Patent Quality Valuation with Deep Learning Models

  • Hongjie Lin
  • Hao Wang
  • Dongfang Du
  • Han Wu
  • Biao Chang
  • Enhong ChenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)


Patenting is of significant importance to protect intellectual properties for individuals, organizations and companies. One of practical demands is to automatically evaluate the quality of new patents, i.e., patent valuation, which can be used for patent indemnification and patent portfolio. However, to solve this problem, most traditional methods just conducted simple statistical analyses based on patent citation networks, while ignoring much crucial information, such as patent text materials and many other useful attributes. To that end, in this paper, we propose a Deep Learning based Patent Quality Valuation (DLPQV) model which can integrate the above information to evaluate the quality of patents. It consists of two parts: Attribute Network Embedding (ANE) and Attention-based Convolutional Neural Network (ACNN). ANE learns the patent embedding from citation networks and attributes, and ACNN extracts the semantic representation from patent text materials. Then their outputs are concatenated to predict the quality of new patents. The experimental results on a real-world patent dataset show our method outperforms baselines significantly with respect to patent valuation.


Patent quality valuation Attribute network embedding Convolutional Neural Network Patent citation network 



This research was partially supported by grants from the National Key Research and Development Program of China (Grant No. 2016YFB1000904), and the National Natural Science Foundation of China (Grants No. U1605251 and 61727809).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Hongjie Lin
    • 1
  • Hao Wang
    • 1
  • Dongfang Du
    • 1
  • Han Wu
    • 1
  • Biao Chang
    • 1
  • Enhong Chen
    • 1
    Email author
  1. 1.Anhui Province Key Laboratory of Big Data Analysis and ApplicationUniversity of Science and Technology of ChinaHefeiChina

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