商标
业务
产业组织
专利分析
并购
空格(标点符号)
知识产权
专利可视化
知识管理
数据科学
财务
计算机科学
操作系统
作者
Zhaoqi Cheng,Ginger Zhe Jin,Mario Leccese,Dokyun Lee,Liad Wagman
出处
期刊:AEA papers and proceedings
[American Economic Association]
日期:2023-05-01
卷期号:113: 288-293
标识
DOI:10.1257/pandp.20231100
摘要
Policymakers are increasingly concerned that incumbent acquisitions of small or young firms may slow down rather than speed up innovation, but it is difficult to identify which firms are related in the fast-changing space of technological innovation. This paper proposes a new, data-driven method to classify patent data into tech-business zones on a probabilistic basis, using patent assignee information. After combining mergers and acquisitions data from S&P Global Market Intelligence with PatentsView data from the US Patent and Trademark Office, we discuss how the zone classification can aid merger reviews and other lines of research.
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