BACKGROUND

Machines and automated agents are increasingly involved in market activities, including for data collection, forecasting, planning, transaction execution, and other activities. This includes increasingly high-performance systems, such as used in high-speed trading. A need exists for methods and systems that improve the machines that enable markets, including for increased efficiency, speed, reliability, and the like for participants in such markets.

Many markets are increasingly distributed, rather than centralized, with distributed ledgers like Blockchain, peer-to-peer interaction models, and micro-transactions replacing or complementing traditional models that involve centralized authorities or intermediaries. A need exists for improved machines that enable distributed transactions to occur at scale among large numbers of participants, including human participants and automated agents.

Operations on blockchains, such as ones using cryptocurrency, increasingly require energy-intensive computing operations, such as calculating very large hash functions on growing chains of blocks. Systems using proof-of-work, proof-of-stake, and the like have led to “mining” operations by which computer processing power is applied at a large scale in order to perform calculations that support collective trust in transactions that are recorded in blockchains.

Many applications of artificial intelligence also require energy-intensive computing operations, such as where very large neural networks, with very large numbers of interconnections, perform operations on large numbers of inputs to produce one or more outputs, such as a prediction, classification, optimization, control output, or the like.

The growth of the Internet of Things and cloud computing platforms have also led to the proliferation of devices, applications, and connections among them, such that data centers, housing servers and other IT components, consume a significant fraction of the energy consumption of the United States and other developed countries.

As a result of these and other trends, energy consumption has become a major factor in utilization of computing resources, such that energy resources and computing resources (or simply “energy and compute”) have begun to converge from various standpoints, such as requisitioning, purchasing, provisioning, configuration, and management of inputs, activities, outputs and the like. Projects have been undertaken, for example, to place large scale computing resource facilities, such as Bitcoin™ or other cryptocurrency mining operations, in close proximity to large-scale hydropower sources, such as Niagara Falls.

A major challenge for facility owners and operators is the uncertainty involved in optimizing a facility, such as resulting from volatility in the cost and availability of inputs (in particular where less stable renewable resources are involved), variability in the cost and availability of computing and networking resources (such as where network performance varies), and volatility and uncertainty in various end markets to which energy and compute resources can be applied (such as volatility in cryptocurrencies, volatility in energy markets, volatility in pricing in various other markets, and uncertainty in the utility of artificial intelligence in a wide range of applications), among other factors.

A need exists for a flexible, intelligent energy and compute facility that adjust in response to uncertainty and volatility, as well as for an intelligent energy and compute resource management system, such as one that includes capabilities for data collection, storage and processing, automated configuration of inputs, resources and outputs, and learning on a training set of facility outcomes, facility parameters, and data collected from data sources to train an artificial intelligence/machine learning system to optimize various relevant parameters for such a facility.

SUMMARY

Machine learning potentially enables machines that enable or interact with automated markets to develop understanding, such as based on IoT data, social network data, and other non-traditional data sources, and execute transactions based on predictions, such as by participating in forward markets for energy, compute, advertising and the like. Blockchain and cryptocurrencies may support a variety of automated transactions, and the intersection of blockchain and AI potentially enables radically different transaction infrastructure. As energy is increasingly used for computation, machines that efficiently allocate available energy sources among storage, compute, and base tasks become possible. These and other concepts are addressed by the methods and systems disclosed herein.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure can include access a distributed ledger including an instruction set, tokenize the instruction set, interpret an instruction set access request, and in response to the instruction set access request, provide a provable access to the instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a coating process.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a 3D printer operation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a semiconductor fabrication process.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a field programmable gate array (FPGA) instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a food preparation instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a polymer production instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a chemical synthesis instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a biological production instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a crystal fabrication system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to interpret an execution operation of the instruction set, and to record a transaction on the distributed ledger in response to the execution operation.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure can include access a distributed ledger including an instruction set, wherein the distributed ledger includes a smart wrapper for the instruction set, tokenize the instruction set, interpret an instruction set access request, and in response to the instruction set access request, provide a provable access to the instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the smart wrapper is configured to provide a feature selected from a list of features consisting of: provable access control, validation of terms, and tracking of utilization for an operation performed on the distributed ledger.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the operation on the distributed ledger includes triggering access controls within a content management system upon a validation of actions.

The present disclosure describes a transaction-enabling system including a smart contract wrapper, the contract wrapper according to one disclosed non-limiting embodiment of the present disclosure may be configured to access a distributed ledger including a plurality of embedded contract terms and a plurality of data values, interpret an access request value for the plurality of data values, and, in response to the access request value, provide access to at least a portion of the plurality of data values, and commit an entity providing the access request value to at least one of the plurality of embedded contract terms.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of data values includes intellectual property (IP) data corresponding to a plurality of IP assets, and wherein the plurality of embedded contract terms includes a plurality of intellectual property (IP) licensing terms for the corresponding plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the smart contract wrapper is further configured to commit the entity providing the access request value to corresponding IP licensing terms for accessed ones of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the smart contract wrapper is further configured to interpret an IP description value and an IP addition request, and to add additional IP data to the plurality of data values in response to the IP description value and the IP addition request, wherein the additional IP data includes IP data corresponding to an additional IP asset.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of data values further includes a plurality of owning entities corresponding to the plurality of IP assets, and wherein the smart contract wrapper is further configured to apportion royalties from the plurality of IP assets to the plurality of owning entities in response to the corresponding IP licensing terms.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the smart contract wrapper is further configured to: interpret an IP description value, an IP addition request, and an IP addition entity, add additional IP data to the plurality of data values in response to the IP description value and the IP addition request, commit the IP addition entity to the IP licensing terms, and further apportion royalties from the plurality of IP assets to the plurality of owning entities in response to the additional IP data and the IP addition entity.

A method for executing a smart contract wrapper for a distributed ledger may include accessing a distributed ledger including a plurality of embedded contract terms and a plurality of data values, interpreting an access request value for the plurality of data values, and, in response to the access request value, providing access to at least a portion of the plurality of data values, and committing an entity providing the access request value to at least one of the plurality of embedded contract terms.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include including providing the entity providing the access request value with a user interface including a contract acceptance input, and wherein the providing access and committing the entity is in response to a user input on the user interface.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include including providing an access option to the user interface, and adjusting at least one of the providing access and the committed contract terms in response to a user input on the user interface responsive to the access option.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of data values includes intellectual property (IP) data corresponding to a plurality of IP assets, and wherein the plurality of embedded contract terms includes a plurality of intellectual property (IP) licensing terms for the corresponding plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include committing the entity providing the access request value to corresponding IP licensing terms for accessed ones of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an IP description value and an IP addition request, and adding additional IP data to the plurality of data values in response to the IP description value and the IP addition request, wherein the additional IP data includes IP data corresponding to an additional IP asset.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of data values further includes a plurality of owning entities corresponding to the plurality of IP assets, the method further including apportioning royalties from the plurality of IP assets to the plurality of owning entities in response to the corresponding IP licensing terms.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an IP description value, an IP addition request, and an IP addition entity, adding additional IP data to the plurality of data values in response to the IP description value and the IP addition request, committing the IP addition entity to the IP licensing terms, and further apportioning royalties from the plurality of IP assets to the plurality of owning entities in response to the additional IP data and the IP addition entity.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include updating a valuation for at least one of the plurality of IP assets, and updating the apportioning royalties from the plurality of IP assets in response to the updated valuation for the at least one of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining that at least one of the plurality of IP assets has expired, and updating the apportioning royalties from the plurality of IP assets in response to the determining that the at least one of the plurality of IP assets has expired.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining that an owning entity corresponding to at least one of the plurality of IP assets has changed, and updating the apportioning royalties from the plurality of IP assets in response to the change of the owning entity.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing a user interface to a new owning entity of the at least one of the plurality of IP assets where an ownership has changed, and committing the new owning entity to the IP licensing terms in response to a user input on the user interface.

The present disclosure describes a transaction-enabling system including a smart contract wrapper, the smart contract wrapper according to one disclosed non-limiting embodiment of the present disclosure may be configured to access a distributed ledger including a plurality of intellectual property (IP) licensing terms corresponding to a plurality of IP assets, wherein the plurality of IP assets include an aggregate stack of IP, interpret an IP description value and an IP addition request, and, in response to the IP addition request and the IP description value, to add an IP asset to the aggregate stack of IP.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the smart contract wrapper is further configured to interpret an IP licensing value corresponding to the IP description value, and to add the IP licensing value to the plurality of IP licensing terms in response to the IP description value and the IP addition request.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the smart contract wrapper is further configured to associate at least one of the plurality of IP licensing terms to an added IP asset.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include a data store having a copy of at least one of the IP assets stored thereon, and wherein the aggregate stack of IP further includes a reference to the data store for the at least one of the IP assets.

A method according to one disclosed non-limiting embodiment of the present disclosure may include accessing a distributed ledger including a plurality of intellectual property (IP) licensing terms corresponding to a plurality of IP assets, wherein the plurality of IP assets include an aggregate stack of IP, interpreting an IP description value and an IP addition request, and, in response to the IP addition request and the IP description value, adding an IP asset to the aggregate stack of IP.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an IP licensing value corresponding to the IP description value, and adding the IP licensing value to the plurality of IP licensing terms in response to the IP description value and the IP addition request.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include associating at least one of the plurality of IP licensing terms to the added IP asset.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include storing at least one of the IP assets on a data store, and wherein the aggregate stack of IP includes a reference to the stored at least one of the IP assets on the data store.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include apportioning royalties from the plurality of IP assets to a plurality of owning entities corresponding to the aggregate stack of IP in response to the corresponding IP licensing terms.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an IP addition entity corresponding to the IP addition request and the IP description value, and committing the IP addition entity to the IP licensing terms.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include apportioning royalties from the plurality of IP assets to the plurality of owning entities in response to the IP addition entity.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include updating a valuation for at least one of the plurality of IP assets, and updating the apportioning royalties from the plurality of IP assets in response to the updated valuation for the at least one of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining that at least one of the plurality of IP assets has expired, and updating the apportioning royalties from the plurality of IP assets in response to the determining that the at least one of the plurality of IP assets has expired.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining that an owning entity corresponding to at least one of the plurality of IP assets has changed, and updating the apportioning royalties from the plurality of IP assets in response to the change of the owning entity.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing a user interface to a new owning entity of the at least one of the plurality of IP assets where an ownership has changed, and committing the new owning entity to the IP licensing terms in response to a user input on the user interface.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure may be configured to: access a distributed ledger including an instruction set, tokenize the instruction set, interpret an instruction set access request, and, in response to the instruction set access request, provide a provable access to the instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a coating process.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a 3D printer operation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes and instruction set for a semiconductor fabrication process.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a field programmable gate array (FPGA) instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a food preparation instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a polymer production instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a chemical synthesis instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a biological production instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a crystal fabrication system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to interpret an execution operation of the instruction set, and to record a transaction on the distributed ledger in response to the execution operation.

