Beyond Patent Citations as Measures of Innovative Search and Success

数以百计，甚至可能是数以千计的研究论文使用专利引用数据来研究创新搜索和影响。 然而，最近的批评，提出了实质性的问题，专利引用措施是否准确地捕捉专利的新颖性和重要性。 使用大数据技术，包括LASSO内容标签，这些标签的语义距离测量，以及专利申请人及其审查员之间的通信的计算语言学分析，这项研究将补充（甚至可能取代）现有的方法来衡量专利的重要性及其相对于其他专利发明的位置。 专利重要性的新衡量标准是基于未来有多少专利申请被特定专利"阻止"，需要验证的假设是，"阻止专利"是有价值的技术空间。为了表征技术空间中的专利，本研究将使用计算机文本分析来识别给定专利相对于1975年以来授予的所有其他专利而言最具区别性的词语，并测试类似“标记”专利在技术空间中彼此接近的假设，从而代表本地搜索。这些技术和公共数据将开启创新史研究、社会福利和科技政策的政策评估、发明人和受让人的消歧、现有技术搜索以及用于计量经济学估计的处理和控制专利的匹配。 随着大型数据库的广泛使用，利用专利数据研究创新及其社会和经济影响已变得越来越普遍。 这种方法的优点是易于计算，并且可以在数百万文档和数千种技术中进行一致的测量。 其缺点是，这些措施（例如，专利的现有技术参考）与实际现象（例如，新颖性，经济影响，创造就业机会或专利诉讼的可能性）之间的相关性很差。 本研究利用计算社会科学，应用改进的大数据技术，结合调查验证，可以改善缺点，并提供给研究人员，显着改善创新和技术战略文献的研究。 除了能够更好地进行研究外，这项工作还提供了更广泛的社会效益，帮助发明人、律师、审查员和投资者进行现有技术检索，改善专利数据库的消歧，并更快地识别重要专利。

Hundreds, possibly thousands, of research papers have used patent citation data to study innovative search and impact. Recent critiques, however, have raised substantial questions about whether patent citation measures accurately capture a patent's novelty and importance. Using big data techniques including LASSO content labels, semantic distance measures with those labels, and computational linguistics analysis of communication between patent applicants and their examiners, this research will supplement (or possibly even replace) existing approaches to measuring a patent's importance and its position relative to other patented inventions. The new measure of patent importance is based on how many future patent applications are "blocked" by a particular patent, and the assumption to be validated is that "blocking patents" are valuable pieces of technology space. To characterize patents in technology space, this research will use computerized text analysis to identify the most differentiating words for a given patent, relative to all other patents granted since 1975, and test the hypothesis that similarly "tagged" patents are close to one another in technology space, and thus represent local search. The techniques and public data will open up research in the history of innovation, policy assessments of social welfare and technology and science policy, inventor and assignee disambiguation, prior art search, and matching of treated and control patents for econometric estimation. Broader impacts The use of patent data to study innovation and its social and economic impact has become increasingly common with the widespread availability of large databases. The advantages of such approaches are easily calculated and consistent measures across millions of documents and thousands of technologies. The disadvantages are that the measures (prior art references by patents, for example) provide poor and indirect correlations with actual phenomena (such as novelty, economic impact, job creation, or potential for patent litigation). Using computational social science, this research applies improved big data techniques, combined with survey validation, that could ameliorate the disadvantages, and made available to researchers, significantly improve research in the innovation and technology strategy literatures. In addition to enabling better research, this work provides wider societal benefit in helping inventors, lawyers, examiners, and investors in prior art search, improved disambiguation of the patent database, and faster identification of important patents.