Method and apparatus for predicting destinations in a navigation context based upon observed usage patterns

• US 7,580,930 B2
• Filed: 02/08/2006
• Issued: 08/25/2009
• Est. Priority Date: 12/27/2005
• Status: Expired due to Fees

• Current Assignee: Monetate, Inc. (StartApp, Inc.)
• Original Assignee: Baynote Incorporated (StartApp, Inc.)
• Inventors: Brave, Scott, Bradshaw, Robert, Minson, Christopher, Jia, Jack
• Primary Examiner(s): Cottingham; John R.
• Assistant Examiner(s): Bromell; Alexandria Y
• Application Number:
  US11/350,646
• Publication Number:
  US 20070150464A1
• Time in Patent Office: 1,294 Days
• Field of Search: 707/3, 707/6, 709/224, 715/205
• US Class Current: 1/1
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: Y10S 707/966 Distributed
• CPC Class Codes: Y10S 707/99936 Pattern matching access

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 automatically determining within an on-line community any of importance of an on-line asset, topic of said asset, and relationships among assets, without asking members of said on-line community directly, comprising the processor executed steps of:

observing usage patterns by a community of peers and experts who show high affinity to a topic related to a navigation context;

employing automatic techniques to extract patterns from said usage;

identifying usefulness of an online asset by observing user implicit behaviors in connection with said usage patterns of said online asset and by extracting behavioral patterns from said observations;

refining said identified online asset usefulness by context, wherein the context of each online asset is automatically detected based on observed terms/topics from individual and group user behaviors when said online asset is determined to be useful based upon said individual and group user behaviors;

determining a user'"'"'s current context/topic based on an aggregation of the identified context of each online asset that the said user has found useful during their navigation, where an asset'"'"'s contribution to the aggregate context is weighted by an asset'"'"'s recency of use;

refining said identified user context based on searches performed during the user'"'"'s navigation, where a search'"'"'s contribution to the aggregate context is weighted by the recency of the search;

comparing the identified context of the current user with the identified context of all assets within the system and assigning to each asset a similarity score based on its similarity to the current user'"'"'s context;

using said similarity score to predict a desired destination of users in said navigation context; and

refining said predicted destination based on observed navigation patterns of previous users;

said observed usage patterns comprising user online search, navigation, and interaction behavior, said behavior including any of searches performed and position in user trail;

assets viewed and position in user trail;

dwell, range, scrolling, think time, and mouse movement on an asset;

anchors and lines used in asset text;

virtual bookmarks and virtual printing; and

explicit downloading, emailing, printing, saving, and removing to and/or from a computer hardware memory.

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

maintaining navigational patterns on a per-user basis; and

determining group navigational patterns by combining individual patterns in an additive fashion.

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

differentiating navigation information on a one per-asset basis; and

basing predictions on a combination of information from all points in a user'"'"'s session navigation history.

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

classifying usage of a given asset by a given user as successful or unsuccessful use based on a combination of factors comprising any of time spent on an asset, explicit user actions taken on said asset, said actions comprising any of print, save, email, and bookmark, mouse and keyboard behavior, length and distribution of stable pauses between activity, navigation to and from said asset, and browser window state, said state comprising any of foreground, background, minimized, maximized, and closed.

5. 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.

6. 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.

7. 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.

8. 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.

9. The method of claim 8, 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.

10. The method 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.

11. An apparatus for automatically determining within an on-line community any of importance of an on-line asset, topic of said asset, and relationships among assets, without asking members of said on-line community directly, comprising:

means for observing usage patterns by a community of peers and experts who show high affinity to a topic related to a navigation context;

a processor for employing automatic techniques to extract patterns from said usage;

said processor identifying usefulness of an online asset by observing user implicit behaviors in connection with said usage patterns of said online asset and by extracting behavioral patterns from said observations;

said processor refining said identified online asset usefulness by context, wherein the context of each online asset is automatically detected based on observed terms/topics from individual and group user behaviors when said online asset is determined to be useful based upon said individual and group user behaviors;

said processor determining a user'"'"'s current context/topic based on an aggregation of the identified context of each online asset that the said user has found useful during their navigation, where an asset'"'"'s contribution to the aggregate context is weighted by an asset'"'"'s recency of use;

said processor refining said identified user context based on searches performed during the user'"'"'s navigation, where a search'"'"'s contribution to the aggregate context is weighted by the recency of the search;

said processor comparing the identified context of the current user with the identified context of all assets within the system and assigning to each asset a similarity score based on its similarity to the current user'"'"'s context;

said processor using said similarity score to predict a desired destination of users in a navigation context; and

said processor refining said predicted destination based on observed navigation patterns of previous users;

said observed usage patterns comprising user online search, navigation, and interaction behavior, said behavior including any of searches performed and position in user trail;

assets viewed and position in user trail;

dwell, range, scrolling, think time, and mouse movement on an asset;

anchors and lines used in asset text;

virtual bookmarks and virtual printing; and

explicit downloading, emailing, printing, saving, and removing.

12. The apparatus of claim 11, further comprising:

said processor maintaining navigational patterns on a per-user basis; and

said processor determining group navigational patterns by combining individual patterns in an additive fashion.

13. The apparatus of claim 11, further comprising:

said processor differentiating navigation information on a per-asset basis; and

said processor basing predictions on a combination of information from all points in a user'"'"'s session navigation history.

14. The apparatus of claim 11, further comprising:

said processor classifying usage of a given asset by a given user as successful or unsuccessful use based on a combination of factors comprising any of time spent on an asset, explicit user actions taken on said asset, said actions comprising any of print, save, email, and bookmark, mouse and keyboard behavior, length and distribution of stable pauses between activity, navigation to and from said asset, and browser window state, said state comprising any of foreground, background, minimized, maximized, and closed.

15. The apparatus of claim 11, further comprising:

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

16. The apparatus of claim 11, further comprising:

said processor analyzing all observations; and

via said analysis, said processor 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.

17. The apparatus of claim 11, further comprising:

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, said processor providing recommendations to said user that allow said user to navigate said site more efficiently.

18. The apparatus of claim 11, further comprising:

said processor 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, said processor using said recommendations to refine and augment a resulting search.

19. The apparatus of claim 18, further comprising:

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

20. The apparatus of claim 11, further comprising:

said processor 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.