Context search system

• US 10,346,926 B2
• Filed: 10/28/2005
• Issued: 07/09/2019
• Est. Priority Date: 09/09/2002
• Status: Active Grant

• Current Assignee: Xenogenic Development Limited Liability Company (Intellectual Ventures LLC)
• Original Assignee: Xenogenic Development LLC
• Inventors: Eder, Jeffrey Scott
• Primary Examiner(s): Mansfield, Thomas L
• Application Number:
  US11/262,146
• Publication Number:
  US 20060059028A1
• Time in Patent Office: 5,002 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 chances in the measure performance situation.

Claims

1. A computer-implemented management method, comprising:

storing, by a computer processor, a measure definition of a measurable and a plurality of level definitions of organization levels for one or more organizations;

collecting data, by the computer processor, from a plurality of data sources;

developing, by the computer processor, one or more context layers for each of the organization levels from the data and the measurable, wherein the one or more context layers include at least an instant impact context layer that comprises information regarding a short-term impact of one or more business actions, and a mission impact context layer that comprises information regarding a long-term impact on each of one or more mission measures;

developing, by the computer processor, a context frame that comprises a measure model of the measurable and one or more of the context layers, including the instant impact context layer and the mission impact context layer, for the organization levels of the one or more organizations by learning from the data, wherein the context frame summarizes a measure performance situation for the one or more organization levels; and

providing, by the computer processor, one or more applications to manage a measure performance for the one or more organization levels;

wherein the applications utilize the context frame that includes the instant impact context layer and the mission impact context layer, the measure model comprises a single quantitative output variable, and the measure model identifies a relative contribution of one or more elements and one or more factors to the measurable for the one or more organizations.

2. The method of claim 1, wherein the organization levels are selected from the group consisting of a group, a team, a division, an enterprise, an organization and combinations thereof.

3. The method of claim 2, wherein, in addition to the instant impact context layer and the mission impact context layer, the one or more context layers include a context layer selected from the group consisting of physical, tactical, social environment, organization, and combinations thereof.

4. The method of claim 1, wherein the one or more applications are selected from the group consisting of analysis, sustainability forecast and planning.

5. The method of claim 1,

wherein the measure model comprises a linear model or a nonlinear model,

wherein the learning from the data comprises completing a multi-stage process,

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, and

wherein the plurality of modeling algorithms are selected from the group including:

a neural network,

a classification and regression tree,

a generalized autoregressive conditional heteroskedasticity,

projection pursuit regression,

a generalized additive model,

a redundant regression network,

a path analysis,

Bayesian,

multivariate adaptive regression spline, and

support vector method.

6. A management system, comprising:

a machine containing one or more central processing units for executing non-transitory programs;

an interface for receiving event messages and connecting with a plurality data sources; and

a search engine module executable by the one or more central processing units, the module comprising instructions for performing at least;

storing a measure definition of a measurable and a plurality of level definitions of organization levels for one or more organizations;

aggregating narrow system data of a plurality of narrow systems, external data and Web data from a plurality of data sources for processing;

developing the data of one or more context layers for each of the organization levels from the data and the measurable, wherein the one or more context layers include at least an instant impact context layer that comprises information regarding a short-term impact of one or more business actions, and a mission impact context layer that comprises information regarding a long-term impact on each of one or more mission measures;

developing a context frame that comprises a measure model of the measurable and one or more of the context layers, including the instant impact context layer and the mission impact context layer, for the organization levels of the one or more organizations by learning from the data, wherein the context frame summarizes a measure performance situation for the one or more organization levels; and

providing one or more applications to manage a measure performance for the one or more organization levels;

wherein the applications utilize the context frame that includes the instant impact context layer and the mission impact context layer, the measure model comprises a single quantitative output variable, and the measure model identifies a relative contribution of one or more elements and one or more factors to the measurable for the one or more organizations.

7. The management system of claim 6, wherein the organization levels are selected from the group consisting of a group, a team, a division, an enterprise, an organization and combinations thereof.

8. The management system of claim 6, wherein the one or more applications are selected from the group consisting of analysis, sustainability forecast and planning.

9. The management system of claim 6, wherein, in addition to the instant impact context layer and the mission impact context layer, the one or more context layers include a context layer selected from the group consisting of physical, tactical, social environment, organization, and combinations thereof.

10. The management system of claim 6, wherein the one or more context layers are stored in a database that automatically captures and incorporates any changes in the measure performance situation.

