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Preventing Patent Risks in Artificial Intelligence Industry for Sustainable Development: A Multi-Level Network Analysis


  • Xi Yang

    () (Center for Studies of Intellectual Property Rights, Zhongnan University of Economics and Law, Wuhan 430073, China)

  • Xiang Yu

    () (School of Management, Huazhong University of Science and Technology, Wuhan 430074, China)


In recent years, assessing patent risks has attracted fast-growing attention from both researchers and practitioners in studies of technological innovation. Following the existing literature on risks and intellectual property (IP) risks, we define patent risks as the lack of understanding of the distribution of patents that lead to losing a key patent, increased research and development costs, and, potentially, infringement litigation. This paper aims to propose an explorative approach to investigating patent risks in the target technology field by integrating social network analysis and patent analysis. Compared to previous research, this study makes an important contribution toward identifying patent risks in the overall technological field by employing a patent-based multi-level network model that has not appeared in existing methodologies of patent risks. In order to verify the effectiveness of this approach, we take artificial intelligence (AI) as an example. Data collected from the Derwent Innovation Index (DII) database were used to build the patent-based multi-level network on patent risks from market, technology, and assignee perspectives. The results indicate that the lack of international collaborations among assignees and industry–university–research collaboration may lead to patent collaboration risks. Regarding patent market risks, the lack of overseas patent applications, especially the lack of distribution in the main competitive markets, is a key factor. As for patent technology risks, most of the leading assignees lack awareness of the distribution in the following technological fields: industrial electric equipment, engineering instrumentation, and automotive electrics. In summary, assignees from the U.S. with first mover advantages are still powerful leaders in the AI technology field. Although China is catching up very rapidly in the total number of AI patents, the apparent patent risks under the perspectives of collaboration, market, and technology will obviously hamper the catch-up efforts of China’s AI industry. We conclude that, in practice, the proposed patent-based multi-level network model not only plays an important role in helping stakeholders in the AI technological field to prevent patent risks, find new technology opportunities, and obtain sustainable development, but also has significance for guiding the industrial development of various emerging technology fields.

Suggested Citation

  • Xi Yang & Xiang Yu, 2020. "Preventing Patent Risks in Artificial Intelligence Industry for Sustainable Development: A Multi-Level Network Analysis," Sustainability, MDPI, Open Access Journal, vol. 12(20), pages 1-21, October.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8667-:d:431264

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    References listed on IDEAS

    1. Elisa Bellotti & Luka Kronegger & Luigi Guadalupi, 2016. "The evolution of research collaboration within and across disciplines in Italian Academia," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 783-811, November.
    2. Hochull Choe & Duk Hee Lee, 2017. "The structure and change of the research collaboration network in Korea (2000–2011): network analysis of joint patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 917-939, May.
    3. Brennecke, Julia & Rank, Olaf, 2017. "The firm’s knowledge network and the transfer of advice among corporate inventors—A multilevel network study," Research Policy, Elsevier, vol. 46(4), pages 768-783.
    4. Kim, Hyoungshick & Song, JaeSeung, 2013. "Social network analysis of patent infringement lawsuits," Technological Forecasting and Social Change, Elsevier, vol. 80(5), pages 944-955.
    5. Andreas Panagopoulos, 2011. "The Effect of IP Protection on Radical and Incremental Innovation," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 2(3), pages 393-404, September.
    6. Jia Zheng & Zhi-yun Zhao & Xu Zhang & Dar-zen Chen & Mu-hsuan Huang, 2014. "International collaboration development in nanotechnology: a perspective of patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 683-702, January.
    7. Li Tang & Philip Shapira & Jan Youtie, 2015. "Is there a clubbing effect underlying Chinese research citation Increases?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(9), pages 1923-1932, September.
    8. Goeldner, Moritz & Herstatt, Cornelius & Tietze, Frank, 2015. "The emergence of care robotics — A patent and publication analysis," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 115-131.
    9. Péter Érdi & Kinga Makovi & Zoltán Somogyvári & Katherine Strandburg & Jan Tobochnik & Péter Volf & László Zalányi, 2013. "Prediction of emerging technologies based on analysis of the US patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 225-242, April.
    10. repec:dau:papers:123456789/1095 is not listed on IDEAS
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    1. Youngho Kim & Sangsung Park & Junseok Lee & Dongsik Jang & Jiho Kang, 2021. "Integrated Survival Model for Predicting Patent Litigation Hazard," Sustainability, MDPI, Open Access Journal, vol. 13(4), pages 1-15, February.

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