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Improving the Discovery of Technological Opportunities Using Patent Classification Based on Explainable Neural Networks

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  • Tadeusz A. Grzeszczyk
  • Michal K. Grzeszczyk

Abstract

Purpose: The paper aims to present an approach supporting the improvement of technological opportunities discovery using patent classification based on explainable neural networks. Design/Methodology/Approach: Empirical research was conducted applying a dataset containing U.S. patent documents. Firstly, this dataset was checked for the correctness of the saved patent data to be further analyzed. Then, a custom Bidirectional Encoder Representations from Transformers (BERT) Neural Network was developed and trained. Finally, the Local Interpretable Model-agnostic Explanations (LIME) method was applied for interpreting the results achieved with the BERT classifier. Findings: The studied classifier achieved high quality (precision of 80.6%), allowing correct classification of the technologies described in the patents. Such neural classifiers are easy to use in practice and highly versatile; however, there is an insufficient trust of managers in the decisions suggested by that black-box method. The proposed new approach may help overcome the lack of trust of the users of neural models towards the technological opportunities suggested by them. Practical Implications: Various patent databases are often used to discover innovative solutions, as well as economic and technological opportunities, because they contain vast resources of prosperous and extensive information recorded in patent documentation. Such analyzes are critical to businesses and public organizations as they help them make decisions about carrying out strategic investment projects. The presented approach, which supports the improvement of automated processes of technological opportunities discovery, may increase confidence in the results obtained using neural classifiers. Originality/Value: Earlier studies focused mainly on using more effective classifiers and better learning algorithms. Progress in this type of research did not help solve the problem of the lack of reliable justification for individual decisions indicated by machine learning models. In this study, a proposal for an approach enables the discovery of technological opportunities using patent classification based on explainable neural networks.

Suggested Citation

  • Tadeusz A. Grzeszczyk & Michal K. Grzeszczyk, 2021. "Improving the Discovery of Technological Opportunities Using Patent Classification Based on Explainable Neural Networks," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 402-409.
    • Handle: RePEc:ers:journl:v:xxiv:y:2021:i:3:p:402-409
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    References listed on IDEAS

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    1. 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.
    2. Lee, Jeongjin & Kim, Changseok & Shin, Juneseuk, 2017. "Technology opportunity discovery to R&D planning: Key technological performance analysis," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 53-63.
    3. Park, Youngjin & Yoon, Janghyeok, 2017. "Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 170-183.
    4. Lee, Changyong & Kang, Bokyoung & Shin, Juneseuk, 2015. "Novelty-focused patent mapping for technology opportunity analysis," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 355-365.
    5. A.A. Chursin & V.V. Strenalyuk, 2018. "Synergy Effect in Innovative Activities and its Accounting in the Technological Competencies of an Enterprise," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 151-161.
    6. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    7. Shaobo Li & Jie Hu & Yuxin Cui & Jianjun Hu, 2018. "DeepPatent: patent classification with convolutional neural networks and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 721-744, November.
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    9. Song, Kisik & Kim, Karp Soo & Lee, Sungjoo, 2017. "Discovering new technology opportunities based on patents: Text-mining and F-term analysis," Technovation, Elsevier, vol. 60, pages 1-14.
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    1. Jeon, Eunji & Yoon, Naeun & Sohn, So Young, 2023. "Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).

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    More about this item

    Keywords

    Patent analysis; artificial neural networks; BERT; explainable classification.;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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