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Automating the search for a patent’s prior art with a full text similarity search

Author

Listed:
  • Lea Helmers
  • Franziska Horn
  • Franziska Biegler
  • Tim Oppermann
  • Klaus-Robert Müller

Abstract

More than ever, technical inventions are the symbol of our society’s advance. Patents guarantee their creators protection against infringement. For an invention being patentable, its novelty and inventiveness have to be assessed. Therefore, a search for published work that describes similar inventions to a given patent application needs to be performed. Currently, this so-called search for prior art is executed with semi-automatically composed keyword queries, which is not only time consuming, but also prone to errors. In particular, errors may systematically arise by the fact that different keywords for the same technical concepts may exist across disciplines. In this paper, a novel approach is proposed, where the full text of a given patent application is compared to existing patents using machine learning and natural language processing techniques to automatically detect inventions that are similar to the one described in the submitted document. Various state-of-the-art approaches for feature extraction and document comparison are evaluated. In addition to that, the quality of the current search process is assessed based on ratings of a domain expert. The evaluation results show that our automated approach, besides accelerating the search process, also improves the search results for prior art with respect to their quality.

Suggested Citation

  • Lea Helmers & Franziska Horn & Franziska Biegler & Tim Oppermann & Klaus-Robert Müller, 2019. "Automating the search for a patent’s prior art with a full text similarity search," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-17, March.
    • Handle: RePEc:plo:pone00:0212103
    DOI: 10.1371/journal.pone.0212103
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    References listed on IDEAS

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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Titipat Achakulvisut & Daniel E Acuna & Tulakan Ruangrong & Konrad Kording, 2016. "Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-11, July.
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    Cited by:

    1. Zheng Liu & Jialing Zhang & Tingting Qin & Yanwen Qu & Yun Li, 2022. "One-to-many comparative summarization for patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1969-1993, April.
    2. Daniel E. Ho & Lisa Larrimore Ouellette, 2020. "Improving Scientific Judgments in Law and Government: A Field Experiment of Patent Peer Review," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 17(2), pages 190-223, June.
    3. Kong, Nancy & Dulleck, Uwe & Jaffe, Adam B. & Sun, Shupeng & Vajjala, Sowmya, 2023. "Linguistic metrics for patent disclosure: Evidence from university versus corporate patents," Research Policy, Elsevier, vol. 52(2).
    4. Weiwei Deng & Jian Ma, 2022. "A knowledge graph approach for recommending patents to companies," Electronic Commerce Research, Springer, vol. 22(4), pages 1435-1466, December.
    5. Jie Chen & Jialin Chen & Shu Zhao & Yanping Zhang & Jie Tang, 2020. "Exploiting word embedding for heterogeneous topic model towards patent recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2091-2108, December.
    6. Shicheng Tan & Tao Zhang & Shu Zhao & Yanping Zhang, 2023. "Self-supervised scientific document recommendation based on contrastive learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5027-5049, September.

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