Identification of promising patents for technology transfers using TRIZ evolution trends

• Published in: Expert systems with applications Vol. 40; no. 2; pp. 736 - 743
• Main Authors: Park, Hyunseok, Ree, Jason Jihoon, Kim, Kwangsoo
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.02.2013; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Criteria; Economics; Evolution; Exact sciences and technology; Expert systems; Firm modelling; General aspects; Information systems. Data bases; Memory organisation. Data processing; Open innovation; Operational research and scientific management; Operational research. Management science; Patent analysis; Patent evaluation; Patent mining; SAO; Software; Speech and sound recognition and synthesis. Linguistics; Subject–Action–Object; Tasks; Technology evaluation; Technology transaction; Technology transfer; Text mining; Trends; Wind turbines; Technology transaction; Text mining; SAO; Patent evaluation; Patent mining; Subject–Action–Object; Technology evaluation; Open innovation; Patent analysis; Performance evaluation; Very large databases; Wind generator; Data mining; Innovation; Systems engineering; Technology transfer; Object oriented; Advanced technology; Patents; Text; Transaction processing; High value; Organizational models; Intellectual property; Open systems; Patent rights; Subject-Action-Object
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.08.008

► We propose a new method to identify promising patents for technology transfer. ► We adopt TRIZ trends as criteria to evaluate technologies in patents. ► TRIZ trends can be classified by considering characteristics of lifecycle stage. ► We adopt SAO-based text mining to analyze big patent data auto...