The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 23, 2021

Filed:

Feb. 09, 2016
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Jie Lu, Hawthorne, NY (US);

Jeffrey S. Boston, Wappingers Falls, NY (US);

Anni R. Coden, Bronx, NY (US);

Jennifer Lai, Garrison, NY (US);

Shimei Pan, Armonk, NY (US);

Mercan Topkara, Scarsdale, NY (US);

Zhen Wen, Springfield, NJ (US);

Stephen P. Wood, Thornwood, NY (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 5/04 (2006.01); G06Q 10/06 (2012.01); G06N 5/02 (2006.01);
U.S. Cl.
CPC ...
G06N 5/04 (2013.01); G06N 5/02 (2013.01); G06Q 10/06 (2013.01);
Abstract

A system includes a user model module that generates a plurality of topic-specific user knowledge models for each user of a plurality of users, each topic-specific user knowledge model representing a level of knowledge possessed by a respective user on a single topic from a set of globally defined topics shared among the plurality of users, a expertise model building module that generates a plurality of topic-specific expert knowledge models, each topic-specific expert knowledge model representing an aggregate level of knowledge possessed by a plurality of expert users on a single topic from a set of globally defined topics shared among the plurality of users, and a processor of a computer that executes instructions for comparing the topic-specific user knowledge model of the first user with the topic-specific expert knowledge model for a respective topic to determine a distance between a user knowledge level and an aggregate expert knowledge level for the topic.


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