11. A non-transitory program storage device readable by a computer, tangibly embodying a program of instructions executable by at least the computer to perform at least:

storing a measure definition of a measurable and a plurality of level definitions of organization levels for one or more organizations;

aggregating narrow system data of a plurality of narrow systems, external data and Web data from a plurality of data sources for processing;

developing one or more context layers for each of the organization levels from the data and the measurable, wherein the one or more context layers include at least an instant impact context layer that comprises information regarding a short-term impact of one or more business actions, and a mission impact context layer that comprises information regarding a long-term impact on each of one or more mission measures;

developing a context frame that comprises a measure model of the measurable and one or more of the context layers, including the instant impact context layer and the mission impact context layer, for the organization levels of the one or more organizations by learning from the data, wherein the context frame summarizes a measure performance situation for the one or more organization levels; and

providing one or more applications to manage a measure performance of the one or more organization levels;

wherein the applications utilize the context frame that includes the instant impact context layer and the mission impact context layer, the measure model comprises a single quantitative output variable, and the measure model identifies a relative contribution of one or more elements and one or more factors to the measurable for the one or more organizations.

12. The program storage device of claim 11, wherein the organization levels are selected from the group consisting of a group, a team, a division, a company, an organization and combinations thereof.

13. The program storage device of claim 11, wherein the one or more applications are selected from the group consisting of analysis, sustainability forecast and planning.

14. The program storage device of claim 12, wherein, in addition to the instant impact context layer and the mission impact context layer, the one or more context layers include a context layer selected from the group consisting of physical, tactical, social environment, organization, and combinations thereof.

15. The program storage device of claim 11,

wherein the measure model comprises a linear model or a nonlinear model,

wherein the learning from the data comprises completing a multi-stage process,

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, and

wherein the plurality of modeling algorithms are selected from the group including:

neural network,

classification and regression tree,

generalized autoregressive conditional heteroskedasticity,

projection pursuit regression,

generalized additive model,

a redundant regression network,

a path analysis,

Bayesian,

multivariate adaptive regression spline, and

a support vector method.

16. The program storage device of claim 11, wherein the one or more context layers are stored in a database configured to automatically capture and incorporate any changes in the measure performance situation.

17. The program storage device of claim 11, wherein the instant impact context layer further comprises information about a short term impact of each of one or more events and an expected short term impact of each of one or more of commitments.

18. A non-transitory computer program product, tangibly embodying a program of instructions executable by at least a computer to perform:

storing two or more measure definitions of measurables and a plurality of level definitions of organization levels for one or more organizations;

aggregating narrow system data of a plurality of narrow systems, external data and Web data from a plurality of data sources for processing;

developing one or more context layers for each of the organization levels from the data and the measurables, wherein the one or more context layers include at least an instant impact context layer that comprises information regarding a short-term impact of one or more business actions, and a mission impact context layer that comprises information regarding a long-term impact on each of one or more mission measures;

developing a context frame that comprises a measure model for each of the measurables, a measure relevance and one or more of the context layers, including the instant impact context layer and the mission impact context layer, for the organization levels of the one or more organizations by learning from the data, wherein the context frame summarizes a measure performance situation for the one or more organization levels; and

providing one or more applications to manage a measure performance for the one or more organization levels,

wherein the applications utilize the context frame that includes the instant impact context layer and the mission impact context layer, the measure models of the measurables each comprise a single quantitative output variable, and each of the measure models identifies a relative contribution of one or more elements and one or more factors to the corresponding measurable.

19. The non-transitory computer program product of claim 18, wherein the organization levels are selected from the group consisting of an individual, a group, a team, a division, a company, an organization and combinations thereof.

20. The non-transitory computer program product of claim 18, wherein the one or more applications are selected from the group consisting of analysis, sustainability forecast and planning.

21. The non-transitory computer program product of claim 18, wherein, in addition to the instant impact context layer and the mission impact context layer, the one or more context layers include a context layer selected from the group consisting of physical, tactical, social environment, organization, and combinations thereof.

22. The non-transitory computer program product of claim 18,

wherein the measure models each comprise a linear predictive model or a nonlinear predictive model,

wherein learning from the data comprises completing a multi-stage process,

wherein each stage of the 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, and

wherein the plurality of modeling algorithms are selected from the group including:

a neural network;

classification and regression tree,

a generalized autoregressive conditional heteroskedasticity,

a projection pursuit regression,

generalized additive model,

redundant regression network,

a path analysis,

Bayesian,

a multivariate adaptive regression spline, and

a support vector method.

23. The non-transitory computer program product of claim 18, wherein the context layers are stored in a database configured to automatically capture and incorporate any changes in the measure performance situation.