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The effect of patent family information in patent citation network analysis: a comparative case study in the drivetrain domain

Author

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  • Hiroko Nakamura

    (The University of Tokyo)

  • Shinji Suzuki

    (The University of Tokyo)

  • Yuya Kajikawa

    (Tokyo Institute of Technology)

  • Masataka Osawa

    (Toyota Central R&D Lab)

Abstract

Previous researchers of citation analysis often analyze patent data of a single authority because of the availability of the data and the simplicity of analysis. Patent analysis, on the other hand, is used not only for filing and litigation, but also for technology trend analysis. However, global technology trends cannot be understood only with the analysis of patent data issued by a single authority. In this paper, we propose the use of patents from multiple authorities and discuss the effect of bundling patent family information. We investigate the effect of patent families with cases from automobile drivetrain technology. Based on the results, we conclude that the use of multiple authorities’ patent data bundled with the patent family information can significantly improve the coverage and practicability of patent citation analysis.

Suggested Citation

  • Hiroko Nakamura & Shinji Suzuki & Yuya Kajikawa & Masataka Osawa, 2015. "The effect of patent family information in patent citation network analysis: a comparative case study in the drivetrain domain," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(2), pages 437-452, August.
    • Handle: RePEc:spr:scient:v:104:y:2015:i:2:d:10.1007_s11192-015-1626-2
    DOI: 10.1007/s11192-015-1626-2
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    References listed on IDEAS

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    Cited by:

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    3. Lai, Kuei-Kuei & Bhatt, Priyanka C. & Kumar, Vimal & Chen, Hsueh-Chen & Chang, Yu-Hsin & Su, Fang-Pei, 2021. "Identifying the impact of patent family on the patent trajectory: A case of thin film solar cells technological trajectories," Journal of Informetrics, Elsevier, vol. 15(2).
    4. Mejia, Cristian & Kajikawa, Yuya, 2020. "Emerging topics in energy storage based on a large-scale analysis of academic articles and patents," Applied Energy, Elsevier, vol. 263(C).
    5. Zhao Qu & Shanshan Zhang & Chunbo Zhang, 2017. "Patent research in the field of library and information science: Less useful or difficult to explore?," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 205-217, April.
    6. Takano, Yasutomo & Mejia, Cristian & Kajikawa, Yuya, 2016. "Unconnected component inclusion technique for patent network analysis: Case study of Internet of Things-related technologies," Journal of Informetrics, Elsevier, vol. 10(4), pages 967-980.
    7. Higham, Kyle & Contisciani, Martina & De Bacco, Caterina, 2022. "Multilayer patent citation networks: A comprehensive analytical framework for studying explicit technological relationships," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    8. Yoon, Naeun & Sohn, So Young, 2024. "Assessment framework for automotive suppliers' technological adaptability in the electric vehicle era," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    9. Jun Hong Park & Hyunseog Chung & Ki Hong Kim & Jin Ju Kim & Chulung Lee, 2021. "The Impact of Technological Capability on Financial Performance in the Semiconductor Industry," Sustainability, MDPI, vol. 13(2), pages 1-20, January.
    10. Kok, Holmer & Faems, Dries & de Faria, Pedro, 2020. "Ties that matter: The impact of alliance partner knowledge recombination novelty on knowledge utilization in R&D alliances," Research Policy, Elsevier, vol. 49(7).
    11. Zhenfu Li & Yixuan Wang & Zhao Deng, 2022. "Research on Evolution Characteristics and Factors of Nordic Green Patent Citation Network," Sustainability, MDPI, vol. 14(13), pages 1-21, June.
    12. Takano, Yasutomo & Kajikawa, Yuya, 2019. "Extracting commercialization opportunities of the Internet of Things: Measuring text similarity between papers and patents," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 45-68.

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