Identification of promising patents for technology transfers using TRIZ evolution trends

被引：149

作者：

Park, Hyunseok ^{[2
]}

Ree, Jason Jihoon ^{[1
]}

Kim, Kwangsoo ^{[1
]}

机构：

[1] Pohang Univ Sci & Technol, Dept Ind & Management Engn, Pohang 790784, South Korea

[2] Pohang Univ Sci & Technol, Dept Technol & Innovat Management, Pohang 790784, South Korea

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2013年 / 40卷 / 02期

基金：

新加坡国家研究基金会;

关键词：

Open innovation; Technology transaction; Patent evaluation; Technology evaluation; Patent mining; Patent analysis; Text mining; Subject-Action-Object; SAO; RESEARCH-AND-DEVELOPMENT; INDUSTRY;

D O I：

10.1016/j.eswa.2012.08.008

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：736 / 743

页数：8

共 40 条

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[2]

[Anonymous], TRIZ J

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