TRIZ是能有效幫助解決矛盾問題的一個理論方法，TRIZ中的矛盾矩陣（Contradiction Matrix）常被用來幫助解決參數間的矛盾問題，在矛盾矩陣中一對工程參數（Engineering Parameters）可能對應到數個建議採用的發明原則（Inventive Principles）。傳統的專利分類研究幾乎都是根據專利所屬的領域來分類，但這種分類方式並不適合TRIZ，雖然近年來開始有些依據發明原則來分類專利的研究被提出，但就我們所知直到現在仍未有研究是依據工程參數來分類專利。事實上採用相同發明原則來解決矛盾問題的數篇專利可能解決的是不同工程參數組合的矛盾問題。對TRIZ使用者來說，找出已經解決相同矛盾問題的一些專利對幫助使用者解決這類問題是有幫助的，矛盾的工程參數是造成矛盾的原因，而數種不同的矛盾問題卻可能用相同發明原則來解決，因此，比起發明原則，依據工程參數來分類解決相同矛盾問題的專利是較為合理的。在論文中，我們提出MCIVC演算法用來依據工程參數來分類解決相同矛盾問題的專利。此演算法是一多層分類演算法並結合基於關聯規則方法及偷懶學習法，MCIVC是一同時顧及到單字語意及語法間關聯性的演算法。

TRIZ, thought to be a useful theory to solve the engineering contradiction problems. One of technological methods of TRIZ is Contradiction Matrix, widely used for the solution of taking care of the trade-off problems. In Contradiction Matrix, each pair of Engineering Parameters may correspond to some Inventive Principles. In most of the traditional patent classification researches, patents are classified according to their technology field; however this type of the patent classifications is unsuitable for the TRIZ users. In the recent years, although there have been some researches proposing some approaches to classify patents according to Inventive Principles, we have not found any research working on patents contradiction classifying based on the Engineering Parameters in our survey. As matter as fact, even though some inventions solved by the same Inventive Principle, which does not mean they had certainly solved the same contradiction. For the TRIZ users, finding out some patent documents which had solved the same contradiction is helpful to solve the problems of this type of contradiction. Because Engineering Parameters are the causing factors of the contradiction, and each Inventive Principle may correspond to more than one type of contradictions, therefore, classifying patents contradiction based on the Engineering Parameters is more reasonable than the Inventive Principles. In this article, a new algorithm named MCIVC for classifying patents technical contradiction based on Engineering Parameters is proposed. This multi-layer classification algorithm adopts the associated rule-based approach combining the lazy learning. It does not only consider the semantic relationship among terms, but also consider the syntactic structure between words.

1. R. H. Tan, Theory of Inventive Problem Solving: TRIZ. China: Science Press, 2004.
2. Terninko, J., Zusman, A., & Zlotin, B. (1998). Systematic Innovation: An Introduction to TRIZ. St. Lucie Press.
3. Liang, Y., Tan, R., & Ma, J. (2008). Patent analysis with text mining for TRIZ. In Management of innovation and technology, 2008. ICMIT 2008. 4th IEEE international conference on, 21–24 Sept. 2008 (pp. 1147–1151).
4. Altshuller, G. S. (1997), 40 principles: TRIZ keys to technical innovation, translated and edited by Shulyak, L., Rodman, S., Technical Innovation Center, Worcester, MA.
5. Altshuller G. The innovation algorithm: TRIZ, systematic innovation and technical creativity. Technical Innovation Center; 1999.
6. Altshuller, G. S. (1984), Creativity as an Exact Science: The Theory of the Solution of Inventive Problems, Gordon and Breach Science, New York.
7. Larkey, L. S. (1998). Some issues in the automatic classification of US patents. AAAi-98 working notes.
8. Larkey, L. S. (1999). A patent search and classification systems. In E. A. Fox & N. Rowe (Eds.), Proceedings of DL-99. Fourth ACM conference on digital libraries (pp. 179–187). New York, US: ACM Press.
9. Gey, F. C., Buckland, M., Chen, C., & Larson, R. (2001). Entry vocabulary – a technology to enhance digital search. In: Proceedings of the First International Conference on Human Language Technology, (pp. 91–95).
10. Fall, C. J., Torcsvari, A., Benzineb, K., & Karetka, G. (2003). Automated categorization in the international patent classification. ACM SI IR Forum, 37.
11. F. Sebastiani, "A Tutorial on Automated Text Categorisation", A. Amandi and A. Zunino (eds.), Proceedings of the 1st Argentinean Symposium on Artificial Intelligence, ASAI, Buenos Aires, 1999, pp. 7-35.
12. Makarov M. The seventh edition of the IPC. World Patent Inform 2000;22:53 –8.
13. Loh, H. T., He, C., & Shen, L. X. (2006). Automatic classification of patent documents for TRIZ users. World Patent Information, 28, 6–13.
