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Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation

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  • Changyong Lee

    (Ulsan National Institute of Science and Technology)

  • Gyumin Lee

    (Ulsan National Institute of Science and Technology)

Abstract

This research responds to the need for the use of quantitative data and scientific methods for technology opportunity analysis by focusing on idea generation. Interpreting innovation as a process of recombinant search, we propose a patent landscape analysis to generate ideas which are likely to have more novelty and value than others. For this, first, a patent landscape is constructed from patent classification information as a vector space model, where each position represents a configuration of technological components and corresponds to an idea and, if they exist, relevant patented inventions. Second, the novelty of ideas is assessed via the modified local outlier factor based on the distribution of existing patented inventions on the landscape. Finally, the value of ideas is estimated via naïve Bayes models based on the forward citations of existing patented inventions. In addition, this study also investigates the recombinant synergies between different technological components and the relationships between novelty and value of ideas. A case study of pharmaceutical technology shows that our approach can guide organisations towards setting up effective search strategies for new technology development.

Suggested Citation

  • Changyong Lee & Gyumin Lee, 2019. "Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 603-632, November.
    • Handle: RePEc:spr:scient:v:121:y:2019:i:2:d:10.1007_s11192-019-03224-7
    DOI: 10.1007/s11192-019-03224-7
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    19. Li, Xin & Wu, Yundi & Cheng, Haolun & Xie, Qianqian & Daim, Tugrul, 2023. "Identifying technology opportunity using SAO semantic mining and outlier detection method: A case of triboelectric nanogenerator technology," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    20. Jeon, Daeseong & Lee, Junyoup & Ahn, Joon Mo & Lee, Changyong, 2023. "Measuring the novelty of scientific publications: A fastText and local outlier factor approach," Journal of Informetrics, Elsevier, vol. 17(4).

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