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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.

Suggested Citation

  • 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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    12. Chiarello, Filippo & Fantoni, Gualtiero & Hogarth, Terence & Giordano, Vito & Baltina, Liga & Spada, Irene, 2021. "Towards ESCO 4.0 – Is the European classification of skills in line with Industry 4.0? A text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
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