Semantically-Based Patent Thicket Identification

71 Pages Posted: 6 Mar 2020 Last revised: 20 Mar 2020

See all articles by Mateusz Gatkowski

Mateusz Gatkowski

University of Essex - Centre for Computational Finance and Economic Agents

Marek Dietl

Warsaw School of Economics (SGH) - Collegium of World Economy

Łukasz Skrok

affiliation not provided to SSRN

Ryan Whalen

National University of Singapore (NUS) - Faculty of Law

Katharine Rockett

University of Essex - Department of Economics; Centre for Economic Policy Research (CEPR)

Date Written: March 1, 2020

Abstract

Patent thickets have been identified as a major stumbling block in the development of new technologies, creating the need to accurately identify thicket membership. Various citations-based methodologies (Graevenitz et al, 2011; Clarkson, 2005) have been proposed, which have relied on broad survey results (Cohen et al, 2000) for validation. Expert evaluation is an alternative direct method of judging thicket membership at the individual patent level. While this method potentially is robust to drafting and jurisdictional differences in patent design, it is also costly to use on a large scale. We employ a natural language processing technique, which does not carry these large costs, to proxy expert views closely. Furthermore, we investigate the relation between our semantic measure and citation based measures, finding them quite distinct. We then combine a variety of thicket indicators into a statistical model to assess the probability that a newly added patent belongs to a thicket. We also study the role each measure plays, as part of creating a prospective screening model that could improve efficiency of the patent system, in response to Lemley (2001).

Keywords: Patent Thicket, Intellectual Property, Semantic Distance, Latent Semantic Analysis, Natural Language Processing, Complexity

JEL Classification: L13, L20, O34

Suggested Citation

Gatkowski, Mateusz and Dietl, Marek and Skrok, Łukasz and Whalen, Ryan and Rockett, Katharine, Semantically-Based Patent Thicket Identification (March 1, 2020). Research Policy, Vol. 49, No. 2, 2020, University of Hong Kong Faculty of Law Research Paper No. 2020/009, Available at SSRN: https://ssrn.com/abstract=3535135

Mateusz Gatkowski

University of Essex - Centre for Computational Finance and Economic Agents ( email )

Wivenhoe Park
Colchester, Essex CO4 3SQ
United Kingdom

Marek Dietl

Warsaw School of Economics (SGH) - Collegium of World Economy ( email )

Al. Niepodległości 164
Warszawa, 02-554
Poland

Łukasz Skrok

affiliation not provided to SSRN

Ryan Whalen (Contact Author)

National University of Singapore (NUS) - Faculty of Law ( email )

469G Bukit Timah Road
Eu Tong Sen Building
Singapore, 259776
Singapore

Katharine Rockett

University of Essex - Department of Economics ( email )

Wivenhoe Park
Colchester CO4 3SQ
United Kingdom
+44 1206 873 333 (Phone)
+44 1206 873 724 (Fax)

Centre for Economic Policy Research (CEPR)

London
United Kingdom

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