Identification of promising patents for technology transfers using TRIZ evolution trends

Authors:
Hyunseok Park;Jason Jihoon Ree;Kwangsoo Kim
Affiliations:
Department of Technology and Innovation Management, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang 790-784, Republic of Korea;Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang 790-784, Republic of Korea;Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang 790-784, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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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.