基于相似论文增广的深度学习专利质量评估  

Patent quality evaluation using deep learning with similar papers as augmented dataset

作　　者：韦伟[1] 李小娟[1] WEI Wei;LI Xiaojuan(Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]中国科学院计算技术研究所,北京100190

出　　处：《计算机应用》2020年第4期966-971,共6页journal of Computer Applications

基　　金：国家重点研发计划项目(2017YFB1401904)。

摘　　要：实际操作中的专利质量评估多采用专家打分或者使用专家设计的质量评价指标,这导致评价过程存在主观性强、评价双方认可分歧大的问题,因此提出一种基于相似论文增广的深度学习专利质量评估方法。首先以论文作为客观评价数据,使用论文计算相似度作为增广数据来进行筛选,然后利用深度神经网络训练出能够实现论文相似性对待评估专利质量的映射的质量评估模型,最后利用评估模型估计专利质量。仿真结果表明不同领域下,在以满分为100分的前提下,所提方法得出的专利质量评估分数与对应的专家评价结果的平均误差均低于4,表明所提方法具备有效的专利质量评估能力。In practical application,the patent quality evaluation is usually adopted by experts scoring or the quality evaluation index designed by the experts,so that the evaluation results are subjective and cannot be agreed by the both sides of the evaluation at the same time.In order to solve these problems,a deep learning patent quality evaluation method based on paper similarity calculation was proposed.Firstly,the papers were selected as the objective evaluation data,and the papers were used to calculate the similarity with the patent for augmented data.Then,a deep neural network was introduced to train the quality evaluation model,which was able to realize the map between the similarity of the paper and the quality of the patent to be evaluated.Finally,the quality evaluation model was used to access the patent quality.With perfect score of 100,the simulation results show that in different fields,compared to the corresponding expert evaluation result,the deviation of patent quality evaluation scores obtained by the proposed method is lower than 4,indicating that the proposed method has an effective patent quality evaluation ability.

关 键 词：专利质量评估 论文质量评估 相似度计算 深度神经网络 质量特征指标 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]