Abstract
Altshuller contradiction matrix is one of the most popular tools among TRIZ practitioners, especially beginners, due to its simplicity and intuitive design. However, scientific and technological progress induces the constant appearance of new scientific vocabulary, which lower accuracy when using this static tool from the end of the sixties. Some attempts to rebuild the matrix or update it has been made within the past four decades but without any successful legitimation due to the lack of scientific proof regarding its relevance. Our recent findings in the use of Natural Language Processing (NLP) techniques allow the creation of a methodology for automatic extraction of the necessary information for establishing a domain-restricted contradiction matrix. In this paper, we relate a technique that exploits the internal language semantic structure to mine the causal relation between terms in patent texts. Moreover, the subject or domain restriction for a patent collection allows observing the links between extracted information at the over-text level. Such an approach relies on inter-and extra-textual features and permits a real-time extraction of contradictory relations between elements. These extracted elements could be presented in matrix form, inspired by The Altshuller contradiction matrix. We postulate that such a representation allows the construction of a state of the art in each domain, which will facilitate the use of TRIZ to solve contradictions within it.
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Notes
- 1.
Bidirectional Encoder Representations from Transformers (BERT).
- 2.
Generative Pre-trained Transformer.
- 3.
- 4.
Accessible in https://huggingface.co/datasets/sem_eval_2010_task_8.
References
Marconi, J.: ARIZ : the algorithm for inventive problem solving. Triz J. [Online]. Available: https://triz-journal.com/ariz-algorithm-inventive-problem-solving/ (Apr 1998). Accessed 08 Apr 2021
Dubois, S., Lutz, P., Rousselot, F., Caillaud, E.: A formal model for the representation of problems based on TRIZ. In: International Conference on Engineering Design, ICED 05, Melbourne, Australia, pp. NA. [Online]. Available: https://hal.archives-ouvertes.fr/hal-00340989 (Aug 2005)
Souili, A., Cavallucci, D.: Automated extraction of knowledge useful to populate inventive design ontology from patents. In: TRIZ – The Theory of Inventive Problem Solving, pp. 43–62. Springer (2017). https://doi.org/10.1007/978-3-319-56593-4_2
Altshuller, G.: 40 Principles: TRIZ Keys to Innovation. Technical Innovation Center, Inc. (2002)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. ArXiv181004805 Cs. [Online]. Available: http://arxiv.org/abs/1810.04805 (May 2019). Accessed 29 Apr 2022
Altshuller, G.: Creativity As an Exact Science. Taylor & Francis (1984)
Altshuller, G., Altov, G.: And Suddenly the Inventor Appeared: TRIZ, the Theory of Inventive Problem Solving. Technical Innovation Center, Inc. (1996)
Souchkov, V.: Differentiating among the five levels of solutions. Online TRIZ J. (2007)
Cavallucci, D.: Contribution à la conception de nouveaux systemes mécaniques par integration methodologique. These de doctorat, Université Louis Pasteur (Strasbourg) (1971–2008). [Online]. Available: http://www.theses.fr/1999STR13238 (1999). Accessed 07 Apr 2021
Rousselot, F., Zanni-Merk, C., Cavallucci, D.: Towards a formal definition of contradiction in inventive design. Comput. Ind. 63(3), 231–242 (2012). https://doi.org/10.1016/j.compind.2012.01.001
Zlotin, B., Zusman, A., Kaplan, L., Visnepolschi, S., Proseanic, V., Malkin, S.: TRIZ beyond technology: the theory and practice of applying TRIZ to nontechnical areas. TRIZ J. 6(1) (2001)
Cavallucci, D.: From TRIZ to Inventive Design Method (IDM): towards a formalization of Inventive Practices in R&D Departments. In: Japan TRIZ Symposium 2012, pp. 2 (2012)
Mann, D.: Re-thinking physical contradictions #1: technical problems. Triz J. [Online]. Available: https://triz-journal.com/re-thinking-physical-contradictions-1-technical-problems/ (Mar 2018). Accessed 08 Apr 2021
Vaswani, A., et al.: Attention is all you need. Advances in Neural Information Processing Systems, 30. [Online]. Available: https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf (2017)
Wolf, T., et al.: Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Online, pp. 38–45 (Oct 2020). https://doi.org/10.18653/v1/2020.emnlp-demos.6
Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing: Google AI Blog. http://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html. Accessed 30 Apr 2022
Rogers, A., Kovaleva, O., Rumshisky, A.: A primer in BERTology: what we know about how BERT works. ArXiv200212327 Cs. [Online]. Available: http://arxiv.org/abs/2002.12327 (Nov 2020). Accessed 29 Apr 2022
Yako, T., 八箇毅: Insert molding of plate glass fitted product. JPH06246782A. [Online]. Available: https://patents.google.com/patent/JPH06246782A/en (06 Sep 1994). Accessed 29 Apr 2022
Akbik, A., Bergmann, T., Blythe, D., Rasul, K., Schweter, S., Vollgraf, R.: FLAIR: an easy-to-use framework for state-of-the-art NLP. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), Minneapolis, Minnesota, pp. 54–59 (Jun 2019). https://doi.org/10.18653/v1/N19-4010
Hendrickx, I., et al.: SemEval-2010 task 8: multi-way classification of semantic relations between pairs of nominal. In: Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions - DEW ’09, Boulder, Colorado, pp. 94 (2009). https://doi.org/10.3115/1621969.1621986
Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. ArXiv190711692 Cs. [Online]. Available: http://arxiv.org/abs/1907.11692 (Jul 2019). Accessed 30 Apr 2022
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Berdyugina, D., Cavallucci, D. (2022). Exploitation of Causal Relation for Automatic Extraction of Contradiction from a Domain-Restricted Patent Corpus. In: Nowak, R., ChrzÄ…szcz, J., Brad, S. (eds) Systematic Innovation Partnerships with Artificial Intelligence and Information Technology. TFC 2022. IFIP Advances in Information and Communication Technology, vol 655. Springer, Cham. https://doi.org/10.1007/978-3-031-17288-5_8
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