A method may include accessing a distributed ledger including an instruction set, tokenizing the instruction set, interpreting an instruction set access request, and, in response to the instruction set access request, providing a provable access to the instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a coating process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the coating process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include recording a transaction on the distributed ledger in response to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a 3D printing process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the 3D printing process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include recording a transaction on the distributed ledger in response to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a semiconductor fabrication process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the semiconductor fabrication process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include recording a transaction on the distributed ledger in response to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes field programmable gate array (FPGA) instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an execution operation of the FPGA instruction set, and recording a transaction on the distributed ledger in response to the execution operation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a food preparation process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the food preparation process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include recording a transaction on the distributed ledger in response to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a polymer production process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the polymer production process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include recording a transaction on the distributed ledger in response to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a chemical synthesis process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the chemical synthesis process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include recording a transaction on the distributed ledger in response to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a biological production process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the biological production process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include recording a transaction on the distributed ledger in response to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a crystal fabrication process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the crystal fabrication process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include recording a transaction on the distributed ledger in response to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an execution operation of the instruction set, and recording a transaction on the distributed ledger in response to the execution operation.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure may be configured to access a distributed ledger including executable algorithmic logic, tokenize the executable algorithmic logic, interpret an access request for the executable algorithmic logic, and, in response to the access request, provide a provable access to the executable algorithmic logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to provide the executable algorithmic logic as a black box, and wherein the instruction set further includes an interface description for the executable algorithmic logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to interpret an execution operation of the executable algorithmic logic, and to record a transaction on the distributed ledger in response to the execution operation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the executable algorithmic logic further includes an application programming interface (API) for the executable algorithmic logic.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure may include accessing a distributed ledger including executable algorithmic logic, tokenizing the executable algorithmic logic, interpreting an access request for the executable algorithmic logic, and, in response to the access request, providing a provable access to the executable algorithmic logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing an interface description for the executable algorithmic logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing an application programming interface (API) for the executable algorithmic logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an execution operation of the executable algorithmic logic, and recording a transaction on the distributed ledger in response to the execution operation.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure may be configured to access a distributed ledger including a firmware data value, tokenize the firmware data value, interpret an access request for the firmware data value, and, in response to the access request, provide a provable access to a firmware corresponding to the firmware data value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to provide a notification to an accessor of the firmware data value in response to an update of the firmware data value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to interpret one of a download operation or an install operation of a firmware asset corresponding to the firmware data value, and to record a transaction on the distributed ledger in response to the one of the download operation or the install operation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the firmware data value includes firmware for a component of a production process.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the component of the production process includes a production tool.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the production tool includes a production tool for a process selected from the processes consisting of: a coating process, a 3D printing process, a semiconductor fabrication process, a food preparation process, a polymer production process, a chemical synthesis process, a biological production process, and a crystal fabrication process.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the firmware data value includes firmware for one of a compute resource and a networking resource.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure may include accessing a distributed ledger including a firmware data value, tokenizing the firmware data value, interpreting an access request for the firmware data value, and, in response to the access request, providing a provable access to the firmware corresponding to the firmware data value.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing a notification to an accessor of the firmware data value in response to an update of the firmware data value.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting a download operation of a firmware asset corresponding to the firmware data value, and recording a transaction on the distributed ledger in response to the download operation.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an install operation of a firmware asset corresponding to the firmware data value, and recording a transaction on the distributed ledger in response to the install operation.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure may be configured to access a distributed ledger including serverless code logic, tokenize the serverless code logic, interpret an access request for the serverless code logic, and, in response to the access request, provide a provable access to the serverless code logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to provide the serverless code logic as a black box, and wherein the serverless code logic further includes an interface description for the serverless code logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to interpret an execution operation of the serverless code logic, and to record a transaction on the distributed ledger in response to the execution operation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to interpret an execution operation of the serverless code logic, and to record a transaction on the distributed ledger in response to the execution operation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set further includes an application programming interface (API) for the serverless code logic.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure may include accessing a distributed ledger including serverless code logic, tokenizing the serverless code logic, interpreting an access request for the serverless code logic, and in response to the access request, providing a provable access to the serverless code logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing an interface description for the serverless code logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing an application programming interface (API) for the serverless code logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an execution operation of the serverless code logic, and recording a transaction on the distributed ledger in response to the execution operation.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure may be configured to access a distributed ledger including an aggregated data set, interpret an access request for the aggregated data set, and, in response to the access request, provide a provable access to the aggregated data set, wherein the provable access includes at least one of which parties have accessed the aggregated data set and how many parties have accessed the aggregated data set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the distributed ledger includes a block chain, and wherein the aggregated data set includes one of a trade secret and proprietary information.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include an expert wrapper for the distributed ledger, wherein the expert wrapper is configured to tokenize the aggregated data set and to validate the one of the trade secret and the proprietary information.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the distributed ledger includes a set of instructions, and wherein the controller is further configured to interpret an instruction update value, and to update the set of instructions in response to the access request and the instruction update value.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include a smart wrapper for the distributed ledger, wherein the smart wrapper is configured to allocate a plurality of sub-sets of instructions to the distributed ledger as the aggregated data set, and manage access to the plurality of sub-sets of instructions in response to the access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to interpret an access of one of the plurality of sub-sets of instructions, and to record a transaction on the distributed ledger in response to the access.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to interpret an execution operation of one of the plurality of sub-sets of instructions, and to record a transaction on the distributed ledger in response to the access.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure may include accessing a distributed ledger including an aggregated data set, interpreting an access request for the aggregated data set, and, in response to the access request, providing a provable access to the aggregated data set, wherein the provable access includes at least one of which parties have accessed the aggregated data set and how many parties have accessed the aggregated data set.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include operating an expert wrapper for the distributed ledger, wherein the expert wrapper is configured to tokenize the aggregated data set and to validate at least one of trade secret or proprietary information of the aggregated data set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the distributed ledger further includes a set of instructions, the method further including interpreting an instruction update value, and updating the set of instructions in response to the access request and the instruction update value.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include allocating a plurality of sub-sets of instructions to the distributed ledger as the aggregated data set, and managing access to the plurality of sub-sets of instructions in response to the access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an access of one of the plurality of sub-sets of instructions, and recording a transaction on the distributed ledger in response to the access.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an execution operation of one of the plurality of sub-sets of instructions, and recording a transaction on the distributed ledger in response to the access.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure may access a distributed ledger including a plurality of intellectual property (IP) data corresponding to a plurality of IP assets, wherein the plurality of IP assets include an aggregate stack of IP, tokenize the IP data, interpret a distributed ledger operation corresponding to at least one of the plurality of IP assets, determine an analytic result value in response to the distributed ledger operation and the tokenized IP data, and provide a report of the analytic result value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the distributed ledger operation includes at least one operation selected from the operations consisting of accessing IP data corresponding to one of the plurality of IP assets, executing a process utilizing IP data corresponding to one of the plurality of IP assets, adding IP data corresponding to an additional IP asset to the aggregate stack of IP, and removing IP data corresponding to one of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the analytic result value includes at least one result value selected from the result values consisting of a number of access events corresponding to at least one of the plurality of IP assets, statistical information corresponding to access events for a plurality of IP assets, a distribution of the plurality of IP assets according to access event rates, one of access times or processing times corresponding to at least one of the plurality of IP assets, and unique entity access events corresponding to at least one of the plurality of IP assets.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure may include accessing a distributed ledger including a plurality of IP data corresponding to a plurality of IP assets, wherein the plurality of IP assets include an aggregate stack of IP, tokenizing the IP data, interpreting a distributed ledger operation corresponding to at least one of the plurality of IP assets, determining an analytic result value in response to the distributed ledger operation and the tokenized IP data, and providing a report of the analytic result value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the analytic result value includes determining a number of access events corresponding to at least one of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the analytic result value includes determining one of an access time or a processing time corresponding to at least one of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the analytic result value includes determining a number of unique entity access events corresponding to at least one of the plurality of IP assets.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure can be configured to interpret a resource utilization requirement for a task system having at least one of a compute task, a network task, or a core task; interpret a plurality of external data sources, wherein the plurality of external data sources includes at least one data source outside of the task system; operate an expert system to predict a forward market price for a resource in response to the resource utilization requirement and the plurality of external data sources; and execute a transaction on a resource market in response to the predicted forward market price.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of external data sources includes an internet-of-things (IoT) data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of external data sources includes a social media data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes a first resource, and wherein the resource of the forward market price includes at least one of: the first resource, and a second resource that can be substituted for the first resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to operate the expert system to determine a substitution cost of the second resource, and to execute the transaction on the resource market further in response to the substitution cost of the second resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the expert system is further configured to determine at least a portion of the substitution cost of the second resource as an operational change cost for the task system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes at least one resource selected from the resources consisting of: a compute resource, a network bandwidth resource, a spectrum resource, a data storage resource, an energy resource, and an energy credit resource.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a resource utilization requirement for a task system having at least one of a compute task, a network task, or a core task; interpreting a plurality of external data sources, wherein the plurality of external data sources includes at least one data source outside of the task system; operating an expert system to predict a forward market price for a resource in response to the resource utilization requirement and the plurality of external data sources; and executing a transaction on a resource market in response to the predicted forward market price.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of external data sources includes an internet-of-things (IoT) data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of external data sources includes a social media data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes a first resource, and wherein the resource of the forward market price includes at least one of: the first resource, and a second resource that can be substituted for the first resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include operating the expert system to determine a substitution cost of the second resource, and executing the transaction on the resource market further in response to the substitution cost of the second resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining at least a portion of the substitution cost of the second resource as an operational change cost for the task system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes at least one resource selected from the resources consisting of: a compute resource, a network bandwidth resource, a spectrum resource, a data storage resource, an energy resource, and an energy credit resource.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure can be configured to interpret a resource utilization requirement for a task system having at least one of a compute task, a network task, or a core task; interpret a behavioral data source; operate a machine to forecast a forward market value for a resource in response to the resource utilization requirement and the behavioral data source; and perform one of adjusting an operation of the task system or executing a transaction in response to the forecast of the forward market value for the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market value for the resource includes a forward market for energy prices.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market value for the resource includes a forward market for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market value for the resource includes a forward market for a compute resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market value for the resource includes a forward market for an energy credit resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes a first resource, and wherein the resource of the forward market value includes at least one of: the first resource, and a second resource that can be substituted for the first resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to operate the machine to determine a substitution cost of the second resource, and to perform the one of adjusting the operation of the task system or executing the transaction further in response to the substitution cost of the second resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the machine is further configured to determine at least a portion of the substitution cost of the second resource as an operational change cost for the task system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the performing includes executing the transaction, wherein the transaction includes one of purchasing or selling one of the first resource or the second resource on a market for at least one of the first resource or the second resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes at least one resource selected from the resources consisting of: a compute resource, a network bandwidth resource, a spectrum resource, a data storage resource, an energy resource, and an energy credit resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the performing includes adjusting the operation of the task system, and wherein the adjusting further includes at least one operation selected from the operations consisting of: adjusting operations of the task system to increase or reduce the resource utilization requirement, adjusting operations of the task system to time shift at least a portion of the resource utilization requirement, adjusting operations of the task system to substitute utilization of a first resource for utilization of a second resource, and accessing an external provider to provide at least a portion of at least one of the compute task, the network task, or the core task.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the performing includes executing the transaction, wherein the transaction includes one of purchasing or selling the resource on a market for the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the market for the resource includes a forward market for the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the market for the resource includes a spot market for the resource.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a resource utilization requirement for a task system having at least one of a compute task, a network task, or a core task; interpreting a behavioral data source; operating a machine to forecast a forward market value for a resource in response to the resource utilization requirement and the behavioral data source; and performing one of adjusting an operation of the task system or executing a transaction in response to the forecast of the forward market value for the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market value for the resource includes a forward market for energy prices.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market value for the resource includes a forward market for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market value for the resource includes a forward market for a compute resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market value for the resource includes a forward market for an energy credit resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes a first resource, and wherein the resource of the forward market value includes at least one of: the first resource, and a second resource that can be substituted for the first resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include operating the machine to determine a substitution cost of the second resource, and performing the one of adjusting the operation of the task system or executing the transaction further in response to the substitution cost of the second resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining at least a portion of the substitution cost of the second resource as an operational change cost for the task system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the performing includes executing the transaction, wherein the transaction includes one of purchasing or selling one of the first resource or the second resource on a market for at least one of the first resource or the second resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes at least one resource selected from the resources consisting of: a compute resource, a network bandwidth resource, a spectrum resource, a data storage resource, an energy resource, and an energy credit resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the performing includes adjusting the operation of the task system, and wherein the adjusting further includes at least one operation selected from the operations consisting of: adjusting operations of the task system to increase or reduce the resource utilization requirement, adjusting operations of the task system to time shift at least a portion of the resource utilization requirement, adjusting operations of the task system to substitute utilization of a first resource for utilization of a second resource, and accessing an external provider to provide at least a portion of at least one of the compute task, the network task, or the core task.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the performing includes executing the transaction, wherein the transaction includes one of purchasing or selling the resource on a market for the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the market for the resource includes a forward market for the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the market for the resource includes a spot market for the resource.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure can be configured to interpret a resource utilization requirement for a task system having at least one of a compute task, a network task, or a core task; interpret a plurality of external data sources, wherein the plurality of external data sources includes at least one data source outside of the task system; operate an expert system to predict a forward market price for a resource in response to the resource utilization requirement and the plurality of external data sources; and execute a cryptocurrency transaction on a resource market in response to the predicted forward market price.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of external data sources includes an internet-of-things (IoT) data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of external data sources includes a social media data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes a first resource, and wherein the resource of the forward market price includes at least one of: the first resource, and a second resource that can be substituted for the first resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to operate the expert system to determine a substitution cost of the second resource, and to execute the cryptocurrency transaction on the resource market further in response to the substitution cost of the second resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the expert system is further configured to determine at least a portion of the substitution cost of the second resource as an operational change cost for the task system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes at least one resource selected from the resources consisting of: a compute resource, a network bandwidth resource, a spectrum resource, a data storage resource, an energy resource, and an energy credit resource.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a resource utilization requirement for a task system having at least one of a compute task, a network task, or a core task; interpreting a plurality of external data sources, wherein the plurality of external data sources includes at least one data source outside of the task system; operating an expert system to predict a forward market price for a resource in response to the resource utilization requirement and the plurality of external data sources; and executing a cryptocurrency transaction on a resource market in response to the predicted forward market price.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of external data sources includes an internet-of-things (IoT) data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of external data sources includes a social media data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes a first resource, and wherein the resource of the forward market price includes at least one of: the first resource, and a second resource that can be substituted for the first resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include operating the expert system to determine a substitution cost of the second resource, and executing the cryptocurrency transaction on the resource market further in response to the substitution cost of the second resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining at least a portion of the substitution cost of the second resource as an operational change cost for the task system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes at least one resource selected from the resources consisting of: a compute resource, a network bandwidth resource, a spectrum resource, a data storage resource, an energy resource, and an energy credit resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include operating the expert system to predict a forward market price for a plurality of forward market time frames.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the executing a cryptocurrency transaction on a resource market in response to the predicted forward market price includes providing for an improved cost of operation of the task system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes a first resource, and wherein the method further includes: determining a second resource that can be substituted for the first resource, wherein operating the expert system to predict the forward market price for the plurality of forward market time frames further includes predicting the forward market price for both of the first resource and the second resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the executing the cryptocurrency transaction on the resource market in response to the predicted forward market price includes providing for an improved cost of operation of the task system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the providing for an improved cost of operation of the task system further includes determining a resource utilization profile, wherein the resource utilization profile includes a utilization of each of the first resource and the second resource.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a machine having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; and a controller, comprising: a resource requirement circuit structured to determine an amount of a resource for the machine to service at least one of the compute task requirement, the networking task requirement, and the energy consumption task requirement; a forward resource market circuit structured to access a forward resource market; a resource market circuit structured to access a resource market; and a resource distribution circuit structured to execute a transaction of the resource on at least one of the resource market or the forward resource market in response to the determined amount of the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve at least one of an output of the machine or a resource utilization of the machine.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit further includes at least one of a machine learning component, an artificial intelligence component, or a neural network component.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource includes a compute resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource includes an energy resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource includes an energy credit resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include situations wherein the resource requirement circuit is further structured to determine a second amount of a second resource for the machine to service at least one of the compute task requirement, the networking task requirement, and the energy consumption task requirement; and wherein the resource distribution circuit is further structured to execute a first transaction of the first resource on one of the resource market or the forward resource market, and to execute a second transaction of the second resource on the other of the one of the resource market or the forward resource market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the second resource includes a substitute resource for the first resource during at least a portion of an operating condition for the machine.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward resource market includes a futures market for the resource at a first time scale, and wherein the resource market includes one of: a spot market for the resource, or a futures market for the resource at a second time scale.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the transaction includes at least one transaction type selected from the transaction types consisting of: a sale of the resource; a purchase of the resource; a short sale of the resource; a call option for the resource; a put option for the resource; and any of the foregoing with regard to at least one of a substitute resource or a correlated resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to determine at least one of a substitute resource or a correlated resource, and to further execute at least one transaction of the at least one of the substitute resource or the correlated resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to execute the at least one transaction of the at least one of the substitute resource or the correlated resource as a replacement for the transaction of the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to execute the at least one transaction of the at least one of the substitute resource or the correlated resource in concert with the transaction of the resource.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include determining an amount of a first resource for a machine to service at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; accessing a forward resource market; accessing a resource market; and executing a transaction of the first resource on at least one of the resource market or the forward resource market in response to the determined amount of the first resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining a second amount of a second resource for the machine to service at least one of the compute task requirement, the networking task requirement, and the energy consumption task requirement; and executing a first transaction of the first resource on one of the resource market or the forward resource market, and executing a second transaction of the second resource on the other of the at least one of the resource market or the forward resource market.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining at least one of a substitute resource or a correlated resource, and executing at least one transaction of the at least one of the substitute resource or the correlated resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include executing the at least one transaction of the at least one of the substitute resource or the correlated resource as a replacement for the transaction of the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include executing the at least one transaction of the at least one of the substitute resource or the correlated resource in concert with the transaction of the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the correlated resource includes at least one operation selected from the operations consisting of: determining the correlated resource for the machine as a resource to service alternate tasks that provide acceptable functionality for the machine; determining the correlated resource as a resource that is expected to be correlated with the resource in regard to at least one of a price or an availability; and determining the correlated resource as a resource that is expected to have a corresponding price change with the resource, such that a subsequent sale of the correlated resource combined with a spot market purchase of the resource provides for a planned economic outcome.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a transaction detection circuit structured to interpret a transaction request value, wherein the transaction request value includes a transaction description for one of a proposed or an imminent transaction, and wherein the transaction description includes a cryptocurrency type value and a transaction amount value, a transaction locator circuit structured to determine a transaction location parameter in response to the transaction request value, wherein the transaction location parameter includes at least one of a transaction geographic value or a transaction jurisdiction value, and a transaction execution circuit structured to provide a transaction implementation command in response to the transaction location parameter.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the transaction locator circuit is further structured to determine the transaction location parameter based on a tax treatment of the one of the proposed or imminent transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the transaction locator circuit is further structured to select the one of the transaction geographic value or the transaction jurisdiction value from a plurality of available geographic values or jurisdiction values that provides an improved tax treatment relative to a nominal one of the plurality of available geographic values or jurisdiction values.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the transaction locator circuit is further structured to determine the transaction location parameter in response to a tax treatment of at least one of the cryptocurrency type value or a type of the one of the proposed or imminent transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the transaction request value further includes a transaction location value, and wherein the transaction locator circuit is further structured to provide the transaction location parameter as the transaction location value in response to determining that a tax treatment of the one of the proposed or imminent transaction meets a threshold tax treatment value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the transaction locator circuit is further structured to operate an expert system configured to use machine learning to continuously improve the determination of the transaction location parameter relative to a tax treatment of transactions processed by the controller.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the transaction locator circuit is further structured to operate an expert system, wherein the expert system is configured to aggregate regulatory information for cryptocurrency transactions from a plurality of jurisdictions, and to continuously improve the determination of the transaction location parameter based on the aggregated regulatory information.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the expert system is further configured to use machine learning to continuously improve the determination of the transaction location parameter relative to secondary jurisdictional costs related to the cryptocurrency transactions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the expert system is further configured to use machine learning to continuously improve the determination of the transaction location parameter relative to a transaction speed for the cryptocurrency transactions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the expert system is further configured to use machine learning to continuously improve the determination of the transaction location parameter relative to a tax treatment for the cryptocurrency transactions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the expert system is further configured to use machine learning to continuously improve the determination of the transaction location parameter relative to a favorability of contractual terms related to the cryptocurrency transactions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the expert system is further configured to use machine learning to continuously improve the determination of the transaction location parameter relative to a compliance of the cryptocurrency transactions within the aggregated regulatory information.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include a transaction engine responsive to the transaction implementation command.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a transaction request value, wherein the transaction request value includes a transaction description for one of a proposed or an imminent transaction, and wherein the transaction description includes a cryptocurrency type value and a transaction amount value, determining a transaction location parameter in response to the transaction request value, wherein the transaction location parameter includes at least one of a transaction geographic value or a transaction jurisdiction value, and providing a transaction implementation command in response to the transaction location parameter.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining the transaction location parameter based on a tax treatment of the one of the proposed or imminent transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include selecting at least one of the transaction geographic value or the transaction jurisdiction value from a plurality of available geographic values or jurisdiction values that provides an improved tax treatment relative to a nominal one of the plurality of available geographic values or jurisdiction values.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining the transaction location parameter in response to a tax treatment of at least one of the cryptocurrency type value or a type of the one of the proposed or imminent transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the transaction request value further includes a transaction location value, the method further including providing the transaction location parameter as the transaction location value in response to determining that a tax treatment of the one of the proposed or imminent transaction meets a threshold tax treatment value.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include aggregating regulatory information for cryptocurrency transactions from a plurality of jurisdictions, and continuously improving the determination of the transaction location parameter based on the aggregated regulatory information.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include applying machine learning to continuously improve the determination of the transaction location parameter relative to at least one parameter selected from the parameters consisting of: secondary jurisdictional costs related to the transactions, a transaction speed for the transactions, a tax treatment for the transactions, a favorability of contractual terms related to the transactions, and a compliance of the transactions within the aggregated regulatory information.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a transaction request value, wherein the transaction request value includes a transaction description for one of a proposed or an imminent transaction, and wherein the transaction description includes a cryptocurrency type value and a transaction amount value, determining a transaction location parameter in response to the transaction request value, wherein the transaction location parameter includes at least one of a transaction geographic value or a transaction jurisdiction value, and executing a transaction in response to the transaction location parameter.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining the transaction location parameter based on a tax treatment of the one of the proposed or imminent transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include selecting the one of the transaction geographic value or the transaction jurisdiction value from a plurality of available geographic values or jurisdiction values that provides an improved tax treatment relative to a nominal one of the plurality of available geographic values or jurisdiction values.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include aggregating regulatory information for cryptocurrency transactions from a plurality of jurisdictions, and continuously improving the determination of the transaction location parameter based on the aggregated regulatory information.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include applying machine learning to continuously improve the determination of the transaction location parameter relative to at least one parameter selected from the parameters consisting of: secondary jurisdictional costs related to the transactions, a transaction speed for the transactions, a tax treatment for the transactions, a favorability of contractual terms related to the transactions, and a compliance of the transactions within the aggregated regulatory information.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a controller. The controller including a smart wrapper structured to interpret a transaction request value from a user, wherein the transaction request value includes a transaction description for an incoming transaction, and wherein the transaction description includes a transaction amount value and at least one of a cryptocurrency type value and a transaction location value, a transaction locator circuit structured to determine a transaction location parameter in response to the transaction request value and further in response to a plurality of tax treatment values corresponding to a plurality of transaction locations, wherein the transaction location parameter includes at least one of a transaction geographic value or a transaction jurisdiction value, and wherein the smart wrapper is further structured to direct an execution of the incoming transaction in response to the transaction location parameter.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the transaction locator circuit is further structured to select an available one of the plurality of transaction locations having a favorable tax treatment value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the transaction location value includes at least one location value corresponding to: a location of a purchaser of the transaction, a location of a seller of the transaction, a location of a delivery of a product or service of the transaction, a location of a supplier of a product or service of the transaction, a residence location of one of the purchaser, seller, or supplier of the transaction, and a legally available location for the transaction.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a transaction request value from a user, wherein the transaction request value includes a transaction description for an incoming transaction, and wherein the transaction description includes a transaction amount value and at least one of a cryptocurrency type value and a transaction location value, determining a transaction location parameter in response to the transaction request value and further in response to a plurality of tax treatment values corresponding to a plurality of transaction locations, wherein the transaction location parameter includes at least one of a transaction geographic value or a transaction jurisdiction value, and directing an execution of the incoming transaction in response to the location parameter.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include selecting an available one of the plurality of transaction locations having a favorable tax treatment value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the transaction location value includes selecting the transaction location value from a list of locations consisting of: a location of a purchaser of the transaction, a location of a seller of the transaction, a location of a delivery of a product or service of the transaction, a location of a supplier of a product or service of the transaction, a residence location of one of the purchaser, seller, or supplier of the transaction, and a legally available location for the transaction.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a controller. The controller according to one disclosed non-limiting embodiment of the present disclosure can include a transaction detection circuit structured to interpret a plurality of transaction request values, wherein each transaction request value includes a transaction description for one of a proposed or an imminent transaction, and wherein the transaction description includes a cryptocurrency type value and a transaction amount value, a transaction support circuit structured to interpret a support resource description including at least one supporting resource for the plurality of transactions, a support utilization circuit structured to operate an expert system, wherein the expert system is configured to use machine learning to continuously improve at least one execution parameter for the plurality of transactions relative to the support resource description, and a transaction execution circuit structured to command execution of the plurality of transactions in response to the improved at least one execution parameter.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the support resource description includes an energy price description for an energy source available to power the execution of the plurality of transactions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the energy price description includes at least one of a forward price prediction and a spot price for the energy source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the support resource description includes a plurality of energy sources available to power the execution of the plurality of transactions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the support resource description includes at least one of a state of charge and a charge cycle cost description for an energy storage source available to power the execution of the plurality of transactions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the energy storage source includes a battery, and wherein the expert system is further configured to user machine learning to improve at least one parameter selected from the parameters consisting of: a battery energy transfer efficiency value, a battery life value, and a battery lifetime utilization cost value.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a plurality of transaction request values, wherein each transaction request value includes a transaction description for one of a proposed or an imminent transaction, and wherein the transaction description includes a cryptocurrency type value and a transaction amount value, interpreting a support resource description including at least one supporting resource for the plurality of transactions, operating an expert system, wherein the expert system is configured to use machine learning to continuously improve at least one execution parameter for the plurality of transactions relative to the support resource description, and commanding execution of the plurality of transactions in response to the improved at least one execution parameter.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the commanding execution includes utilizing the continuously improved at least one execution parameter.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include commanding execution of a first transaction in response to the at least one execution parameter, wherein the continuously improving the at least one execution parameter includes updating the at least one execution parameter, the method further including commanding execution of a second transaction using the updated at least one execution parameter.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the support resource description includes an energy price description for an energy source available to power the execution of the plurality of transactions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the energy price description includes at least one of a forward price prediction and a spot price for the energy source.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a controller. The controller according to one disclosed non-limiting embodiment of the present disclosure can include an attention market access circuit structured to interpret a plurality of attention-related resources available on an attention market, an intelligent agent circuit structured to determine an attention-related resource acquisition value based on a cost parameter of at least one of the plurality of attention-related resources, and an attention acquisition circuit structured to solicit an attention-related resource in response to the attention-related resource acquisition value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the attention acquisition circuit is further structured to perform the soliciting the attention-related resource by performing at least one operation selected from the operations consisting of purchasing the attention-related resource from the attention market, selling the attention-related resource to the attention market, making an offer to sell the attention-related resource to a second intelligent agent, and making an offer to purchase the attention-related resource to the second intelligent agent.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of attention-related resources includes at least one resource selected from a list consisting of an advertising placement, a search listing, a keyword listing, a banner advertisement, a video advertisement, an embedded video advertisement, a panel activity participation, a survey activity participation, a trial activity participation, and a pilot activity placement or participation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the attention market includes a spot market for at least one of the plurality of attention-related resources.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the cost parameter of at least one of the plurality of attention-related resources includes a future predicted cost of the at least one of the plurality of attention-related resources, and wherein the intelligent agent circuit is further structured to determine the attention-related resource acquisition value in response to a comparison of a first cost on the spot market with the cost parameter.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the attention market includes a forward market for at least one of the plurality of attention-related resources, and wherein the cost parameter of the at least one of the plurality of attention-related resources includes a predicted future cost.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the cost parameter of at least one of the plurality of attention-related resources includes a future predicted cost of the at least one of the plurality of attention-related resources, and wherein the intelligent agent circuit is further structured to determine the attention-related resource acquisition value in response to a comparison of a first cost on the forward market with the cost parameter.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the intelligent agent circuit is further structured to determine the attention-related resource acquisition value in response to the cost parameter of the at least one of the plurality of attention-related resources having a value that is outside of an expected cost range for the at least one of the plurality of attention-related resources.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the intelligent agent circuit is further structured to determine the attention-related resource acquisition value in response to a function of the cost parameter of the at least one of the plurality of attention-related resources, and an effectiveness parameter of the at least one of the plurality of attention-related resources.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller further includes an external data circuit structured to interpret a social media data source, and wherein the intelligent agent circuit is further structured to determine, in response to the social media data source, at least one of a future predicted cost of the at least one of the plurality of attention-related resources, and to utilize the future predicted cost as the cost parameter, and the effectiveness parameter of the at least one of the plurality of attention-related resources.

