Method and apparatus for determining usefulness of a digital asset
First Claim
1. A method for identifying, extracting using a processor, capturing, and leveraging expertise and knowledge comprising:
- observing, between and among peers and experts who show high affinity with regard to any of the users, assets, and topics/terms, a user heartbeat which comprises user activity, including mouse movement and stable pauses on an asset, while the asset is in a user display foreground;
based upon the observing, detecting user think time for the asset;
employing automatic techniques to extract patterns from at least the user think time; and
learning affinities between and among any of the users, assets and topics/terms from the extracted patterns for any of;
automatically determining each user'"'"'s peer group,predicting a desired destination of the users in a navigation context,calculating an expert and peer impact factor, an asset impact factor, or rareness,determining importance of an asset and/or expertise that individuals possess, without asking the individuals directly,automatically disambiguating query terms in an online search, and/oreffecting a predictive query by suggesting search terms to the users or automatically inserting search terms into user queries to expand a search, wherein learning affinities includes applying a formula to lower weights of the term as the terms become associated with more assets.
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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 emcompasses 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.
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Citations
38 Claims
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1. A method for identifying, extracting using a processor, capturing, and leveraging expertise and knowledge comprising:
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observing, between and among peers and experts who show high affinity with regard to any of the users, assets, and topics/terms, a user heartbeat which comprises user activity, including mouse movement and stable pauses on an asset, while the asset is in a user display foreground; based upon the observing, detecting user think time for the asset; employing automatic techniques to extract patterns from at least the user think time; and learning affinities between and among any of the users, assets and topics/terms from the extracted patterns for any of; automatically determining each user'"'"'s peer group, predicting a desired destination of the users in a navigation context, calculating an expert and peer impact factor, an asset impact factor, or rareness, determining importance of an asset and/or expertise that individuals possess, without asking the individuals directly, automatically disambiguating query terms in an online search, and/or effecting a predictive query by suggesting search terms to the users or automatically inserting search terms into user queries to expand a search, wherein learning affinities includes applying a formula to lower weights of the term as the terms become associated with more assets. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method for identifying, extracting using a processor, capturing, and leveraging expertise and knowledge comprising:
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observing, between and among peers and experts who show high affinity with regard to any of the users, assets, and topics/terms, a user heartbeat which comprises user activity, including mouse movement and stable pauses on an asset, while the asset is in a user display foreground; based upon the observing, detecting whether the asset is or is not useful for a given user; employing automatic techniques to extract patterns from at least the detecting whether the asset is or is not useful for a given user; and learning affinities between and among any of the users, assets and topics/terms from the extracted patterns for any of; automatically determining each user'"'"'s peer group, predicting a desired destination of the users in a navigation context, calculating an expert and peer impact factor, an asset impact factor, or rareness, determining importance of an asset and/or expertise that individuals possess, without asking the individuals directly, automatically disambiguating query terms in an online search, and/or effecting a predictive query by suggesting search terms to the users or automatically inserting search terms into user queries to expand a search, wherein learning affinities includes applying a formula to lower weights of the term as the terms become associated with more assets. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
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20. An apparatus for identifying, extracting, capturing, and leveraging expertise and knowledge, comprising:
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means for observing, between and among peers and experts who show high affinity with regard to any of the users, assets, and topics/terms, a user heartbeat which comprises any user activity, including mouse movement and stable pauses on an asset, while the asset is in a user display foreground; based upon the observing, a processor for detecting user think time for the asset; the processor employing automatic techniques to extract patterns from at least the user think time; and the processor learning affinities between and among any of the users, assets and topics/terms from the extracted patterns for any of; automatically determining each user'"'"'s peer group, predicting a desired destination of the users in a navigation context, calculating an expert and peer impact factor, an asset impact factor, or rareness, determining importance of an asset and/or expertise that individuals possess, without asking the individuals directly, automatically disambiguating query terms in an online search, and/or effecting a predictive query by suggesting search terms to the users or automatically inserting search terms into user queries to expand a search, wherein learning affinities includes applying a formula to lower weights of the terms as the terms become associated with more assets. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29)
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30. An apparatus for identifying, extracting, capturing, and leveraging expertise and knowledge, comprising:
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means for, observing, between and among peers and experts who show high affinity with regard to any of users, assets, and topics/terms, a user heartbeat which comprises user activity, including mouse movement and stable pauses on an asset, while the asset is in a user display foreground; based upon the observing, a processor for detecting whether the asset is or is not useful for a given user; the processor employing automatic techniques to extract patterns from at least detecting whether the asset is or is not useful for a given user; and the processor learning affinities between and among any of the users, assets and topics/terms from the extracted patterns for any of; automatically determining each user'"'"'s peer group, predicting a desired destination of the users in a navigation context, calculating an expert and peer impact factor, an asset impact factor, or rareness, determining importance of an asset and/or expertise that individuals possess, without asking the individuals directly, automatically disambiguating query terms in an online search, and/or effecting a predictive query by suggesting search terms to the users or automatically inserting search terms into user queries to expand a search, wherein learning affinities includes applying a formula to lower weights of the terms as the terms become associated with more assets. - View Dependent Claims (31, 32, 33, 34, 35, 36, 37, 38)
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Specification