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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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