Expertise discovery methods and systems

• US 8,645,312 B2
• Filed: 10/07/2011
• Issued: 02/04/2014
• Est. Priority Date: 03/29/2011
• Status: Active Grant

• Current Assignee: Manyworlds Incorporated
• Original Assignee: Manyworlds Incorporated
• Inventors: Flinn, Steven Dennis, Moneypenny, Naomi Felina
• Primary Examiner(s): Holmes, Michael B
• Application Number:
  US13/268,025
• Publication Number:
  US 20120254095A1
• Time in Patent Office: 851 Days
• Field of Search: None
• US Class Current: 706/62
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 40/56 Natural language generation
• CPC Class Codes: G06K 7/10366 the interrogation device being adapted for miscellaneous applications
• CPC Class Codes: G06K 7/10475 arrangements to facilitate interaction with further interrogation devices, e.g. such that at least two interrogation devices may function and cooperate in a network of such devices
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06N 7/02 using fuzzy logic computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A computer-implemented expertise discovery method and system generates recommendations of people in accordance with inferences of levels of expertise from behavioral information. The generation of the recommendations may be in accordance with comparisons of two or more expertise vectors and associated correlations between the vectors. The inferences of expertise may be with respect to specified topical neighborhoods. Explanations as to why recommendations were made are delivered to the recommendation recipients, and may include references to the inferred levels of expertise, the behavioral bases for the inferences, and/or the confidence in the inferences.

Claims

1. A computer-implemented method, comprising:

receiving a recommendation of a person from an expertise discovery function executed on a processor-based device that generates the recommendation in accordance with an inference of a level of expertise from a plurality of usage behaviors; and

receiving an explanation from a computer-implemented explanatory function, wherein the explanation comprises a reason as to why the person was recommended.

2. The method of claim 1, further comprising:

receiving the recommendation of a person from the expertise discovery function executed on a processor-based device that generates the recommendation in accordance with the inference of a level of expertise from a plurality of usage behaviors, wherein the inference is in accordance with a specified topical neighborhood.

3. The method of claim 1, further comprising:

receiving the recommendation, wherein the recommendation is based on a comparison of two expertise vectors.

4. The method of claim 1, further comprising:

receiving the recommendation from the expertise discovery function executed on a processor-based device that generates the recommendation in accordance with the inference of a level of expertise from a plurality of usage behaviors, wherein at least one behavior of the plurality of behaviors is a physical location.

5. The method of claim 1, further comprising:

receiving the explanation, wherein the explanation references at least one behavior of the plurality of behaviors.

6. The method of claim 1, further comprising:

receiving the explanation, wherein the explanation references the inferred level of expertise.

7. The method of claim 1, further comprising:

receiving the explanation, wherein the explanation indicates a level of confidence with respect to the recommendation.

8. A computer-implemented system, comprising:

an expertise recommender function executed on a processor-based computing device that generates a recommendation of person for delivery to a recommendation recipient based on an inference of a level of expertise from a plurality of usage behaviors; and

a computer-implemented explanatory function that delivers an explanation to the recommendation recipient as to why the person was recommended.

9. The system of claim 8, further comprising:

the expertise recommender function executed on a processor-based computing device that generates the recommendation of a person for delivery to a recommendation recipient based on the inference of a level of expertise from the plurality of usage behaviors, wherein the inference is in accordance with a specified topical neighborhood.

10. The system of claim 8, further comprising:

the expertise recommender function, wherein the expertise recommender function generates the recommendation in accordance with a comparison of two expertise vectors.

11. The system of claim 8, further comprising:

the expertise recommender function that generates the recommendation based on the inference of a level of expertise from the plurality of usage behaviors, wherein at least one behavior of the plurality of behaviors is a physical location.

12. The system of claim 8, further comprising:

the computer-implemented explanatory function that delivers the explanation to the recommendation recipient as to why the person was recommended, wherein the explanation references at least one behavior of the plurality of behaviors.

13. The system of claim 8, further comprising:

the computer-implemented explanatory function that delivers an explanation to the recommendation recipient as to why the person was recommended, wherein the explanation references the inferred level of expertise.

14. The system of claim 8, further comprising:

the computer-implemented explanatory function that delivers an explanation to the recommendation recipient as to why the person was recommended, wherein the explanation indicates a level of confidence with respect to the recommendation.

15. A computer-implemented system, comprising:

an expertise inferencing function executed on a processor-based computing device, wherein the expertise inferencing function generates a first expertise vector associated with a first user in accordance with an inference of a level of expertise from a plurality of behaviors; and

a computer-implemented expertise discovery function that generates a recommendation of a person for delivery to a user in accordance with the first expertise vector.

16. The system of claim 15, further comprising:

the computer-implemented expertise discovery function that generates the recommendation, wherein the recommendation is generated in accordance with a comparison of the first expertise vector with a second expertise vector.

17. The system of claim 16, further comprising:

the computer-implemented expertise discovery function that generates the recommendation, wherein the recommendation is generated in accordance with comparing the first expertise vector with the second expertise vector, and wherein the second expertise vector is generated based on an inferred expertise level associated with a second user.

18. The system of claim 17, further comprising:

an expertise correlation function that identifies a negative correlation between the first expertise vector and the second expertise vector.

19. The system of claim 16, further comprising:

the computer-implemented expertise discovery function that generates the recommendation, wherein the recommendation is generated in accordance with comparing the first expertise vector with the second expertise vector, and wherein at least one value of the second expertise vector is established by the recommendation recipient.

20. The system of claim 15, further comprising:

the computer-implemented expertise discovery function that generates the recommendation, wherein the recommendation is generated in response to a search request.