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Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework

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  • Yoon, Janghyeok
  • Park, Hyunseok
  • Seo, Wonchul
  • Lee, Jae-Min
  • Coh, Byoung-youl
  • Kim, Jonghwa

Abstract

Research into deriving technology opportunities from existing technologies or products (ETPs) is useful to technology-based firms, which have to expand their technology portfolios with limited resources. Therefore, this study proposes a function-based framework for technology opportunity discovery (TOD) from a firm's ETPs. This framework consists of: (1) a TOD knowledge base that structures information on products, technologies, and their functions, which has been extracted from 223,603 patents in various technical fields, and (2) a TOD logic that derives potential technology opportunities from an ETP using semantic functional similarities between technologies or products. This framework returns technology opportunities in terms of the four types of TOD paths: existing technology (eT) to technologies that can be developed by modifying eT, eT to products that can be produced by using eT, existing product (eP) to products that can be developed by modifying eP, and eP to technologies that can be adopted to improve eP. We implement the proposed framework in a prototype system, and demonstrate TOD cases, using several ETPs. This framework will contribute to creating systematic TOD based on current technological capability over a wide range of technologies, and become a basis for developing future automated technology intelligence systems.

Suggested Citation

  • Yoon, Janghyeok & Park, Hyunseok & Seo, Wonchul & Lee, Jae-Min & Coh, Byoung-youl & Kim, Jonghwa, 2015. "Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 153-167.
    • Handle: RePEc:eee:tefoso:v:100:y:2015:i:c:p:153-167
    DOI: 10.1016/j.techfore.2015.04.012
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    References listed on IDEAS

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    1. Janghyeok Yoon & Sungchul Choi & Kwangsoo Kim, 2011. "Invention property-function network analysis of patents: a case of silicon-based thin film solar cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(3), pages 687-703, March.
    2. Janghyeok Yoon & Hyunseok Park & Kwangsoo Kim, 2013. "Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 313-331, January.
    3. Karvonen, Matti & Kässi, Tuomo, 2013. "Patent citations as a tool for analysing the early stages of convergence," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1094-1107.
    4. Sungchul Choi & Janghyeok Yoon & Kwangsoo Kim & Jae Yeol Lee & Cheol-Han Kim, 2011. "SAO network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 863-883, September.
    5. Gaetano Cascini & Davide Russo, 2007. "Computer-aided analysis of patents and search for TRIZ contradictions," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 4(1/2), pages 52-67.
    6. Janghyeok Yoon & Kwangsoo Kim, 2011. "Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 213-228, July.
    7. Janghyeok Yoon & Kwangsoo Kim, 2012. "Detecting signals of new technological opportunities using semantic patent analysis and outlier detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 445-461, February.
    8. Jan M. Gerken & Martin G. Moehrle, 2012. "A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 645-670, June.
    9. Hyunseok Park & Janghyeok Yoon & Kwangsoo Kim, 2012. "Identifying patent infringement using SAO based semantic technological similarities," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 515-529, February.
    10. Martin G. Moehrle, 2010. "Measures for textual patent similarities: a guided way to select appropriate approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 95-109, October.
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