14. Liang, Y., Tan, R., Wang, C., & Li, Z. (2009b). Computer-aided classification of patents oriented to TRIZ. In Industrial engineering and engineering management, 2009. IEEM 2009. IEEE international conference on, 8–11 Dec. 2009 (pp. 2389–2393).
15. He, C., & Loh, H. T. (2007). Grouping of TRIZ inventive principles to facilitate automatic patent classification. Expert System with Applications, 34(3).
16. Cong, H. and Loh, H. T. (2010). Pattern-Oriented Associative Rule-Based Patent Classification. Expert Systems with Applications, 37(3), 2395-2404.
17. Adams C, Tate D. Computer-aided TRIZ ideality and level of invention estimation using natural language processing and machine learning. In: Tan R, Cao G, Leon N, editors. Growth and development of computer-aided innovation: third international federation of information processing (IFIP) WG 5.4 working conference, 2009 proceedings. Berlin: Springer; 2009. p. 27–37.
18. Li, Z., Tate, D., Lane, C., Adams, C., 2012. A framework for automatic TRIZ level of invention estimation of patents using natural language processing, knowledge-transfer and patent citation metrics. Computer-Aided Design 44 (10), 987e1010.
19. JOACHIMS, T. 1999. Transductive inference for text classification using support vector machines. In Proceedings of ICML-99, 16th International Conference on Machine Learning (Bled, Slovenia, 1999), 200–209.
20. F. Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1–47, 2002.
21. G. D. Guo, H. Wang, D. Bell, Y. X. Bi, and K. Greer. Using kNN model for automatic text categorization. Soft Computing, 10(5):423–430, 2006.
22. M.-L. Antonie and O. R. Zaiane. An associative classifier based on positive and negative rules. In 9th ACM SIGMOD workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD-04) Proceedings, pages 64–69, 2004.
23. F. Thabtah, A review of associative classification mining, The Knowledge Engineering Review 22 (2007) 37–65.
24. Hu, M., and Liu, B. 2004. Mining and summarizing customer reviews. KDD’04, 2004.
25. B. Liu, M. Hu, M. and J. Cheng. Opinion Observer: Analyzing and comparing opinions on the Web. WWW-05, 2005.
26. http://www.merriam-webster.com/thesaurus/
27. Computer aided innovation system: InventionTool 3.0, Chinese patent of software, 2006SR13729, 2006
28. TechOptimizer: http://www.invention-machine.com, 2009/03
29. Pro/Innovator: http://www.iwint.com.cn/, 2009/03
30. CREAX INNOVATION SUITE 3.1, http://www.creaxinnovationsuite.com/, 2009/03
31. Goldfire: http://gli.goldfire.com, 2009/03
32. V. K. Gupta, N. B. Pangannaya, “Carbon nanotubes: bibliometric analysis of patents”, World Patent Information, vol.22, issue 3, pp185-189, Sep. 2000.
33. J. Michel, B. Bettels, “Patent citation analysis: a closer look at the basic input data from patent search reports”, Scientometrics, vol. 51, no. 1 pp185–201, 2001
34. Y. H. Tseng, C. J. Lin, Y. I. Lin, “Text mining for patent map analysis”. Information Processing & Management, vol. 43, issue 5, pp1216-1247, Sep. 2007.
35. Y. Q. Liu, D. H. Zhu, L. Lv, “Technology maturity of product forecasting based on text mining”, Computer Integrated Manufacturing Systems, Vol.14, No.3, Mar. 2008, pp.506-510 (in Chinese)
36. G. Cascini, D. Russo, “Computer-aided analysis of patents and search for TRIZ contradictions”, Int. J. Product Development, vol.4, no. 1/2, 2007
37. M. F. Porter, “An algorithm for suffix stripping”, Program, vol.14, no.3, 1980, pp130-137
38. H. Schmid. Treetagger. In TC project at the Institute for Computational Linguistics of the University of Stuttgart, 1994.
39. Ueda, N., & Saito, K. (2003). Parametric mixture models for multi-labeled text. In Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press.
40. P. F. Brown, V. J. Della Pietra, P. V. deSouza, J. C. Lai, and R. L. Mercer, “Class-based n-gram models of natural language”, Computational Linguistics, vol. 18, pp. 467–479, 1992.
41. C AVNAR, W. B. AND T RENKLE, J. M. 1994. N-gram-based text categorization. In Proceedings of SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval (Las Vegas, NV, 1994), 161–175.
42. Stevenson, M., Greenwood, M., 2006. Comparing information extraction pattern models. In: Proc. Workshop on Information Extraction Beyond the Document, Sydney, pp. 12–19.
43. Ozgur, L., Gungor, T., 2010. Text classification with the support of pruned dependency patterns. Pattern Recognition Lett. 31, 1598–1607.
44. Ozgur, L., Gungor, T (2012) Optimization of dependency and pruning usage in text classification. Pattern Anal Appl 15(1):45–58
45. D’hondt, E., Verberne, S., Koster, K., and Boves, L. (2012). Text Representations for Patent Classification.