Patent-to-Patent Similarity: A Vector Space Model

39 Pages Posted: 30 Dec 2015 Last revised: 19 Aug 2016

Kenneth A. Younge

École Polytechnique Fédérale de Lausanne (EPFL-CDM)

Jeffrey M. Kuhn

University of North Carolina (UNC) at Chapel Hill - Management-Strategy Area

Date Written: July 30, 2016

Abstract

Current measures of patent similarity rely on the manual classification of patents into taxonomies. In this project, we leverage information retrieval theory and Big Data methods to develop a machine-automated measure of patent-to-patent similarity. We validate the measure and demonstrate that it significantly improves upon existing patent classification systems. Moreover, we illustrate how a pairwise similarity comparison of any and every two patents in the USPTO patent space can open new avenues of research in economics, management, and public policy. We make the data available for future scholarship through the Patent Research Foundation.

Keywords: patent data, technology space, similarity, relatedness

Suggested Citation

Younge, Kenneth A. and Kuhn, Jeffrey M., Patent-to-Patent Similarity: A Vector Space Model (July 30, 2016). Available at SSRN: https://ssrn.com/abstract=2709238 or http://dx.doi.org/10.2139/ssrn.2709238

Kenneth A. Younge (Contact Author)

École Polytechnique Fédérale de Lausanne (EPFL-CDM) ( email )

Station 5
1015 Lausanne
Switzerland

Jeffrey M. Kuhn

University of North Carolina (UNC) at Chapel Hill - Management-Strategy Area ( email )

Chapel Hill, NC 27599
United States

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