PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation

Abstract

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 (r=0.938) with expert annotations, significantly outperforming embedding-based models and advanced prompt engineering methods.These results highlight the effectiveness of modular reasoning frameworks in overcoming key limitations of embedding-based methods for analyzing patent similarity.

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