A Novel Matching Algorithm for Academic Patent Paper Pairs: An Exploratory Study of Japan's national research universities and laboratories

Nguyen Van-Thien and René Carraz

Working Papers of BETA from Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg

Abstract: This paper proposes a new method for matching patents with academic publications to create patent-paper pairs (PPP). These pairs can identify instances where a research result is both applied in a patent and published in a paper. The study focuses on a sample of top research-intensive universities and laboratories in Japan, utilizing a new dataset that contains patent-to-article citations and a machine learning model as part of the matching process. Expert consultations were conducted to enhance the robustness of the methodology. Focusing on a set of 14 Japanese universities and 3 national research laboratories, using patent (USPTO) and publication data (OpenAlex) between 1998 and 2018, we built a dataset of 3,177 PPPs out of 7,766 granted patents and 91,213 publications. The results demonstrate that this phenomenon is widespread in academia and our data show the diversity of the academic disciplines and technical field involved, highlighting the intricate connections between scientific and technical concepts and communities. On the methodological side, we documented in-depth complementary validation techniques to enhance the precision and reliability of our matching algorithm. Using open-source data, our methodology is adaptable to diverse national contexts and can be readily adopted by other research teams investigating similar topics.

Keywords: Patent Paper Pair; Methodology; Matching algorithm; Academic patent; Japan. (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-big, nep-ino, nep-ipr, nep-sog and nep-tid
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Persistent link: https://EconPapers.repec.org/RePEc:ulp:sbbeta:2023-29

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