CROSS-REFERENCE TO RELATED APPLICATIONS

ApplicationNo.Date FiledTitleCurrentHerewithTRANSFER LEARNING ANDapplicationDOMAIN ADAPTATION USINGDISTRIBUTABLE DATA MODELSis a continuation of:15/835,436Dec. 7, 2017TRANSFER LEARNING ANDU.S. Pat. No.:Issue Date:DOMAIN ADAPTATION USING10,572,828Feb. 25, 2020DISTRIBUTABLE DATA MODELSwhich is a continuation-in-part of:15/790,457Oct. 23, 2017DISTRIBUTABLE MODEL WITHBIASES CONTAINED WITHINDISTRUBUTED DATAWhich claims benefit of provisional62/568,298Oct. 4, 2017DISTRIBUTABLE MODEL WITHBIASES CONTAINED WITHINDISTRUBUTED DATAAnd is also continuation-in-part of:15/790,327Oct. 23, 2017DISTRIBUTABLE MODEL WITHDISTRUBUTED DATAWhich claims benefit of provisional:62/568,291Oct. 4, 2017DISTRIBUTABLE MODEL WITHDISTRUBUTED DATAAnd is also continuation-in-part of:15/141,752Apr. 28, 2016SYSTEM FOR FULLY INTEGRATEDCAPTURE, AND ANALYSIS OFBUSINESS INFORMATION RESULTINGIN PREDICTIVE DECISION MAKINGAND SIMULATIONwhich is a continuation-in-part of:15/091,563Apr. 5, 2016SYSTEM FOR CAPTURE, ANALYSISU.S. Pat. No.:Issue Date:AND STORAGE OF TIME SERIES10,204,147Feb. 12, 2019DATA FROM SENSORS WITHHETEROGENEOUS REPORTINTERVAL PROFILESAnd is also continuation-in-part of:14/925,974Oct. 28, 2015RAPID PREDICTIVE ANALYSISOF VERY LARGE DATA SETSUSING THE DISTRIBUTEDCOMPUTATIONAL GRAPHAnd is also continuation-in-part of:14/986,536Dec. 31, 2015DISTRIBUTED SYSTEM FORU.S. Pat. No.:Issue Date:LARGE VOLUME DEEP WEB10,210,255Feb. 19, 2019DATA EXTRACTIONCurrentHerewithTRANSFER LEARNING ANDapplicationDOMAIN ADAPTATION USINGDISTRIBUTABLE DATA MODELSis a continuation of:15/835,436Dec. 7, 2017TRANSFER LEARNING ANDU.S. Pat. No.:Issue Date:DOMAIN ADAPTATION USING10,572,828Feb. 25, 2020DISTRIBUTABLE DATA MODELSwhich is a continuation-in-part of:15/790,457Oct. 23, 2017DISTRIBUTABLE MODEL WITHBIASES CONTAINED WITHINDISTRUBUTED DATAwhich is continuation-in-part of:15/790,327Oct. 23, 2017DISTRIBUTABLE MODEL WITHDISTRUBUTED DATAwhich is continuation-in-part of:15/616,427Jun. 7, 2017RAPID PREDICTIVE ANALYSISOF VERY LARGE DATA SETS USINGAN ACTOR-DRIVEN DISTRIBUTEDCOMPUTATIONAL GRAPHwhich is continuation-in-part of:14/925,974Oct. 28, 2015RAPID PREDICTIVE ANALYSISOF VERY LARGE DATA SETSUSING THE DISTRIBUTEDCOMPUTATIONAL GRAPHthe entire specification of each of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

Field of the Invention

The disclosure relates to the field of machine learning, more particularly to model improvement using biases contained in data distributed across multiple devices.

Discussion of the State of the Art

In traditional machine learning, data is generally gathered and processed at a central location. The gathered data may then be used to train models. However, gatherable data, through means such as web scraping, or news aggregation, may be relatively narrow in scope compared to what is stored on devices, for instance a person's mobile device or crime data from a local police station. This data may seldom leave the devices it is stored on due to the possibility of it containing sensitive data, and also the bandwidth required to transfer the data may be extensive.

Generator models trained on data sets (whether anonymized or not) may be restricted for transfer (for example, due to GDPR). Existing tools such as SNORKEL™ can provide substantial mechanisms to accelerate generating realistic and labelled training data from small seeds. The same concept can support transferring valuable models without moving the restricted information itself. This also applies to other sharing of models, data sets, visualizations, pipelines, and other data, such as for multi-tenant deployments, inter-organizational sharing or hybrid cloud-edge use cases.

What is needed is a system for using transfer learning to accommodate data models trained in particular environments or applications that may not have identical feature space or distributions within target data or applications.

SUMMARY OF THE INVENTION

Accordingly, the inventor has conceived a system and methods for using transfer learning to accommodate data models trained in particular environments or applications that may not have identical feature space or distributions within target data or applications.

In a preferred embodiment, a model source may serve instances of a variety of distributable models: a generalized model in which data used to train it has had biases weighted and corrected, and also a bias-specific model in which biases are utilized. The instances are served to distributed devices, where they may be trained by the devices. The devices each generate an update report, which is transferred back to the model source, where the model source may use the report to improve the main models.

In one aspect of the invention, a system for improving a distributable model with biases contained in distributed data is provided, comprising a network-connected distributable model source comprising a memory, a processor, and a plurality of programming instructions stored in the memory thereof and operable on the processor thereof, wherein the programmable instructions, when operating on the processor, cause the processor to serve instances of a plurality of distributable models; and a directed computation graph module comprising a memory, a processor, and a plurality of programming instructions stored in the memory thereof and operable on the processor thereof, wherein the programmable instructions, when operating on the processor, cause the processor to receive at least an instance of at least one of the distributable models from the network-connected computing system, create a cleansed dataset from data stored in the memory based at least in part by biases contained within the data stored in memory, train the instance of the distributable model with the cleansed dataset, and generate an update report based at least in part by updates to the instance of the distributable model.

In another embodiment of the invention, at least a portion the cleansed dataset is data that has had sensitive information removed. In another embodiment of the invention, at least a portion of the update report is used by the network-connected distributable model source to improve the distributable model. In another embodiment of the invention, at least a portion of the biases contained within the data stored in memory is based on geography. In another embodiment of the invention, at least a portion of the biases contained within the data stored in memory is based on age. In another embodiment of the invention, at least a portion of the biases contained within the data stored in memory is based on gender.

In another aspect of the invention, a method for improving a distributable model with biases contained in distributed data is provided, comprising the steps of: (a) serving instances of a plurality of distributable models with a network-connected distributable model source; (b) receiving at least an instance of at least one of the distributable models from the network-connected computing system with a directed computation graph module; (c) creating a cleansed dataset from data stored in the memory based at least in part by biases contained within the data stored in memory with the directed computation graph module; (d) training the instance of the distributable model with the cleansed dataset with the directed computation graph module; and (e) generating an update report based at least in part by updates to the instance of the distributable model with the directed computation graph module.

