An AI-Driven Methodology for Patent Evaluation in the IoT Sector: Assessing Relevance and Future Impact

Lelio Campanile, Renato Zona, Antonio Perfetti, Franco Rosatelli

2025

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.

Paper Citation

in Harvard Style

Campanile L., Zona R., Perfetti A. and Rosatelli F. (2025). An AI-Driven Methodology for Patent Evaluation in the IoT Sector: Assessing Relevance and Future Impact. In Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: AI4EIoT; ISBN 978-989-758-750-4, SciTePress, pages 501-508. DOI: 10.5220/0013519700003944

in Bibtex Style

@conference{ai4eiot25,
author={Lelio Campanile and Renato Zona and Antonio Perfetti and Franco Rosatelli},
title={An AI-Driven Methodology for Patent Evaluation in the IoT Sector: Assessing Relevance and Future Impact},
booktitle={Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: AI4EIoT},
year={2025},
pages={501-508},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013519700003944},
isbn={978-989-758-750-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: AI4EIoT
TI - An AI-Driven Methodology for Patent Evaluation in the IoT Sector: Assessing Relevance and Future Impact
SN - 978-989-758-750-4
AU - Campanile L.
AU - Zona R.
AU - Perfetti A.
AU - Rosatelli F.
PY - 2025
SP - 501
EP - 508
DO - 10.5220/0013519700003944
PB - SciTePress