System for refining cognitive insights using cognitive graph vectors

• US 9,990,582 B2
• Filed: 01/27/2017
• Issued: 06/05/2018
• Est. Priority Date: 06/09/2014
• Status: Active Grant

• Current Assignee: Tecnotree Technologies Incorporated
• Original Assignee: Cognitive Scale Inc (Tecnotree Oyj)
• Inventors: Sanchez, Matthew
• Primary Examiner(s): Chen, Alan
• Application Number:
  US15/417,986
• Publication Number:
  US 20170140275A1
• Time in Patent Office: 494 Days
• Field of Search: None
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/043 Distributed expert systems; Blackboards

Abstract

A method, system and computer-usable medium for using cognitive graph vectors to refine cognitive insights comprising storing data from a plurality of data sources within a cognitive graph via a cognitive inference and learning system; associating a first set of the data within the cognitive graph with a first cognitive graph vector of a plurality of cognitive graph vectors via the cognitive inference and learning system; associating a second set of the data within the cognitive graph with a second cognitive graph vector of the plurality of cognitive graph vectors via the cognitive inference and learning system; processing the data from the plurality of data sources to provide cognitive insights via the cognitive inference and learning system; and refining the cognitive insights based upon a limitation relating to one of the plurality of cognitive graph vectors via the cognitive inference and learning system.

Claims

1. A method for refining cognitive insights using cognitive graph vectors comprising:

storing data from a plurality of data sources within a cognitive graph via a cognitive inference and learning system, the cognitive graph representing a data domain;

associating a first set of the data of the data domain within the cognitive graph with a first cognitive graph vector of a plurality of cognitive graph vectors via the cognitive inference and learning system, the first cognitive graph vector extending away from a cognitive graph nexus in a first direction;

associating a second set of the data within the cognitive graph with a second cognitive graph vector of the plurality of cognitive graph vectors via the cognitive inference and learning system, the first cognitive graph vector extending away from a cognitive graph nexus in a first direction;

processing the data from the plurality of data sources to provide cognitive insights via the cognitive inference and learning system; and

refining the cognitive insights based upon a limitation relating to one of the plurality of cognitive graph vectors via the cognitive inference and learning system, the limitation corresponding to a selected cognitive graph vector parameter.

2. The method of claim 1, wherein:

the first cognitive graph vector comprises a plurality of first cognitive graph vector indices extending along the first cognitive graph vector away from a cognitive graph nexus;

the second cognitive graph vector comprises a plurality of second cognitive graph vector indices extending along the second cognitive graph vector away from the cognitive graph nexus;

the limitation comprises limiting the first set of data to data within a first certain index of the plurality of first cognitive graph vector indices; and

the refining comprising limiting the second set of data to data within a second certain index of the second cognitive graph vector indices.

3. The method of claim 1, further comprising:

associating a third set of data within the cognitive graph with a third cognitive graph vector of the plurality of cognitive graph vectors; and

, wherein

the refining the cognitive insights based upon the limitation relating to one of the plurality of cognitive graph vectors further comprises identifying a limitation on one of the first, second and third cognitive graph vectors and refining another of the first, second and third cognitive graph vectors based upon the limitation of one of the first, second and third cognitive graph vectors.

4. The method of claim 3, wherein:

the first cognitive graph vector comprises a plurality of first cognitive graph vector indices extending along the first cognitive graph vector away from a cognitive graph nexus;

the second cognitive graph vector comprises a plurality of second cognitive graph vector indices extending along the second cognitive graph vector away from the cognitive graph nexus;

the third cognitive graph vector comprises a plurality of third cognitive graph vector indices extending along the third cognitive graph vector away from the cognitive graph nexus;

the limitation comprises limiting the first set of data to data within a first certain index of the plurality of first cognitive graph vector indices;

the refining comprising limiting the second set of data to data within a second certain index of the second cognitive graph vector indices and data within a third; and

the refining further comprising limiting the third set of data to data within a third certain index of the third cognitive graph vector indices.

5. The method of claim 4, wherein:

at least some of the first cognitive graph vector indices, second cognitive graph vector indices and third vector graph indices are different magnitudes.

6. The method of claim 4, wherein:

at least some of the first cognitive graph vector indices, second cognitive graph vector indices and third vector graph indices have a corresponding magnitude.

7. An information processing system comprising:

a processor;

a data bus coupled to the processor; and

a computer-usable medium embodying computer program code, the computer-usable medium being coupled to the data bus, the computer program code used for refining cognitive insights using cognitive graph vectors and comprising instructions executable by the processor and configured for;

storing data from a plurality of data sources within a cognitive graph via a cognitive inference and learning system executing on the processor, the cognitive graph representing a data domain;

associating a first set of the data of the data domain within the cognitive graph with a first cognitive graph vector of a plurality of cognitive graph vectors via the cognitive inference and learning system, the first cognitive graph vector extending away from a cognitive graph nexus in a first direction;

associating a second set of the data within the cognitive graph with a second cognitive graph vector of the plurality of cognitive graph vectors via the cognitive inference and learning system, the first cognitive graph vector extending away from a cognitive graph nexus in a first direction;

processing the data from the plurality of data sources to provide cognitive insights via the cognitive inference and learning system; and

refining the cognitive insights based upon a limitation relating to one of the plurality of cognitive graph vectors via the cognitive inference and learning system, the limitation corresponding to a selected cognitive graph vector parameter.

