Patent similarity evaluation plays a critical role in intellectual property analysis. However, existing methods often overlook the intricate structure of patent documents, which integrate technical specifications, legal boundaries, and application contexts. We introduce PatentMind, a novel framework for patent similarity assessment based on a Multi-Aspect Reasoning Graph (MARG). PatentMind decomposes patents into three core dimensions: technical feature, application domain, and claim scope, to compute dimension-specific similarity scores. These scores are dynamically weighted through a four-stage reasoning process which integrates contextual signals to emulate expert-level judgment. To support evaluation, we construct PatentSimBench, a human-annotated benchmark comprising 500 patent pairs. Experimental results demonstrate that PatentMind achieves a strong correlation () with expert annotations, significantly outperforming embedding-based models and advanced prompt engineeringthis http URLresults highlight the effectiveness of modular reasoning frameworks in overcoming key limitations of embedding-based methods for analyzing patent similarity.
View on arXiv@article{yoo2025_2505.19347,
title={ PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation },
author={ Yongmin Yoo and Qiongkai Xu and Longbing Cao },
journal={arXiv preprint arXiv:2505.19347},
year={ 2025 }
}