Abstract
This paper proposes an idea for developing a computational model of creative processes in design. This model facilitates and accelerates idea generation in the inventive design, increasing the solution space definition by suggesting technical actions and graphical triggers.
The problem solver has to state the required design objective using any verbal action, then an automatic system generates an appropriate set of triggering actions indicating different ways of accomplishing that goal. In addition, for each verb is associated a list of evocative images indicating how that action can be implemented in space/time and through specific physical effects. The system is capable of handling the huge number of verbs that the English language offers. To select all functional verbs of the technical lexicon, the patent database has been processed using the most advanced text mining techniques. Among them, a customized version of Word2Vec model has been exploited to learn word/actions associations from a large corpus of patents.
The article explains how the libraries have been created, the progress the software prototype and the results of a first validation campaign.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tsourikov, V.M.: Inventive machine: second generation. AI & Soc. 7(1), 62–77 (1993)
Kucharavy, D.: Thoughts about history of Inventive Machine Projects. Presentation at LICIA/LGECO‐Design Engineering Laboratory INSA Strasbourg (2011). http://www.seecore.org/d/20110923(2).pdf
Montecchi, T., Russo, D.: FBOS: function/behaviour–oriented search. Procedia Eng. 131, 140–149 (2015)
Zhang, P., Cavallucci, D., Bai, Z., Zanni-Merk, C.: Facilitating engineers abilities to solve inventive problems using CBR and semantic similarity. In: Cavallucci, D., De Guio, R., Koziołek, S. (eds.) TFC 2018. IFIP Advances in Information and Communication Technology, vol. 541, pp. 204–212. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02456-7_17
Rakov, D.: Okkam-advanced morphological approach as method for computer aided innovation (CAI). MATEC Web Conf. 298, 00120, 1–9 (2019)
Korobkin, D., Fomenkov, S., Vereschak, G., Kolesnikov, S., Tolokin, D., Kravets, A.G.: The formation of morphological matrix based on an ontology “patent representation of technical systems” for the search of innovative technical solutions. In: Kravets, A.G., Bolshakov, A.A., Shcherbakov, M.V. (eds.) Cyber-Physical Systems. Studies in Systems, Decision and Control, vol. 350, pp. 149–160. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67892-0_13
Hanifi, M., et al.: Problem formulation in inventive design using Doc2vec and Cosine Similarity as Artificial Intelligence methods and Scientific Papers. Eng. Appl. Artif. Intell. 109, 104661 (2022)
Wendrich, R.E.: Computer aided creative thinking machines (CaXTus). Comput.-Aided Des. Appl. 18(6), 1390–1409 (2021)
Devlin, J., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Belski, I., Skiadopoulos, A., Aranda-Mena, G., Cascini, G., Russo, D.: Engineering creativity: the influence of general knowledge and thinking heuristics. In: Chechurin, L., Collan, M. (eds.) Advances in Systematic Creativity, pp. 245–263. Palgrave Macmillan, Cham (2019). https://doi.org/10.1007/978-3-319-78075-7_15
Eberle, B.: Scamper on: Games for Imagination Development. Prufrock Press Inc., Austin (1996)
Hirtz, J., et al.: A functional basis for engineering design: reconciling and evolving previous efforts. Res. Eng. Des. 13(2), 65–82 (2002)
Russo, D., Spreafico, M., Precorvi, A.: Discovering new business opportunities with dependent semantic parsers. Comput. Ind. 123, 103330 (2020)
High, R.: The era of cognitive systems: an inside look at IBM Watson and how it works. IBM Corporation, Redbooks, vol. 1, p. 16 (2012)
Floridi, L., Chiriatti, M.: GPT-3: its nature, scope, limits, and consequences. Minds Mach. 30(4), 681–694 (2020)
Lee, J.-S., Hsiang, J.: Patentbert: patent classification with fine-tuning a pre-trained BERT model. arXiv preprint arXiv:1906.02124 (2019)
Levin, B.: English Verb Classes and Alternations a Preliminary Investigation. The University of Chicago Press, Chicago (1993)
Mikolov, T., et al.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Srebrovic, R., Yonamine, J.: Leveraging the BERT algorithm for Patents with TensorFlow and BigQuery. https://services.google.com/fh/files/blogs/bert_for_patents_white_paper.pdf
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 IFIP International Federation for Information Processing
About this paper
Cite this paper
Russo, D., Gervasoni, D. (2022). AI Based Patent Analyzer for Suggesting Solutive Actions and Graphical Triggers During Problem Solving. In: Nowak, R., Chrząszcz, 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_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-17288-5_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17287-8
Online ISBN: 978-3-031-17288-5
eBook Packages: Computer ScienceComputer Science (R0)
