Artificial Life · 2019 · 14 citations · 22 references
ManagementData ScienceData MiningInnovationIntellectual PropertyInnovation ClustersPatent AnalysisTechnological ChangeTechnological InnovationEmerging Technology DetectionOpen-ended EvolutionPatent Classification SystemsPatent ClusteringPatent Corpus MiningTechnology Discovery
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.
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Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text
Laurens van der Maaten, Geoffrey E. Hinton · Journal of Machine Learning Research · 2008 · 35.7K citations
Software Framework for Topic Modelling with Large Corpora
Petr Sojka · 2010 · 3.8K citations · Full text