CROSS-REFERENCE TO RELATED APPLICATIONS

This application relates to, and claims the benefit of the filing date of, U.S. provisional patent application Ser. No. 61/433,314, entitled SYSTEM FOR FOSTERING INNOVATION AMONG A GROUP OF USERS, filed Jan. 17, 2011, and India provisional patent application serial no. 3510/CHE/20910, entitled SYSTEM FOR FOSTERING INNOVATION AMONG A GROUP OF USERS, filed Nov. 23, 2010, the entire contents of which are incorporated herein by reference for all purposes. The Application is also a continuation in part of, and claims the benefit of the filing date of, application Ser. No. 13/301,590, entitled “SYSTEM FOR FOSTERING INNOVATION AMONG A GROUP OF USERS filed Nov. 21, 2011, which is incorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to software systems, and more specifically to a system for fostering innovation among a group of users.

BACKGROUND

Innovation broadly refers to the generation and implementation of creative ideas targeted to produce new or improved solutions to: product/service development and marketing (e.g., products/services sold to consumers); work processes (e.g., making users more productive); knowledge management (e.g., supervising employee talent and knowledge); organizational leadership (e.g., developing business models and organizational culture); and stakeholder relations (e.g., relations with customers, suppliers and society). The term “innovation” is contrasted to the term “creativity” in that creativity is related to coming up with new, original ideas, while innovation is related to putting those ideas to work and creating a benefit.

Fostering innovation among a group of users refers to promoting/further encouraging the growth or development of innovations/innovative techniques among the users. Such fostering enables the users to be more innovative in terms of the number and/or the quality of the products/processes implemented as a group. The group of users may be an organized set of users or an unorganized assemblage. The group of users may contain only a single user or a large number of users (in the range 1000+). Examples of groups are the employees/consultants working for a business organization, the members of a society/organization such as IEEE, ad hoc groups formed for solving specific problems/tasks, etc.

There is a need in the art for fostering innovation among users in groups.

Moreover, in data base management, when the number of data variables (forces) in a dynamic system grows, the set of relationships between variables and between the tables needed to represent such variables becomes more and more complex. This complexity impacts the speed by which the computer can access needed data and carry out computations with those complex relationships, and the memory needed during to conduct those functions and to store the results for future processing. Increasing the speed of access and computation while reducing needed memory improves the computer-related technology by which the data base manages the dynamic changes in the complex relationships of variables.

Quantifiable data within a data base can be represented as either a scalar or a vector. Scalars are quantities that distinguish a single numerical value like an integer are fully described by a magnitude alone. Vectors are quantities that are fully described by both a magnitude and a direction.Scalar data are stored using a matrix of cells, with a specified identity for each cell so the computer processor can retrieve specified data to use in computations.Vector data are stored in a vector-based system as arrays of data with both magnitude and direction, as defined by coordinates marking the beginning and end points

Each individual piece of quantifiable data is considered a “singular object” that can be accessed and utilized in computer processing. A vector is itself considered to be an object that can contain multiple singular objects. With basic vector-based data systems, a data “object” exists as a vector in a 3-dimensional space and can be manipulated in accord with 3-dimensional Euclidean vector calculations such as addition, subtraction, multiplication and cross product. With CPUs, vector processing implements an instruction set on vector objects that contain multiple individual data objects, whereas scalar (integer) processing instructions operate on single data objects.

Using vector instructions dramatically outperforms integer instructions. Vector processing originally was the domain of super computers, but approximately as of the year 2015 most commodity CPUs implement architectures that feature instructions for a form of vector processing on multiple (vectorized) data sets, typically known as SIMD (Single Instruction, Multiple Data). Thus, microcomputers have started to incorporate the specialized instructions that support vector algebra by manipulating strings of bits (vectors) in a single instruction. These vector bit strings reflect a compression of data that can be loaded into microprocessors more effectively, thus improving CPU processing.

Along with this evolution in CPU, there has been an evolution in data base management, employing vectors in a columnar database that effectively manages vectors into cache. A vector data base management system (DBMS) will process compressed vector data directly while the integer DBMS will expend resources to un-compress data and then take up to 4× or more of memory. This 4× compression advantage means that the vector processor is more likely to find data in the fast Level 1 cache and in the mid-range L2 cache of memory. The use of vector algebra thereby provides another 8×-50× boost to the speed of calculation compared with integer-based mathematics. (Reference: Rob Klopp, “Database Super Computing,” Apr. 10, 2015. https://skylandtech.net/215/04/30/database-super-computing/)

Conventional vector DBMSs are built around data “objects” existing as vectors in 1-, 2-, or 3-dimensional space and are aimed at situations involving spatial relationships or images, such as geographic applications (see VDMS: A Vector Data Management System for GIS Applications; https://ieeexplore.ieee.org/document/4579604/?reload=true) and image data (U.S. Pat. No. 6,539,126B1 and US706562B1). Patents for the use of n-dimensional vectors have been issued for methods of breaking down the components of those n-dimensional vectors into simpler vectors (U.S. Pat. No. 7,729,553B2). Other n-dimensional vectors patents focus on novel means of data compression (U.S. Pat. No. 8,811,156B1), transformation of the data vectors (US20070192316A1), and data searching (U.S. Pat. No. 7,325,001B2).

