Using machine learning approaches to identify emergence: Case of vehicle related patent data

Samira Ranaei, Arho Suominen

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    2 Citations (Scopus)

    Abstract

    Bibliometric studies have long used simple search strings, publications count, and word counts to track the emergence of technologies. Novel machine learning methods open new possibilities to study bibliometric data and use algorithmic approaches to uncover emergence of a technology. This study looks at the large and complex dataset of vehicle related patents to uncover emergence indicators. By using machine learning methods this study focuses on if, and to what extent different methods can produce patterns of emergence from the data directly. The data extracted from PATSTAT contains 711296 granted US patent abstracts between the years 1980 and 2014 resulting from a search for "vehicle" creating a complex dataset of technologies from automotive to medical applications. Using Latent Dirichlet Allocation and Dynamic Topic Modeling we show different emergence patterns. Finally, we discuss in detail the possibilities of using machine learning approaches to draw emergence dynamics of technologies.

    Original languageEnglish
    Title of host publicationPICMET 2017 - Portland International Conference on Management of Engineering and Technology
    Subtitle of host publicationTechnology Management for the Interconnected World, Proceedings
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages1-8
    Number of pages8
    Volume2017-January
    ISBN (Electronic)9781890843366
    DOIs
    Publication statusPublished - 29 Nov 2017
    MoE publication typeA4 Article in a conference publication
    Event2017 Portland International Conference on Management of Engineering and Technology, PICMET 2017 - Portland, United States
    Duration: 9 Jul 201713 Jul 2017

    Conference

    Conference2017 Portland International Conference on Management of Engineering and Technology, PICMET 2017
    Abbreviated titlePICMET 2017
    CountryUnited States
    CityPortland
    Period9/07/1713/07/17

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    Learning systems
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    Medical applications
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    Machine learning
    Learning methods
    Bibliometrics
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    Cite this

    Ranaei, S., & Suominen, A. (2017). Using machine learning approaches to identify emergence: Case of vehicle related patent data. In PICMET 2017 - Portland International Conference on Management of Engineering and Technology: Technology Management for the Interconnected World, Proceedings (Vol. 2017-January, pp. 1-8). IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.23919/PICMET.2017.8125290
    Ranaei, Samira ; Suominen, Arho. / Using machine learning approaches to identify emergence : Case of vehicle related patent data. PICMET 2017 - Portland International Conference on Management of Engineering and Technology: Technology Management for the Interconnected World, Proceedings. Vol. 2017-January IEEE Institute of Electrical and Electronic Engineers , 2017. pp. 1-8
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    title = "Using machine learning approaches to identify emergence: Case of vehicle related patent data",
    abstract = "Bibliometric studies have long used simple search strings, publications count, and word counts to track the emergence of technologies. Novel machine learning methods open new possibilities to study bibliometric data and use algorithmic approaches to uncover emergence of a technology. This study looks at the large and complex dataset of vehicle related patents to uncover emergence indicators. By using machine learning methods this study focuses on if, and to what extent different methods can produce patterns of emergence from the data directly. The data extracted from PATSTAT contains 711296 granted US patent abstracts between the years 1980 and 2014 resulting from a search for {"}vehicle{"} creating a complex dataset of technologies from automotive to medical applications. Using Latent Dirichlet Allocation and Dynamic Topic Modeling we show different emergence patterns. Finally, we discuss in detail the possibilities of using machine learning approaches to draw emergence dynamics of technologies.",
    author = "Samira Ranaei and Arho Suominen",
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    Ranaei, S & Suominen, A 2017, Using machine learning approaches to identify emergence: Case of vehicle related patent data. in PICMET 2017 - Portland International Conference on Management of Engineering and Technology: Technology Management for the Interconnected World, Proceedings. vol. 2017-January, IEEE Institute of Electrical and Electronic Engineers , pp. 1-8, 2017 Portland International Conference on Management of Engineering and Technology, PICMET 2017, Portland, United States, 9/07/17. https://doi.org/10.23919/PICMET.2017.8125290

    Using machine learning approaches to identify emergence : Case of vehicle related patent data. / Ranaei, Samira; Suominen, Arho.

    PICMET 2017 - Portland International Conference on Management of Engineering and Technology: Technology Management for the Interconnected World, Proceedings. Vol. 2017-January IEEE Institute of Electrical and Electronic Engineers , 2017. p. 1-8.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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    Ranaei S, Suominen A. Using machine learning approaches to identify emergence: Case of vehicle related patent data. In PICMET 2017 - Portland International Conference on Management of Engineering and Technology: Technology Management for the Interconnected World, Proceedings. Vol. 2017-January. IEEE Institute of Electrical and Electronic Engineers . 2017. p. 1-8 https://doi.org/10.23919/PICMET.2017.8125290