The present disclosure describes a system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a fleet of machines, each one of the fleet of machines including a task system having a core task and at least one of a compute task or a network task, a controller, including an attention market access circuit structured to interpret a plurality of attention-related resources available on an attention market, and an intelligent agent circuit structured to determine an attention-related resource acquisition value based on a cost parameter of at least one of the plurality of attention-related resources, and further based on the core task for a corresponding machine of the fleet of machines, an attention purchase aggregating circuit structured to determine an aggregate attention-related resource purchase value in response to the plurality of attention-related resource acquisition values from each intelligent agent circuit corresponding to each machine of the fleet of the machines, and an attention acquisition circuit structured to purchase an attention-related resource in response to the aggregate attention-related resource purchase value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the attention purchase aggregating circuit is positioned at a location selected from the locations consisting of at least partially distributed on a plurality of the controllers corresponding to machines of the fleet of machines, on a selected controller corresponding to one of the machines of the fleet of machines, and on a system controller communicatively coupled to the plurality of the controllers corresponding to machines of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the attention purchase acquisition circuit is positioned at a location selected from the locations consisting of at least partially distributed on a plurality of the controllers corresponding to machines of the fleet of machines, on a selected controller corresponding to one of the machines of the fleet of machines, and on a system controller communicatively coupled to the plurality of the controllers corresponding to machines of the fleet of machines.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a plurality of attention-related resources available on an attention market, determining an attention-related resource acquisition value based on a cost parameter of at least one of the plurality of attention-related resources, soliciting an attention-related resource in response to the attention-related resource acquisition value.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include performing the soliciting the attention-related resource by performing at least one operation selected from the operations consisting of purchasing the attention-related resource from the attention market, selling the attention-related resource to the attention market, making an offer to sell the attention-related resource to a second intelligent agent, and making an offer to purchase the attention-related resource to the second intelligent agent.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the cost parameter of at least one of the plurality of attention-related resources includes a future predicted cost of the at least one of the plurality of attention-related resources, the method further including determining the attention-related resource acquisition value in response to a comparison of a first cost on the attention market with the cost parameter.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting a social media data source and determining, in response to the social media data source, at least one of a future predicted cost of the at least one of the plurality of attention-related resources, and to utilize the future predicted cost as the cost parameter, and an effectiveness parameter of the at least one of the plurality of attention-related resources, and wherein the determining the attention-related resource acquisition value is further based on the at least one of the future predicted cost or the effectiveness parameter.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a plurality of attention-related resources available on an attention market, determining an attention-related resource acquisition value for each machine of a fleet of machines based on a cost parameter of at least one of the plurality of attention-related resources, and further based on a core task for each of a corresponding machine of the fleet of machines, determining an aggregate attention-related resource purchase value in response to the plurality of attention-related resource acquisition values corresponding to each machine of the fleet of the machines, and an attention acquisition circuit structured to purchase an attention-related resource in response to the aggregate attention-related resource purchase value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the cost parameter of at least one of the plurality of attention-related resources includes a future predicted cost of the at least one of the plurality of attention-related resources, the method further including determining each attention-related resource acquisition value in response to a comparison of a first cost on a spot market for attention-related resources with the cost parameter.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting a social media data source and determining, in response to the social media data source, at least one of a future predicted cost of the at least one of the plurality of attention-related resources, and to utilize the future predicted cost as the cost parameter, and an effectiveness parameter of the at least one of the plurality of attention-related resources, and wherein the determining the attention-related resource acquisition value is further based on the at least one of the future predicted cost or the effectiveness parameter.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a production facility including a core task, wherein the core task includes a production task; a controller, including: a facility description circuit structured to interpret a plurality of historical facility parameter values and a corresponding plurality of historical facility outcome values; a facility prediction circuit structured to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the plurality of historical facility parameter values and the corresponding plurality of historical facility outcome values; wherein the facility description circuit is further structured to interpret a plurality of present state facility parameter values; and wherein the facility prediction circuit is further structured to operate the adaptive learning system to predict a present state facility outcome value in response to the plurality of present state facility parameter values.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the present state facility outcome value includes a facility production outcome.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the present state facility outcome value includes a facility production outcome probability distribution.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the present state facility outcome value includes at least one value selected from the values consisting of: a production volume description of the production task; a production quality description of the production task; a facility resource utilization description; an input resource utilization description; and a production timing description of the production task.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility description circuit is further structured to interpret historical external data from at least one external data source, and wherein the adaptive learning system is further configured to train the facility production predictor in response to the historical external data.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the at least one external data source includes at least one data source selected from the data sources consisting of: a social media data source; a behavioral data source; a spot market price for an energy source; and a forward market price for an energy source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility description circuit is further structured to interpret present external data from the at least one external data source, and wherein the adaptive learning system is further configured to predict the present state facility outcome value in response to the present external data.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a plurality of historical facility parameter values and a corresponding plurality of historical facility outcome values; operating an adaptive learning system, thereby training a facility production predictor in response to the plurality of historical facility parameter values and the corresponding plurality of historical facility outcome values; interpreting a plurality of present state facility parameter values; and operating the adaptive learning system to predict a present state facility outcome value in response to the plurality of present state facility parameter values.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the present state facility outcome value includes a facility production outcome.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the present state facility outcome value includes a facility production outcome probability distribution.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the present state facility outcome value includes at least one value selected from the values consisting of: a production volume description of a production task; a production quality description of a production task; a facility resource utilization description; an input resource utilization description; and a production timing description of a production task.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting historical external data from at least one external data source, and operating the adaptive learning system to further train the facility production predictor in response to the historical external data.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the at least one external data source includes at least one data source selected from the data sources consisting of: a social media data source; a behavioral data source; a spot market price for an energy source; and a forward market price for an energy source.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting present external data from the at least one external data source, and operating the adaptive learning system to predict the present state facility outcome value further in response to the present external data.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a facility including a core task; a controller, including: a facility description circuit structured to interpret a plurality of historical facility parameter values and a corresponding plurality of historical facility outcome values; a facility prediction circuit structured to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility resource allocation circuit in response to the plurality of historical facility parameter values and the corresponding plurality of historical facility outcome values; wherein the facility description circuit is further structured to interpret a plurality of present state facility parameter values; and wherein the trained facility resource allocation circuit is further structured to adjust, in response to the plurality of present state facility parameter values, a plurality of facility resource values.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of facility resource values include: a provisioning and an allocation of facility energy resources; and a provisioning and an allocation of facility compute resources.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the trained facility resource allocation circuit is further structured to adjust the plurality of facility resource values by one of producing or selecting a favorable facility resource utilization profile from among a set of available facility resource utilization profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the trained facility resource allocation circuit is further structured to adjust the plurality of facility resource values by one of producing or selecting a favorable facility resource output selection from among a set of available facility resource output values.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the trained facility resource allocation circuit is further structured to adjust the plurality of facility resource values by one of producing or selecting a favorable facility resource input profile from among a set of available facility resource input profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the trained facility resource allocation circuit is further structured to adjust the plurality of facility resource values by one of producing or selecting a favorable facility resource configuration profile from among a set of available facility resource configuration profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility description circuit is further structured to interpret historical external data from at least one external data source, and wherein the adaptive learning system is further configured to train the facility resource allocation circuit in response to the historical external data.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the at least one external data source includes at least one data source selected from the data sources consisting of: a social media data source; a behavioral data source; a spot market price for an energy source; and a forward market price for an energy source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility description circuit is further structured to interpret present external data from the at least one external data source, and wherein the trained facility resource allocation circuit is further structured to adjust the plurality of facility resource values in response to the present external data.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a plurality of historical facility parameter values and a corresponding plurality of historical facility outcome values; operating an adaptive learning system, thereby training a facility resource allocation circuit in response to the plurality of historical facility parameter values and the corresponding plurality of historical facility outcome values; interpreting a plurality of present state facility parameter values; and adjusting, in response to the plurality of present state facility parameter values, a plurality of facility resource values.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of facility resource values include: a provisioning and an allocation of facility energy resources; and a provisioning and an allocation of facility compute resources.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by selecting a favorable facility resource utilization profile from among a set of available facility resource utilization profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by producing a favorable facility resource utilization profile relative to a set of available facility resource utilization profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include updating the set of available facility resource utilization profiles in response to the plurality of facility resource values.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by selecting a favorable facility resource output selection from among a set of available facility resource output values.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by producing a facility resource output selection relative to a set of available facility resource output values.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include updating the set of available facility resource output values in response to the plurality of facility resource values.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by selecting a favorable facility resource input profile from among a set of available facility resource input profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by producing a facility resource input profile relative to a set of available facility resource input profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include updating the set of available facility resource input profiles in response to the plurality of facility resource values.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by selecting a favorable facility resource configuration profile from among a set of available facility resource configuration profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by producing a facility resource configuration profile relative to a set of available facility resource configuration profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include updating the set of available facility resource configuration profiles in response to the plurality of facility resource values.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting historical external data from at least one external data source, and operating the adaptive learning system to further train the facility resource allocation circuit in response to the historical external data.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the at least one external data source includes at least one data source selected from the data sources consisting of: a social media data source; a behavioral data source; a spot market price for an energy source; and a forward market price for an energy source.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting present external data from the at least one external data source, and further adjusting the plurality of facility resource values in response to the present external data.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a facility including a core task, wherein the core task includes a customer relevant output; a controller, including: a facility description circuit structured to interpret a plurality of historical facility parameter values and a corresponding plurality of historical facility outcome values; a facility prediction circuit structured to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the plurality of historical facility parameter values and the corresponding plurality of historical facility outcome values; wherein the facility description circuit is further structured to interpret a plurality of present state facility parameter values; wherein the trained facility production predictor is configured to determine a customer contact indicator in response to the plurality of present state facility parameter values; and a customer notification circuit structured to provide a notification to a customer in response to the customer contact indicator.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the customer includes one of a current customer and a prospective customer.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the customer contact indicator includes performing at least one operation selected from the operations consisting of: determining whether the customer relevant output will meet a volume request from the customer; determining whether the customer relevant output will meet a quality request from the customer; determining whether the customer relevant output will meet a timing request from the customer; and determining whether the customer relevant output will meet an optional request from the customer.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility description circuit is further structured to interpret historical external data from at least one external data source, and wherein the adaptive learning system is further configured to train the facility production predictor in response to the historical external data.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the at least one external data source includes at least one data source selected from the data sources consisting of: a social media data source; a behavioral data source; a spot market price for an energy source; and a forward market price for an energy source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility description circuit is further structured to interpret present external data from the at least one external data source, and wherein the trained facility production predictor is further configured to determine the customer contact indicator in response to the present external data.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a plurality of historical facility parameter values and a corresponding plurality of historical facility outcome values; operating an adaptive learning system, thereby training a facility production predictor in response to the plurality of historical facility parameter values and the corresponding plurality of historical facility outcome values; interpreting a plurality of present state facility parameter values; operating the trained facility production predictor to determine a customer contact indicator in response to the plurality of present state facility parameter values; and providing a notification to a customer in response to the customer contact indicator.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the customer includes one of a current customer and a prospective customer.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the customer contact indicator includes determining whether a customer relevant output will meet a volume request from the customer.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the customer contact indicator includes determining whether a customer relevant output will meet a quality request from the customer.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the customer contact indicator determining whether a customer relevant output will meet a timing request from the customer.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the customer contact indicator includes determining whether a customer relevant output will meet an optional request from the customer.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include including interpreting historical external data from at least one external data source, and operating the adaptive learning system to further train the facility production predictor in response to the historical external data.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the at least one external data source includes at least one data source selected from the data sources consisting of: a social media data source; a behavioral data source; a spot market price for an energy source; and a forward market price for an energy source.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting present external data from the at least one external data source, and operating the trained facility production predictor to further determine the customer contact indicator in response to the present external data.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a facility including a core task, wherein the core task includes a customer relevant output; a controller, including: a facility description circuit structured to interpret a plurality of historical facility parameter values and a corresponding plurality of historical facility outcome values; a facility prediction circuit structured to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the plurality of historical facility parameter values and the corresponding plurality of historical facility outcome values; wherein the facility description circuit is further structured to interpret a plurality of present state facility parameter values; wherein the trained facility production predictor is configured to determine a customer contact indicator in response to the plurality of present state facility parameter values; and a customer notification circuit structured to provide a notification to a customer in response to the customer contact indicator.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the customer includes one of a current customer and a prospective customer.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the customer contact indicator includes performing at least one operation selected from the operations consisting of: determining whether the customer relevant output will meet a volume request from the customer; determining whether the customer relevant output will meet a quality request from the customer; determining whether the customer relevant output will meet a timing request from the customer; and determining whether the customer relevant output will meet an optional request from the customer.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility description circuit is further structured to interpret historical external data from at least one external data source, and wherein the adaptive learning system is further configured to train the facility production predictor in response to the historical external data.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the at least one external data source includes at least one data source selected from the data sources consisting of: a social media data source; a behavioral data source; a spot market price for an energy source; and a forward market price for an energy source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility description circuit is further structured to interpret present external data from the at least one external data source, and wherein the trained facility production predictor is further configured to determine the customer contact indicator in response to the present external data.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a plurality of historical facility parameter values and a corresponding plurality of historical facility outcome values; operating an adaptive learning system, thereby training a facility production predictor in response to the plurality of historical facility parameter values and the corresponding plurality of historical facility outcome values; interpreting a plurality of present state facility parameter values; operating the trained facility production predictor to determine a customer contact indicator in response to the plurality of present state facility parameter values; and providing a notification to a customer in response to the customer contact indicator.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the customer includes one of a current customer and a prospective customer.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the customer contact indicator includes determining whether a customer relevant output will meet a volume request from the customer.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the customer contact indicator includes determining whether a customer relevant output will meet a quality request from the customer.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the customer contact indicator determining whether a customer relevant output will meet a timing request from the customer.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the customer contact indicator includes determining whether a customer relevant output will meet an optional request from the customer.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include including interpreting historical external data from at least one external data source, and operating the adaptive learning system to further train the facility production predictor in response to the historical external data.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the at least one external data source includes at least one data source selected from the data sources consisting of: a social media data source; a behavioral data source; a spot market price for an energy source; and a forward market price for an energy source.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting present external data from the at least one external data source, and operating the trained facility production predictor to further determine the customer contact indicator in response to the present external data.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include an energy and compute facility including: at least one of a compute task or a compute resource; and at least one of an energy source or an energy utilization requirement; and a controller, including: a facility description circuit structured to interpret detected conditions, wherein the detected conditions include at least one condition selected from the conditions consisting of: an input resource for the facility; a facility resource; an output parameter for the facility; and an external condition related to an output of the facility; and a facility configuration circuit structured to operate an adaptive learning system, wherein the adaptive learning system is configured to adjust a facility configuration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adaptive learning system includes at least one of a machine learning system and an artificial intelligence (AI) system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein adjusting the facility configuration further includes at least one operation selected from the operations consisting of: performing a purchase or sale transaction on one of an energy spot market or an energy forward market; performing a purchase or sale transaction on one of a compute resource spot market or a compute resource forward market; and performing a purchase or sale transaction on one of an energy credit spot market or an energy credit forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility further includes a networking task, and wherein adjusting the facility configuration further includes at least one operation selected from the operations consisting of: performing a purchase or sale transaction on one of a network bandwidth spot market or a network bandwidth forward market; and performing a purchase or sale transaction on one of a spectrum spot market or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration further includes adjusting at least one task of the facility to reduce the energy utilization requirement.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting detected conditions relative to a facility, wherein the detected conditions include at least one condition selected from the conditions consisting of: an input resource for the facility; a facility resource; an output parameter for the facility; and an external condition related to an output of the facility; and operating an adaptive learning system, thereby adjusting a facility configuration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein adjusting the facility configuration further includes performing a purchase or sale transaction on one of an energy spot market or an energy forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein adjusting the facility configuration further includes performing a purchase or sale transaction on one of a compute resource spot market or a compute resource forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein adjusting the facility configuration further includes performing a purchase or sale transaction on one of an energy credit spot market or an energy credit forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein adjusting the facility configuration further includes performing a purchase or sale transaction on one of a network bandwidth spot market or a network bandwidth forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein adjusting the facility configuration further includes performing a purchase or sale transaction on one of a spectrum spot market or a spectrum forward market.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include an energy and compute facility including: at least one of a compute task or a compute resource; and at least one of an energy source or an energy utilization requirement; and a controller, including: a facility description circuit structured to interpret detected conditions, wherein the detected conditions relate to a set of input resources for the facility; and a facility configuration circuit structured to operate an adaptive learning system, wherein the adaptive learning system is configured to adjust a facility configuration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adaptive learning system includes at least one of a machine learning system and an artificial intelligence (AI) system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein adjusting the facility configuration further includes at least one operation selected from the operations consisting of: performing a purchase or sale transaction on one of an energy spot market or an energy forward market; performing a purchase or sale transaction on one of a compute resource spot market or a compute resource forward market; and performing a purchase or sale transaction on one of an energy credit spot market or an energy credit forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility further includes a networking task, and wherein adjusting the facility configuration further includes at least one operation selected from the operations consisting of: performing a purchase or sale transaction on one of a network bandwidth spot market or a network bandwidth forward market; and performing a purchase or sale transaction on one of a spectrum spot market or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration further includes adjusting at least one task or configuration of a resource of the facility to change an input resource requirement for the facility.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting detected conditions relative to a facility, wherein the detected conditions relate to a set of input resources for the facility; and operating an adaptive learning system, thereby adjusting a facility configuration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting includes performing a purchase or sale transaction on one of an energy spot market or an energy forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting includes performing a purchase or sale transaction on one of a compute resource spot market or a compute resource forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting includes performing a purchase or sale transaction on one of an energy credit spot market or an energy credit forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting includes performing a purchase or sale transaction on one of a network bandwidth spot market or a network bandwidth forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting includes performing a purchase or sale transaction on one of a spectrum spot market or a spectrum forward market.