Clustering triz analysis method

Details Clustering triz analysis method Clustering triz analysis method

2011-06-07

2011-06-07

Sparks, Donald (Department: 2129)

Data processing: artificial intelligence

Knowledge processing system

Knowledge representation and reasoning technique

C706S020000, C706S012000

Reexamination Certificate

active

07958078

ABSTRACT:
The TRIZ decision process of the clustering method proposed by this invention uses the characteristics and invention rules from the contradiction matrix table resulting from massive quantities of patent inferences to find a similar or approximate character group and invention rule group of the physical meanings, and also applies statistics to calculate the number of display times of the groups to be the basic foundation. Apart from the number of display times, Bayes probability, fuzzy object oriented method and Bayes probability combined with fuzzy object oriented method can be used as the system. The reading value is utilized as a foundation for prioritizing the sequence of consideration for the groups, in which the system reading value constructed by different models gives designers lots of options to perform the reading, so as to acquire the undesired result features of the prioritized consideration.

REFERENCES:
patent: 7716226 (2010-05-01), Barney
Low et al., M., “Product to Service Eco-Innovation: The TRIZ Model of Creativity Explored”, IEEE, pp. 209-214, 2000.
Timm et al., H., “Fuzzy Cluster Analysis of Classified Data”, IEEE, pp. 1431-1436, 2001.
Ross, V., “A Comparison of Tools Based on the Inventive Principles of TRIZ”, The TRIZ Journal, pp. 1-96, Nov. 2006.
Salamatov, Y., “TRIZ: The Right Solution at the Right Time”, pp. 1-20, 1999.
Nurnberger et al., A., “Improving Naive Bayes Classifiers Using Neuro-Fuzzy Learning”, IEEE, pp. 154-159, 1999.

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