Context search system

• US 10,719,888 B2
• Filed: 06/14/2019
• Issued: 07/21/2020
• Est. Priority Date: 09/09/2002
• Status: Active Grant

• Current Assignee: Xenogenic Development Limited Liability Company (Intellectual Ventures LLC)
• Original Assignee: Xenogenic Development Limited Liability Company (Intellectual Ventures LLC)
• Inventors: Eder, Jeffrey Scott
• Primary Examiner(s): Mansfield, Thomas L
• Application Number:
  US16/441,761
• Publication Number:
  US 20190362436A1
• Time in Patent Office: 403 Days
• Field of Search: None
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

A computer based media, method and system for developing at least one context frame that summarizes a measure performance situation for one or more levels of one or more organizations, providing applications for managing the measure performance that adapt to the performance situation by using a context frame and a database that automatically captures and incorporates any changes in the measure performance situation.

Claims

1. A system for implementing an architecture for context aware computing for an organization, comprising:

a computer with a processor having circuitry to execute instructions; and

a storage device available to the processor with one or more sequences of instructions stored therein, which when executed cause the processor to;

provide a data access layer operable to exchange data with a user, one or more external databases and a plurality of systems, accept an organization structure definition that comprises a plurality of organization levels, accept a quantitative measure definition and store the data and definitions in one or more context layers;

provide a modeling layer operable to develop a measure context layer and an organization context layer for at least one combination of the organization levels by learning from the stored data, wherein developing the measure context layer by learning from the data comprises completing a multi-stage process that includes multiple stages for developing a linear or nonlinear predictive measure model wherein each stage of the multi-stage process comprises an automated selection of an output from a plurality of outputs produced by a plurality of modeling algorithms after processing at least part of the data wherein a linearity of the measure model is determined by an evaluation of an accuracy of at least one linear model and at least one nonlinear model in a final stage of the multi-stage process; and

provide a context frame layer that develops and outputs a context frame comprising the measure and organization context layers and one or more other context layers for the at least one combination of organization levels wherein the context frame comprises an importance of one or more elements to the measure and a quantified dependence between the elements wherein the context frame enables the output of a graph that comprises a node for each element and the importance measure for each element.

2. The system of claim 1, wherein the processor further provides one or more applications for managing a measure performance of the at least one combination of organization levels wherein the applications utilize the context frame to summarize a measure performance situation and wherein the one or more applications are selected from a group consisting of analysis, forecast, optimization, planning and project management wherein the project management application identifies a set of project features that optimize a project value and a project risk for the context frame.

3. The system of claim 1, wherein the one or more other context layers are selected from a group consisting of physical, tactical, and social environment.

4. The system of claim 1, wherein the plurality of organization levels are selected from a group consisting of team, group, department, division, company, and enterprise for one or more organizations.

5. The system of claim 1, wherein the plurality of modeling algorithms comprise two or more predictive model algorithms for at least one first stage of the multi-stage process and two or more causal model algorithms for at least one second stage of the multi-stage process.

6. The system of claim 1, further comprising, a ContextBase that stores data in accordance with a context by a context layer.

7. A method for implementing an architecture for context aware computing for an organization, comprising:

using a computer to complete at least one of the steps of performing operations, including at least one of the following by using a computer;

providing a data access layer operable to exchange data with a plurality of sources including a user, one or more external databases and a plurality of systems, accept an organization structure definition that comprises a plurality of organization levels, accept one quantitative measure definitions and store the data and the definitions in one or more context layers;

providing a modeling layer operable to develop a measure context layer and an organization context layer for at least one combination of the organization levels by learning from the stored data, wherein developing the measure context layer by learning from the data comprises completing a multi-stage process for developing a predictive measure model wherein each stage of the multi-stage process comprises an automated selection of an output from a plurality of outputs produced by a plurality of modeling algorithms after processing at least part of the data wherein a linearity of each measure model is determined by an evaluation of an accuracy of at least one linear model and at least one nonlinear model in a final stage of the multi-stage process and wherein each stage is completed with and without clustering; and

providing a context frame layer that develops and outputs a context frame comprising the measure layer, the organization context layer and one or more additional context layers wherein the context frame comprises an importance of one or more elements and one or more factors to the measure and a quantified dependence between the elements and factors wherein the context frame enables the output of a graph that comprises a node for each element and factor and the importance measure for each element and factor.