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include an energy and compute facility including: at least one of a compute task or a compute resource; and at least one of an energy source or an energy utilization requirement; and a controller, including: a facility description circuit structured to interpret detected conditions, wherein the detected conditions relate to at least one resource of the facility; and a facility configuration circuit structured to operate an adaptive learning system, wherein the adaptive learning system is configured to adjust a facility configuration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adaptive learning system includes at least one of a machine learning system and an artificial intelligence (AI) system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein adjusting the facility configuration further includes at least one operation selected from the operations consisting of: performing a purchase or sale transaction on one of an energy spot market or an energy forward market; performing a purchase or sale transaction on one of a compute resource spot market or a compute resource forward market; and performing a purchase or sale transaction on one of an energy credit spot market or an energy credit forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility further includes a networking task, and wherein adjusting the facility configuration further includes at least one operation selected from the operations consisting of: performing a purchase or sale transaction on one of a network bandwidth spot market or a network bandwidth forward market; and performing a purchase or sale transaction on one of a spectrum spot market or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility further includes at least one additional facility resource; wherein the adjusting the facility configuration further includes adjusting a utilization of the compute resource and the at least one additional facility resource; and wherein the at least one additional facility resource includes at least one of a network resource, a data storage resource, or a spectrum resource.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting detected conditions relative to a facility, wherein the detected conditions relate to at least one resource of the facility; and operating an adaptive learning system, thereby adjusting a facility configuration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of an energy spot market or an energy forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of a spectrum spot market or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of a compute resource spot market or a compute resource forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of an energy credit spot market or an energy credit forward market.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include an energy and compute facility including: at least one of a compute task or a compute resource; and at least one of an energy source or an energy utilization requirement; and a controller, including: a facility description circuit structured to interpret detected conditions, wherein the detected conditions include an output parameter for the facility; and a facility configuration circuit structured to operate an adaptive learning system, wherein the adaptive learning system is configured to adjust a facility configuration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adaptive learning system includes at least one of a machine learning system and an artificial intelligence (AI) system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein adjusting the facility configuration further includes at least one operation selected from the operations consisting of: performing a purchase or sale transaction on one of an energy spot market or an energy forward market; performing a purchase or sale transaction on one of a compute resource spot market or a compute resource forward market; and performing a purchase or sale transaction on one of an energy credit spot market or an energy credit forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility further includes a networking task, and wherein adjusting the facility configuration further includes at least one operation selected from the operations consisting of: performing a purchase or sale transaction on one of a network bandwidth spot market or a network bandwidth forward market; and performing a purchase or sale transaction on one of a spectrum spot market or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration further includes adjust one task of the facility to provide at least one of: an increased facility output volume, an increased facility quality value, or an adjusted facility output time value.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting detected conditions relative to a facility, wherein the detected conditions include an output parameter for the facility; and operating an adaptive learning system thereby adjusting a facility configuration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of an energy spot market or an energy forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of a spectrum spot market or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of a compute resource spot market or a compute resource forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of an energy credit spot market or an energy credit forward market.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include an energy and compute facility including: at least one of a compute task or a compute resource; and at least one of an energy source or an energy utilization requirement; and a controller, including: a facility description circuit structured to interpret detected conditions, wherein the detected conditions include a utilization parameter for an output of the facility; and a facility configuration circuit structured to operate an adaptive learning system, wherein the adaptive learning system is configured to adjust a facility configuration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adaptive learning system includes at least one of a machine learning system and an artificial intelligence (AI) system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein adjusting the facility configuration further includes at least one operation selected from the operations consisting of: performing a purchase or sale transaction on one of an energy spot market or an energy forward market; performing a purchase or sale transaction on one of a compute resource spot market or a compute resource forward market; and performing a purchase or sale transaction on one of an energy credit spot market or an energy credit forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility further includes a networking task, and wherein adjusting the facility configuration further includes at least one operation selected from the operations consisting of: performing a purchase or sale transaction on one of a network bandwidth spot market or a network bandwidth forward market; and performing a purchase or sale transaction on one of a spectrum spot market or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration further includes adjusting at least one task of the facility to reduce the utilization parameter for the facility.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting detected conditions relative to a facility, wherein the detected conditions include a utilization parameter for an output of the facility; and operating an adaptive learning system, thereby adjusting a facility configuration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of an energy spot market or an energy forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of a spectrum spot market or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of a compute resource spot market or a compute resource forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of an energy credit spot market or an energy credit forward market.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include an energy and compute facility including: at least one of a compute task or a compute resource; and at least one of an energy source or an energy utilization requirement; and a controller, including: a facility model circuit structured to operate a digital twin for the facility; a facility description circuit structured to interpret a set of parameters from the digital twin for the facility; and a facility configuration circuit structured to operate an adaptive learning system, wherein the adaptive learning system is configured to adjust a facility configuration based on the set of parameters from the digital twin for the facility.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adaptive learning system includes at least one of a machine learning system and an artificial intelligence (AI) system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein adjusting the facility configuration further includes at least one operation selected from the operations consisting of: performing a purchase or sale transaction on one of an energy spot market or an energy forward market; performing a purchase or sale transaction on one of a compute resource spot market or a compute resource forward market; and performing a purchase or sale transaction on one of an energy credit spot market or an energy credit forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility further includes a networking task, and wherein adjusting the facility configuration further includes at least one operation selected from the operations consisting of: performing a purchase or sale transaction on one of a network bandwidth spot market or a network bandwidth forward market; and performing a purchase or sale transaction on one of a spectrum spot market or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility description circuit is further structured to interpret detected conditions, wherein the detected conditions include at least one condition selected from the conditions consisting of: an input resource for the facility; a facility resource; an output parameter for the facility; and an external condition related to an output of the facility; and wherein the facility model circuit is further structured to update the digital twin for the facility in response to the detected conditions.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include operating a model including a digital twin for a facility interpreting a set of parameters from the digital twin for the facility operating an adaptive learning system, thereby adjusting a facility configuration based on the set of parameters from the digital twin for the facility.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of an energy spot market or an energy forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of a spectrum spot market or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of a compute resource spot market or a compute resource forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of an energy credit spot market or an energy credit forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adjusting the facility configuration includes performing a purchase or sale transaction on one of a network bandwidth spot market or a network bandwidth forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting detected conditions relative to the facility, wherein the detected conditions include at least one condition selected from the conditions consisting of: an input resource for the facility; a facility resource; an output parameter for the facility; and an external condition related to an output of the facility; and operating the adaptive learning system, thereby updating the digital twin for the facility in response to the detected conditions.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a machine having an associated regenerative energy facility, the machine having a requirement for at least one of a compute task, a networking task, and an energy consumption task; and a controller, comprising: an energy requirement circuit structured to determine an amount of energy for the machine to service the at least one of the compute task, the networking task, and the energy consumption task in response to the requirement for the at least one of the compute task, the networking task, and the energy consumption task; and an energy distribution circuit structured to adaptively improve an energy delivery of energy produced by the associated regenerative energy facility between the at least one of the compute task, the networking task, and the energy consumption task.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the energy consumption task includes s a core task.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller further includes an energy market circuit structured to access an energy market, and wherein the energy distribution circuit is further structured to adaptively improve the energy delivery of the energy produced by the associated regenerative energy facility between the compute task, the networking task, the energy consumption task, and a sale of the energy produced on the energy market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the energy market comprises at least one of a spot market or a forward market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the energy distribution circuit further comprises at least one of a machine learning component, an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include determining an amount of energy for a machine to service at least one of a compute task, a networking task, or an energy consumption task in response to a compute task requirement, a networking task requirement, and an energy consumption task requirement; adaptively improving an energy delivery between: the compute task, the networking task, and the energy consumption task; wherein the energy delivery is of energy produced by a regenerative energy facility of the machine.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include accessing an energy market, and adaptively improving the energy delivery of the energy produced by the regenerative energy facility between: the compute task, the networking task, the energy consumption task, and a sale of the energy produced on the energy market.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a machine having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; and a controller, comprising: a resource requirement circuit structured to determine an amount of a resource for the machine to service at least one of the compute task requirement, the networking task requirement, and the energy consumption task requirement; a forward resource market circuit structured to access a forward resource market; and a resource distribution circuit structured to execute a transaction of the resource on the forward resource market in response to the determined amount of the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a compute resource, and wherein the forward resource market comprises a forward market for compute resources.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a spectrum allocation resource, and wherein the forward resource market comprises a forward market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy credit resource, and wherein the forward resource market comprises a forward market for energy credits.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy resource, and wherein the forward resource market comprises a forward market for energy.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a data storage resource, and wherein the forward resource market comprises a forward market for data storage capacity.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy storage resource, and wherein the forward resource market comprises a forward market for energy storage capacity.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a network bandwidth resource, and wherein the forward resource market comprises a forward market for network bandwidth.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the transaction of the resource on the forward resource market comprises one of buying or selling the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve one of an output value of the machine or a cost of operation of the machine using executed transactions on the forward resource market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit further comprises at least one of a machine learning component, an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include determining an amount of a resource for a machine to service at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement of a machine; accessing a forward resource market; and executing a transaction of the resource on the forward resource market in response to the determined amount of the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a compute resource, and wherein the forward resource market comprises a forward market for compute resources.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a spectrum allocation resource, and wherein the forward resource market comprises a forward market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy credit resource, and wherein the forward resource market comprises a forward market for energy credits.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy resource, and wherein the forward resource market comprises a forward market for energy.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a data storage resource, and wherein the forward resource market comprises a forward market for data storage capacity.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy storage resource, and wherein the forward resource market comprises a forward market for energy storage capacity.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a network bandwidth resource, and wherein the forward resource market comprises a forward market for network bandwidth.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein executing the transaction of the resource on the forward resource market comprises one of buying or selling the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adaptively improving improve one of an output value of the machine or a cost of operation of the machine using executed transactions on the forward resource market.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a fleet of machines each having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; and a controller, comprising: a resource requirement circuit structured to determine an amount of a resource for each of the machines to service at least one of the compute task requirement, the networking task requirement, and the energy consumption task requirement for each corresponding machine; a forward resource market circuit structured to access a forward resource market; and a resource distribution circuit structured to execute an aggregated transaction of the resource on the forward resource market in response to the determined amount of the resource for each of the machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a compute resource, and wherein the forward resource market comprises a forward market for compute resources.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a spectrum allocation resource, and wherein the forward resource market comprises a forward market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy credit resource, and wherein the forward resource market comprises a forward market for energy credits.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy resource, and wherein the forward resource market comprises a forward market for energy.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a data storage resource, and wherein the forward resource market comprises a forward market for data storage capacity.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy storage resource, and wherein the forward resource market comprises a forward market for energy storage capacity.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a network bandwidth resource, and wherein the forward resource market comprises a forward market for network bandwidth.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the aggregated transaction of the resource on the forward resource market comprises one of buying or selling the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve one of an aggregate output value of the fleet of machines or a cost of operation of the fleet of machines using executed aggregated transactions on the forward resource market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit further comprises at least one of a machine learning component, an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include determining an amount of a resource, for each of machine of a fleet of machines, to service at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement for each corresponding machine; accessing a forward resource market executing an aggregated transaction of the resource on the forward resource market in response to the determined amount of the resource for each of the machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a compute resource, and wherein the forward resource market comprises a forward market for compute resources.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a spectrum allocation resource, and wherein the forward resource market comprises a forward market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy credit resource, and wherein the forward resource market comprises a forward market for energy credits.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy resource, and wherein the forward resource market comprises a forward market for energy.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a data storage resource, and wherein the forward resource market comprises a forward market for data storage capacity.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy storage resource, and wherein the forward resource market comprises a forward market for energy storage capacity.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a network bandwidth resource, and wherein the forward resource market comprises a forward market for network bandwidth.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein executing the aggregated transaction of the resource on the forward resource market comprises one of buying or selling the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adaptively improving one of an aggregate output value of the fleet of machines or a cost of operation of the fleet of machines using executed aggregated transactions on the forward resource market.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a fleet of machines each having a requirement for at least one of a compute task, a networking task, and an energy consumption task; and a controller, comprising: a resource requirement circuit structured to determine an amount of a resource for each of the machines to service the requirement for the at least one of the compute task, the networking task, and the energy consumption task for each corresponding machine; and a resource distribution circuit structured to adaptively improve a resource utilization of the resource for each of the machines between the compute task, the networking task, and the energy consumption task for each corresponding machine.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a compute resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a spectrum allocation resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy credit resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a data storage resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy storage resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit further comprises at least one of a machine learning component, an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include determining an amount of a resource, for each of machine of a fleet of machines, to service a requirement of at least one of a compute task, a networking task, and an energy consumption task for each corresponding machine; and adaptively improving a resource utilization of the resource for each of the machines between the compute task, the networking task, and the energy consumption task for each corresponding machine.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a compute resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a spectrum allocation resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy credit resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a data storage resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy storage resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a network bandwidth resource.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a machine having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; and a controller, comprising: a resource requirement circuit structured to determine an amount of a resource for the machine to service at least one of the compute task requirement, the networking task requirement, and the energy consumption task requirement; a resource market circuit structured to access a resource market; and a resource distribution circuit structured to execute a transaction of the resource on the resource market in response to the determined amount of the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy resource, and wherein the resource market comprises a spot market for energy.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy credit resource, and wherein the resource market comprises a spot market for energy credits.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a spectrum allocation resource, and wherein the resource market comprises a spot market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve one of an output value of the machine or a cost of operation of the machine using executed transactions on the resource market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit further comprises at least one of a machine learning component, an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include determining an amount of a resource for a machine to service at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; accessing a resource market; and executing a transaction of the resource on the resource market in response to the determined amount of the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy resource, and wherein the resource market comprises a spot market for energy.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy credit resource, and wherein the resource market comprises a spot market for energy credits.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a spectrum allocation resource, and wherein the resource market comprises a spot market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adaptively improving one of an output value of the machine or a cost of operation of the machine using executed transactions on the resource market.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a fleet of machines each having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; and a controller, comprising: a resource requirement circuit structured to determine an amount of a resource for each of the machines to service at least one of the compute task requirement, the networking task requirement, and the energy consumption task requirement for each corresponding machine; a resource market circuit structured to access a resource market; and a resource distribution circuit structured to execute an aggregated transaction of the resource on the resource market in response to the determined amount of the resource for each of the machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy resource, and wherein the resource market comprises a spot market for energy.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy credit resource, and wherein the resource market comprises a spot market for energy credits.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a spectrum allocation resource, and wherein the resource market comprises a spot market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve one of an aggregate output value of the fleet of machines or a cost of operation of the fleet of machines using executed transactions on the resource market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit further comprises at least one of a machine learning component, an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include determining an amount of a resource, for each of machine of a fleet of machines, to service at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement for each corresponding machine; accessing a resource market; and executing an aggregated transaction of the resource on the resource market in response to the determined amount of the resource for each of the machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy resource, and wherein the resource market comprises a spot market for energy.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy credit resource, and wherein the resource market comprises a spot market for energy credits.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a spectrum allocation resource, and wherein the resource market comprises a spot market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adaptively improving one of an aggregate output value of the fleet of machines or a cost of operation of the fleet of machines using executed transactions on the resource market.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a machine having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; and a controller, comprising: a resource requirement circuit structured to determine an amount of a resource for the machine to service at least one of the compute task requirement, the networking task requirement, and the energy consumption task requirement; a social media data circuit structured to interpret data from a plurality of social media data sources; a forward resource market circuit structured to access a forward resource market; a market forecasting circuit structured to predict a forward market price of the resource on the forward resource market in response to the plurality of social media data sources; and a resource distribution circuit structured to execute a transaction of the resource on the forward resource market in response to the determined amount of the resource and the predicted forward market price of the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a spectrum allocation resource, and wherein the forward resource market comprises a forward market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy credit resource, and wherein the forward resource market comprises a forward market for energy credits.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy resource, and wherein the forward resource market comprises a forward market for energy.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the transaction of the resource on the forward resource market comprises one of buying or selling the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the market forecasting circuit further comprises at least one of a machine learning component, an artificial intelligence component, or a neural network component.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve one of an output value of the machine or a cost of operation of the machine using executed transactions on the forward resource market.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit further comprises at least one of a machine learning component, an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include determining an amount of a resource for a machine to service at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; interpreting data from a plurality of social media data sources; accessing a forward resource market; predicting a forward market price of the resource on the forward resource market in response to the plurality of social media data sources; and executing a transaction of the resource on the forward resource market in response to the determined amount of the resource and the predicted forward market price of the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a spectrum allocation resource, and wherein the forward resource market comprises a forward market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy credit resource, and wherein the forward resource market comprises a forward market for energy credits.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy resource, and wherein the forward resource market comprises a forward market for energy.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein executing the transaction of the resource on the forward resource market comprises one of buying or selling the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adaptively improving one of an output value of the machine or a cost of operation of the machine using executed transactions on the forward resource market.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a machine having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; and a controller. The controller including a resource requirement circuit structured to determine an amount of a resource for the machine to service at least one of the compute task requirement, the networking task requirement, and the energy consumption task requirement; a resource market circuit structured to access a resource market; a market testing circuit structured to execute a first transaction of the resource on the resource market in response to the determined amount of the resource; and an arbitrage execution circuit structured to execute a second transaction of the resource on the resource market in response to the determined amount of the resource and further in response to an outcome of the execution of the first transaction, wherein the second transaction comprises a larger transaction than the first transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a compute resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a spectrum allocation resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy credit resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a data storage resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy storage resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the arbitrage execution circuit is further structured to adaptively improve an arbitrage parameter by adjusting a relative size of the first transaction and the second transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the arbitrage parameter comprises at least one parameter selected from the parameters consisting of: a similarity value in a market response of the first transaction and the second transaction; a confidence value of the first transaction to provide test information for the second transaction; and a market effect of the first transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the arbitrage execution circuit further comprises at least one of a machine learning component, an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include determining an amount of a resource for a machine to service at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; accessing a resource market; executing a first transaction of the resource on the resource market in response to the determined amount of the resource; and executing a second transaction of the resource on the resource market in response to the determined amount of the resource and further in response to an outcome of the execution of the first transaction, wherein the second transaction comprises a larger transaction than the first transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a compute resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a spectrum allocation resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy credit resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a data storage resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises an energy storage resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adaptively improving an arbitrage parameter by adjusting a relative size of the first transaction and the second transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the arbitrage parameter comprises at least one parameter selected from the parameters consisting of: a similarity value in a market response of the first transaction and the second transaction; a confidence value of the first transaction to provide test information for the second transaction; and a market effect of the first transaction.

