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Introducing Patents with Indirect Connection (PIC) for Establishing Patent Strategies

Author

Listed:
  • Juhyun Lee

    (Department of Industrial Management Engineering, Korea University, Seoul 02841, Korea)

  • Sangsung Park

    (Department of Big Data and Statistics, Cheongju University, Chungbuk 28503, Korea)

  • Jiho Kang

    (Machine Learning Big Data Institute, Korea University, Seoul 02841, Korea)

Abstract

A patent system requires novelty and progressiveness so that new patents do not infringe on the rights of prior art. Patent investigation including a prior art search is essential to the process of commercialization of technology. In general, patent investigation has been conducted by experts based on their qualitative judgement. However, the number of patents has increased so fast that it has become difficult to handle the quantitative burdens of the search with a conventional approach. There have been previous studies dealing with patent investigation to find similar technologies. They had limitations as they did not utilize the citation relationship and similarity between patents in a comprehensive way. In addition, they could not properly reflect the sequential citation relationship of patents though this is effective in discovering similar patents. In this study, we propose an efficient methodology to discover similar technologies by comprehensively considering the similarity and citation relationship between patents. In particular, we intended to reflect the citation sequence and indirect citation relationship in the process of searching for similar patents. For this, we introduced the concept of “patents with indirect connections” (PICs) and devised an algorithm to efficiently detect patent pairs having such a relationship. The proposed methodology of this study contributes to preventing patent litigation in advance by discovering patents with such potential risks. It is expected that this method will provide patent applicants with the opportunity to establish appropriate strategies against competitors with similar technologies. In order to examine the practical applicability of the proposed method, Korean patents related to machine learning and deep learning were collected. As a result of the experiment, it was possible to identify 24 pairs of similar patents without a direct citation relationship and derive appropriate counter strategies.

Suggested Citation

  • Juhyun Lee & Sangsung Park & Jiho Kang, 2021. "Introducing Patents with Indirect Connection (PIC) for Establishing Patent Strategies," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:820-:d:481038
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    References listed on IDEAS

    as
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    2. Andrew Rodriguez & Byunghoon Kim & Mehmet Turkoz & Jae-Min Lee & Byoung-Youl Coh & Myong K. Jeong, 2015. "New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 565-581, May.
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    4. Arora, Ashish & Fosfuri, Andrea, 2003. "Licensing the market for technology," Journal of Economic Behavior & Organization, Elsevier, vol. 52(2), pages 277-295, October.
    5. Nakamura, Hiroko & Suzuki, Shinji & Sakata, Ichiro & Kajikawa, Yuya, 2015. "Knowledge combination modeling: The measurement of knowledge similarity between different technological domains," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 187-201.
    6. Storey, Chris & Easingwood, Christopher J., 1999. "Types of New Product Performance: Evidence from the Consumer Financial Services Sector," Journal of Business Research, Elsevier, vol. 46(2), pages 193-203, October.
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