This paper describes an efficiently scalable approach to measure technological similarity between patents by combining embedding techniques from natural language processing with nearest-neighbor approximation. Using this methodology we are able to compute existing similarities between all patents, which in turn enables us to represent the whole patent universe as a technological network. We validate both technological signature and similarity in various ways, and demonstrate at the case of electric vehicle technologies their usefulness to measure knowledge flows, map technological change, and create patent quality indicators. Thereby the paper contributes to the growing literature on text-based indicators for patent landscaping.

翻译：本文描述了一种有效的可扩展方法,通过将自然语言处理技术嵌入技术与近邻近似法相结合,衡量专利之间的技术相似性。我们用这种方法可以计算出所有专利之间的现有相似性,这反过来又使我们能够将整个专利宇宙作为一个技术网络来代表。我们以各种方式验证技术特征和相似性,并在电动车辆技术方面证明它们对于测量知识流动、绘制技术变化图和创建专利质量指标的有用性。因此,该文件促进了关于基于文本的专利园林化指标的文献的发展。