The present disclosure describes an apparatus, the apparatus according to one disclosed non-limiting embodiment of the present disclosure can include a resource requirement circuit structured to determine an amount of a resource for a machine to service at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; a resource market circuit structured to access a resource market; a market testing circuit structured to execute a first transaction of the resource on the resource market in response to the determined amount of the resource; and an arbitrage execution circuit structured to execute a second transaction of the resource on the resource market in response to the determined amount of the resource and further in response to an outcome of the execution of the first transaction, wherein the second transaction comprises a larger transaction than the first transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource comprises at least one resource selected from the resources consisting of: a compute resource; a spectrum allocation resources; an energy credit resource; an energy resource; a data storage resource; an energy storage resource; and a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the arbitrage execution circuit is further structured to adaptively improve an arbitrage parameter by adjusting a relative size of the first transaction and the second transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the arbitrage parameter comprises a similarity value in a market response of the first transaction and the second transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the arbitrage parameter further comprises a market effect of the first transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the arbitrage parameter comprises a confidence value of the first transaction to provide test information for the second transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the arbitrage parameter further comprises a market effect of the first transaction.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the arbitrage execution circuit further comprises at least one of a machine learning component, an artificial intelligence component, or a neural network component.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a machine having an associated resource capacity for a resource, the machine having a requirement for at least one of a core task, a compute task, an energy storage task, a data storage task, and a networking task; and a controller, comprising: a resource requirement circuit structured to determine an amount of the resource to service the requirement of the at least one of the core task, the compute task, the energy storage task, the data storage task, and the networking task in response to the requirement of the at least one of the core task, the compute task, the energy storage task, the data storage task, and the networking task; and a resource distribution circuit structured to adaptively improve, in response to the associated resource capacity, a resource delivery of the resource between the core task, the compute task, the energy storage task, the data storage task, and the networking task.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the associated resource capacity comprises a compute capacity for a compute resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the associated resource capacity comprises an energy capacity for an energy resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the associated resource capacity comprises a network bandwidth capacity for a networking resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the associated resource capacity comprises an energy storage capacity for an energy storage resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve the resource delivery in response to one of a quality and an output associated with the core task.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve the resource delivery in response to a cost of operation of the machine.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit further comprises at least one of a machine learning component, an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include determining an amount of a resource to service a core task, a compute task, an energy storage task, a data storage task, and a networking task of a machine, in response to at least one of a core task requirement, a compute task requirement, an energy storage task requirement, a data storage task requirement, and a networking task requirement of the machine; and adaptively improving, in response to an associated resource capacity of the machine, a resource delivery of the resource between the core task, the compute task, the energy storage task, the data storage task, and the networking task.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the associated resource capacity comprises a compute capacity for a compute resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the associated resource capacity comprises an energy capacity for an energy resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the associated resource capacity comprises a network bandwidth capacity for a networking resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the associated resource capacity comprises an energy storage capacity for an energy storage resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adaptively improving the resource delivery in response to one of a quality and an output associated with the core task of the machine.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adaptively improving the resource delivery in response to a cost of operation of the machine.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a fleet of machines each having an associated resource capacity for a resource, and each machine of the fleet of machines further having a requirement for at least one of a core task, a compute task, an energy storage task, a data storage task, and a networking task; and a controller. The controller including a resource requirement circuit structured to determine an aggregated amount of the resource to service the at least one of the core task, the compute task, the energy storage task, the data storage task, and the networking task for each of the fleet of machines in response to the requirement of the at least one of the core task, the compute task, the energy storage task, the data storage task, and the networking task for each one of the fleet of machines; and a resource distribution circuit structured to adaptively improve, in response to an aggregated associated resource capacity, an aggregated resource delivery of the resource between the core task, the compute task, the energy storage task, the data storage task, and the networking task for each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the aggregated associated resource capacity comprises a compute capacity for a compute resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the aggregated associated resource capacity comprises an energy capacity for an energy resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the aggregated associated resource capacity comprises a network bandwidth capacity for a networking resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the aggregated associated resource capacity comprises an energy storage capacity for an energy storage resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve the aggregated resource delivery in response to one of a quality and an output associated with the core task for each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve the aggregated resource delivery in response to an aggregated one of a quality and an output associated with the core task for the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to interpret a resource transferability value between at least two machines of the fleet of machines, and to adaptively improve the aggregated resource delivery further in response to the resource transferability value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve the aggregated resource delivery in response to a cost of operation of each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve the aggregated resource delivery in response to an aggregated cost of operation of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit further comprises at least one of a machine learning component, an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include determining an aggregated amount of a resource to service a core task, a compute task, an energy storage task, a data storage task, and a networking task for each machine of a fleet of machines, in response to at least one of a core task requirement, a compute task requirement, an energy storage task requirement, a data storage task requirement, and a networking task requirement for each machine of the fleet of machines; and adaptively improving, in response to an aggregated associated resource capacity of the fleet of machines, an aggregated resource delivery of the resource between the core task, the compute task, the energy storage task, the data storage task, and the networking task for each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adaptively improving the aggregated resource delivery in response to one of a quality and an output associated with the core task for each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adaptively improving the aggregated resource delivery in response to an aggregated one of a quality and an output associated with the core task for each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting a resource transferability value between at least two machines of the fleet of machines, and adaptively improving the aggregated resource delivery further in response to the resource transferability value.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adaptively improving the aggregated resource delivery in response to a cost of operation of each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adaptively improving the aggregated resource delivery in response to an aggregated cost of operation of the fleet of machines.

