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Machine learning and natural language processing on the patent corpus: Data, tools, and new measures

Author

Listed:
  • Benjamin Balsmeier
  • Mohamad Assaf
  • Tyler Chesebro
  • Gabe Fierro
  • Kevin Johnson
  • Scott Johnson
  • Guan‐Cheng Li
  • Sonja Lück
  • Doug O'Reagan
  • Bill Yeh
  • Guangzheng Zang
  • Lee Fleming

Abstract

Drawing upon recent advances in machine learning and natural language processing, we introduce new tools that automatically ingest, parse, disambiguate, and build an updated database using U.S. patent data. The tools identify unique inventor, assignee, and location entities mentioned on each granted U.S. patent from 1976 to 2016. We describe data flow, algorithms, user interfaces, descriptive statistics, and a novelty measure based on the first appearance of a word in the patent corpus. We illustrate an automated coinventor network mapping tool and visualize trends in patenting over the last 40 years. Data and documentation can be found at https://console.cloud.google.com/launcher/partners/patents-public-data.

Suggested Citation

  • Benjamin Balsmeier & Mohamad Assaf & Tyler Chesebro & Gabe Fierro & Kevin Johnson & Scott Johnson & Guan‐Cheng Li & Sonja Lück & Doug O'Reagan & Bill Yeh & Guangzheng Zang & Lee Fleming, 2018. "Machine learning and natural language processing on the patent corpus: Data, tools, and new measures," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 27(3), pages 535-553, September.
    • Handle: RePEc:bla:jemstr:v:27:y:2018:i:3:p:535-553
    DOI: 10.1111/jems.12259
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    References listed on IDEAS

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