8. The method of claim 7, wherein the method further comprises providing one or more applications for managing a measure performance of the at least one combination of organization levels wherein the applications utilize the context frame for summarizing a measure performance situation and wherein the one or more applications are selected from a group consisting of analysis, forecast, optimization, planning, and project management.

9. The method of claim 7, wherein the plurality of systems are selected from a group consisting of accounting systems, alliance management systems, asset management systems, brand management systems, budgeting/financial planning systems, business intelligence systems, call management systems, cash management systems, channel management systems, commodity risk management systems, content management systems, contract management systems, credit-risk management system, customer relationship management systems, data integration systems, demand chain systems, decision support systems, document management systems, email management systems, employee relationship management systems, energy risk management systems, executive dashboard systems, expense report processing systems, fleet management systems, fraud management systems, freight management systems, human capital management systems, human resource management systems, incentive management systems, innovation management systems, insurance management systems, intellectual property management systems, intelligent storage systems, interest rate risk management systems, investor relationship management systems, knowledge management systems, learning management systems, location management systems, maintenance management systems, material requirement planning systems, metrics creation system, online analytical processing systems, ontology management systems, partner relationship management systems, payroll systems, performance management systems, price optimization systems, private exchanges, process management systems, product life-cycle management systems, project management systems, project portfolio management systems, revenue management systems, risk management information system, risk simulation systems, sales force automation systems, scorecard systems, sensor grid systems, service management systems, six-sigma quality management systems, strategic planning systems, supply chain systems, supplier relationship management systems, support chain systems, taxonomy development systems, technology chain systems, unstructured data management systems, visitor (web site) relationship management systems, weather risk management systems, workforce management systems, yield management systems and combinations thereof.

10. The method of claim 7, wherein the one or more additional context layers are selected from a group consisting of physical, tactical and social environment and wherein the context frame defines a context.

11. The method of claim 7, wherein the plurality of organization levels are selected from a group consisting of individual, team, group, department, division, company, and enterprise for one or more organizations.

12. The method of claim 7, wherein the method further comprises identifying a set of project features that optimize a project value and a project risk for the at least one context frame.

13. The method of claim 7, further comprising, developing a ContextBase that stores data in accordance with a context by a context layer.

14. The method of claim 7, wherein the plurality of modeling algorithms comprise two or more predictive model algorithms for at least one first stage of the multi-stage process and two or more causal model algorithms selected from the group consisting of Tetrad, LaGrange, Bayes and path analysis for at least one second stage of the multi-stage process.

15. A computer program product tangibly embodied on a computer readable medium and comprising a non-transitory program code for directing at least one computer to perform operations in accordance with an architecture for context aware computing for an organization, the operations comprising:

providing a data access layer operable to exchange data with a plurality of sources including a user, one or more external databases and a plurality of systems, accept an organization structure definition that comprises a plurality of organization levels, accept one quantitative measure definitions and store the data and the definitions in one or more context layers;

providing a modeling layer operable to develop a measure context layer and an organization context layer for at least one combination of the organization levels by learning from the stored data, wherein developing the measure context layer by learning from the data comprises completing a multi-stage process for developing a predictive measure model wherein each stage of the multi-stage process comprises an automated selection of an output from a plurality of outputs produced by a plurality of modeling algorithms after processing at least part of the data wherein a linearity of each measure model is determined by an evaluation of an accuracy of at least one linear model and at least one nonlinear model in a final stage of the multi-stage process; and

providing a context frame layer that develops a context frame comprising the measure layer, the organization context layer and one or more additional context layers wherein the context frame comprises a contribution of one or more elements to the measure and a quantified dependence between the elements wherein the context frame enables the output of a graph that comprises a node for each element and the importance measure for each element.

