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Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents

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  • Song, Kisik
  • Kim, Kyuwoong
  • Lee, Sungjoo

Abstract

This study suggests a patent-based methodology for identifying emerging technologies by combining a retrospective technological feature analysis and a prospective market-needs analysis. To do this, first, the candidate promising technologies were identified by applying bibliographic coupling to patents, thus producing a list of outlier patents. Then, the measures to evaluate both technological and market characteristics of the candidate technologies were developed, where retrospective patent analysis and sentiment analysis on customer opinions are required. Finally, the candidate technologies are mapped onto two-dimensional space according to the values of the two measures; the final promising technologies are determined to be those that have high values for either technological characteristics or market characteristics. The suggested methodology was applied to an automobile industry, through which its feasibility and usability were verified. This study is one of the few studies to develop technology-evaluation measures based on an ad-hoc analysis of technological characteristics. In addition, it attempts to link patent databases to market databases, aiming to directly reflect customer needs to evaluate the potential of a technology in a market. The approach suggested in this study can be applied to recent patents with little citation information for assessing their value to be deemed as promising technologies; this is expected to contribute both academically and practically to the existing literature on patent analysis.

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  • Song, Kisik & Kim, Kyuwoong & Lee, Sungjoo, 2018. "Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 118-132.
    • Handle: RePEc:eee:tefoso:v:128:y:2018:i:c:p:118-132
    DOI: 10.1016/j.techfore.2017.11.008
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    5. Serkan Altuntas & Zulfiye Erdogan & Turkay Dereli, 2020. "A clustering-based approach for the evaluation of candidate emerging technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1157-1177, August.
    6. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    7. Lai, Kuei-Kuei & Bhatt, Priyanka C. & Kumar, Vimal & Chen, Hsueh-Chen & Chang, Yu-Hsin & Su, Fang-Pei, 2021. "Identifying the impact of patent family on the patent trajectory: A case of thin film solar cells technological trajectories," Journal of Informetrics, Elsevier, vol. 15(2).
    8. Kajikawa, Yuya & Mejia, Cristian & Wu, Mengjia & Zhang, Yi, 2022. "Academic landscape of Technological Forecasting and Social Change through citation network and topic analyses," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    9. Sajad Ashouri & Anne-Laure Mention & Kosmas X. Smyrnios, 2021. "Anticipation and analysis of industry convergence using patent-level indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5727-5758, July.
    10. Zhai, Dongsheng & Zhai, Liang & Li, Mengyang & He, Xijun & Xu, Shuo & Wang, Feifei, 2022. "Patent representation learning with a novel design of patent ontology: Case study on PEM patents," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    11. de Paulo, Alex Fabianne & Nunes, Breno & Porto, Geciane, 2020. "Emerging green technologies for vehicle propulsion systems," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    12. Youngjae Choi & Sanghyun Park & Sungjoo Lee, 2021. "Identifying emerging technologies to envision a future innovation ecosystem: A machine learning approach to patent data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5431-5476, July.
    13. Haoyang Song & Jianhua Hou & Yang Zhang, 2022. "Patent protection: does it promote or inhibit the patented technological knowledge diffusion?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2351-2379, May.
    14. Hugo Confraria & Vitor Hugo Ferreira & Manuel Mira Godinho, 2021. "Emerging 21st Century technologies: Is Europe still falling behind?," Working Papers REM 2021/0188, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    15. Jinho Choi & Nina Shin & Yong Sik Chang, 2021. "Strategic Investment Decisions for Emerging Technology Fields in the Health Care Sector Based on M&A Analysis," Sustainability, MDPI, vol. 13(7), pages 1-20, March.

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