Complete context™ search system

• US 20120066217A1
• Filed: 11/20/2011
• Published: 03/15/2012
• Est. Priority Date: 03/31/2005
• Status: Active Grant

• Current Assignee: Xenogenic Development Limited Liability Company (Intellectual Ventures LLC)
• Original Assignee: Square Halt Solutions Limited Liability Company (Intellectual Ventures LLC)
• Inventors: Eder, Jeffrey Scott
• Granted Patent: US 8,713,025 B2
• Time in Patent Office: Days
• US Class Current: 707/723
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06Q 10/00 Administration; Management

Abstract

A system, method and computer program product for developing an entity context frame or situation summary before using said context frame/situation summary to develop an index, perform a context search and return prioritized results. The search results may comprise a plurality of health related data where said health related data comprises a plurality of microbiome data.

Claims

1. A non-transitory computer program product, tangibly embodied in a machine readable storage device, comprising one or more instructions that, when executed, cause at least one processor to perform operations comprising:

obtain a subject entity definition, a node depth criteria and an impact cutoff criteria,

aggregate and prepare a plurality of data items that includes data related to the subject entity for processing where said data comprises at least one entity function, one or more entity function measures and a creation date for each of the data items,

store the aggregated data items in one or more context layers by a component of context,

develop a subject entity situation summary by analyzing said subject entity related data where said summary comprises a linear or nonlinear regression model of each of the one or more entity function measures, a relevance for each of the measures and one or more of the context layers,

use the subject entity situation summary, the node depth criteria and the impact cutoff criteria to identify the components of context to include in an index,

create a composite index for the data associated with the identified components of context where said index comprises a column for the creation data of the data items, a column for each identified component of context and a ranking for each of the indexed data items,

receive a search request, and

provide a plurality of results in response to said request where the results are prioritized using a weight comprised of a mathematical combination of the index position ranking and a ranking provided by a relevance measure.

2. The computer program product of claim 1, wherein the search results comprise a plurality of health related data where said health related data comprises a plurality of microbiome data.

3. The computer program product of claim 1, wherein the subject entity is selected from the group consisting of team, group, department, division, company, organization or multi-entity organization.

4. The computer program product of claim 1, wherein the situation summary comprises a context frame and wherein the operations further comprise developing a prioritized context frame that consists of the components of context that meet the impact cutoff and node depth criteria specified by the user and providing one or more applications that use the prioritized context frame to adapt to and manage a performance situation for the entity where said applications are selected from the group consisting of:

benefit plan analysis, customization, database, display, exchange, forecast, metric development, optimization, planning, profile development, review, rule development, summary, sustainability forecast and wellness program optimization.

5. The computer program product of claim 1, wherein the search request is received from a browser.

6. The computer program product of claim 1, wherein the relevance measure is selected from the group consisting of cover density rankings, vector space model measurements, okapi similarity measurements, three level relevance scores and hypertext induced topic selection algorithm scores where reinforcement learning determines which relevance measure is selected.

7. The computer program product of claim 1, wherein the search request comprises one or more keywords or a question where said search request is received from a natural language interface or an anticipated need for data automatically initiates the search request.

8. The computer program product of claim 1, wherein the one or more context layers are stored in a database that automatically captures and incorporates any changes in the measure performance situation.

9. The computer program product of claim 1, wherein the at least one processor comprises at least one processor in a computer, at least one processor in a mobile access device or a combination thereof.

10. The computer program product of claim 1, wherein the linear or nonlinear regression model is developed using automated learning where the automated learning comprises completing a multi stage process where each stage of said process comprises an automated selection of an output from a plurality of output produced by a plurality of modeling algorithms after processing at least part of the data where the linearity of the predictive model is determined by learning from the data and where the plurality of modeling algorithms are selected from the group consisting of neural network;

classification and regression tree;

generalized autoregressive conditional heteroskedasticity, regression;

generalized additive model;

linear regression, path analysis;

Bayesian;

multivariate adaptive regression spline and support vector method.