16. The computer program product of claim 15, wherein the non-transitory program code further directs the at least one computer to provide one or more applications for managing a measure performance of the at least one combination of organization levels wherein the applications utilize the context frame for summarizing a measure performance situation and wherein the one or more applications are selected from a group consisting of analysis, forecast, optimization, planning, and project management.

17. The computer program product of claim 15, wherein the one or more additional context layers are selected from a group consisting of physical, tactical, instant and social environment.

18. The computer program product of claim 15, wherein the non-transitory program code further directs the at least one computer to identify a set of project features that optimize a project value and a project risk for the context frame.

19. The computer program product of claim 15, wherein the plurality of organization levels are selected from a group consisting of individual, team, group, department, division, company, and enterprise.

20. The computer program product of claim 15, wherein the context frame defines a context.

21. The computer program product of claim 15, wherein the processing provides a ContextBase that stores data in accordance with a context by a context layer.

22. The computer program product of claim 15, wherein the plurality of modeling algorithms comprise two or more algorithms selected from a group consisting of neural network, classification and regression trees, projection pursuit regression, generalized additive model, redundant regression network, linear regression, support vector method, MARS and stepwise regression for at least one first stage of multi-stage process and two or more causal model algorithms for at least one second stage of multi-stage process.

23. An advanced system for implementing an architecture for context aware computing for an organization, comprising:

a computer with a processor having circuitry to execute instructions; and

a storage device available to the processor with one or more sequences of instructions stored therein, which when executed cause the processor to;

provide a data access layer operable to obtain data from a plurality of sources including a user, one or more external databases, a world wide web and a plurality of narrow systems, accept an organization structure definition that comprises a plurality of organization levels, accept one or more quantitative measure definitions and store the data and the definitions in one or more context layers;

provide a modeling layer operable to develop a measure context layer and an organization context layer for at least one combination of the organization levels by learning from the stored data; and

provide a context frame layer operable to define and output a context frame that comprises the measure context layer, the organization context layer and one or more of the other context layers wherein the context frame comprises a contribution of each of one or more elements and each of one or more factors to each of the measures and a quantified dependence between each of the elements and factors wherein the context frame enables the output of a graph that comprises a node for each element and factor and the contribution measure for each element and factor.

24. The system of claim 23, wherein the plurality of organization levels are selected from a group consisting of individual, team, group, department, division, company, and enterprise.

25. The system of claim 23, wherein the context frame defines a context.

26. The system of claim 23, wherein the system further comprises a ContextBase that stores data in accordance with a context by a context layer.

27. The system of claim 23, wherein the plurality of narrow systems are selected from a group consisting of accounting systems, alliance management systems, asset management systems, brand management systems, budgeting/financial planning systems, business intelligence systems, call management systems, cash management systems, channel management systems, commodity risk management systems, content management systems, contract management systems, credit-risk management system, customer relationship management systems, data integration systems, demand chain systems, decision support systems, document management systems, email management systems, employee relationship management systems, energy risk management systems, executive dashboard systems, expense report processing systems, fleet management systems, fraud management systems, freight management systems, human capital management systems, human resource management systems, incentive management systems, innovation management systems, insurance management systems, intellectual property management systems, intelligent storage systems, interest rate risk management systems, investor relationship management systems, knowledge management systems, learning management systems, location management systems, maintenance management systems, material requirement planning systems, metrics creation system, online analytical processing systems, ontology management systems, partner relationship management systems, payroll systems, performance management systems, price optimization systems, private exchanges, process management systems, product life-cycle management systems, project management systems, project portfolio management systems, revenue management systems, risk management information system, risk simulation systems, sales force automation systems, scorecard systems, sensor grid systems, service management systems, six-sigma quality management systems, strategic planning systems, supply chain systems, supplier relationship management systems, support chain systems, taxonomy development systems, technology chain systems, unstructured data management systems, visitor (web site) relationship management systems, weather risk management systems, workforce management systems and yield management systems.

