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Technology opportunity discovery under the dynamic change of focus technology fields: Application of sequential pattern mining to patent classifications

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  • Choi, Jaewoong
  • Jeong, Byeongki
  • Yoon, Janghyeok

Abstract

Technology opportunity discovery (TOD), have evolved over time from technology forecasting to an approach based on existing technology capabilities for increased practicality. Unfortunately, TOD studies are still lacking, in that they do not consider the unique direction of the target firm in technology development or the recent trends of technological fields. Consequently, this paper proposes an improved methodology for identifying technology opportunities with less uncertainty by focusing on the target firm's dynamic change of focus technology fields. The proposed approach is as follows: 1) generate a sequence database, containing firms' dynamic change of focus technology fields; 2) explore the frequent sequential patterns from a precedence enterprise (PE) sequence database using the PrefixSpan algorithm to identify the technology candidates from a PE sequence similar with that of the target firm; and 3) evaluate the candidates on technological similarity, business stability, and recency. The results of the proposed approach are expected to help firms discover appropriate technology opportunities by considering both their existing technological capacities and the dynamic change of their focus technology fields. Furthermore, the proposed approach can identify the most appropriate technology opportunities with less uncertainty in real-life business environments by evaluating technological similarity, business stability, and recency.

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  • Choi, Jaewoong & Jeong, Byeongki & Yoon, Janghyeok, 2019. "Technology opportunity discovery under the dynamic change of focus technology fields: Application of sequential pattern mining to patent classifications," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    • Handle: RePEc:eee:tefoso:v:148:y:2019:i:c:s0040162518312745
    DOI: 10.1016/j.techfore.2019.119737
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    Cited by:

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    3. Jinzhu Zhang & Wenqian Yu, 2020. "Early detection of technology opportunity based on analogy design and phrase semantic representation," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 551-576, October.
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    8. Jeon, Eunji & Yoon, Naeun & Sohn, So Young, 2023. "Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    9. Seunghyun Oh & Jaewoong Choi & Namuk Ko & Janghyeok Yoon, 2020. "Predicting product development directions for new product planning using patent classification-based link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1833-1876, December.
    10. Lee, MyoungHoon & Kim, Suhyeon & Kim, Hangyeol & Lee, Junghye, 2022. "Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    11. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).
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