Identification of promising patents for technology transfers using TRIZ evolution trends

Hyunseok Park, Jason Jihoon Ree, Kwangsoo Kim

Research output: Contribution to journalArticle

69 Scopus citations

Abstract

Technology transfer is one of the most important mechanisms for acquiring knowledge from external sources to secure innovative and advanced technologies in high-tech industries. For successful technology transfer, identification of high-value technologies is a fundamental task. In particular, identifying future promising patents is important, because most technology transfer transactions are aimed at acquiring technologies for future uses. This paper proposes a new approach to identification of promising patents for technology transfer. We adopted TRIZ evolution trends as criteria to evaluate technologies in patents, and Subject-Action-Object (SAO)-based text-mining technique to deal with big patent data and analyze them automatically. The applicability of the proposed method was verified by applying it to technologies related to floating wind turbines.

Original languageEnglish
Pages (from-to)736-743
Number of pages8
JournalExpert Systems with Applications
Volume40
Issue number2
DOIs
Publication statusPublished - 2013 Feb 1

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Keywords

  • Open innovation
  • Patent analysis
  • Patent evaluation
  • Patent mining
  • SAO
  • Subject-Action-Object
  • Technology evaluation
  • Technology transaction
  • Text mining

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