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Patent 'Sightings': A Comparative Analysis of Patent Citation Search Tools Using Case Studies from the Engineering Literature

机译:专利“瞄准”:使用工程文学案例研究的专利引用搜索工具的比较分析

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Citation searching is a well-known and widely used technique for locating relevant articles via networks of cited references. Specialized citation databases such as Google Scholar, Scopus, and Web of Science facilitate citation searching by indexing hundreds of millions of references from a vast body of journal and conference literature. In recent years, many other discipline-specific databases have added citation indexing and search tools. Academic researchers also use citation metrics such as the Impact Factor (IF) and h-index in order to assess the value and impact of their publications. The techniques used in citation searching and the calculation of citation metrics can also be applied, with appropriate care, to the patent literature. Searching citations in patents and cited patents can retrieve new and relevant information on an infinite number of engineering topics. It can also reveal connections between the journal literature and patents and expose knowledge gaps for further exploration. Universities are increasingly interested in assessing the value and impact of patents awarded to their faculty. A small but growing number of universities led by the University of Maryland and Texas A&M now give credit for patents in faculty tenure and promotion reviews. This paper explores the tools and strategies for searching cited patents and non-patent literature (NPL) references cited in patents using examples from the engineering literature. The author discusses patent citation practices and how citations appear in patent documents and databases. Strategies for searching patent and NPL citations in patents in selected databases are compared and discussed, noting their respective advantages and limitations. The author also explains the potential benefits and pitfalls of applying popular citation metrics to faculty patents and university patent portfolios.
机译:引文搜索是一种通过引用的引用网络定位相关文章的知名和广泛使用的技术。专业引文数据库,如Google Scholar,Scopus和科学网络,有助于通过索引来自庞大的日记和会议文学的数亿参考资料来搜索。近年来,许多其他特定的特定数据库已经增加了引文索引和搜索工具。学术研究人员还使用引文指标,例如影响因子(IF)和H-Index,以评估其出版物的价值和影响。在引文搜索和引用度量计算中使用的技术也可以用适当的小心应用于专利文献。在专利中搜索引用和引用专利可以检索有关无限数量的工程主题的新信息和相关信息。它还可以揭示期刊文学和专利之间的联系,并揭示知识差距以获得进一步的探索。大学越来越有兴趣评估授予其教师的专利的价值和影响。 Maryland大学和德克萨斯州A&M领导的一大学较小但越来越多的大学现在为教师托管和促进评论提供赞赏。本文探讨了使用工程文献中的示例在专利中搜索引用专利和非专利文献(NPL)参考文献的工具和策略。作者讨论了专利引用实践以及引文如何出现在专利文献和数据库中。比较和讨论在所选数据库专利中搜索专利和NPL引文的策略,并讨论,并注意到它们各自的优缺点。作者还解释了将流行引用指标应用于教师专利和大学专利组合的潜在利益和陷阱。

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