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One-to-many comparative summarization for patents

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Abstract

Patents bring technology companies commercial values in modern business operations. However, companies have to bear the high cost of handling patent applications or infringement cases. A common yet expensive task among these jobs is to analyze relevant patent literature. Lengthy and technically complicated patents require a large number of human efforts. This paper focuses on automatically analyzing the similar contents between a patent and its relevant literature, relevant patents specifically, to help experts review the similarities among these patents. We formulate this as a one-to-many document comparison problem by generating a comparative summary of a given patent and its relevant patents. We extract essential technical features from semantic dependency trees based on sentences in claims and construct a multi-relational graph to model the relevance between features and patents. The key to generating the comparative summary is selecting comparative essential technical features, which we formulate as an optimization problem and solve by a fast greedy algorithm. Experiments on real-world datasets and case studies demonstrate the effectiveness and efficiency of the proposed methods.

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Funding

Funding was provided by Nanjing University of Posts and Telecommunications (Grant No. NY219084).

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Authors and Affiliations

  1. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

    Zheng Liu, Jialing Zhang, Tingting Qin & Yun Li

  2. School of Computer Information and Engineering, Jiangxi Normal University, Nanchang, China

    Yanwen Qu

Authors
  1. Zheng Liu
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  2. Jialing Zhang
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  3. Tingting Qin
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  4. Yanwen Qu
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  5. Yun Li
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Correspondence to Zheng Liu.

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Liu, Z., Zhang, J., Qin, T. et al. One-to-many comparative summarization for patents. Scientometrics 127, 1969–1993 (2022). https://doi.org/10.1007/s11192-022-04307-8

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  • DOI: https://doi.org/10.1007/s11192-022-04307-8

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