An Italian Patent Multi-Label Classification System to Support the Innovation Demand and Supply Matching

Nicola Amoroso, Annamaria Demarinis Loiotile (), Ester Pantaleo, Giuseppe Conti, Shiva Loccisano, Sabina Tangaro, Alfonso Monaco and Roberto Bellotti
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Nicola Amoroso: Department of Pharmacy-Pharmaceutical Sciences, University of Bari Aldo Moro, 70125 Bari, Italy
Annamaria Demarinis Loiotile: Department of Electrical and Information Engineering, Polytechnic University of Bari, 70125 Bari, Italy
Ester Pantaleo: Interuniversity Department of Physics, University of Bari Aldo Moro, 70125 Bari, Italy
Giuseppe Conti: Netval—Network for Research Valorisation, 23900 Lecco, Italy
Shiva Loccisano: Netval—Network for Research Valorisation, 23900 Lecco, Italy
Sabina Tangaro: Italian Institute of Nuclear Physics, Bari Section, 70125 Bari, Italy
Alfonso Monaco: Italian Institute of Nuclear Physics, Bari Section, 70125 Bari, Italy
Roberto Bellotti: Italian Institute of Nuclear Physics, Bari Section, 70125 Bari, Italy

Sustainability, 2025, vol. 17, issue 14, 1-27

Abstract: The innovation demand and supply matching requires an accurate and time-consuming analysis of patents and the identification of their technological domains; since these tasks can be particularly challenging, this is why recent studies have evaluated the possibility of adopting Artificial Intelligence based on NLP techniques. Here, we present an automated workflow for patent analysis and classification devoted to the Italian patent scenario. High-quality data from the online platform KnowledgeShare (KS) were investigated: KS is the first patent management platform on the Italian innovation scene. A not secondary aspect consisted in determining which words mostly influenced patent classification, thus characterizing the corresponding research areas. Several models were compared to ensure the workflow’s robustness; Logistic Regression (LR) resulted in the best-performing model, and its performance compared well with the State of the Art. For each technological domain in the KS database, we evaluated and discussed its characteristic words; furthermore, a further analysis was focused on explaining why some domains, such as “Packaging” and “Environment,” were particularly confounding. This last aspect is of paramount importance to identify cross-contamination effects among research areas.

Keywords: patent analysis; technological areas; technology transfer; innovation demand and supply matching (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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