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What makes the first forward citation of a patent occur earlier?

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  • Jungpyo Lee

    (Yonsei University)

  • So Young Sohn

    (Yonsei University)

Abstract

Identifying the economic value of a patent is crucial to technology management. It also facilitates the commercialization and transactions of patents. As a proxy of patent value, forward citation counting is widely used, but it takes a long time for a patent to be sufficiently cited. In this context, we suggest the first citation lag, namely the time taken until the first citation, as a proxy of patent value, as it is positively correlated with the patent value and considers the fact that important patents with a high economic value tend to receive many citations in a short space of time. We explore the influential patent attributes related to the first citation lag to build a model to predict the patent value. By using the Cox proportional hazard model on green inventory patent data, we find that patents with a shorter technology cycle time and a larger number of IPC four-digit classes, claims, patent family, and backward citations are associated with the shorter first citation lag, while a patent’s science linkage has an inverse U-shaped relationship with the first citation lag. Further, patents having an unconventional technological core have a longer first citation lag and among patents having an unconventional technological core, the presence of a novel element makes the first citation earlier. Our study is expected to help patent evaluation in the early stage of technology transfer.

Suggested Citation

  • Jungpyo Lee & So Young Sohn, 2017. "What makes the first forward citation of a patent occur earlier?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 279-298, October.
  • Handle: RePEc:spr:scient:v:113:y:2017:i:1:d:10.1007_s11192-017-2480-1
    DOI: 10.1007/s11192-017-2480-1
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    Cited by:

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    6. Hu, Zewen & Zhou, Xiji & Lin, Angela, 2023. "Evaluation and identification of potential high-value patents in the field of integrated circuits using a multidimensional patent indicators pre-screening strategy and machine learning approaches," Journal of Informetrics, Elsevier, vol. 17(2).
    7. Kim, Tae San & Sohn, So Young, 2020. "Machine-learning-based deep semantic analysis approach for forecasting new technology convergence," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    8. Won Sang Lee & So Young Sohn, 2017. "Identifying Emerging Trends of Financial Business Method Patents," Sustainability, MDPI, vol. 9(9), pages 1-21, September.
    9. Chung, Park & Sohn, So Young, 2020. "Early detection of valuable patents using a deep learning model: Case of semiconductor industry," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
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    11. Marusaki, Koji & Nakai, Kensei & Kataoka, Shotaro & Kawano, Seiya & Hentona, Asahi & Sakumoto, Takeshi & Yamamoto, Yuta & Mori, Kaede & Nonaka, Hirofumi, 2024. "A study on patent term prediction by survival time analysis using neural hazard model," Technological Forecasting and Social Change, Elsevier, vol. 203(C).

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    Keywords

    Patent value; First citation; Short-term value; Survival analysis; Novelty;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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