商标
封面(代数)
新颖性
数据科学
欧洲专利局
编码(集合论)
计算机科学
业务
工程类
商业
心理学
社会心理学
机械工程
操作系统
集合(抽象数据类型)
程序设计语言
作者
Sam Arts,Jianan Hou,Juan Carlos Gómez
出处
期刊:Research Policy
[Elsevier]
日期:2021-03-01
卷期号:50 (2): 104144-104144
被引量:114
标识
DOI:10.1016/j.respol.2020.104144
摘要
We develop natural language processing techniques to identify the creation and impact of new technologies in the population of U.S. patents. We validate the new techniques and their improvement over traditional metrics based on patent classification and citations in two case-control studies. First, we collect patents linked to awards such as the Nobel prize and the National Inventor Hall of Fame. These patents likely cover radically new technologies with a major impact on technological progress and patenting. Second, we identify patents granted by the United States Patent and Trademark Office but simultaneously rejected by both the European and Japanese patent office. Such patents arguably lack novelty or cover small incremental advances over prior art and should have little impact on technological progress. We provide open access to code, data, and new measures for all utility patents granted by the USPTO up to May 2018 (see https://zenodo.org/record/3515985, DOI: 10.5281/zenodo.3515985).
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