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.
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Acknowledgements
The authors warmly thank reviewers for their valuable suggestions. This research was supported by National Natural Science Foundation of China [Grant Number: 71373291]. This research was supported by the Science and Technology Planning Project of Guangdong Province (China) [Grant Number: 2016B030303003].
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Lu, Y., Xiong, X., Zhang, W. et al. Research on classification and similarity of patent citation based on deep learning. Scientometrics 123, 813–839 (2020). https://doi.org/10.1007/s11192-020-03385-w
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DOI: https://doi.org/10.1007/s11192-020-03385-w