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Patent Similarity Data and Innovation Metrics

Author

Listed:
  • Ryan Whalen
  • Alina Lungeanu
  • Leslie DeChurch
  • Noshir Contractor

Abstract

We introduce and describe the Patent Similarity Dataset, comprising vector space model‐based similarity scores for U.S. utility patents. The dataset provides approximately 640 million pre‐calculated similarity scores, as well as the code and computed vectors required to calculate further pairwise similarities. In addition to the raw data, we introduce measures that leverage patent similarity to provide insight into innovation and intellectual property law issues of interest to both scholars and policymakers. Code is provided in accompanying scripts to assist researchers in obtaining the dataset, joining it with other available patent data, and using it in their research.

Suggested Citation

  • Ryan Whalen & Alina Lungeanu & Leslie DeChurch & Noshir Contractor, 2020. "Patent Similarity Data and Innovation Metrics," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 17(3), pages 615-639, September.
    • Handle: RePEc:wly:empleg:v:17:y:2020:i:3:p:615-639
    DOI: 10.1111/jels.12261
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    Citations

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    Cited by:

    1. Natalie A. Carlson, 2023. "Differentiation in microenterprises," Strategic Management Journal, Wiley Blackwell, vol. 44(5), pages 1141-1167, May.
    2. A‐Sung Hong, 2024. "Beyond the finish line: How losing in patent race drives post‐race innovation," Strategic Management Journal, Wiley Blackwell, vol. 45(5), pages 968-993, May.
    3. Kong, Nancy & Dulleck, Uwe & Jaffe, Adam B. & Sun, Shupeng & Vajjala, Sowmya, 2023. "Linguistic metrics for patent disclosure: Evidence from university versus corporate patents," Research Policy, Elsevier, vol. 52(2).
    4. de Rassenfosse, Gaétan & Pellegrino, Gabriele & Raiteri, Emilio, 2024. "Do patents enable disclosure? Evidence from the invention secrecy act," International Journal of Industrial Organization, Elsevier, vol. 92(C).
    5. Meijun Liu & Sijie Yang & Yi Bu & Ning Zhang, 2023. "Female early-career scientists have conducted less interdisciplinary research in the past six decades: evidence from doctoral theses," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    6. Higham, Kyle & de Rassenfosse, Gaétan & Jaffe, Adam B., 2021. "Patent Quality: Towards a Systematic Framework for Analysis and Measurement," Research Policy, Elsevier, vol. 50(4).
    7. Lorenz Brachtendorf & Fabian Gaessler & Dietmar Harhoff, 2023. "Truly standard‐essential patents? A semantics‐based analysis," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 32(1), pages 132-157, January.
    8. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    9. Teng, Hao & Wang, Nan & Zhao, Hongyu & Hu, Yingtong & Jin, Haitao, 2024. "Enhancing semantic text similarity with functional semantic knowledge (FOP) in patents," Journal of Informetrics, Elsevier, vol. 18(1).
    10. Ashtor, Jonathan H., 2022. "Modeling patent clarity," Research Policy, Elsevier, vol. 51(2).
    11. Guangtong Li & L. Siddharth & Jianxi Luo, 2023. "Embedding knowledge graph of patent metadata to measure knowledge proximity," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(4), pages 476-490, April.

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