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   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