28. The system of claim 23, wherein the processor further provides one or more applications for managing a measure performance of the at least one combination of organization levels wherein the applications utilize the context frame for summarizing a measure performance situation and wherein the one or more applications are selected from a group consisting of analysis, forecast, optimization, planning and project management.

29. The system of claim 23, wherein developing the measure context layer by learning from the data comprises completing a multi-stage process for developing a predictive model for each measure wherein each stage of the multi-stage process comprises an automated selection of an output from a plurality of outputs produced by a plurality of modeling algorithms after processing at least part of the data wherein a linearity of each measure model is determined by an evaluation of an accuracy of at least one linear model and at least one nonlinear model in a final stage of the multi-stage process.

30. The system of claim 23, wherein the plurality of modeling algorithms comprise two or more algorithms selected from a group consisting of neural network, classification and regression trees, projection pursuit regression, generalized additive model, redundant regression network, linear regression, support vector method, MARS and stepwise regression for at least one first stage of the multi-stage process and two or more algorithms selected from a group consisting of Tetrad, LaGrange, Bayes, MML and path analysis for at least one second stage of multi-stage process.

31. The system of claim 23, wherein the processor further provides an application for identifying a set of project features that optimize a project value and a project risk for the at least one context frame wherein the context frame comprises a project sponsor.

32. A non-transitory computer program product tangibly embodied on a computer readable medium and comprising program code for directing at least one computer to perform operations in accordance with an advanced architecture for context aware computing for an organization and to provide the following:

a data access layer operable to obtain data from a plurality of sources including a user, one or more external databases, a world wide web and a plurality of narrow systems, accept an organization structure definition that comprises a plurality of organization levels, accept one or more quantitative measure definitions and store the data and the definitions in one or more context layers;

a modeling layer operable to develop a measure context layer and an organization context layer for at least one combination of the organization levels by learning from the stored data; and

a context frame layer operable to define an entity and output a context frame that comprises the measure context layer, the organization context layer, and one or more of the other context layers wherein the context frame comprises a contribution of each of one or more elements and each of one or more factors to each of the measures and a quantified dependence between each of the elements and factors wherein the context frame enables the output of a graph that comprises a node for each element and factor and the contribution measure for each element and factor.

33. The computer program product of claim 32, wherein the plurality of organization levels are selected from a group consisting of individual, team, group, department, division, company, and enterprise.

34. The computer program product of claim 32, wherein the context frame defines a context.

35. The computer program product of claim 32, wherein the context frame further comprises a ContextBase that stores data in accordance with a context by a context layer.

36. The computer program product of claim 32, wherein the non-transitory program code further directs the at least one computer to provide one or more applications for managing a measure performance of the at least one combination of organization levels wherein the applications utilize the context frame for summarizing a measure performance situation and wherein the one or more applications are selected from a group consisting of analysis, forecast, optimization, planning, project management and review.

37. The computer program product of claim 32, wherein developing the measure context layer by learning from the data comprises completing a multi-stage process for each model wherein each stage of the multistage process comprises an automated selection of an output from a plurality of outputs produced by a plurality of modeling algorithms after processing at least part of the data wherein a linearity of each measure model is determined by an evaluation of an accuracy of at least one linear model and at least one nonlinear model in a final stage of the multi-stage process.

38. The computer program product of claim 37, wherein the plurality of modeling algorithms comprise two or more predictive model algorithms selected from a group consisting of neural network, classification and regression tree, projection pursuit regression, generalized additive model, redundant regression network, linear regression, support vector method, multivariate adaptive regression spline and stepwise regression for at least one first stage of the multi-stage process and two or more causal model algorithms selected from a group consisting of Tetrad, LaGrange, Bayes, MML and path analysis for at least one second stage of the multi-stage process.

39. The computer program product of claim 32, wherein the non-transitory program code further directs the at least one computer to identify a set of project features that maximize a project value and minimize a project risk for the context frame.

