Venue & metadata

• Journal/Proceedings: International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings
• Pages: 501 – 508
• Note: Cited by: 0; All Open Access, Hybrid Gold Open Access
• Author keywords: LLM; Machine Learning; Patent Classification; Patent Evaluation

Abstract

The rapid expansion of the Internet of Things has led to a surge in patent filings, creating challenges in evaluating their relevance and potential impact. Traditional patent assessment methods, relying on manual review and keyword-based searches, are increasingly inadequate for analyzing the complexity of emerging IoT technologies. In this paper, we propose an AI-driven methodology for patent evaluation that leverages Large Language Models and machine learning techniques to assess patent relevance and estimate future impact. Our framework integrates advanced Natural Language Processing techniques with structured patent metadata to establish a systematic approach to patent analysis. The methodology consists of three key components: (1) feature extraction from patent text using LLM embeddings and conventional NLP methods, (2) relevance classification and clustering to identify emerging technological trends, and (3) an initial formulation of impact estimation based on semantic similarity and citation patterns. While this study focuses primarily on defining the methodology, we include a minimal validation on a sample dataset to illustrate its feasibility and potential. The proposed approach lays the groundwork for a scalable, automated patent evaluation system, with future research directions aimed at refining impact prediction models and expanding empirical validation. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

Keywords

Cluster analysis GS Computational grammars GS Natural language processing systems GS Keyword-based search GS Language model GS LLM GS Machine-learning GS Patent assessment GS Patent classifications GS Patent evaluation GS Patent filing GS Potential impacts GS Rapid expansion GS Patents and inventions GS

Links & artifacts

DOI Publisher Copy BibTeX Download .bib

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Suggested citation

Campanile, L., Zona, R., Perfetti, A., & Rosatelli, F. (2025). An AI-Driven Methodology for Patent Evaluation in the IoT Sector: Assessing Relevance and Future Impact [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 501–508. https://doi.org/10.5220/0013519700003944