11. A context search method, comprising:

using a computer and a mobile access device to complete a series of processing steps, comprising;

obtain a subject entity definition, a node depth criteria and an impact cutoff criteria,

aggregate and prepare a plurality of data items that includes data related to the subject entity for processing where said data comprises at least one entity function, one or more entity function measures and a creation date for each of the data items,

store the aggregated data items in one or more context layers by a component of context,

develop a subject entity situation summary by analyzing said subject entity related data where said summary comprises a linear or nonlinear regression model of each of the one or more entity function measures, a relevance for each of the measures and one or more of the context layers,

use the subject entity situation summary, the node depth criteria and the impact cutoff criteria to identify the components of context to include in an index,

create a composite index for the data associated with the identified components of context where said index comprises a column for the creation data of the data items, a column for each identified component of context and a ranking for each of the indexed data items,

receive a search request, and

provide a plurality of results in response to said request where the results are prioritized using a weight comprised of a mathematical combination of the index position ranking and a ranking provided by a relevance measure and where the subject entity physically exists

where the situation summary supports a graphical display of a relative contribution of one or more drivers to the function measures.

12. The method of claim 11, wherein the search results comprise a plurality of health related data where said health related data comprises a plurality of microbiome data.

13. The method of claim 11, wherein the one or more context layers are selected from the group consisting of Physical, Tactical, Organization, Social Environment and combinations thereof when the subject entity is selected from the group consisting of team, group, department, division, company, organization or multi-entity organization and has a single financial or a single non-financial function and wherein the one or more context layers are selected from the group consisting of Element, Environment, Resource, Reference Frame, Relationship, Transaction and combinations thereof when the subject entity is selected from the group consisting of team, group, department, division, company, organization or multi-entity organization and has two or more functions and when the subject entity is not a member of the group consisting of team, group, department, division, company, organization or multi-entity organization.

14. The method of claim 11, wherein the situation summary comprises a context frame and wherein the method further comprises developing a prioritized context frame that consists of the components of context that meet the impact cutoff and node depth criteria specified by the user and providing one or more applications that use the prioritized context frame to adapt to and manage a performance situation for the entity measures where said applications are selected from the group consisting of:

benefit plan analysis, customization, database, display, exchange, forecast, metric development, optimization, planning, profile development, review, rule development, summary, sustainability forecast and wellness program optimization.

15. The method of claim 11, wherein the search request is received from a browser.

16. The method of claim 11, wherein the relevance measure is selected from the group consisting of cover density rankings, vector space model measurements, okapi similarity measurements, three level relevance scores and hypertext induced topic selection algorithm scores where reinforcement learning determines which relevance measure is selected.

17. The method of claim 11, wherein the search request comprises one or more keywords or a question where said search request is received from a natural language interface or an anticipated need for data automatically initiates the search request.

18. The method of claim 11, wherein the one or more context layers are stored in a database that automatically captures and incorporates any changes in the measure performance situation.

19. A context search system, comprising:

a computer with one or more processors having circuitry to execute instructions;

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

obtain a subject entity definition, a node depth criteria and an impact cutoff criteria,

aggregate and prepare a plurality of data items that includes data related to the subject entity for processing where said data comprises at least one entity function, one or more entity function measures and a creation date for each of the data items,

store the aggregated data items in one or more context layers by a component of context,

develop a subject entity situation summary by analyzing said subject entity related data where said summary comprises a linear or nonlinear regression model of each of the one or more entity function measures, a relevance for each of the measures and one or more of the context layers,

use the subject entity situation summary, the node depth criteria and the impact cutoff criteria to identify the components of context to include in an index,

create a composite index for the data associated with the identified components of context where said index comprises a column for the creation data of the data items, a column for each identified component of context and a ranking for each of the indexed data items,

receive a search request from a mobile access device, and

provide a plurality of results in response to said request where the results are prioritized using a weight comprised of a mathematical combination of the index position ranking and a ranking provided by a relevance measure where at least part of the data and the search request are obtained from a mobile device.

20. The system of claim 19, wherein the linear or nonlinear regression model is developed using automated learning where the automated learning comprises completing a multi stage process where each stage of said process comprises an automated selection of an output from a plurality of output produced by a plurality of modeling algorithms after processing at least part of the data where the linearity of the predictive model is determined by learning from the data and where the plurality of modeling algorithms are selected from the group consisting of neural network;

classification and regression tree;

generalized autoregressive conditional heteroskedasticity, regression;

generalized additive model;

linear regression, path analysis;

Bayesian;

multivariate adaptive regression spline and support vector method.