A Study of TRIZ for Patent Mapping – A Case Study for Green Industries

碩士 === 國立臺北科技大學 === 工業工程與管理研究所 === 97 === The inventor of Soviet Union, Genrich Altshuller developed TRIZ to present as well-known to the public. The aim of this research is to map 1,000 Chinese patents of R.O.C Green-related industry into TRIZ Inventive Principles by using text mining technique, an...

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Bibliographic Details
Main Authors: Tzu-Hung Yu, 游子鋐
Other Authors: 葉繼豪
Format: Others
Language:zh-TW
Published: 2009
Online Access:http://ndltd.ncl.edu.tw/handle/2twbdt
Description
Summary:碩士 === 國立臺北科技大學 === 工業工程與管理研究所 === 97 === The inventor of Soviet Union, Genrich Altshuller developed TRIZ to present as well-known to the public. The aim of this research is to map 1,000 Chinese patents of R.O.C Green-related industry into TRIZ Inventive Principles by using text mining technique, and develop a computer-aided mechanism to patents searching by Contradiction Matrix tool and Inventive Principles. There are two parts in the reaserch. First at all, using on-line auto-tag system provided by Academia Sinica to break every sentence in a document into several keywords and label these keywords manually. Secondly, calculating text frequency (TF) and inverse document frequency (IDF) in the corresponding document. Then, chi-square statistics and correlation coefficient approaches are used to select and sort word features which are highly correlated to 40 TRIZ Inventive Principles. In addition, TFIDF and weight-TFIDF values are inputs for further classifiers such as support vector machine (SVM) and k-nearest neighbor classifier (KNN). Finally, SVM and KNN evaluate the performances of 1,000 Chinese R.O.C patents of green-related industry into 40 TRIZ Inventive Principles. Experimental results of part 1 show that, Both of SVM and KNN perform well and the accuracy between them was only 1%. However, used the comparative measure F(2)-value to assess performance of SVM and KNN. KNN could deliver better performance. At part 2, according to text frequency (TF) and KNN build relationships between integrated 39 TRIZ Parameters, 40 TRIZ Inventive Principles and patents. We can build the TRIZ high-correlation R.O.C patents searching mechanism of green-related industry by Integrated 39×39 TRIZ Contradiction Matrix, which combined original classical TRIZ matrix with Matrix2003 developed by E. Domb & M. Slocum. Experimental results of part 2 show that, the user could use the TRIZ high-correlation R.O.C patents searching mechanism of green-related industry to get TRIZ Inventive Principles by analysis of problems to find the hidden contradictions and input one or more pair-wise integrated 39 TRIZ Parameters. Moreover, searching the high-correlation patents of green industry by sorted the correlation of the integrated 39 TRIZ Parameters and 40 Inventive Principles in descending order. We hope that it could provide users with the concept of eco-design and eco-innovation for the phase of product design or problem solving.