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Patent valuation based on text mining and survival analysis

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  • Eun Han
  • So Sohn

Abstract

Assessing the value of a patent is crucial not only at the licensing stage but also during the resolution of a patent infringement lawsuit. In this study, we use text mining to identify important factors associated with patent value as represented by its survival period. The variables retrieved from text mining were the Euclidian distance of patent claims between a patent and its backward cited patents, or forward cited patents, and the singular value decompositions (SVDs) of patent claims. After applying Weibull regression to 3D printing patents, the following factors were found to have significant associations with the survival time of a patent: the Euclidian distance of claims between a patent and its forward cited patents, the average number of forward citations, whether Germany is included among the family patent countries, whether the patent is transferred to another, and the presence of five SVDs. Our study is expected to contribute to enhancing patent valuation in consideration of patent infringement risk. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Eun Han & So Sohn, 2015. "Patent valuation based on text mining and survival analysis," The Journal of Technology Transfer, Springer, vol. 40(5), pages 821-839, October.
    • Handle: RePEc:kap:jtecht:v:40:y:2015:i:5:p:821-839
    DOI: 10.1007/s10961-014-9367-6
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    Cited by:

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    3. Abeliansky, Ana Lucia & Martínez-Zarzoso, Inmaculada & Prettner, Klaus, 2020. "3D printing, international trade, and FDI," Economic Modelling, Elsevier, vol. 85(C), pages 288-306.
    4. Junhyeog Choi & Sunghae Jun & Sangsung Park, 2016. "A Patent Analysis for Sustainable Technology Management," Sustainability, MDPI, vol. 8(7), pages 1-13, July.
    5. Ben Zhang & Lei Ma & Zheng Liu & Ping Wang, 2019. "Sustainable Technology Innovation Path Recognition: An Evaluation of Patent Risk of International Trade," Sustainability, MDPI, vol. 11(18), pages 1-17, September.
    6. Kim, Dong-hyu & Lee, Heejin & Kwak, Jooyoung, 2017. "Standards as a driving force that influences emerging technological trajectories in the converging world of the Internet and things: An investigation of the M2M/IoT patent network," Research Policy, Elsevier, vol. 46(7), pages 1234-1254.
    7. Joon Hyung Cho & So Young Sohn, 2017. "Competing risk model for predicting stabilization period of university spin-off ventures," International Entrepreneurship and Management Journal, Springer, vol. 13(3), pages 777-796, September.
    8. Youngho Kim & Sangsung Park & Junseok Lee & Dongsik Jang & Jiho Kang, 2021. "Integrated Survival Model for Predicting Patent Litigation Hazard," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    9. Won Sang Lee & Hyo Shin Choi & So Young Sohn, 2018. "Forecasting new product diffusion using both patent citation and web search traffic," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-12, April.
    10. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    11. 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.

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    More about this item

    Keywords

    Patent valuation; Patent infringement risk; Singular value decomposition; Text mining; Survival analysis; C10; O30;
    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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