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Analysis of the relationship between citation frequency of patents and diversity of their backward citations for Japanese patents

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  • Fuyuki Yoshikane

    (University of Tsukuba)

  • Yutaka Suzuki

    (University of Tsukuba)

  • Keita Tsuji

    (University of Tsukuba)

Abstract

Conventional patent citation analyses have focused mainly on the presence of citation relationships, the number of patents cited by the subject patent, and the number of times the subject patent is cited by others (i.e., the numbers of backward and forward citations of the subject patent). However, most of them have not focused on patent classifications. Assuming that a patent based on a variety of technological bases tends to be an important patent that is cited more often, this study examines and clarifies the relationship between the diversity of classifications assigned to backward citations and the number of forward citations for Japanese patents. The results show notable differences in the number of classifications assigned to backward citations between the often cited and less frequently cited groups. It is considered that the diversity of backward citations can be utilized in the evaluation criteria for grouping that roughly identifies the often cited patents or eliminates a large part of less frequently cited patents.

Suggested Citation

  • Fuyuki Yoshikane & Yutaka Suzuki & Keita Tsuji, 2012. "Analysis of the relationship between citation frequency of patents and diversity of their backward citations for Japanese patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(3), pages 721-733, September.
    • Handle: RePEc:spr:scient:v:92:y:2012:i:3:d:10.1007_s11192-011-0612-6
    DOI: 10.1007/s11192-011-0612-6
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    References listed on IDEAS

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    Cited by:

    1. Fuyuki Yoshikane, 2013. "Multiple regression analysis of a patent’s citation frequency and quantitative characteristics: the case of Japanese patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 365-379, July.
    2. Fang Han & Shengtai Zhang & Junpeng Yuan & Li Wang, 2022. "Assessing future technological impacts of patents based on the classification algorithms in machine learning: The case of electric vehicle domain," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-17, December.
    3. Fuyuki Yoshikane & Takafumi Suzuki, 2014. "Diversity of fields in patent citations: synchronic and diachronic changes," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1879-1897, March.
    4. Neil Gandal & Michal Shur-Ofry & Michael Crystal & Royee Shilony, 2021. "Out of sight: patents that have never been cited," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2903-2929, April.

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