Book chapter
Using Classification Code Hierarchies for Patent Prior Art Searches
Current Challenges in Patent Information Retrieval, pp.287-304
Information Retrieval Series, Springer Nature
01/01/2011
DOI: 10.1007/978-3-642-19231-9_14
Abstract
Searches in patent collections to determine if a given patent application has related prior art patents is non-trivial and often requires extensive manpower. When time is constrained, an automatically generated, ranked list of prior art patents associated with a given patent application decreases search costs and improves search efficiency. One may view the discovery of this prior art patent set as a problem of finding patents 'related' to the patent application. To accomplish this, we examine whether semantic relations between patent classification codes can aid in the recognition of related prior art patents. We explore similarity measures for hierarchically ordered patent classes and subclasses for this purpose. Next, we examine various patent feature-weighting schemes to achieve the best similarities between our patent applications and related prior art patents. Finally, we provide a method and demonstrate that patent prior art searches can successfully be used as an aid in patent ranking.
Details
- Title: Subtitle
- Using Classification Code Hierarchies for Patent Prior Art Searches
- Creators
- Christopher G. Harris - Univ Iowa, Informat Program, Iowa City, IA 52242 USARobert Arens - Nuance Communications (United States)Padmini Srinivasan - University of Iowa
- Contributors
- Mihai Lupu (Editor)Katja Mayer (Editor)John Tait (Editor)Anthony J Trippe (Editor)
- Resource Type
- Book chapter
- Publication Details
- Current Challenges in Patent Information Retrieval, pp.287-304
- Series
- Information Retrieval Series
- DOI
- 10.1007/978-3-642-19231-9_14
- ISSN
- 1387-5264
- Publisher
- Springer Nature; DORDRECHT
- Number of pages
- 18
- Language
- English
- Date published
- 01/01/2011
- Academic Unit
- Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984430338602771
Metrics
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