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• W1992300369 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." @default.
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• W1992300369 date "2013-02-01" @default.
• W1992300369 modified "2023-10-06" @default.
• W1992300369 title "Identification of promising patents for technology transfers using TRIZ evolution trends" @default.
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• W1992300369 doi "https://doi.org/10.1016/j.eswa.2012.08.008" @default.
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