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Natural Language Processing Patents Landscape Analysis

Author

Listed:
  • Hend S. Al-Khalifa

    (Department of Information Technology, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia
    iWAN Research Group, King Saud University, Riyadh 11543, Saudi Arabia)

  • Taif AlOmar

    (iWAN Research Group, King Saud University, Riyadh 11543, Saudi Arabia)

  • Ghala AlOlyyan

    (iWAN Research Group, King Saud University, Riyadh 11543, Saudi Arabia)

Abstract

Understanding NLP patents provides valuable insights into innovation trends and competitive dynamics in artificial intelligence. This study uses the Lens patent database to investigate the landscape of NLP patents. The overall patent output in the NLP field on a global scale has exhibited a rapid growth over the past decade, indicating rising research and commercial interests in applying NLP techniques. By analyzing patent assignees, technology categories, and geographic distribution, we identify leading innovators as well as research hotspots in applying NLP. The patent landscape reflects intensifying competition between technology giants and research institutions. This research aims to synthesize key patterns and developments in NLP innovation revealed through patent data analysis, highlighting implications for firms and policymakers. A detailed understanding of NLP patenting activity can inform intellectual property strategy and technology investment decisions in this burgeoning AI domain.

Suggested Citation

  • Hend S. Al-Khalifa & Taif AlOmar & Ghala AlOlyyan, 2024. "Natural Language Processing Patents Landscape Analysis," Data, MDPI, vol. 9(4), pages 1-17, March.
    • Handle: RePEc:gam:jdataj:v:9:y:2024:i:4:p:52-:d:1367920
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Zhao, Hongye & Zhao, Yi & Zhang, Chengzhi, 2026. "Quantifying the knowledge proximity between academic and industry research: An entity and semantic perspective," Technological Forecasting and Social Change, Elsevier, vol. 226(C).

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