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Patent Thickets Identification

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  • Gątkowski, Mateusz
  • Dietl, Marek
  • Skrok, Lukasz
  • Whalen, Ryan
  • Rockett, Katharine

Abstract

Patent thickets have been identified by various citations-based techniques, such as Graevenitz et al (2011) and Clarkson (2005). An alternative direct measurement is based on expert opinion. We use natural language processing techniques to measure pairwise semantic similarity of patents identified as thicket members by experts to create a semantic network. We compare the semantic similarity scores for patents in different expert-identified thickets: those within the same thicket, those in different thickets, and those not in thickets. We show that patents within the same thicket are significantly more semantically similar than other pairs of patents. We then present a statistical model to assess the probability of a newly added patent belonging to a thicket based on semantic networks as well as other measures from the existing thicket literature (the triples of Graevenitz and Clarkson’s density ratio). We conclude that combining information from semantic distance with other sources can be helpful to isolate the patents that are likely to be members of thickets.

Suggested Citation

  • Gątkowski, Mateusz & Dietl, Marek & Skrok, Lukasz & Whalen, Ryan & Rockett, Katharine, 2018. "Patent Thickets Identification," Economics Discussion Papers 22928, University of Essex, Department of Economics.
    • Handle: RePEc:esx:essedp:22928
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    References listed on IDEAS

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    1. Wesley M. Cohen & Richard R. Nelson & John P. Walsh, 2000. "Protecting Their Intellectual Assets: Appropriability Conditions and Why U.S. Manufacturing Firms Patent (or Not)," NBER Working Papers 7552, National Bureau of Economic Research, Inc.
    2. Antonin Bergeaud & Yoann Potiron & Juste Raimbault, 2017. "Classifying patents based on their semantic content," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-22, April.
    3. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    4. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
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    Cited by:

    1. Marcel Seip & Carolina Castaldi & Meindert Flikkema & Ard-Pieter de Man, 2019. "A taxonomy of firm-level IPR application practices to inform policy debates," LEM Papers Series 2019/03, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

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    More about this item

    Keywords

    Patent Thickets Identification; Intellectual Property; Patenting; Patent Thickets; Semantic Distance; Latent Semantic Analysis; Natural Language Processing; Complexity;
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