40. The computer program product of claim 32, wherein the plurality of narrow systems are selected from a group consisting of accounting systems, alliance management systems, asset management systems, brand management systems, budgeting/financial planning systems, business intelligence systems, call management systems, cash management systems, channel management systems, commodity risk management systems, content management systems, contract management systems, credit-risk management system, customer relationship management systems, data integration systems, demand chain systems, decision support systems, document management systems, email management systems, employee relationship management systems, energy risk management systems, executive dashboard systems, expense report processing systems, fleet management systems, fraud management systems, freight management systems, human capital management systems, human resource management systems, incentive management systems, innovation management systems, insurance management systems, intellectual property management systems, intelligent storage systems, interest rate risk management systems, investor relationship management systems, knowledge management systems, learning management systems, location management systems, maintenance management systems, material requirement planning systems, metrics creation system, online analytical processing systems, ontology management systems, partner relationship management systems, payroll systems, performance management systems, price optimization systems, private exchanges, process management systems, product life-cycle management systems, project management systems, project portfolio management systems, revenue management systems, risk management information system, risk simulation systems, sales force automation systems, scorecard systems, sensor grid systems, service management systems, six-sigma quality management systems, strategic planning systems, supply chain systems, supplier relationship management systems, support chain systems, taxonomy development systems, technology chain systems, unstructured data management systems, visitor (web site) relationship management systems, weather risk management systems, workforce management systems and yield management systems.

41. An apparatus for implementing an architecture for context aware computing for an organization, comprising:

a memory configured to store data; and

a processor configured to;

accept and store an organization structure definition that comprises a plurality of organization levels;

accept and store one or more quantitative measure definitions;

obtain data from a plurality of sources comprising a user, one or more external databases, a world wide web and a plurality of narrow systems;

prepare and store the data obtained from the sources in one or more context layers;

develop and output a predictive model for each of the defined measures for at least one combination of the organization levels;

develop and output a measure relevance for the at least one combination of the organization levels;

develop and output a measure layer for the at least one combination of the organization levels comprised of the measure relevance and the one or more predictive measure models;

develop and output an organization layer for the at least one combination of the organization levels from the data obtained from the plurality of sources; and

define a context frame for the at least one combination of the organization levels that comprises the measure layer the organization layer and one or more of the other context layers wherein the context frame comprises a contribution of each of one or more elements and each of one or more factors to each of the measures and a quantified dependence between each of the elements and factors wherein the context frame enables the output of a graph that comprises a node for each element and factor and the contribution measure for each element and factor.

42. The apparatus of claim 41, wherein the context frame defines a context.

43. The apparatus of claim 41, wherein the memory stores data in accordance with a context by a context layer.

44. The apparatus of claim 41, wherein the apparatus further comprises a component configured to provide one or more applications for managing a measure performance of the at least one combination of organization levels wherein the applications utilize the context frame to summarize a measure performance situation and wherein the one or more applications are selected from a group consisting of analysis, forecast, optimization, planning, project management and review.

45. The apparatus of claim 41, wherein the processor is further configured to complete a multi-stage process for each model wherein each stage of the multi-stage process comprises an automated selection of an output from a plurality of outputs produced by a plurality of modeling algorithms after processing at least part of the data wherein a linearity of each measure model is determined by an evaluation of an accuracy of at least one linear model and at least one nonlinear model in a final stage of the multi-stage process.

46. The apparatus of claim 45, wherein the plurality of modeling algorithms comprise two or more algorithms selected from a group consisting of neural network, classification and regression trees, projection pursuit regression, generalized additive model, redundant regression network, linear regression, support vector method, MARS and stepwise regression for at least one first stage of the multi-stage process and two or more causal model algorithms selected from a group consisting of Tetrad, LaGrange, MML, Bayes and path analysis for at least one second stage of the multi-stage process.

47. The apparatus of claim 41, wherein the output from each of the measure models comprises a relative contribution from one or more elements of performance and one or more external factors to each measure.

48. The apparatus of claim 41, wherein the plurality of organization levels are selected from a group consisting of individual, team, group, department, division, company, and enterprise.

49. The apparatus of claim 41, wherein the processor is further configured to identify a set of project features that maximize a project value and minimize a project risk for the context frame wherein the context frame comprises a project sponsor.