Adaptive expertise clustering system and method

• US 8,694,457 B2
• Filed: 10/07/2011
• Issued: 04/08/2014
• 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): Gaffin, Jeffrey A
• Assistant Examiner(s): Kim, David H
• Application Number:
  US13/268,145
• Publication Number:
  US 20120254098A1
• Time in Patent Office: 914 Days
• Field of Search: 706/52
• US Class Current: 706/46
• 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 adaptive expertise clustering system and method generates expertise cohorts based on inferences from behavioral information and applies the expertise cohorts in generating recommendations for delivery to users. The expertise clustering may be informed by the evaluation of the contents of computer-implemented objects. Behaviors associated with members of specific expertise cohorts after specific events may inform the recommendations. Recommendations may comprise, for example, other users or process steps. Explanations for the recommendations may be provided to recommendation recipients.

Claims

1. A computer-implemented method, comprising:

contributing a plurality of behaviors to an expertise clustering function executed on a processor-based device, wherein the expertise clustering function assigns a user to an expertise cohort based, at least in part, on an inference from the plurality of behaviors; and

receiving a recommendation generated by a computer-implemented recommender function, wherein the recommender function generates the recommendation in accordance with the expertise cohort.

2. The method of claim 1, further comprising:

contributing the plurality of behaviors to the expertise clustering function, wherein the expertise clustering function assigns the user to the expertise cohort based, at least in part, on an evaluation of the contents of a plurality of objects.

3. The method of claim 1, further comprising:

receiving the recommendation, wherein the recommendation comprises a person.

4. The method of claim 1, further comprising:

receiving the recommendation, wherein the recommendation comprises a process step.

5. The method of claim 1, further comprising:

receiving the recommendation, wherein the recommendation is generated in accordance with a recommendation preference control setting.

6. The method of claim 1, further comprising:

receiving the recommendation, wherein the recommendation is generated in accordance with a specified topical neighborhood.

7. The method of claim 1, further comprising:

receiving from a computer-implemented explanatory function an explanation comprising a reason for the recommendation.

8. A computer-implemented system, comprising:

an expertise clustering function executed on a processor-based computing device, wherein the expertise clustering function assigns a user to an expertise cohort based, at least in part, on an inference from a plurality of behaviors; and

a computer-implemented recommender function, wherein the recommender function generates a recommendation for delivery to the user in accordance with the expertise cohort.

9. The system of claim 8, further comprising:

the expertise clustering function, wherein the expertise clustering function assigns the user to the expertise cohort based, at least in part, on an evaluation of the contents of a plurality of objects.

10. The system of claim 8, further comprising:

the recommendation, wherein the recommendation comprises a person.

11. The system of claim 8, further comprising:

the recommendation, wherein the recommendation comprises a process step.

12. The system of claim 8, further comprising:

the computer-implemented recommender function, wherein the recommender function generates the recommendation in accordance with a recommendation preference control setting.

13. The system of claim 8, further comprising:

the computer-implemented recommender function, wherein the recommender function generates the recommendation in accordance with a specified topical neighborhood.

14. The system of claim 8, further comprising:

an explanatory function that delivers to the user an explanation comprising a reason for the delivery of the recommendation.

15. A computer-implemented system, comprising:

a computer-implemented function that clusters a plurality of users into a plurality of expertise cohorts based, at least in part, on an inference from a first plurality of behaviors;

a computer-implemented assessment function that assesses a second plurality of behaviors associated with members of at least one of the expertise cohorts; and

a recommender function executed on a processor-based computing device that generates a recommendation based, at least in part, on the assessment of the second plurality of behaviors.

16. The system of claim 15, further comprising:

the computer-implemented function that clusters users into the plurality of expertise cohorts, wherein the clustering is based, at least in part, on an evaluation of the contents of a plurality of objects.

17. The system of claim 15, further comprising:

the computer-implemented function that clusters users into the plurality of expertise cohorts, wherein the clustering is based, at least in part, on a specified topical neighborhood.

18. The system of claim 15, further comprising:

the computer-implemented assessment function that assesses the second plurality of behaviors associated with members of at least one of the expertise cohorts, wherein the second plurality of behaviors occur after a specific event.

19. The system of claim 15, further comprising:

the computer-implemented recommender function that generates the recommendation, wherein the recommendation is based, at least in part, on the expertise cohort of which the recommendation recipient is a member.

20. The system of claim 15 further comprising:

the computer-implemented recommender function that generates the recommendation, wherein the recommendation comprises a process step.