Research on classification and similarity of patent citation based on deep learning

Yonghe Lu (), Xin Xiong (), Weiting Zhang (), Jiaxin Liu () and Ruijie Zhao ()
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Yonghe Lu: Sun Yat-sen University
Xin Xiong: Sun Yat-sen University
Weiting Zhang: Sun Yat-sen University
Jiaxin Liu: Sun Yat-sen University
Ruijie Zhao: Sun Yat-sen University

Scientometrics, 2020, vol. 123, issue 2, No 12, 813-839

Abstract: Abstract This paper proposes a patent citation classification model based on deep learning, and collects the patent datasets in text analysis and communication area from Google patent database to evaluate the classification effect of the model. At the same time, considering the technical relevance between the examiners’ citations and the pending patent, this paper proposes a hypothesis to take the output value of the model as the technology similarity of two patents. The rationality of the hypothesis is verified from the perspective of machine statistics and manual spot check. The experimental results show that the model effect based on deep learning proposed in this paper is significantly better than the traditional text representation and classification method, while having higher robustness than the method combining Doc2vec and traditional classification technology. In addition, we compare between the proposed method based on deep learning and the traditional similarity method by a triple verification. It shows that the proposed method is more accurate in calculating technology similarity of patents. And the results of manual sampling show that it is reasonable to use the output value of the proposed model to represent the technology similarity of patents.

Keywords: Deep learning; Patent citation; Classification; Technology similarity (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11192-020-03385-w

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