Investigating Patterns of Technological Innovation

Juste Raimbault (), Antonin Bergeaud and Yoann Potiron
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Juste Raimbault: LVMT - Laboratoire Ville, Mobilité, Transport - IFSTTAR - Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux - UPEM - Université Paris-Est Marne-la-Vallée - ENPC - École nationale des ponts et chaussées, GC (UMR_8504) - Géographie-cités - UP1 - Université Paris 1 Panthéon-Sorbonne - UPD7 - Université Paris Diderot - Paris 7 - CNRS - Centre National de la Recherche Scientifique

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Abstract: The understanding of technological innovation's patterns is crucial for both the theory of economic growth and practical applications in research and development. Yet a precise characterization of breakthrough inventions has not been fully investigated. We address this issue using a large-scale data-mining and network approach on patent data. We extract from US Patent Office raw data an open consolidated database which includes detailed patent information, technological classifications, citation links, and abstract texts. This yields a database of around 4.10^6 patents on a time range from 1976 to 2012. We aim to capture the semantic information contained in texts which has been shown to be complementary to classification data. To do that, we extract relevant n-gram keywords and obtain for each year a semantic network based on co-occurrences. The multi-objective optimization of network modularity and size is performed on network construction parameters (filtering thresholds) through high performance computing. We obtain for each year a multi-layer network, containing semantic community relations, technological classes relations and citation relations between patents. The mining of network layers yields interesting results, such as an increase in time of patent semantic originality combined with a counter-intuitive loss of class-level interdisciplinarity. This corroborates the stylized facts of both invention refinement and specialization in time. Citation-level interdisciplinarity is investigated by combining the different layers. Finally, we plan further work towards the use of these heterogeneous features produced by multi-layer network analysis into machine learning models to predict success and breakthrough level of inventions. Our contribution to the study of socio-technical complex systems is thematic, with the construction of an open-access large scale consolidated patent database and insights into the temporal evolution of inventions, as well as methodological with a technique that can be generated to any network whose nodes contain a textual description.

Keywords: Technological Innovation; Text-mining; Multilayer Network (search for similar items in EconPapers)
Date: 2016-09-19
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Published in Conference on Complex Systems 2016, Sep 2016, Amsterdam, Netherlands

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-01370528

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