8. The information processing system of claim 7, wherein:

the first cognitive graph vector comprises a plurality of first cognitive graph vector indices extending along the first cognitive graph vector away from a cognitive graph nexus;

the second cognitive graph vector comprises a plurality of second cognitive graph vector indices extending along the second cognitive graph vector away from the cognitive graph nexus;

the limitation comprises limiting the first set of data to data within a first certain index of the plurality of first cognitive graph vector indices; and

the refining comprising limiting the second set of data to data within a second certain index of the second cognitive graph vector indices.

9. The information processing system of claim 7, further comprising:

associating a third set of data within the cognitive graph with a third cognitive graph vector of the plurality of cognitive graph vectors; and

, wherein

the refining the cognitive insights based upon the limitation relating to one of the plurality of cognitive graph vectors further comprises identifying a limitation on one of the first, second and third cognitive graph vectors and refining another of the first, second and third cognitive graph vectors based upon the limitation of one of the first, second and third cognitive graph vectors.

10. The information processing system of claim 9, wherein:

the first cognitive graph vector comprises a plurality of first cognitive graph vector indices extending along the first cognitive graph vector away from a cognitive graph nexus;

the second cognitive graph vector comprises a plurality of second cognitive graph vector indices extending along the second cognitive graph vector away from the cognitive graph nexus;

the third cognitive graph vector comprises a plurality of third cognitive graph vector indices extending along the third cognitive graph vector away from the cognitive graph nexus;

the limitation comprises limiting the first set of data to data within a first certain index of the plurality of first cognitive graph vector indices;

the refining comprising limiting the second set of data to data within a second certain index of the second cognitive graph vector indices and data within a third; and

the refining further comprising limiting the third set of data to data within a third certain index of the third cognitive graph vector indices.

11. The information processing system of claim 10, wherein:

at least some of the first cognitive graph vector indices, second cognitive graph vector indices and third vector graph indices are different magnitudes.

12. The information processing system of claim 10, wherein:

at least some of the first cognitive graph vector indices, second cognitive graph vector indices and third vector graph indices have a corresponding magnitude.

13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:

storing data from a plurality of data sources within a cognitive graph via a cognitive inference and learning system, the cognitive graph representing a data domain;

associating a first set of the data of the data domain within the cognitive graph with a first cognitive graph vector of a plurality of cognitive graph vectors via the cognitive inference and learning system, the first cognitive graph vector extending away from a cognitive graph nexus in a first direction;

associating a second set of the data within the cognitive graph with a second cognitive graph vector of the plurality of cognitive graph vectors via the cognitive inference and learning system, the first cognitive graph vector extending away from a cognitive graph nexus in a first direction;

processing the data from the plurality of data sources to provide cognitive insights via the cognitive inference and learning system; and

refining the cognitive insights based upon a limitation relating to one of the plurality of cognitive graph vectors via the cognitive inference and learning system, the limitation corresponding to a selected cognitive graph vector parameter.

14. The non-transitory, computer-readable storage medium of claim 13, wherein:

the first cognitive graph vector comprises a plurality of first cognitive graph vector indices extending along the first cognitive graph vector away from a cognitive graph nexus;

the second cognitive graph vector comprises a plurality of second cognitive graph vector indices extending along the second cognitive graph vector away from the cognitive graph nexus;

the limitation comprises limiting the first set of data to data within a first certain index of the plurality of first cognitive graph vector indices; and

the refining comprising limiting the second set of data to data within a second certain index of the second cognitive graph vector indices.

15. The non-transitory, computer-readable storage medium of claim 13, further comprising:

associating a third set of data within the cognitive graph with a third cognitive graph vector of the plurality of cognitive graph vectors; and

, wherein

the refining the cognitive insights based upon the limitation relating to one of the plurality of cognitive graph vectors further comprises identifying a limitation on one of the first, second and third cognitive graph vectors and refining another of the first, second and third cognitive graph vectors based upon the limitation of one of the first, second and third cognitive graph vectors.

16. The non-transitory, computer-readable storage medium of claim 15, wherein:

the first cognitive graph vector comprises a plurality of first cognitive graph vector indices extending along the first cognitive graph vector away from a cognitive graph nexus;

the second cognitive graph vector comprises a plurality of second cognitive graph vector indices extending along the second cognitive graph vector away from the cognitive graph nexus;

the third cognitive graph vector comprises a plurality of third cognitive graph vector indices extending along the third cognitive graph vector away from the cognitive graph nexus;

the limitation comprises limiting the first set of data to data within a first certain index of the plurality of first cognitive graph vector indices;

the refining comprising limiting the second set of data to data within a second certain index of the second cognitive graph vector indices and data within a third; and

the refining further comprising limiting the third set of data to data within a third certain index of the third cognitive graph vector indices.

17. The non-transitory, computer-readable storage medium of claim 16, wherein:

at least some of the first cognitive graph vector indices, second cognitive graph vector indices and third vector graph indices are different magnitudes.

18. The non-transitory, computer-readable storage medium of claim 16, wherein:

at least some of the first cognitive graph vector indices, second cognitive graph vector indices and third vector graph indices have a corresponding magnitude.

19. The non-transitory, computer-readable storage medium of claim 13, wherein the computer executable instructions are deployable to a client system from a server system at a remote location.

20. The non-transitory, computer-readable storage medium of claim 13, wherein the computer executable instructions are provided by a service provider to a user on an on-demand basis.