The present disclosure describes an apparatus, the apparatus according to one disclosed non-limiting embodiment of the present disclosure can include a resource requirement circuit structured to determine an aggregated amount of a resource to service a core task, a compute task, an energy storage task, a data storage task, and a networking task for each machine of a fleet of machines in response to at least one of a core task requirement, a compute task requirement, an energy storage task requirement, a data storage task requirement, and a networking task requirement for each machine of the fleet of machines; and a resource distribution circuit structured to adaptively improve, in response to an aggregated associated resource capacity of the fleet of machines, an aggregated resource delivery of the resource between the core task, the compute task, the energy storage task, the data storage task, and the networking task for each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the aggregated associated resource capacity comprises a compute capacity for a compute resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the aggregated associated resource capacity comprises an energy capacity for an energy resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the aggregated associated resource capacity comprises a network bandwidth capacity for a networking resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the aggregated associated resource capacity comprises an energy storage capacity for an energy storage resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve the aggregated resource delivery in response to a quality associated with the core task for each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve the aggregated resource delivery in response to an output associated with the core task for each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve the aggregated resource delivery in response to an aggregated quality associated with the core task for the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve the aggregated resource delivery in response to an aggregated output associated with the core task for the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to interpret a resource transferability value between at least two machines of the fleet of machines, and to adaptively improve the aggregated resource delivery further in response to the resource transferability value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve the aggregated resource delivery in response to a cost of operation of each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit is further structured to adaptively improve the aggregated resource delivery in response to an aggregated cost of operation of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource distribution circuit further comprises at least one of a machine learning component, an artificial intelligence component, or a neural network component.

