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From stones to jewellery: Investigating technology opportunities from expired patents

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  • Yun, Siyeong
  • Song, Kisik
  • Kim, Chulhyun
  • Lee, Sungjoo

Abstract

This study focuses on the potential of expired patents, that is, patents freely available in the public domain, to become new technology opportunities. These technologies can create a new value, if they are well utilized and combined with other technologies, or go through further commercialization processes. Furthermore, such value can be obtained effectively, given their initial usability, proven by a patent system and high accessibility. Despite the numerous studies aimed at seeking technological opportunities from patents, however, little attention in academia has been given to those expired patents as a source of such opportunities; accordingly, the distinguishing features of expired patents as opportunities have been largely overlooked in the existing studies. To fill this research gap, we propose a novel approach to identify technology opportunities using the expired patents, where two types of opportunities are investigated: 1) opportunities within the target technological field named as promising technology; and 2) opportunities from other technological fields named as convergence technology. After key technological topics, either in the target field or in the reference field, are identified using LDA topic modelling, a target topic of interest is chosen, and its corresponding patents are evaluated from their patent quality and risk of implementation. Finally, the patents with high patent quality and low risk of implementation are identified as technology opportunities. The proposed approach is applied to biocosmetics products, and the research findings indicate that the expired patents can have as much importance as the valid patents as sources of new technology opportunities. Hence, by guiding the use of expired patents, the proposed approach is expected to reduce the costs and risks of adopting those patents for a firm's own business.

Suggested Citation

  • Yun, Siyeong & Song, Kisik & Kim, Chulhyun & Lee, Sungjoo, 2021. "From stones to jewellery: Investigating technology opportunities from expired patents," Technovation, Elsevier, vol. 103(C).
    • Handle: RePEc:eee:techno:v:103:y:2021:i:c:s016649722100016x
    DOI: 10.1016/j.technovation.2021.102235
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    References listed on IDEAS

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    Cited by:

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    2. Jang, Hyejin & Lee, Suyeong & Yoon, Byungun, 2023. "Data-driven techno-socio co-evolution analysis based on a topic model and a hidden Markov model," Technovation, Elsevier, vol. 126(C).
    3. Choi, Jaewoong & Lee, Changyong & Yoon, Janghyeok, 2023. "Exploring a technology ecology for technology opportunity discovery: A link prediction approach using heterogeneous knowledge graphs," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    4. Seol, Youngjin & Lee, Seunghyun & Kim, Cheolhan & Yoon, Janghyeok & Choi, Jaewoong, 2023. "Towards firm-specific technology opportunities: A rule-based machine learning approach to technology portfolio analysis," Journal of Informetrics, Elsevier, vol. 17(4).
    5. Silva, Lucas Emmanuel Nascimento & Gomes, Leonardo Augusto de Vasconcelos & Faria, Aline Mariane de & Borini, Felipe Mendes, 2024. "Innovation processes in ecosystem settings: An integrative framework and future directions," Technovation, Elsevier, vol. 132(C).
    6. Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    7. Gozuacik, Necip & Sakar, C. Okan & Ozcan, Sercan, 2023. "Technological forecasting based on estimation of word embedding matrix using LSTM networks," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

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