Method and apparatus for identifying, extracting, capturing, and leveraging expertise and knowledge
First Claim
1. A computer implemented method performed by a processor, comprising:
- observing usage patterns by one or more users in an on-line community in connection with an on-line asset;
identifying usefulness of the on-line asset by observing user implicit behaviors in connection with the usage patterns of the on-line asset and by extracting behavioral patterns from the user implicit behaviors;
refining the identified on-line asset usefulness by context, wherein a context of the on-line asset is automatically detected based on one or more observed terms obtained by observing user implicit behaviors with respect to the identified on-line asset, wherein the identified on-line asset has a plurality of term vectors;
assigning a term vector entry of a term vector of the plurality of term vectors that describes a degree to which the identified on-line asset has an affinity with the observed terms, wherein each term vector of the plurality of term vectors is associated with a different user of the one or more users;
identifying for each user of the one or more users an expertise vector byidentifying on-line assets with respect to which the each user has engaged in one or more of the user implicit behaviors; and
generating the expertise vector by summing the plurality of term vectors for the identified on-line assets;
receive query including query terms;
obtaining search result documents;
determining that at least one search result document is an identified on-line asset for an expert user of the one or more users; and
ranking the at least one search result based on a relationship of the query terms to the expertise vector of the expert user.
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Accused Products
Abstract
The invention comprises a set of complementary techniques that dramatically improve enterprise search and navigation results. The core of the invention is an expertise or knowledge index, called UseRank that tracks the behavior of website visitors. The expertise-index is designed to focus on the four key discoveries of enterprise attributes: Subject Authority, Work Patterns, Content Freshness, and Group Know-how. The invention produces useful, timely, cross-application, expertise-based search and navigation results. In contrast, traditional Information Retrieval technologies such as inverted index, NLP, or taxonomy tackle the same problem with an opposite set of attributes than what the enterprise needs: Content Population, Word Patterns, Content Existence, and Statistical Trends. Overall, the invention encompasses Baynote Search—a enhancement over existing IR searches, Baynote Guide—a set of community-driven navigations, and Baynote Insights—aggregated views of visitor interests and trends and content gaps.
100 Citations
38 Claims
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1. A computer implemented method performed by a processor, comprising:
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observing usage patterns by one or more users in an on-line community in connection with an on-line asset; identifying usefulness of the on-line asset by observing user implicit behaviors in connection with the usage patterns of the on-line asset and by extracting behavioral patterns from the user implicit behaviors; refining the identified on-line asset usefulness by context, wherein a context of the on-line asset is automatically detected based on one or more observed terms obtained by observing user implicit behaviors with respect to the identified on-line asset, wherein the identified on-line asset has a plurality of term vectors; assigning a term vector entry of a term vector of the plurality of term vectors that describes a degree to which the identified on-line asset has an affinity with the observed terms, wherein each term vector of the plurality of term vectors is associated with a different user of the one or more users; identifying for each user of the one or more users an expertise vector by identifying on-line assets with respect to which the each user has engaged in one or more of the user implicit behaviors; and generating the expertise vector by summing the plurality of term vectors for the identified on-line assets; receive query including query terms; obtaining search result documents; determining that at least one search result document is an identified on-line asset for an expert user of the one or more users; and ranking the at least one search result based on a relationship of the query terms to the expertise vector of the expert user. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38)
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Specification