Template-type: ReDIF-Paper 1.0
Author-Name: Manera, Maria
Author-Email: maria.manera@unito.it
Author-Workplace-Name: Department of Economics and Statistics Cognetti de
Martiis, University of Turin, Italy
Author-Workplace-Homepage: https://www.est.unito.it
Author-Name: Fusillo, Fabrizio
Author-Email: fabrizio.fusillo@unito.it
Author-Workplace-Name: Department of Economics and Statistics Cognetti de
Martiis, University of Turin, Italy
Author-Workplace-Homepage: https://www.est.unito.it
Author-Name: Orsatti, Gianluca
Author-Email: gianluca.orsatti@unito.it
Author-Workplace-Name: Department of Economics and Statistics Cognetti de
Martiis, University of Turin, Italy
Author-Workplace-Homepage: https://www.est.unito.it
Author-Name: Quatraro, Francesco
Author-Email: francesco.quatraro@unito.it
Author-Workplace-Name: Department of Economics and Statistics Cognetti de
Martiis, University of Turin, Italy
Author-Workplace-Homepage: https://www.est.unito.it

Title: Addressing the identification of Critical Raw Material Patents Using
Pretrained and Large Language Models
Abstract: In modern technologies, critical raw materials (CRMs) have gained
attention due to supply chain risks, environmental concerns, and their
essential role in industries such as renewable energy, electric vehicles,
and advanced electronics. However, identifying and classifying CRM-related
patents, and thus technologies, remains challenging due to the lack of
specific classification systems. Traditional approaches, such as keyword-
based searches and Cooperative Patent Classification (CPC) and
International Patent Classification (IPC) codes, suffer from inaccuracies
due to evolving terminology, ambiguous context, as well as the inability in
recognizing alternative material usage. This study proposes a novel
methodology leveraging advanced natural language processing (NLP) tools to
overcome these limitations. Our approach addresses two key objectives: (1)
distinguishing between substitutable and non-substitutable CRMs in patent
abstracts through the GPT-3.5-turbo-16k model and (2) identifying CRM-
related patents via a fine-tuned BERT for Patents model. Our findings
reveal distinct geographical, technological, and temporal patterns in CRM-
related innovation, emphasizing the significance of NLP techniques in
overcoming traditional classification challenges. This research offers
policymakers and industry stakeholders valuable insights into CRM
innovation trends, supporting strategic decision-making for sustainable
resource management.
Length: 49 pages
Creation-Date: 2025-05

File-URL: https://www.est.unito.it/do/home.pl/Download?doc=/allegati/wp2025dip/wp_05_2025.pdf
File-Format: Application/PDF
Handle: RePEc:uto:dipeco:202505