IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0282361.html
   My bibliography  Save this article

IPC prediction of patent documents using neural network with attention for hierarchical structure

Author

Listed:
  • Yuki Hoshino
  • Yoshimasa Utsumi
  • Yoshiro Matsuda
  • Yoshitoshi Tanaka
  • Kazuhide Nakata

Abstract

International patent classifications (IPCs) are assigned to patent documents; however, since the procedure for assigning classifications is manually done by the patent examiner, it takes a lot of time and effort to select some IPCs from about 70,000 IPCs. Hence, some research has been conducted on patent classification with machine learning. However, patent documents are very voluminous, and learning with all the claims (the part describing the content of the patent) as input would run out of the necessary memory, even if the batch size is set to a very small size. Therefore, most of the existing methods learn by excluding some information, such as using only the first claim as input. In this study, we propose a model that considers the contents of all claims by extracting important information for input. In addition, we focus on the hierarchical structure of the IPC, and propose a new decoder architecture to consider it. Finally, we conducted an experiment using actual patent data to verify the accuracy of the prediction. The results showed a significant improvement in accuracy compared to existing methods, and the actual applicability of the method was also discussed.

Suggested Citation

  • Yuki Hoshino & Yoshimasa Utsumi & Yoshiro Matsuda & Yoshitoshi Tanaka & Kazuhide Nakata, 2023. "IPC prediction of patent documents using neural network with attention for hierarchical structure," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-17, March.
    • Handle: RePEc:plo:pone00:0282361
    DOI: 10.1371/journal.pone.0282361
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282361
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0282361&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0282361?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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 - Part ), pages 402-409.
    2. Anqi Ma & Yu Liu & Xiujuan Xu & Tao Dong, 2021. "A deep-learning based citation count prediction model with paper metadata semantic features," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6803-6823, August.
    3. Bernhard Ganglmair & Alexander Kann, 2025. "Looking for Innovation Beyond the Patent System: Evidence from Research Disclosures," CRC TR 224 Discussion Paper Series crctr224_2025_656, University of Bonn and University of Mannheim, Germany.
    4. Juite Wang, 0000. "Analyzing and Predicting R&D Collaboration Networks in the Metaverse Industry," Proceedings of Economics and Finance Conferences 14716418, International Institute of Social and Economic Sciences.
    5. 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).
    6. Arousha Haghighian Roudsari & Jafar Afshar & Wookey Lee & Suan Lee, 2022. "PatentNet: multi-label classification of patent documents using deep learning based language understanding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 207-231, January.
    7. 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.
    8. Choi, Seokkyu & Lee, Hyeonju & Park, Eunjeong & Choi, Sungchul, 2022. "Deep learning for patent landscaping using transformer and graph embedding," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    9. Peng Shao & Runhua Tan & Qingjin Peng & Wendan Yang & Fang Liu, 2023. "An Integrated Method to Acquire Technological Evolution Potential to Stimulate Innovative Product Design," Mathematics, MDPI, vol. 11(3), pages 1-24, January.
    10. Ascione, Grazia Sveva, 2023. "Technological diversity to address complex challenges: the contribution of American universities to sdgs," MPRA Paper 119452, University Library of Munich, Germany.
    11. Danielle Lee, 2024. "Exploring the determinants of research performance for early-career researchers: a literature review," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(1), pages 181-235, January.
    12. repec:ers:journl:v:xxiv:y:2021:i:3:p:402-409 is not listed on IDEAS
    13. Liang Chen & Shuo Xu & Lijun Zhu & Jing Zhang & Xiaoping Lei & Guancan Yang, 2020. "A deep learning based method for extracting semantic information from patent documents," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 289-312, October.
    14. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    15. Hamid Bekamiri & Daniel S. Hain & Roman Jurowetzki, 2021. "PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT," Papers 2103.11933, arXiv.org, revised Oct 2021.
    16. Mark Bukowski & Sandra Geisler & Thomas Schmitz-Rode & Robert Farkas, 2020. "Feasibility of activity-based expert profiling using text mining of scientific publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 579-620, May.
    17. Meindl, Benjamin & Ayala, Néstor Fabián & Mendonça, Joana & Frank, Alejandro G., 2021. "The four smarts of Industry 4.0: Evolution of ten years of research and future perspectives," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    18. Doina Caragea & Theodor Cojoianu & Mihai Dobri & Andreas Hoepner & Oana Peia & Davide Romelli, 2024. "Competition and Innovation in the Financial Sector: Evidence from the Rise of FinTech Start-ups," Journal of Financial Services Research, Springer;Western Finance Association, vol. 65(1), pages 103-140, February.
    19. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    20. Liyuan Zhang & Wei Liu & Yufei Chen & Xiaodong Yue, 2022. "Reliable Multi-View Deep Patent Classification," Mathematics, MDPI, vol. 10(23), pages 1-13, December.
    21. Yuan Zhou & Fang Dong & Yufei Liu & Zhaofu Li & JunFei Du & Li Zhang, 2020. "Forecasting emerging technologies using data augmentation and deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 1-29, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0282361. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.