Abstract
Formalisation of heuristic methods for supporting the conceptual design stage of product and technology development has been extensively evolved in industry during the last half of the century and gradually more formally appears in academic context nowadays. Due to the considerable interest from the Industry and the Academia, heuristic approaches such as TRIZ have been strongly developed over the past decades. Thus, TRIZ evolved from a set of empirical inventive principles into a considerably formal approach including techniques for modeling technical problems with the possibility of further overcoming them using formal methods. Moreover, during the last decades, TRIZ has been extensively digitized. Several generations of software have appeared that facilitate the use of inventive methods (Goldfire, Invention Machine). From the trend of digitalisation and the success of machine driven processes, it can be assumed that the further fate of invention methods and formal algorithms for overcoming non-trivial problems lies in the plane of Machine Learning and Artificial Intelligence approaches. The position of the authors is that the idea of automating inventions looks extremely attractive, although in the coming time, digital approaches will rather complement the intelligence of engineers and scientists, rather than replace it. Taking a certain preparatory step towards AI driven inventions, we present a semantic model that can form the basis of future approaches, at the same time, having already sufficient functionality to support the heuristic stage of technology. As part of this work, over 8 millions of patents and scientific publications have been analyzed to extract semantic concepts. A model was built based on Machine Learning methods and Natural Language Processing techniques with the following discussion and application examples.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Salamatov, Y. Souchkov, V.: TRIZ: The Right Solution at the Right Time: A Guide to Innovative Problem Solving, p. 256. Insytec, Hattem (1999)
Altshuller, G., Altov, H.: And Suddenly the Inventor Appeared: TRIZ, The Theory of Inventive Problem Solving. Technical Innovation Center, Inc. (1996)
Litvin, S., Petrov, V., Rubin M.: TRIZ Body of Knowledge. The TRIZ Developers summit 2007 (2007). https://triz-summit.ru/en/203941/
Cavallucci, D., Khomenko, N.: From TRIZ to OTSM-TRIZ: addressing complexity challenges in inventive design. Int. J. Prod. Dev. 4(1–2), 4–21 (2007)
Cascini, G.: State-of-the-art and trends of computer-aided innovation tools. In: Jacquart, R. (ed.) Building the Information Society. IFIP International Federation for Information Processing, vol 156. Springer, Boston, MA (2004). https://doi.org/10.1007/978-1-4020-8157-6_40
http://invention-machine.com/custsupport/to_install.cfm. Accessed Apr 2021
https://ihsmarkit.com/products/enterprise-knowledge.html. Accessed Apr 2021
Savransky, S.D.: Engineering of creativity: Introduction to TRIZ methodology of inventive problem solving. CRC press (2000)
Artificial Intelligence (2019). WIPO Technology Trends (2019). https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf
Loper, E., Bird, S.: NLTK: the natural language toolkit. arXiv preprint cs/0205028 (2002)
Joseph, S.R., Hlomani, H., Letsholo, K., Kaniwa, F., Sedimo, K.: Natural language processing: a review. Nat. Lang. Process. Rev. 6, 207–210 (2016)
Hu, Z., Fang, S., Liang, T.: Empirical study of constructing a knowledge organization system of patent documents using topic modeling. Scientometrics 100(3), 787–799 (2014). https://doi.org/10.1007/s11192-014-1328-1
Ranaei, S., Knutas, A., Salminen, J., Hajikhani, A.: Cloud-based patent and paper analysis tool for comparative analysis of research. In CompSysTech, pp. 315–322, June 2016
Okamoto, M., Shan, Z., Orihara, R.: Applying information extraction for patent structure analysis. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 989–992, August 2017
Montecchi, T., Russo, D., Liu, Y.: Searching in cooperative patent classification: comparison between keyword and concept-based search. Adv. Eng. Inf. 27(3), 335–345 (2013)
Abood, A., Feltenberger, D.: Automated patent landscaping. Artificial Intelligence and Law 26(2), 103–125 (2018). https://doi.org/10.1007/s10506-018-9222-4
Liang, Y., Tan, R., Ma, J.: Patent analysis with text mining for TRIZ. In: 2008 4th IEEE International Conference on Management of Innovation and Technology, pp. 1147–1151. IEEE, September 2008
Cascini, G., Russo, D.: Computer-aided analysis of patents and search for TRIZ contradictions. Int. J. Prod. Dev. 4(1–2), 52–67 (2007)
Ni, X., Samet, A., Cavallucci, D.: 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 (2020). https://doi.org/10.1007/978-3-030-61295-5_6
Berdyugina, D., Cavallucci, D.: Setting up context-sensitive real-time contradiction matrix of a given field using unstructured texts of patent contents and natural language processing. 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 (2020). https://doi.org/10.1007/978-3-030-61295-5_3
Regazzoni, D., Nani, R.: TRIZ-Based Patent Investigation by Evaluating Inventiveness. In: Cascini, G. (ed.) CAI 2008. TIFIP, vol. 277, pp. 247–258. Springer, Boston, MA (2008). https://doi.org/10.1007/978-0-387-09697-1_21
Bergeaud, A., Potiron, Y., Raimbault, J.: Classifying patents based on their semantic content. PloS One 12(4), e0176310 (2017)
Kaliteevskii, V., Deder, A., Peric, N., Chechurin, L.: Conceptual semantic analysis of patents and scientific publications based on TRIZ tools. In: International TRIZ Future Conference, pp. 54–63. Springer, Cham, October 2020
Likas, A., Vlassis, N., Verbeek, J.J.: The global k-means clustering algorithm. Pattern Recogn. 36(2), 451–461 (2003)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Huang, C.H., Yin, J., Hou, F.: A text similarity measurement combining word semantic information with TF-IDF method. Jisuanji Xuebao(Chinese Journal of Computers) 34(5), 856–864 (2011)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Řehůřek, R., Sojka, P.: Gensim—statistical semantics in python. Statistical semantics; gensim; Python; LDA; SVD (2011)
https://www.uspto.gov/. Accessed May 2020
https://core.ac.uk/. Accessed May 2020
Oxford Creativity. Physical effects and functions database. http://wbam2244.dns-systems.net/EDB/index.php. Accessed May 2020
Fomenkov, S.A., Kolesnikov, S.G., Korobkin, D.M., Kamaev, V.A., Orlova, Y.A.: The information filling of the database by physical effects. J. Eng. Appl. Sci. 9(10–12), 422–426 (2014)
Physical Effects database. http://bionicinspiration.org/physical-effects/. Accessed May 2020
Efimov-Soini, N.K., Chechurin, L.S.: Method of ranking in the function model. Procedia CIRP 39, 22–26 (2016)
Renev, I., Chechurin, L., Perlova, E.: Early design stage automation in architecture-engineering-construction (AEC) projects. In: Proceedings of the 35th eCAADe Conference, pp. 373–382 (2017)
Acknowledgement
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement № 722176.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
Kaliteevskii, V., Deder, A., Peric, N., Chechurin, L. (2021). Concept Extraction Based on Semantic Models Using Big Amount of Patents and Scientific Publications Data. In: Borgianni, Y., Brad, S., Cavallucci, D., Livotov, P. (eds) Creative Solutions for a Sustainable Development. TFC 2021. IFIP Advances in Information and Communication Technology, vol 635. Springer, Cham. https://doi.org/10.1007/978-3-030-86614-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-86614-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86613-6
Online ISBN: 978-3-030-86614-3
eBook Packages: Computer ScienceComputer Science (R0)