Article(id=1236346468819325804, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1236346464578891843, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=1660579200000, acceptedDateStr=2022-08-16, onlineDate=1772697568373, onlineDateStr=2026-03-05, pubDate=1684080000000, pubDateStr=2023-05-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772697568373, onlineIssueDateStr=2026-03-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772697568373, creator=13701087609, updateTime=1772697568373, updator=13701087609, issue=Issue{id=1236346464578891843, tenantId=1146029695717560320, journalId=1235980733773295621, year='2023', volume='32', issue='9', pageStart='865', pageEnd='968', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1772697567361, creator=13701087609, updateTime=1772697682369, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1236346947028701631, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1236346464578891843, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1236346947028701632, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1236346464578891843, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=865, endPage=871, ext={EN=ArticleExt(id=1236346469083566972, articleId=1236346468819325804, tenantId=1146029695717560320, journalId=1235980733773295621, language=EN, title=The technology opportunity analysis of patent based on neural network——taking biological nanomedicine as an example, columnId=null, journalTitle=Chinese Journal of New Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective: This study proposes a neural network-based technology opportunity analysis of patent data.
Methods: First, based on learning of the content characteristics of the data by deep learning algorithm, the extraction keyword model was formed. Content similarity between keywords was calculated based on the contents. Second, patent technology network was built based on the keywords. The link prediction algorithm was used to calculate the topology features of the network, including similarity features based on local information, random walk and path. Finally, the three types of network topology features and content features were input to the BP neural network model to predict potential technology opportunities.
Results: The field of biological nanomedicine was selected as the empirical field. The top 10 technological opportunities with the highest probability score in the patented technology network were identified in the field of biomedicine.
Conclusion: The results were analyzed and evaluated through the literature retrospective method, and the latest literature found the research results on these technical opportunities, but they have not successfully applied for patents.
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目的： 本研究以专利数据为基础提出一种基于神经网络的技术机会分析方法。
方法： 首先，基于深度学习算法学习数据的内容特征，形成抽取关键词模型，基于内容计算关键词间内容相似度特征。其次，基于关键词构建专利技术网络。运用链路预测算法，计算网络的拓扑结构特征，包括：基于局部信息的相似性特征、基于随机游走的相似性特征和基于路径的相似性特征。最后，将3类网络拓扑特征和内容特征输入到反向传播神经网络模型中，预测潜在的技术机会。
结果： 选择生物纳米医药领域作为实证领域，对生物纳米医药领域专利技术网络中概率得分最大的前10项技术机会进行识别。
结论： 通过文献回溯法对该结果进行分析评价，通过最新的文献发现学者对这些技术机会的研究成果，但尚未成功申请专利。
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1Institute of Medical Information, Peking Union Medical College/Chinese Academy of Medical Sciences, Beijing 100020, China
2National Science Library, Chinese Academy of Sciences, Beijing 100190, China
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1北京协和医学院/中国医学科学院医学信息研究所，北京 100020
2中国科学院文献情报中心，北京 100190
3中国科学院大学经济与管理学院信息资源管理系，北京 100190, bio={"content":"
单治易，男，博士研究生，研究方向：医学信息分析。E-mail: shan.zhiyi@imicams.ac.cn。
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单治易，男，博士研究生，研究方向：医学信息分析。E-mail: shan.zhiyi@imicams.ac.cn。
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