基于深度学习的专利知识推荐服务研究  

Patent Knowledge Recommendation Service Based on Deep Learning

作　　者：李振宇 战洪飞[1] 余军合[1] 王瑞 邓慧君 LI Zhenyu;ZHAN Hongfei;YU Junhe;WANG Rui;DENG Huijun(College of Mechanical Engineering and Mechanics,Ningbo University,Ningbo,Zhejiang 315211,China;College of Information Science and Engineering,Ningbo University,Ningbo,Zhejiang 315211,China)

机构地区：[1]宁波大学机械工程与力学学院,浙江宁波315211 [2]宁波大学信息科学与工程学院,浙江宁波315211

出　　处：《计算机工程与应用》2022年第15期95-109,共15页Computer Engineering and Applications

基　　金：国家重点研发计划(2019YFB1707101,2019YFB1707103);国家自然科学基金(71671097);浙江省公益技术应用研究计划(LGG20E050010,LGG18E050002);宁波市自然科学基金(2018A610131)。

摘　　要：专利作为一种包含大多数领域中最完整的设计信息,可以为设计者解决设计问题提供有价值的指导。针对现有的专利推荐方法难以有效地推荐跨领域专利的问题,提出一种基于深度学习的跨领域专利知识推荐方法,用于创新产品的概念设计。对产品功能和知识需求情境进行建模,将设计问题进行标准化表达,生成设计问题空间。提出一种半监督学习算法(TG-TCI)将专利功能信息按照功能基自动分类和标记,利用实体识别算法(BERT-BiLSTMCRF)提取专利应用场景术语、技术术语,结合国际专利分类(IPC)信息以表示专利的功能、情境、技术和领域属性,从而生成专利知识空间。通过设计问题空间到专利知识空间的功能基和知识情境映射查找所需的跨领域专利,根据技术和领域属性对它们进行聚类和评估,选出特定的专利以激发设计者的创造力。以一个实际案例进行分析验证,证明了基于深度学习的专利知识推荐模型的可行性及有效性。Patent contains the most complete design information in most domains,it can provide designers with valuable guidance in solving design problems.Aiming at the problem that the existing patent recommendation methods are difficult to effectively recommend cross-domain patents,a cross-domain patent knowledge recommendation method based on deep learning is proposed for the conceptual design of innovative products.The product function and knowledge demand situation are modeled,the design problem is standardized,and the design problem space is generated.A semi-supervised learning algorithm(TG-TCI)is proposed to automatically classify and label patent function information according to the function base,and use entity recognition algorithm(BERT-BiLSTM-CRF)to extract patent application scenario terms and technical terms,combined with international patent classifications(IPC)information is used to represent the function,context,technology and domain attributes of patents,thereby generating patent knowledge space.Find the required cross-domain patents through the functional base and knowledge context mapping from the design problem space to the patent knowledge space,cluster and evaluate them according to the technology and domain attributes,and select specific patents to stimulate the creativity of designers.An actual case is used to analyze and verify the feasibility and effectiveness of the patent knowledge recommendation model based on deep learning.

关 键 词：产品创新 专利数据 深度学习 知识需求情境 知识资源推荐 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]