Text matching to measure patent similarity

Sam Arts, Bruno Cassiman and Juan Carlos Gomez

Strategic Management Journal, 2018, vol. 39, issue 1, 62-84

Abstract: Research Summary: We propose using text matching to measure the technological similarity between patents. Technology experts from different fields validate the new similarity measure and its improvement on measures based on the United States Patent Classification System, and identify its limitations. As an application, we replicate prior findings on the localization of knowledge spillovers by constructing a case–control group of text‐matched patents. We also provide open access to the code and data to calculate the similarity between any two utility patents granted by the United States Patent and Trademark Office between 1976 and 2013, or between any two patent portfolios. Managerial Summary: We propose using text matching to measure the technological similarity between patents. The method can be used by various practitioners such as inventors, attorneys, patent examiners, and managers to search for closely related prior art, to assess the novelty of a patent, to identify R&D opportunities in less crowded areas, to detect in‐ or out‐licensing opportunities, to map companies in technology space, and to find acquisition targets. We use an expert panel to validate the improvement of the new similarity measure on measures based on the United States Patent Classification System, and provide open access to the code and data to calculate the similarity between any two utility patents granted by the USPTO between 1976 and 2013, or between any two patent portfolios.

Date: 2018
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Citations: View citations in EconPapers (90)

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https://doi.org/10.1002/smj.2699

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Working Paper: Text matching to measure patent similarity (2017)
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