Method and apparatus for identifying, extracting, capturing, and leveraging expertise and knowledge

• US 20060200556A1
• Filed: 12/27/2005
• Published: 09/07/2006
• Est. Priority Date: 12/29/2004
• Status: Active Grant

• Current Assignee: Monetate, Inc. (StartApp, Inc.)
• Original Assignee: Baynote Incorporated (StartApp, Inc.)
• Inventors: Brave, Scott, Bradshaw, Robert, Minson, Christopher, Jia, Jack
• Granted Patent: US 7,698,270 B2
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G06F 16/901 Indexing; Data structures therefor; Storage structures for retrieval from the web G06F16/951
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

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.

Claims

1. A computer implemented method for determining knowledge and/or expertise, comprising the step of:

identifying a target community (or subgroup) of users;

observing usage patterns of Web documents and other online assets by said target community (or subgroup) of users; and

based upon said observed usage patterns, performing any of the steps of;

automatically determining the topic of said Web documents and other online assets; and

detecting which of said Web documents and other online assets that are most used and useful to said target community (or subgroup) of users.

2. The method of claim 1, further comprising the step of:

ranking documents in a search or navigation context based on said observed usage patterns.

3. The method of claim 1, further comprising the step of:

comparing online assets and scoring similarity of said assets based on said observed usage patterns.

4. The method of claim 1, further comprising the step of:

representing asset topics using a vector-space model and UF-IDF (usage frequency, inverse document frequency) weighting.

5. The method of claim 4, further comprising the steps of:

maintaining asset topics on a per-user basis; and

determining group topic assessments by combining individual user assessments.

6. The method of claim 5, further comprising the steps of:

representing topic assessments using a vector-space model and UF-IDF (usage frequency, inverse document frequency) weighting; and

combining user topic assessments using a linear algorithm.

7. The method of claim 1, further comprising the steps of:

representing asset topics using a vector-space model and UF-IDF (usage frequency, inverse document frequency) weighting; and

giving said asset topics a similarity score based on a cosine similarity metric.

8. The method of claim 1, further comprising the step of:

determining usage patterns based upon any of search terms used to reach assets, navigation patterns used to reach assets, user asset dwell/think time, and user scrolling, printing, saving, and emailing of assets.

9. The method of claim 1, further comprising the step of:

using time-based phenomena to contribute to a determination of usefulness, said phenomena comprising any of newness of assets, rediscovery of assets, trend-based use, and periodic use of assets.

10. The method of claim 9, further comprising the step of:

refining usefulness by detecting and dampening attractive nuisance assets which show a large amount of activity even though they are not useful.

11. The method of claim 10, further comprising the step of:

basing detection of attractive nuisance assets on a number of revisits an asset has received by users in combination with a number of user visits and overall activity on said asset.

12. The method of claim 1, further comprising the step of:

providing a significantly greater weighting to the importance of implicit observations over the weighting provided to explicit observations.

13. The method of claim 1, further comprising the steps of:

analyzing all observations; and

via said analysis, generating a set of recommendations comprising distilled experiences from a community of users;

wherein said recommendations age over time and are discarded if they have relatively little value; and

wherein recommendations which are most valuable based on repeated usage are stored into a long term memory.

14. The method of claim 1, further comprising the step of:

for a given user who may be anonymous, said user visiting a particular site, and a given context comprising any of what page the user is on and how the user got there, providing recommendations to said user that allow said user to navigate said site more efficiently.

15. The method of claim 1, further comprising the steps of:

generating a set a recommendations that may be applied to a search; and

for a given user who may be anonymous, and a given search query, using said recommendations to refine and augment a resulting search.

16. The method of claim 15, further comprising the step of:

driving said recommendations not just by individual uses, but by the use of communities, leveraging the wisdom of crowds and community emergent behavior.

17. The method of any of claim 1, further comprising the step of:

identifying communities comprising any of peer groups and expert groups based on an information context;

wherein communities are nested and defined by different levels of contexts.

18. An apparatus for determining knowledge and/or expertise, comprising:

means for identifying a target community (or subgroup) of users;

means for observing usage patterns of Web documents and other online assets by said target community (or subgroup) of users; and

based upon said observed usage patterns, means for any of;

automatically determining the topic of said Web documents and other online assets; and

detecting which of said Web documents and other online assets that are most used and useful to said target community (or subgroup) of users.

19. The apparatus of claim 18, further comprising:

means for ranking documents in a search or navigation context based on said observed usage patterns.

20. The apparatus of claim 18, further comprising:

means for comparing online assets and scoring similarity of said assets based on said observed usage patterns.

21. The apparatus of claim 18, further comprising:

means for representing asset topics using a vector-space model and UF-IDF (usage frequency, inverse document frequency) weighting.

22. The apparatus of claim 21, further comprising:

means for maintaining asset topics on a per-user basis; and

means for determining group topic assessments by combining individual user assessments.

23. The apparatus of claim 22, further comprising:

means for representing topic assessments using a vector-space model and UF-IDF (usage frequency, inverse document frequency) weighting; and

means for combining user topic assessments using a linear algorithm.

24. The apparatus of claim 18, further comprising:

means for representing asset topics using a vector-space model and UF-IDF (usage frequency, inverse document frequency) weighting; and

means for giving said asset topics a similarity score based on a cosine similarity metric.

25. The apparatus of claim 18, further comprising:

means for determining usage patterns based upon any of search terms used to reach assets, navigation patterns used to reach assets, user asset dwell/think time, and user scrolling, printing, saving, and emailing of assets.

26. The apparatus of claim 18, further comprising:

means for using time-based phenomena to contribute to a determination of usefulness, said phenomena comprising any of newness of assets, rediscovery of assets, trend-based use, and periodic use of assets.

27. The apparatus of claim 26, further comprising:

means for refining usefulness by detecting and dampening attractive nuisance assets which show a large amount of activity even though they are not useful.

28. The apparatus of claim 27, further comprising:

means for basing detection of attractive nuisance assets on a number of revisits an asset has received by users in combination with a number of user visits and overall activity on said asset.

29. The apparatus of claim 18, further comprising:

means for providing a significantly greater weighting to the importance of implicit observations over the weighting provided to explicit observations.

30. The apparatus of claim 18, further comprising:

means for analyzing all observations; and

via said analysis, means for generating a set of recommendations comprising distilled experiences from a community of users;

wherein said recommendations age over time and are discarded if they have relatively little value; and

wherein recommendations which are most valuable based on repeated usage are stored into a long term memory.

31. The apparatus of claim 18, further comprising:

for a given user who may be anonymous, means for said user visiting a particular site, and a given context comprising any of what page the user is on and how the user got there, providing recommendations to said user that allow said user to navigate said site more efficiently.

32. The apparatus of claim 18, further comprising:

means for generating a set a recommendations that may be applied to a search; and

for a given user who may be anonymous, and a given search query, means for using said recommendations to refine and augment a resulting search.

33. The apparatus of claim 32, further comprising:

means for driving said recommendations not just by individual uses, but by the use of communities, leveraging the wisdom of crowds and community emergent behavior.

34. The apparatus of any of claim 18, further comprising:

means for identifying communities comprising any of peer groups and expert groups based on an information context;

wherein communities are nested and defined by different levels of contexts.