Provided herein are methods and systems that improve the machines that enable markets, including for increased efficiency, speed, reliability, and the like for participants in such markets.

Provided herein are improved machines that enable distributed transactions to occur at scale among large numbers of participants, including human participants and automated agents.

Certain systems and operations are described herein for improving or optimizing energy utilization and/or acquisition for compute, networking, and/or other tasks.

In embodiments, a platform for enabling transactions is provided having a machine with a regenerative energy facility that optimizes allocation of delivery of energy produced among compute tasks, networking tasks and energy consumption tasks.

In embodiments, a platform for enabling transactions is provided having a machine that automatically purchases its energy in a forward market for energy.

In embodiments, a platform for enabling transactions is provided having a machine that automatically purchases energy credits in a forward market.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically aggregate purchasing in a forward market for energy.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically aggregate purchasing energy credits in a forward market.

In embodiments, a platform for enabling transactions is provided having a machine that automatically purchases spectrum allocation in a forward market for network spectrum.

In embodiments, a platform for enabling transactions is provided having a machine that automatically sells its compute capacity on a forward market for compute capacity.

In embodiments, a platform for enabling transactions is provided having a machine that automatically sells its compute storage capacity on a forward market for storage capacity.

In embodiments, a platform for enabling transactions is provided having a machine that automatically sells its energy storage capacity on a forward market for energy storage capacity.

In embodiments, a platform for enabling transactions is provided having a machine that automatically sells its network bandwidth on a forward market for network capacity.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically purchase spectrum allocation in a forward market for network spectrum.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically optimize energy utilization for compute task allocation (e.g., bitcoin mining).

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically aggregate data on collective optimization of forward market purchases of energy.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically aggregate data on collective optimization of forward market purchases of energy credits.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically aggregate data on collective optimization of forward market purchases of network spectrum

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically aggregate data on collective optimization of forward market sales of compute capacity.

In embodiments, a platform for enabling transactions is provided having a machine that automatically purchases its energy in a spot market for energy.

In embodiments, a platform for enabling transactions is provided having a machine that automatically purchases energy credits in a spot market.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically aggregate purchasing in a spot market for energy.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically aggregate purchasing energy credits in a spot market.

In embodiments, a platform for enabling transactions is provided having a machine that automatically purchases spectrum allocation in a spot market for network spectrum.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically purchase spectrum allocation in a spot market for network spectrum.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically optimize energy utilization for compute task allocation (e.g., bitcoin mining).

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically aggregate data on collective optimization of spot market purchases of energy.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically aggregate data on collective optimization of spot market purchases of energy credits.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically aggregate data on collective optimization of spot market purchases of network spectrum.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically sell their aggregate compute capacity on a forward market for compute capacity.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically sell their aggregate compute storage capacity on a forward market for storage capacity.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically sell their aggregate energy storage capacity on a forward market for energy storage capacity.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically sell their aggregate network bandwidth on a forward market for network capacity.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market pricing of energy prices based on information collected from social media data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market pricing of network spectrum based on information collected from social media data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market pricing of energy credits based on information collected from social media data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market value of compute capability based on information collected from social media data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically executes an arbitrage strategy for purchase or sale of compute capacity by testing a spot market for compute capacity with a small transaction and rapidly executing a larger transaction based on the outcome of the small transaction.

In embodiments, a platform for enabling transactions is provided having a machine that automatically executes an arbitrage strategy for purchase or sale of energy storage capacity by testing a spot market for compute capacity with a small transaction and rapidly executing a larger transaction based on the outcome of the small transaction.

In embodiments, a platform for enabling transactions is provided having a machine that automatically executes an arbitrage strategy for purchase or sale of network spectrum or bandwidth by testing a spot market for compute capacity with a small transaction and rapidly executing a larger transaction based on the outcome of the small transaction.

In embodiments, a platform for enabling transactions is provided having a machine that automatically executes an arbitrage strategy for purchase or sale of energy by testing a spot market for compute capacity with a small transaction and rapidly executing a larger transaction based on the outcome of the small transaction.

In embodiments, a platform for enabling transactions is provided having a machine that automatically executes an arbitrage strategy for purchase or sale of energy credits by testing a spot market for compute capacity with a small transaction and rapidly executing a larger transaction based on the outcome of the small transaction.

In embodiments, a platform for enabling transactions is provided having a machine that automatically allocates its energy capacity among a core task, a compute task, an energy storage task, a data storage task and a networking task.

In embodiments, a platform for enabling transactions is provided having a machine that automatically allocates its compute capacity among a core task, a compute task, an energy storage task, a data storage task and a networking task.

In embodiments, a platform for enabling transactions is provided having a machine that automatically allocates its networking capacity among a core task, a compute task, an energy storage task, a data storage task and a networking task.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically allocate collective energy capacity among a core task, a compute task, an energy storage task, a data storage task and a networking task.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically allocate collective compute capacity among a core task, a compute task, an energy storage task, a data storage task and a networking task.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically allocate collective networking capacity among a core task, a compute task, an energy storage task, a data storage task and a networking task.

Certain systems and/or operations for utilizing a blockchain for knowledge to enable transactions are described herein.

In embodiments, a platform for enabling transactions is provided having a smart contract wrapper using a distributed ledger wherein the smart contract embeds IP licensing terms for intellectual property embedded in the distributed ledger and wherein executing an operation on the distributed ledger provides access to the intellectual property and commits the executing party to the IP licensing terms.

In embodiments, a platform for enabling transactions is provided having a distributed ledger for aggregating intellectual property licensing terms, wherein a smart contract wrapper on the distributed ledger allows an operation on the ledger to add intellectual property to an aggregate stack of intellectual property.

In embodiments, a platform for enabling transactions is provided having a distributed ledger for aggregating intellectual property licensing terms, wherein a smart contract wrapper on the distributed ledger allows an operation on the ledger to add intellectual property to agree to an apportionment of royalties among the parties in the ledger.

In embodiments, a platform for enabling transactions is provided having a distributed ledger for aggregating intellectual property licensing terms, wherein a smart contract wrapper on the distributed ledger allows an operation on the ledger to add intellectual property to an aggregate stack of intellectual property.

In embodiments, a platform for enabling transactions is provided having a distributed ledger for aggregating intellectual property licensing terms, wherein a smart contract wrapper on the distributed ledger allows an operation on the ledger to commit a party to a contract term.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes an instruction set, such that operation on the distributed ledger provides provable access to the instruction set.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes executable algorithmic logic, such that operation on the distributed ledger provides provable access to the executable algorithmic logic.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes a 3D printer instruction set, such that operation on the distributed ledger provides provable access to the instruction set.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes an instruction set for a coating process, such that operation on the distributed ledger provides provable access to the instruction set.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes an instruction set for a semiconductor fabrication process, such that operation on the distributed ledger provides provable access to the fabrication process.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes a firmware program, such that operation on the distributed ledger provides provable access to the firmware program.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes an instruction set for an FPGA, such that operation on the distributed ledger provides provable access to the FPGA.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes serverless code logic, such that operation on the distributed ledger provides provable access to the serverless code logic.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes an instruction set for a crystal fabrication system, such that operation on the distributed ledger provides provable access to the instruction set.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes an instruction set for a food preparation process, such that operation on the distributed ledger provides provable access to the instruction set.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes an instruction set for a polymer production process, such that operation on the distributed ledger provides provable access to the instruction set.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes an instruction set for chemical synthesis process, such that operation on the distributed ledger provides provable access to the instruction set.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes an instruction set for a biological production process, such that operation on the distributed ledger provides provable access to the instruction set.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes a trade secret with an expert wrapper, such that operation on the distributed ledger provides provable access to the trade secret and the wrapper provides validation of the trade secret by the expert.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that aggregates views of a trade secret into a chain that proves which and how many parties have viewed the trade secret.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes an instruction set, such that operation on the distributed ledger provides provable access to the instruction set and execution of the instruction set on a system results in recording a transaction in the distributed ledger.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that tokenizes an item of intellectual property and a reporting system that reports an analytic result based on the operations performed on the distributed ledger or the intellectual property.

In embodiments, a platform for enabling transactions is provided having a distributed ledger that aggregates a set of instructions, where an operation on the distributed ledger adds at least one instruction to a pre-existing set of instructions to provide a modified set of instructions.

Certain systems and/or operations for utilizing and/or executing transactions with an intelligent cryptocurrency or cryptocurrency transaction manager are described herein.

In embodiments, a platform for enabling transactions is provided having a smart wrapper for a cryptocurrency coin that directs execution of a transaction involving the coin to a geographic location based on tax treatment of at least one of the coin and the transaction in the geographic location.

In embodiments, a platform for enabling transactions is provided having a smart wrapper for a cryptocurrency coin that directs execution of a transaction involving the coin to a geographic location based on tax treatment of at least one of the coin and the transaction in the geographic location.

In embodiments, a platform for enabling transactions is provided having a self-executing cryptocurrency coin that commits a transaction upon recognizing a location-based parameter that provides favorable tax treatment.

In embodiments, a platform for enabling transactions is provided having an expert system that uses machine learning to optimize the execution of cryptocurrency transactions based on tax status.

In embodiments, a platform for enabling transactions is provided having an expert system that aggregates regulatory information covering cryptocurrency transactions and automatically selects a jurisdiction for an operation based on the regulatory information.

In embodiments, a platform for enabling transactions is provided having an expert system that uses machine learning to optimize the execution of a cryptocurrency transaction based on real time energy price information for an available energy source.

In embodiments, a platform for enabling transactions is provided having an expert system that uses machine learning to optimize the execution of a cryptocurrency transaction based on an understanding of available energy sources to power computing resources to execute the transaction.

In embodiments, a platform for enabling transactions is provided having an expert system that uses machine learning to optimize charging and recharging cycle of a rechargeable battery system to provide energy for execution of a cryptocurrency transaction.

Certain systems and operations for making forward market predictions, and/or enabling transactions utilizing forward market predictions are described herein. In certain embodiments, forward market predictions described herein include non-traditional data, and/or include data for forward market predictions that are not utilized in previously known systems.

In embodiments, a platform for enabling transactions is provided having an expert system that predicts a forward market price in a market based on an understanding obtained by analyzing Internet of Things data sources and executes a transaction based on the forward market prediction.

In embodiments, a platform for enabling transactions is provided having an expert system that predicts a forward market price in a market based on an understanding obtained by analyzing social network data sources and executes a transaction based on the forward market prediction.

