DocumentCode
1073226
Title
Development of New Technology Forecasting Algorithm: Hybrid Approach for Morphology Analysis and Conjoint Analysis of Patent Information
Author
Yoon, Byungun ; Park, Yongtae
Author_Institution
Dongguk Univ., Seoul
Volume
54
Issue
3
fYear
2007
Firstpage
588
Lastpage
599
Abstract
Despite being a strong stimulus for the invention of new alternatives, morphology analysis (MA) suffers the limitations of being a nonquantitative, vague, and static methodology. As a consequence, the MA outcomes typically do not provide practical technology opportunities. This paper, therefore, proposes a new hybrid approach that enhances the performance of MA by combining it with conjoint analysis (CA) and citation analysis of patent information. First, keywords are extracted from patent documents using text mining, and the morphology of existing patents is identified by these keywords. Alternatives for new technology development from among the emerging technologies are presented by combining the valuable levels of each attribute in a morphology matrix predefined by domain experts. Then, configurations of new technology are suggested in order of priority using CA, and the technological feasibility of each new configuration is subsequently investigated. The technological competitiveness of a company can be analyzed by a newly suggested index, ldquotechnology share,rdquo which is analogous to the concept of market share in traditional CA. The proposed MA-CA hybrid process is illustrated with a case example of patent information from the thin film transistor-liquid crystal display (TFT-LCD) patent database.
Keywords
citation analysis; data mining; mathematical morphology; patents; technological forecasting; text analysis; citation analysis; conjoint analysis; morphology analysis; morphology matrix; new technology forecasting algorithm; patent information; text mining; thin film transistor-liquid crystal display patent database; Algorithm design and analysis; Citation analysis; Data mining; Indexes; Information analysis; Morphology; Performance analysis; Technology forecasting; Text mining; Thin film transistors; Conjoint analysis (CA); hybrid approach; morphology analysis (MA); patent information; technology forecasting;
fLanguage
English
Journal_Title
Engineering Management, IEEE Transactions on
Publisher
ieee
ISSN
0018-9391
Type
jour
DOI
10.1109/TEM.2007.900796
Filename
4278022
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