James E Pitkow

6369819, Apr 9, 2002

Apr 17, 1998

09/062513

James E. Pitkow - Palo Alto CA
Peter L. T. Pirolli - San Francisco CA
Ed H. Chi - Minneapolis MN
Stuart K. Card - Los Altos Hills CA
Jock D. Mackinlay - Palo Alto CA

Xerox Corporation - Stamford CT

G06T 1120

345440, 345853, 707 3

A method for displaying in a coherent manner the changes over time of a web sites structure, usage, and content is disclosed. Time tubes are generated by a method of displaying a related series of graphs. Time tubes illustrate changes in a graph that undergoes one or more transformations from one state to another. The transformations are displayed using the length of the cylindrical tube, filling the length of the time tube with planar slices which represent the data at various stages of the transformations. Time tubes may encode several dimensions of the transformations simultaneously by altering the representation of size, color, and layout among the planar slices. Temporal transformations occur when web pages are added or deleted over time. Value-based transformations include node colors, which may be used to encode a specific pages usage parameter. Spatial transformations include the scaling of physical dimension as graphs expand or contract in size.

6415368, Jul 2, 2002

Dec 22, 1999

09/470126

Natalie S. Glance - Meylan, FR
Bernardo A. Huberman - Palo Alto CA
Lada A. Adamic - Redwood City CA
James Pitkow - Palo Alto CA

Xerox Corporation - Stamford CT

G06F 1200

711158, 711151, 711156

A system and method of caching uses quality or value attributes, provided for example, by a recommender system or by a dynamical analysis of site accesses, which are attached to cached information to prioritize items in the cache. Documents are prioritized in the cache according to the relative value of their content. Value data may be provided from a recommender system which provides a value for a document according to user recommendations (using explicit recommendations) or from statistical analysis of site visits from unique users (implicit recommendations) or a combination of the two to identify the higher value documents. The caching method may also be used to improve performance of a recommender system.

6457028, Sep 24, 2002

Sep 29, 1999

09/407787

James E. Pitkow - Palo Alto CA
Peter L. Pirolli - San Francisco CA

Xerox Corporation - Stamford CT

G06F 1721

707513, 707 5, 707514, 707901, 707907

A method and apparatus for identifying related collections of linked documents. In the method the links from a set of related documents are analyzed to identify a plurality of document collections. By analyzing only the link structure, a process intensive content analysis of the documents is avoided. A citation analysis technique, such as co-citation analysis, is performed on the set of documents to extract link information indicating links and link frequency between document collections. For co-citation analysis that information would include the frequency that both are linked to by another document collection. By using the link information, related document collections may then be identified using a suitable analysis technique, such as clustering or spreading activation.

6493702, Dec 10, 2002

May 5, 1999

09/305844

Eytan Adar - Foster City CA
Thomas M. Breuel - San Jose CA
Todd A. Cass - San Francisco CA
James E. Pitkow - Palo Alto CA
Hinrich Schuetze - San Francisco CA

Xerox Corporation - Stamford CT

G06F 1730

707 3

A search and recommendation system employs the preferences and profiles of individual users and groups within a community of users, as well as information derived from shared document bookmarks, to augment Internet searches, re-rank search results, and provide recommendations for documents based on a subject-matter query. The search and recommendation system operates in the context of a shared bookmark manager, which stores individual users bookmarks (some of which may be published or shared for group use) on a centralized bookmark database connected to the Internet. The shared bookmark manager is implemented as a distributed program, portions of which operate on users terminals and other portions of which operate on the centralized bookmark database.

6567797, May 20, 2003

Oct 19, 1999

09/425038

Hinrich Schuetze - San Francisco CA
James E. Pitkow - Palo Alto CA
Peter L. Pirolli - San Francisco CA
Ed H. Chi - Palo Alto CA
Jun Li - Seattle WA

Xerox Corporation - Stamford CT

G06F 700

707 2, 707 3, 707 4, 707 5, 707 10, 709203

A system and method for browsing, retrieving, and recommending information from a collection uses multi-modal features of the documents in the collection, as well as an analysis of users prior browsing and retrieval behavior. The system and method are premised on various disclosed methods for quantitatively representing documents in a document collection as vectors in multi-dimensional vector spaces, quantitatively determining similarity between documents, and clustering documents according to those similarities. The system and method also rely on methods for quantitatively representing users in a user population, quantitatively determining similarity between users, clustering users according to those similarities, and visually representing clusters of users by analogy to clusters of documents.

6564202, May 13, 2003

Oct 19, 1999

09/421419

Hinrich Schuetze - San Francisco CA
Peter L. Pirolli - San Francisco CA
James E. Pitkow - Palo Alto CA
Ed H. Chi - Palo Alto CA
Jun Li - Seattle WA

Xerox Corporation - Stamford CT

G06F 1730

707 2

A system and method for browsing, retrieving, and recommending information from a collection uses multi-modal features of the documents in the collection, as well as an analysis of users prior browsing and retrieval behavior. The system and method are premised on various disclosed methods for quantitatively representing documents in a document collection as vectors in multi-dimensional vector spaces, quantitatively determining similarity between documents, and clustering documents according to those similarities. The system and method also rely on methods for quantitatively representing users in a user population, quantitatively determining similarity between users, clustering users according to those similarities, and visually representing clusters of users by analogy to clusters of documents.

6598054, Jul 22, 2003

Oct 19, 1999

09/425039

Hinrich Schuetze - San Francisco CA
Peter L. Pirolli - San Francisco CA
James E. Pitkow - Palo Alto CA
Ed H. Chi - Palo Alto CA
Jun Li - Seattle WA

Xerox Corporation - Stamford CT

G06F 1700

707103R, 7071041, 345650, 345700

A system and method for browsing, retrieving, and recommending information from a collection uses multi-modal features of the documents in the collection, as well as an analysis of users prior browsing and retrieval behavior. The system and method are premised on various disclosed methods for quantitatively representing documents in a document collection as vectors in multi-dimensional vector spaces, quantitatively determining similarity between documents, and clustering documents according to those similarities. The system and method also rely on methods for quantitatively representing users in a user population, quantitatively determining similarity between users, clustering users according to those similarities, and visually representing clusters of users by analogy to clusters of documents.

6631451, Oct 7, 2003

Apr 22, 2002

10/128011

Natalie S. Glance - Meylan, FR
Bernardo A. Huberman - Palo Alto CA
Lada A. Adamic - Redwood City CA
James Pitkow - Palo Alto CA

Xerox Corporation - Stamford CT

G06F 1200

711158, 711151, 711133

A system and method of caching uses quality or value attributes, provided for example, by a recommender system or by a dynamical analysis of site accesses, which are attached to cached information to prioritize items in the cache. Documents are prioritized in the cache according to the relative value of their content. Value data may be provided from a recommender system which provides a value for a document according to user recommendations (using explicit recommendations) or from statistical analysis of site visits from unique users (implicit recommendations) or a combination of the two to identify the higher value documents.