In embodiments, a platform for enabling transactions is provided having an expert system that predicts a forward market price in a market based on an understanding obtained by analyzing Internet of Things data sources and executes a cryptocurrency transaction based on the forward market prediction.

In embodiments, a platform for enabling transactions is provided having an expert system that predicts a forward market price in a market based on an understanding obtained by analyzing social network data sources and executes a cryptocurrency transaction based on the forward market prediction.

In embodiments, a platform for enabling transactions is provided having an expert system that predicts a forward market price in an energy market based on an understanding obtained by analyzing Internet of Things data sources and executes a transaction based on the forward market prediction.

In embodiments, a platform for enabling transactions is provided having an expert system that predicts a forward market price in an energy market based on an understanding obtained by analyzing social network data sources and executes a transaction based on the forward market prediction.

In embodiments, a platform for enabling transactions is provided having an expert system that predicts a forward market price in a market for computing resources based on an understanding obtained by analyzing Internet of Things data sources and executes a transaction based on the forward market prediction.

In embodiments, a platform for enabling transactions is provided having an expert system that predicts a forward market price in a market for spectrum or network bandwidth based on an understanding obtained by analyzing Internet of Things data sources and executes a transaction based on the forward market prediction.

In embodiments, a platform for enabling transactions is provided having an expert system that predicts a forward market price in a market for computing resources based on an understanding obtained by analyzing social network data sources and executes a transaction based on the forward market prediction.

In embodiments, a platform for enabling transactions is provided having an expert system that predicts a forward market price in a market for advertising based on an understanding obtained by analyzing Internet of Things data sources and executes a transaction based on the forward market prediction.

In embodiments, a platform for enabling transactions is provided having an expert system that predicts a forward market price in a market for advertising based on an understanding obtained by analyzing social network data sources and executes a transaction based on the forward market prediction.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market pricing of energy prices based on information collected from automated agent behavioral data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market pricing of network spectrum based on information collected from automated agent behavioral data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market pricing of energy credits based on information collected from automated agent behavioral data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market value of compute capability based on information collected from automated agent behavioral data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market pricing of energy prices based on information collected from business entity behavioral data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market pricing of network spectrum based on information collected from business entity behavioral data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market pricing of energy credits based on information collected from business entity behavioral data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market value of compute capability based on information collected from business entity behavioral data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market pricing of energy prices based on information collected from human behavioral data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market pricing of network spectrum based on information collected from human behavioral data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market pricing of energy credits based on information collected from human behavioral data sources.

In embodiments, a platform for enabling transactions is provided having a machine that automatically forecasts forward market value of compute capability based on information collected from human behavioral data sources.

In embodiments, a platform for enabling transactions is provided having an expert system that predicts a forward market price in a market for spectrum or network bandwidth based on an understanding obtained by analyzing social data sources and executes a transaction based on the forward market prediction.

In embodiments, a platform for enabling transactions is provided having an intelligent agent that is configured to solicit the attention resources of another external intelligent agent.

In embodiments, a platform for enabling transactions is provided having a machine that automatically purchases attention resources in a forward market for attention.

In embodiments, a platform for enabling transactions is provided having a fleet of machines that automatically aggregate purchasing in a forward market for attention.

Provided herein are a flexible, intelligent energy and compute facility, as well as an intelligent energy and compute facility resource management system, including components, systems, services, modules, programs, processes and other enabling elements, such as capabilities for data collection, storage and processing, automated configuration of inputs, resources and outputs, and learning on a training set of facility outcomes, facility parameters, and data collected from data sources to train an artificial intelligence/machine learning system to optimize parameters relevant to such a facility.

In embodiments, provided herein is an information technology system for providing data to an intelligent energy and compute facility resource management system having a system for learning on a training set of facility outcomes, facility parameters, and data collected from data sources to train an artificial intelligence/machine learning system to predict a likelihood of a facility production outcome.

In embodiments, provided herein is an information technology system for providing data to an intelligent energy and compute facility resource management system having a system for learning on a training set of facility outcomes, facility parameters, and data collected from data sources to train an artificial intelligence/machine learning system to predict a facility production outcome.

In embodiments, provided herein is an information technology system for providing data to an intelligent energy and compute facility resource management system having a system for learning on a training set of facility outcomes, facility parameters, and data collected from data sources to train an artificial intelligence/machine learning system to optimize provisioning and allocation of energy and compute resources to produce a favorable facility resource utilization profile among a set of available profiles.

In embodiments, provided herein is an information technology system for providing data to an intelligent energy and compute facility resource management system having a system for learning on a training set of facility outcomes, facility parameters, and data collected from data sources to train an artificial intelligence/machine learning system to optimize provisioning and allocation of energy and compute resources to produce a favorable facility resource output selection among a set of available outputs.

In embodiments, provided herein is an information technology system for providing data to an intelligent energy and compute facility resource management system having a system for learning on a training set of facility outcomes, facility parameters, and data collected from data sources to train an artificial intelligence/machine learning system to optimize requisition and provisioning of available energy and compute resources to produce a favorable facility input resource profile among a set of available profiles.

In embodiments, provided herein is an information technology system for providing data to an intelligent energy and compute facility resource management system having a system for learning on a training set of facility outcomes, facility parameters, and data collected from data sources to train an artificial intelligence/machine learning system to optimize configuration of available energy and compute resources to produce a favorable facility resource configuration profile among a set of available profiles.

In embodiments, provided herein is an information technology system for providing data to an intelligent energy and compute facility resource management system having a system for learning on a training set of facility outcomes, facility parameters, and data collected from data sources to train an artificial intelligence/machine learning system to optimize selection and configuration of an artificial intelligence system to produce a favorable facility output profile among a set of available artificial intelligence systems and configurations.

In embodiments, provided herein is an information technology system for providing data to an intelligent energy and compute facility resource management system having a system for learning on a training set of facility outcomes, facility parameters, and data collected from data sources to train an artificial intelligence/machine learning system to generate an indication that a current or prospective customer should be contacted about an output that can be provided by the facility.

In embodiments, provided herein is a system having an intelligent, flexible energy and compute facility whereby an artificial intelligence/machine learning system configures the facility among a set of available configurations based on a set of detected conditions. relating to at least one of an input resource, a facility resource, an output parameter and an external condition related to the output of the facility.

In embodiments, provided herein is a system having an intelligent, flexible energy and compute facility whereby an artificial intelligence/machine learning system configures the facility among a set of available configurations based on a set of detected conditions relating to a set of input resources.

In embodiments, provided herein is a system having an intelligent, flexible energy and compute facility whereby an artificial intelligence/machine learning system configures the facility among a set of available configurations based on a set of detected conditions relating to a set of facility resources.

In embodiments, provided herein is a system having an intelligent, flexible energy and compute facility whereby an artificial intelligence/machine learning system configures the facility among a set of available configurations based on a set of detected conditions relating to an output parameter.

In embodiments, provided herein is a system having an intelligent, flexible energy and compute facility whereby an artificial intelligence/machine learning system configures the facility among a set of available configurations based on a set of detected conditions relating to a utilization parameter for the output of the facility.

In embodiments, provided herein is a system having an intelligent, flexible energy and compute facility whereby an artificial intelligence/machine learning system configures the facility among a set of available configurations based on a set of parameters received from a digital twin for the facility.

An example transaction-enabling system includes a controller having: an attention market access circuit structured to interpret a number of attention-related resources available on an attention market, an intelligent agent circuit structured to determine an attention-related resource acquisition value based on a cost parameter of at least one of the number of attention-related resources, and an attention acquisition circuit structured to solicit an attention-related resource in response to the attention-related resource acquisition value.

Certain further aspects of an example system are described following, any one or more of which may be present in certain embodiments. An example system includes where the attention acquisition circuit is further structured to perform the soliciting the attention-related resource by performing at least one operation selected from the operations consisting of: purchasing the attention-related resource from the attention market; selling the attention-related resource to the attention market; making an offer to sell the attention-related resource to a second intelligent agent; and making an offer to purchase the attention-related resource to the second intelligent agent. An example system includes where the number of attention-related resources includes at least one resource selected from the list consisting of: an advertising placement; a search listing; a keyword listing; a banner advertisements; a video advertisement; an embedded video advertisement; a panel activity participation; a survey activity participation; a trial activity participation; and a pilot activity placement or participation. An example system includes one or more of: where the attention market includes a spot market for at least one of the number of attention-related resources; where the cost parameter of at least one of the number of attention-related resources includes a future predicted cost of the at least one of the number of attention-related resources, and where the intelligent agent circuit is further structured to determine the attention-related resource acquisition value in response to a comparison of a first cost on the spot market with the cost parameter; where the attention market includes a forward market for at least one of the number of attention-related resources, and where the cost parameter of the at least one of the number of attention-related resources includes a predicted future cost; and/or where the cost parameter of at least one of the number of attention-related resources includes a future predicted cost of the at least one of the number of attention-related resources, and where the intelligent agent circuit is further structured to determine the attention-related resource acquisition value in response to a comparison of a first cost on the forward market with the cost parameter. An example system includes the intelligent agent circuit further structured to determine the attention-related resource acquisition value in response to the cost parameter of the at least one of the number of attention-related resources having a value that is outside of an expected cost range for the at least one of the number of attention-related resources. An example system includes the intelligent agent circuit is further structured to determine the attention-related resource acquisition value in response to a function of: the cost parameter of the at least one of the number of attention-related resources, and/or an effectiveness parameter of the at least one of the number of attention-related resources. In certain further embodiments, an example controller further includes an external data circuit structured to interpret a social media data source, and where the intelligent agent circuit is further structured to determine, in response to the social media data source, at least one of a future predicted cost of the at least one of the number of attention-related resources, and to utilize the future predicted cost as the cost parameter and/or the effectiveness parameter of the at least one of the number of attention-related resources.

An example system includes a fleet of machines, where each machine includes a task system having a core task and further at least one of a compute task or a network task. The system includes a controller having: an attention market access circuit structured to interpret a number of attention-related resources available on an attention market; an intelligent agent circuit structured to determine an attention-related resource acquisition value based on a cost parameter of at least one of the number of attention-related resources, and further based on the core task for the corresponding machine of the fleet of machines; an attention purchase aggregating circuit structured to determine an aggregate attention-related resource purchase value in response to the number of attention-related resource acquisition values from each intelligent agent circuit corresponding to each machine of the fleet of the machines; and an attention acquisition circuit structured to purchase an attention-related resource in response to the aggregate attention-related resource purchase value.

Certain further aspects of an example system are described following, any one or more of which may be present in certain embodiments. An example system includes where the attention purchase aggregating circuit is positioned at a location selected from the locations consisting of: at least partially distributed on a number of the controllers corresponding to machines of the fleet of machines; on a selected controller corresponding to one of the machines of the fleet of machines; and on a system controller communicatively coupled to the number of the controllers corresponding to machines of the fleet of machines. An example system includes where the attention purchase acquisition circuit is positioned at a location selected from the locations consisting of: at least partially distributed on a number of the controllers corresponding to machines of the fleet of machines; on a selected controller corresponding to one of the machines of the fleet of machines; and on a system controller communicatively coupled to the number of the controllers corresponding to machines of the fleet of machines.

An example procedure includes an operation to interpret a number of attention-related resources available on an attention market, an operation to determine an attention-related resource acquisition value based on a cost parameter of at least one of the number of attention-related resources, and an operation to solicit an attention-related resource in response to the attention-related resource acquisition value.

Certain further aspects of an example procedure are described following, any one or more of which may be present in certain embodiments. An example procedure further includes the operation to perform the soliciting the attention-related resource by performing at least one operation selected from the operations consisting of: purchasing the attention-related resource from the attention market; selling the attention-related resource to the attention market; making an offer to sell the attention-related resource to a second intelligent agent; and making an offer to purchase the attention-related resource to the second intelligent agent. An example procedure further includes where the cost parameter of at least one of the number of attention-related resources includes a future predicted cost of the at least one of the number of attention-related resources, the method further including determining the attention-related resource acquisition value in response to a comparison of a first cost on a spot market with the cost parameter. An example procedure further includes an operation to interpret a social media data source and an operation to determine, in response to the social media data source: a future predicted cost of the at least one of the number of attention-related resources, and to utilize the future predicted cost as the cost parameter; and/or an effectiveness parameter of the at least one of the number of attention-related resources. An example procedure further includes where the operation to determine the attention-related resource acquisition value is further based on the at least one of the future predicted cost or the effectiveness parameter.

An example procedure includes an operation to interpret a number of attention-related resources available on an attention market, an operation to determine an attention-related resource acquisition value for each machine of a fleet of machines based on a cost parameter of at least one of the number of attention-related resources, and further based on a core task for each of a corresponding machine of the fleet of machines, an operation to determine an aggregate attention-related resource purchase value in response to the number of attention-related resource acquisition values corresponding to each machine of the fleet of the machines, and an operation to purchase an attention-related resource in response to the aggregate attention-related resource purchase value.

Certain further aspects of an example procedure are described following, any one or more of which may be present in certain embodiments. An example procedure includes where the cost parameter of at least one of the number of attention-related resources includes a future predicted cost of the at least one of the number of attention-related resources, and where the procedure further includes an operation to determine each attention-related resource acquisition value in response to a comparison of a first cost on a spot market for attention-related resources with the cost parameter. An example procedure further includes an operation to interpret a social media data source and an operation to determine, in response to the social media data source: a future predicted cost of the at least one of the number of attention-related resources, and to utilize the future predicted cost as the cost parameter; and/or an effectiveness parameter of the at least one of the number of attention-related resources. An example procedure further includes the operation to determine the attention-related resource acquisition value further based on the future predicted cost and/or the effectiveness parameter.

