Patent Similarity Data and Innovation Metrics
Patent Similarity Data and Innovation Metrics
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专利相似性数据和创新指标
DOI：
10.1111/jels.12261
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发表时间：
2020
影响因子：
1.7
通讯作者：
Contractor, Noshir
中科院分区：
文献类型：
--
作者：
Whalen, Ryan;Lungeanu, Alina;DeChurch, Leslie;Contractor, Noshir
我们介绍并描述了专利相似性数据集，该数据集包括美国实用新型专利的基于向量空间模型的相似性得分。该数据集提供了大约6.4亿个预先计算的相似性分数，以及计算进一步成对相似性所需的代码和计算向量。除了原始数据，我们还引入了利用专利相似性的措施，以深入了解学者和政策制定者感兴趣的创新和知识产权法问题。随附的脚本中提供了代码，以帮助研究人员获取数据集，将其与其他可用的专利数据结合起来，并在研究中使用它。
We introduce and describe the Patent Similarity Dataset, comprising vector space model‐based similarity scores for U.S. utility patents. The dataset provides approximately 640 million pre‐calculated similarity scores, as well as the code and computed vectors required to calculate further pairwise similarities. In addition to the raw data, we introduce measures that leverage patent similarity to provide insight into innovation and intellectual property law issues of interest to both scholars and policymakers. Code is provided in accompanying scripts to assist researchers in obtaining the dataset, joining it with other available patent data, and using it in their research.