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

• URL: http://arxiv.org/abs/2505.19347v1
• Date: Sun, 25 May 2025 22:28:27 GMT
• Title: PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation
• Authors: Yongmin Yoo, Qiongkai Xu, Longbing Cao,
• Abstract summary: We introduce PatentMind, a novel framework for patent similarity assessment based on a Multi-Aspect Reasoning Graph (MARG)<n>PatentMind decomposes patents into three core dimensions: technical feature, application domain, and claim scope, to compute dimension-specific similarity scores.<n>To support evaluation, we construct PatentSimBench, a human-annotated benchmark comprising 500 patent pairs.
• Score: 32.272839191711114
• License: http://creativecommons.org/licenses/by/4.0/
• 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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