CLUSTERING TRIZ ANALYSIS METHOD
First Claim
1. A clustering TRIZ analysis method, comprising:
- (1) constructing cluster elements according to features and inventive principles of a TRIZ matrix by associating items with similar physical meanings as one cluster;
(2) calculating display times of each cluster according to improved feature cluster and inventive principle cluster, calculating the display times of the corresponding inventive principle cluster and the no-worsening feature cluster according to a TRIZ contradiction matrix, and storing results of the calculation in a database;
(3) determining discrimination values of each model according to the display time results in step (2), and using the discrimination values to determine a priority order of the clusters.
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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.
8 Citations
10 Claims
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1. A clustering TRIZ analysis method, comprising:
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(1) constructing cluster elements according to features and inventive principles of a TRIZ matrix by associating items with similar physical meanings as one cluster; (2) calculating display times of each cluster according to improved feature cluster and inventive principle cluster, calculating the display times of the corresponding inventive principle cluster and the no-worsening feature cluster according to a TRIZ contradiction matrix, and storing results of the calculation in a database; (3) determining discrimination values of each model according to the display time results in step (2), and using the discrimination values to determine a priority order of the clusters. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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Specification