Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (9): 2532-2538.DOI: 10.11772/j.issn.1001-9081.2020111887

Special Issue: 人工智能

• Artificial intelligence • Previous Articles     Next Articles

Automatic patent price evaluation based on recurrent neural network

LIU Zichen, LI Xiaojuan, WEI Wei   

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2020-12-02 Revised:2021-02-07 Online:2021-09-10 Published:2021-05-12
  • Supported by:
    This work is partially supported by the National Key Research and Development Program of China (2017YFB1401904).


刘子辰, 李小娟, 韦伟   

  1. 中国科学院 计算技术研究所, 北京 100190
  • 通讯作者: 李小娟
  • 作者简介:刘子辰(1984-),男,山东临沂人,助理研究员,博士,主要研究方向:大数据、分布式系统、数据库系统、人工智能;李小娟(1980-),女,湖南长沙人,高级工程师,硕士,CCF会员,主要研究方向:知识产权管理、专利评估与技术创新、大数据;韦伟(1985-),男,山西汾阳人,助理研究员,硕士,主要研究方向:人工智能、大数据、专利评估与技术创新。
  • 基金资助:

Abstract: Patent price evaluation is an important part of intellectual property right transactions. When evaluating patent prices, the impact of the market, law, and technical dimensions on patent prices was not considered effectively by the existing methods. And the market factor of patent plays an important role in the evaluation of patent prices. Aiming at the above problem, an automatic patent price evaluation method based on recurrent neural network was proposed. In this method, based on the market approach, various other factors were considered comprehensively, and the Gated Recurrent Unit (GRU) neural network method was used to realize the automatic evaluation of patent prices. Example tests show that, with the qualitative evaluation results of experts as the benchmark, the average relative accuracy of the proposed method is 0.85. And this average relative accuracy of the proposed method is increased by 3.66%, 4.94% and 2.41% of the average relative accuracies of Analytic Hierarchy Process (AHP), rough set theory method and Back Propagation (BP) neural network method respectively.

Key words: patent price evaluation, artificial intelligence, Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), knowledge mining

摘要: 专利价格评估是知识产权交易的重要内容,现有方法在进行专利价格评估时没有有效地考虑专利的市场、法律、技术维度对专利价格的影响,而专利的市场因素对专利价格的评估起到关键作用。针对上述问题,提出一种基于循环神经网络(RNN)的专利价格自动评估方法。该方法以市场法为基础,对其他各种因素进行综合考虑,并利用门控循环单元(GRU)构建RNN的方法实现对专利价格的自动评估。实例测试表明,以专家定性评估结果为基准,所提方法的相对准确度平均为0.85,与层次分析法(AHP)、粗糙集理论方法和逆向传播(BP)神经网络方法相比,所提方法这一相对准确度均值分别提升了3.66%、4.94%和2.41%。

关键词: 专利价格评估, 人工智能, 门控循环单元, 循环神经网络, 知识挖掘

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