Human assigned vs. machine created: Links between patents and scholarly publications

Research output: Contribution to conferenceConference AbstractScientific

Abstract

To measure knowledge flows between scholarly literature and patents, studies have used a several approaches, such as keyword search based count of science publications and patenting in a technology with an expectation of linearity of innovation, patents citing publications or vice versa, or author-inventor co-occurrences. Patent citations, specifically non-patent references has often been seen as a proxy of for the "science-dependence" or "science-base" of a technology, although this has been critiqued as an over simplification. Another avenue to classify patents and publications would be to rely on machine learning, specifically unsupervised learning. In this study, we analyse the relationship between publication and patents by looking at the intersection of human assigned and machine learned linkages between science and patents. We use a macro level approach focusing on the whole science publication and patents from one country.
Original languageEnglish
Publication statusPublished - 2017
Event7th Global TechMining Conference, GTM 2017 - Atlanta, United States
Duration: 9 Oct 2017 → …

Conference

Conference7th Global TechMining Conference, GTM 2017
Abbreviated titleGTM 2017
CountryUnited States
CityAtlanta
Period9/10/17 → …

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patent
science
communication sciences
macro level
learning
innovation
knowledge

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Suominen, A. (2017). Human assigned vs. machine created: Links between patents and scholarly publications. Abstract from 7th Global TechMining Conference, GTM 2017, Atlanta, United States.
Suominen, Arho. / Human assigned vs. machine created : Links between patents and scholarly publications. Abstract from 7th Global TechMining Conference, GTM 2017, Atlanta, United States.
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abstract = "To measure knowledge flows between scholarly literature and patents, studies have used a several approaches, such as keyword search based count of science publications and patenting in a technology with an expectation of linearity of innovation, patents citing publications or vice versa, or author-inventor co-occurrences. Patent citations, specifically non-patent references has often been seen as a proxy of for the {"}science-dependence{"} or {"}science-base{"} of a technology, although this has been critiqued as an over simplification. Another avenue to classify patents and publications would be to rely on machine learning, specifically unsupervised learning. In this study, we analyse the relationship between publication and patents by looking at the intersection of human assigned and machine learned linkages between science and patents. We use a macro level approach focusing on the whole science publication and patents from one country.",
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Suominen, A 2017, 'Human assigned vs. machine created: Links between patents and scholarly publications', 7th Global TechMining Conference, GTM 2017, Atlanta, United States, 9/10/17.

Human assigned vs. machine created : Links between patents and scholarly publications. / Suominen, Arho.

2017. Abstract from 7th Global TechMining Conference, GTM 2017, Atlanta, United States.

Research output: Contribution to conferenceConference AbstractScientific

TY - CONF

T1 - Human assigned vs. machine created

T2 - Links between patents and scholarly publications

AU - Suominen, Arho

N1 - Published: Abstract only

PY - 2017

Y1 - 2017

N2 - To measure knowledge flows between scholarly literature and patents, studies have used a several approaches, such as keyword search based count of science publications and patenting in a technology with an expectation of linearity of innovation, patents citing publications or vice versa, or author-inventor co-occurrences. Patent citations, specifically non-patent references has often been seen as a proxy of for the "science-dependence" or "science-base" of a technology, although this has been critiqued as an over simplification. Another avenue to classify patents and publications would be to rely on machine learning, specifically unsupervised learning. In this study, we analyse the relationship between publication and patents by looking at the intersection of human assigned and machine learned linkages between science and patents. We use a macro level approach focusing on the whole science publication and patents from one country.

AB - To measure knowledge flows between scholarly literature and patents, studies have used a several approaches, such as keyword search based count of science publications and patenting in a technology with an expectation of linearity of innovation, patents citing publications or vice versa, or author-inventor co-occurrences. Patent citations, specifically non-patent references has often been seen as a proxy of for the "science-dependence" or "science-base" of a technology, although this has been critiqued as an over simplification. Another avenue to classify patents and publications would be to rely on machine learning, specifically unsupervised learning. In this study, we analyse the relationship between publication and patents by looking at the intersection of human assigned and machine learned linkages between science and patents. We use a macro level approach focusing on the whole science publication and patents from one country.

M3 - Conference Abstract

ER -

Suominen A. Human assigned vs. machine created: Links between patents and scholarly publications. 2017. Abstract from 7th Global TechMining Conference, GTM 2017, Atlanta, United States.