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Patent Quality Valuation with Deep Learning Models

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Database Systems for Advanced Applications (DASFAA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10828))

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Abstract

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.

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Notes

  1. 1.

    http://www.ificlaims.com/, in 2015, IBM received 8,088 granted U.S. patents, followed by Samsung (5,518), Canon (3,665), Qualcomm (2,897), Google (2,835), Intel (2,784), LG (2,428), Microsoft (2,398).

  2. 2.

    http://www.wipo.int.

  3. 3.

    http://www.patentsview.org.

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Acknowledgements

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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Authors and Affiliations

  1. Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China, Hefei, China

    Hongjie Lin, Hao Wang, Dongfang Du, Han Wu, Biao Chang & Enhong Chen

Authors
  1. Hongjie Lin
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  2. Hao Wang
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  3. Dongfang Du
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  4. Han Wu
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  5. Biao Chang
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  6. Enhong Chen
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Corresponding author

Correspondence to Enhong Chen .

Editor information

Editors and Affiliations

  1. Simon Fraser University, Burnaby, BC, Canada

    Jian Pei

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Yannis Manolopoulos

  3. University of Queensland, Brisbane, QLD, Australia

    Shazia Sadiq

  4. University of Western Australia, Crawley, WA, Australia

    Jianxin Li

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Lin, H., Wang, H., Du, D., Wu, H., Chang, B., Chen, E. (2018). Patent Quality Valuation with Deep Learning Models. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10828. Springer, Cham. https://doi.org/10.1007/978-3-319-91458-9_29

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  • DOI: https://doi.org/10.1007/978-3-319-91458-9_29

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