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Automatic extraction of inventive information out of patent texts in support of manufacturing design studies using Natural Languages Processing

Author

Listed:
  • Daria Berdyugina

    (ICUBE/CSIP, INSA of Strasbourg)

  • Denis Cavallucci

    (ICUBE/CSIP, INSA of Strasbourg)

Abstract

Intelligent manufacturing systems are constantly evolving in diversity and complexity. The rise of numeric era, ruled by the keywords industry 4.0 or industry of the future imposes to companies to invent new processes and solve an ever increasing quantity of problems. Paradoxically, even if techniques of inventive problem-solving progress in diversity, their ability to face this world-wide challenge do not grow accordingly. However, thanks to Natural Languages Processing (NLP), actors of invention can now count on information contents as an assistant through its textual data. Patent texts are of particular interest since they are an important and constantly renewed source of inventive information. This situation leads to the difficulty, for scientists and engineers, to permanently manage new masses of information from recent domains well beyond their reading capacity. Our research, based on the combination of the theory of inventive problem-solving (also known as TRIZ) and NLP, has made it possible to extract quickly and in a relevant way from patent texts, concepts that contain information useful for formulating an inventive problem. In this paper, we present our methodology for the automatic extraction of inventive information from patent texts and measure our technique to a classical human-led information gathering. Our results show a significant reduction of experts time solicitation, for an increase of 36% in the extraction of useful information. A case study applied to microplastics harvesting from the ocean illustrates our point.

Suggested Citation

  • Daria Berdyugina & Denis Cavallucci, 2023. "Automatic extraction of inventive information out of patent texts in support of manufacturing design studies using Natural Languages Processing," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2495-2509, June.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:5:d:10.1007_s10845-022-01943-y
    DOI: 10.1007/s10845-022-01943-y
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    References listed on IDEAS

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    1. Denis Cavallucci & Nikolai Khomenko, 2007. "From TRIZ to OTSM-TRIZ: addressing complexity challenges in inventive design," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 4(1/2), pages 4-21.
    2. Norman Dalkey & Olaf Helmer, 1963. "An Experimental Application of the DELPHI Method to the Use of Experts," Management Science, INFORMS, vol. 9(3), pages 458-467, April.
    3. Cristina Feniser & Gheorghe Burz & Marian Mocan & Larisa Ivascu & Vasile Gherhes & Calin Ciprian Otel, 2017. "The Evaluation and Application of the TRIZ Method for Increasing Eco-Innovative Levels in SMEs," Sustainability, MDPI, vol. 9(7), pages 1-19, June.
    4. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    5. Daniel F. Spulber, 2015. "How Patents Provide The Foundation Of The Market For Inventions," Journal of Competition Law and Economics, Oxford University Press, vol. 11(2), pages 271-316.
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