One-to-many comparative summarization for patents

Zheng Liu (), Jialing Zhang, Tingting Qin, Yanwen Qu and Yun Li
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Zheng Liu: Nanjing University of Posts and Telecommunications
Jialing Zhang: Nanjing University of Posts and Telecommunications
Tingting Qin: Nanjing University of Posts and Telecommunications
Yanwen Qu: Jiangxi Normal University
Yun Li: Nanjing University of Posts and Telecommunications

Scientometrics, 2022, vol. 127, issue 4, No 15, 1969-1993

Abstract: 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.

Keywords: Patent comparison; Essential technical feature; Semantic dependency tree; Feature-patent relevance graph (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11192-022-04307-8

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