Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT

As innovative technology is being developed at an accelerated rate, the identification of technology opportunities is especially critical for both companies and governments. Among various approaches to search for opportunities, one of the most frequently used is to discover technology opportunity fr...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Sustainability 2020-01, Vol.12 (1), p.136, Article 136
Hauptverfasser: Feng, Lijie, Niu, Yuxiang, Liu, Zhenfeng, Wang, Jinfeng, Zhang, Ke
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:As innovative technology is being developed at an accelerated rate, the identification of technology opportunities is especially critical for both companies and governments. Among various approaches to search for opportunities, one of the most frequently used is to discover technology opportunity from patent data. In line with it, this paper aims to propose a hybrid approach based on morphological analysis (MA) and unified structured inventive thinking (USIT) for technology opportunity discovery (TOD) through patent analysis using text mining and Word2Vec clustering analysis to explore the intrinsic links of innovation elements. A basic morphology matrix is constructed according to patent information and then is extended using the innovation algorithms that are reorganized from USIT. Technology opportunities are analyzed at two layers to generate new technical ideas. To illustrate the research process and validate its utility, this paper selects the technology of coalbed methane (CBM) extraction as a use case. This hybrid approach contributes by suggesting a semi-autonomous and systematic procedure to perform MA for TOD. By integrating the innovation algorithms, this approach improves the procedure of value extension in MA.
ISSN:2071-1050
2071-1050
DOI:10.3390/su12010136