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Social network analysis of patent infringement lawsuits

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  • Kim, Hyoungshick
  • Song, JaeSeung

Abstract

Using patent lawsuit information, we develop a method to identify companies with a significant legal influence on the technologies used in their industry. We construct a patent-infringement lawsuits graph, using the data from intellectual property lawsuits between companies, and analyse the level of influence of companies by computing the network centrality of each company in the graph. To illustrate the practicality of our method, we apply the proposed method to analyse the patent influence of well-known companies in the smartphone industry. The results of our empirical analysis are well matched to the current smartphone market status — for example, Apple, Nokia and Samsung are identified as the most important companies, which lead the smartphone technology and market. This shows that the proposed approach can be used to evaluate and manage patent portfolios even using a relatively small amount of patent lawsuits data.

Suggested Citation

  • Kim, Hyoungshick & Song, JaeSeung, 2013. "Social network analysis of patent infringement lawsuits," Technological Forecasting and Social Change, Elsevier, vol. 80(5), pages 944-955.
    • Handle: RePEc:eee:tefoso:v:80:y:2013:i:5:p:944-955
    DOI: 10.1016/j.techfore.2012.10.014
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    Citations

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    Cited by:

    1. Xie, Qijun & Su, Jun, 2021. "The spatial-temporal complexity and dynamics of research collaboration: Evidence from 297 cities in China (1985–2016)," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    2. Xi Yang & Xiang Yu, 2020. "Preventing Patent Risks in Artificial Intelligence Industry for Sustainable Development: A Multi-Level Network Analysis," Sustainability, MDPI, vol. 12(20), pages 1-21, October.
    3. Lee, Jong-Seon & Kim, Nami & Bae, Zong-Tae, 2019. "The effects of patent litigation involving NPEs on firms’ patent strategies," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    4. Lee, Pei-Chun & Su, Hsin-Ning, 2014. "How to forecast cross-border patent infringement? — The case of U.S. international trade," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 125-131.
    5. Way-Ren Huang & Chia-Jen Hsieh & Ke-Chiun Chang & Yen-Jo Kiang & Chien-Chung Yuan & Woei-Chyn Chu, 2017. "Network characteristics and patent value—Evidence from the Light-Emitting Diode industry," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-14, August.
    6. Yuan, Xiaodong & Li, Xiaotao, 2020. "A network analytic method for measuring patent thickets: A case of FCEV technology," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    7. Sun, Yutao, 2016. "The structure and dynamics of intra- and inter-regional research collaborative networks: The case of China (1985–2008)," Technological Forecasting and Social Change, Elsevier, vol. 108(C), pages 70-82.
    8. Sun, Yutao & Jiang, Lin & Cao, Cong & Tseng, Fang-Mei, 2024. "From contributors to boundary spanners: Evolving roles of government agencies in China’s innovation policy network (1980–2019)," Technovation, Elsevier, vol. 132(C).
    9. Sun, Yutao & Cao, Cong, 2018. "The evolving relations between government agencies of innovation policymaking in emerging economies: A policy network approach and its application to the Chinese case," Research Policy, Elsevier, vol. 47(3), pages 592-605.
    10. Yu-Hsin Chang & Kuei-Kuei Lai & Chien-Yu Lin & Fang-Pei Su & Ming-Chung Yang, 2017. "A hybrid clustering approach to identify network positions and roles through social network and multivariate analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1733-1755, December.

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