Natural Language Processing Patents Landscape Analysis

Author:

Al-Khalifa Hend S.12ORCID,AlOmar Taif2ORCID,AlOlyyan Ghala2

Affiliation:

1. Department of Information Technology, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia

2. 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.

Funder

Researchers Supporting Project

Publisher

MDPI AG

Reference12 articles.

1. (2023, July 21). Natural Language Processing-Global|Market Forecast, Statista. Available online: https://www.statista.com/outlook/tmo/artificial-intelligence/natural-language-processing/worldwide.

2. Haney, B. (2023, July 21). Patents for NLP Software: An Empirical Review. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3594515.

3. Warin, T., Duc, R.L., and Sanger, W. (2017, January 14–16). Mapping Innovations in Artificial Intelligence through Patents: A Social Data Science Perspective. Proceedings of the 2017 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.

4. Yoo, Y., Lim, D., and Kim, K. (2021). Patent Analysis Using Vector Space Model and Deep Learning Model: A Case of Artificial Intelligence Industry Technology. Preprints, 2021110208.

5. Patent Relating to Artificial Intelligence and Liability for Artificial Intelligence Application from the US Law Perspectives;Ngoc;Vietnam. J. Leg. Sci.,2022

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