Abstract
Inventive Design Method mostly relies on the presence of exploitable knowledge. It has been elaborated to formalize some aspects of TRIZ being expert-dependent. Patents are appropriate candidates since they contain problems and their corresponding partial solutions. When associated with patents of different fields, problems and partial solutions constitute a potential inventive solution scheme for a target problem. Nevertheless, our study found that links between these two major components are worth studying further. We postulate that problem-solution effectively matching contains a hidden value to automate the solution retrieval and uncover inventive details in patents in order to facilitate R&D activities. In this paper, we assimilate this challenge to the field of the Question Answering system instead of the traditional syntactic analysis approaches and proposed a model called IDM-Matching. Technically, a state-of-the-art neural network model named XLNet in the Natural Language Processing field is combined into our IDM-Matching to capture the corresponding partial solution for the given query that we masked using the related problem. Then we construct links between these problems and solutions. The final experimental results on the real-world U.S. patent dataset illustrates our model’s ability to effectively match IDM-related knowledge with each other. A detailed case study is demonstrated to prove the usage and latent perspective of our proposal in the TRIZ field.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Altshuller, G.: 40 Principles: TRIZ Keys to Innovation, vol. 1. Technical Innovation Center Inc., Worcester (2002)
Brill, E., Dumais, S., Banko, M.: An analysis of the AskMSR question-answering system. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 257–264. Association for Computational Linguistics (2002)
Bultey, A., De Bertrand De Beuvron, F., Rousselot, F.: A substance-field ontology to support the TRIZ thinking approach. Int. J. Comput. Appl. Technol. 30(1–2), 113–124 (2007)
Cascini, G., Fantechi, A., Spinicci, E.: Natural language processing of patents and technical documentation. In: Marinai, S., Dengel, A.R. (eds.) DAS 2004. LNCS, vol. 3163, pp. 508–520. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28640-0_48
Cascini, G., Russo, D., et al.: Computer-aided analysis of patents and search for TRIZ contradictions. Int. J. Prod. Dev. 4(1), 52–67 (2007)
Cavallucci, D., Rousselot, F., Zanni, C.: Initial situation analysis through problem graph. CIRP J. Manuf. Sci. Technol. 2(4), 310–317 (2010)
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q.V., Salakhutdinov, R.: Transformer-xl: attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860 (2019)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Jiang, S., Luo, J., Pava, G.R., Hu, J., Magee, C.L.: A CNN-based patent image retrieval method for design ideation. arXiv preprint arXiv:2003.08741 (2020)
Ni, X., Samet, A., Cavallucci, D.: An approach merging the IDM-related knowledge. In: Benmoussa, R., De Guio, R., Dubois, S., Koziołek, S. (eds.) TFC 2019. IAICT, vol. 572, pp. 147–158. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32497-1_13
Peters, M.E., et al.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018)
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)
Rahim, Z.A., Yusof, S.M., Bakar, N.A., Mohamad, W.M.S.W.: The application of computational thinking and TRIZ methodology in patent innovation analytics. In: International Conference of Reliable Information and Communication Technology, pp. 793–802. Springer (2018). https://doi.org/10.1007/978-3-319-99007-1_73
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)
Ravichandran, D., Hovy, E.: Learning surface text patterns for a question answering system. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 41–47. Association for Computational Linguistics (2002)
Savransky, S.D.: Engineering of Creativity: Introduction to TRIZ Methodology of Inventive Problem Solving. CRC Press, Boca Raton (2000)
Souili, A., Cavallucci, D.: Toward an automatic extraction of IDM concepts from patents. In: Chakrabarti, A. (ed.) CIRP Design 2012, pp. 115–124. Springer (2013). https://doi.org/10.1007/978-1-4471-4507-3_12
Souili, A., Cavallucci, D., Rousselot, F.: A lexico-syntactic pattern matching method to extract IDM-TRIZ knowledge from on-line patent databases. Procedia Eng. 131, 418–425 (2015)
Strumsky, D., Lobo, J.: Identifying the sources of technological novelty in the process of invention. Res. Policy 44(8), 1445–1461 (2015)
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, pp. 5754–5764 (2019)
Yeap, T., Loo, G.H., Pang, S.: Computational patent mapping: intelligent agents for nanotechnology. In: Proceedings International Conference on MEMS, NANO and Smart Systems, pp. 274–278. IEEE (2003)
Acknowledgement
This work is supported by China Scholarship Council (CSC). The statements made herein are solely the responsibility of the authors.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 IFIP International Federation for Information Processing
About this paper
Cite this paper
Ni, X., Samet, A., Cavallucci, D. (2020). Build Links Between Problems and Solutions in the Patent. In: Cavallucci, D., Brad, S., Livotov, P. (eds) Systematic Complex Problem Solving in the Age of Digitalization and Open Innovation. TFC 2020. IFIP Advances in Information and Communication Technology, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-030-61295-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-61295-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61294-8
Online ISBN: 978-3-030-61295-5
eBook Packages: Computer ScienceComputer Science (R0)