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Identification of promising patents for technology transfers using TRIZ evolution trends

Title
Identification of promising patents for technology transfers using TRIZ evolution trends
Author
박현석
Keywords
Open innovation; Technology transaction; Patent evaluation; Technology evaluation; Patent mining; Patent analysis; Text mining; Subject-Action-Object
Issue Date
2013-02
Publisher
Elsevier Science B.V., Amsterdam.
Citation
Expert systems with applications , 40, 2, 736 - 743
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. (C) 2012 Elsevier Ltd. All rights reserved.
URI
https://www.sciencedirect.com/science/article/pii/S0957417412009694?via%3Dihubhttp://hdl.handle.net/20.500.11754/44944
ISSN
0957-4174
DOI
10.1016/j.eswa.2012.08.008
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > INFORMATION SYSTEMS(정보시스템학과) > Articles
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