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Computer-aided analysis of patents and search for TRIZ contradictions

Author

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  • Gaetano Cascini
  • Davide Russo

Abstract

TRIZ, the Soviet-initiated Theory of Inventive Problem Solving, is gaining acknowledgement both as a systematic methodology for innovation and a powerful tool for technology forecasting. Nevertheless, the analysis of patents necessary for gathering the data to be used for the previsional activity is very cumbersome and sometimes unworthy due to the intrinsic low reliability of forecasting tasks. With this perspective it is necessary to speed up the identification of the technical/physical conflict(s) overcome by an invention, according to its textual description. Although text-mining tools have reached relevant capabilities for extracting useful information from huge sets of documents, no specific means are available to support the analysis of patents with the aim of identifying the contradiction underlying a given technical system. This paper proposes a computer-aided approach for accomplishing such a task: the algorithm is described and validated by means of practical examples.

Suggested Citation

  • Gaetano Cascini & Davide Russo, 2007. "Computer-aided analysis of patents and search for TRIZ contradictions," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 4(1/2), pages 52-67.
    • Handle: RePEc:ids:ijpdev:v:4:y:2007:i:1/2:p:52-67
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    Cited by:

    1. Olga Kokshagina & Pascal Le Masson & Benoit Weil, 2017. "Should we manage the process of inventing? Designing for patentability," Post-Print hal-01481889, HAL.
    2. Martin G. Moehrle & Jan M. Gerken, 2012. "Measuring textual patent similarity on the basis of combined concepts: design decisions and their consequences," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 805-826, June.
    3. Yoon, Janghyeok & Park, Hyunseok & Seo, Wonchul & Lee, Jae-Min & Coh, Byoung-youl & Kim, Jonghwa, 2015. "Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 153-167.
    4. Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2024. "The impact of ChatGPT on human skills: A quantitative study on twitter data," Technological Forecasting and Social Change, Elsevier, vol. 203(C).

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