| US 7,580,930 B2 | ||
| Method and apparatus for predicting destinations in a navigation context based upon observed usage patterns | ||
| Scott Brave, Mountain View, Calif. (US); Robert Bradshaw, San Jose, Calif. (US); Jack Jia, Los Altos Hills, Calif. (US); and Christopher Minson, Menlo Park, Calif. (US) | ||
| Assigned to Baynote, Inc., Cupertino, Calif. (US) | ||
| Filed on Feb. 08, 2006, as Appl. No. 11/350,646. | ||
| Application 11/350646 is a division of application No. 11/319928, filed on Dec. 27, 2005. | ||
| Prior Publication US 2007/0150464 A1, Jun. 28, 2007 | ||
| This patent is subject to a terminal disclaimer. | ||
| Int. Cl. G06F 17/30 (2006.01) | ||
| U.S. Cl. 707—6 [707/10; 709/224; 715/205] | 20 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.
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