This paper describes a new approach to measure technological similarity between patents by leveraging their textual description. Using embedding techniques from natural language processing, we represent their description as a high dimensional numerical vector, thus capturing their technological signature. Deploying an almost near linear-scaling approximate nearest neighbor matching techniques, we are able to compute technological similarity scores for all existing patents. This enables us to represent the whole patent universe as a technological network. We validate both technological signature and similarity in various ways, and demonstrate their usefulness to create patent quality indicators, measure knowledge flows, and map technological change.

中文翻译：

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基于文本的技术特征和相似之处：如何创建它们以及如何处理它们

本文描述了一种通过利用文本描述来衡量专利之间技术相似性的新方法。使用来自自然语言处理的嵌入技术，我们将它们的描述表示为高维数值向量，从而捕获它们的技术特征。部署几乎接近线性缩放的近似最近邻匹配技术，我们能够计算所有现有专利的技术相似性分数。这使我们能够将整个专利领域表示为一个技术网络。我们以各种方式验证技术特征和相似性，并证明它们在创建专利质量指标、衡量知识流和绘制技术变化图方面的有用性。