A systematic review of artificial intelligence applications and methodological advances in patent analysis

Purpose

This study aims to systematically synthesize the practical applications of artificial intelligence (AI) in patent analysis by constructing a comprehensive matrix that aligns distinct AI techniques with their corresponding analytical tasks. The “AI Technique and Analytical Task” matrix provides a structured framework for understanding how various AI approaches are deployed across different functional objectives within the patent analysis domain.

Design/methodology/approach

This study integrates bibliometric analysis, BERT-based topic modeling, and literature review to explore AI applications in patent analysis. Data were retrieved from the Web of Science Core Collection using a dual-focus search strategy targeting AI techniques and patent analysis tasks. A clear distinction was made to exclude studies analyzing AI trends using patent data, retaining only those applying AI methods to patent analytics. With these strategies, 718 relevant publications were selected as the basis for analysis.

Findings

The results reveal exponential growth in AI-powered patent analysis research since the mid-2010s, with Technological Forecasting and Social Change (TFSC), Scientometrics, and World Patent Information (WPI) identified as the leading publication platforms. Geographical analysis shows that China and South Korea have rapidly increased their research output and institutional engagement, while the U.S. maintains a foundational yet less recent presence.

With topic modeling technique, this study identified eleven major thematic clusters, spanning tasks such as emerging knowledge discovery, technology forecasting, and opportunity identification. These were integrated into “AI Technique and Analytical Task” matrix, which systematically maps the relationships between AI methods (such as pretrained language models, convolutional neural networks, semantic analysis, and topic modeling) and their practical implementations. Among these, patent classification and nature language processing (NLP) emerged as the most impactful applications, underscoring AI's vital role in enabling scalable, data-driven approaches to managing complex patent information.

Originality

This study presents a novel integration of multi-layered literature retrieval strategies, bibliometric analysis, BERT-based topic modeling, and an AI technique-to-analytical task matrix to construct a systematic and structured knowledge framework. This integrative approach not only delineates the interdisciplinary evolution of AI applications in patent analysis but also provides strategic guidance for future research, particularly in advancing empirical validation, informing policy applications, and promoting global inclusivity in this emerging field.