We detect ongoing innovation in empirical data about human technological innovations. Ongoing technological innovation is a form of open-ended evolution, but it occurs in a nonbiological, cultural population that consists of actual technological innovations that exist in the real world. The change over time of this population of innovations seems to be quite open-ended. We take patented inventions as a proxy for technological innovations and mine public patent records for evidence of the ongoing emergence of technological innovations, and we compare two ways to detect it. One way detects the first instances of predefined patent pigeonholes, specifically the technology classes listed in the United States Patent Classification (USPC). The second way embeds patents in a high-dimensional semantic space and detects the emergence of new patent clusters. After analyzing hundreds of years of patent records, both methods detect the emergence of new kinds of technologies, but clusters are much better at detecting innovations that are unanticipated and undetected by USPC pigeonholes. Our clustering methods generalize to detect unanticipated innovations in other evolving populations that generate ongoing streams of digital data.

中文翻译：

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开放式技术创新

我们在有关人类技术创新的经验数据中检测持续创新。持续的技术创新是一种开放式进化形式，但它发生在由现实世界中存在的实际技术创新组成的非生物文化群体中。随着时间的推移，这些创新群体的变化似乎是非常开放的。我们将专利发明作为技术创新的代表，并挖掘公共专利记录作为技术创新不断涌现的证据，我们比较了两种检测方法。一种方法检测预定义专利分类的第一个实例，特别是美国专利分类 (USPC) 中列出的技术类别。第二种方式将专利嵌入高维语义空间，检测新专利集群的出现。在分析了数百年的专利记录后，这两种方法都可以检测到新技术的出现，但集群在检测 USPC 分类器未预料和未检测到的创新方面要好得多。我们的聚类方法可以推广到检测其他不断发展的人群中的意外创新，这些创新会产生持续的数字数据流。