System for categorizing documents in a linked collection of documents
April 9, 2017. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.Patent ReferencesMethod and means for lineage tracing of a spatial information processing and database system Concept matching of natural language queries with a database of document concepts Scatter-gather: a cluster-based method and apparatus for browsing large document collections Method for learning to infer the topical content of documents based upon their lexical content System for generation of user profiles for a system for customized electronic identification of desirable objects Layered storage structure for computer data storage manager Real-time embedded software respository with attribute searching apparatus and method Patent #: 5778368 InventorsAssigneeApplicationNo. 842926 filed on 04/09/1997US Classes:707/102, Generating database or data structure (e.g., via user interface)707/4, Query formulation, input preparation, or translation707/5, Query augmenting and refining (e.g., inexact access)707/6, Pattern matching access707/101, Manipulating data structure (e.g., compression, compaction, compilation)707/104.1Application of database or data structure (e.g., distributed, multimedia, image)ExaminersPrimary: Amsbury, WayneAssistant: Alam, Shahid Attorney, Agent or FirmForeign Patent References
International ClassG06F 017/30AbstractA system for extracting and analyzing information from a collection of linked documents at a locality to enable categorization of documents and prediction of documents relevant to a focus document. The system obtains and analyzes topology, usage and path information from for a collection at a locality, e.g. a web locality on the world wide web. For categorization, document meta information is represented as document vectors. Predefined criteria is applied to the document vectors to create lists of "similar" types of documents. For relevance prediction, networks representing topology, usage path and text similarity amongst the documents in the collection are created. A spreading activation technique is applied to the networks starting at a focus document to predict the documents relevant to the focus document. Using category and relevance prediction information, tools can be built to enable a user to more efficiently traverse through the collection of linked documents.Other References
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