PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation
This addresses the need for more accurate patent similarity assessment in intellectual property analysis, particularly for tasks like infringement risk assessment, though it appears incremental as it builds on existing methods by incorporating multi-aspect reasoning.
The paper tackled the problem of patent similarity evaluation by introducing PatentMind, a framework that decomposes patents into technical, application, and claim aspects and uses a Multi-Aspect Reasoning Graph to compute similarity scores, achieving a correlation of r=0.938 with expert annotations.
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 their three dimensions of technical features, application domains, and claim scopes, then dimension-specific similarity scores are calculated over the MARG. These scores are dynamically weighted through a context-aware reasoning process, which integrates contextual signals to emulate expert-level judgment. To support evaluation, we construct a human-annotated benchmark PatentSimBench, comprising 500 patent pairs. Experimental results demonstrate that the PatentMind-generated scores show a strong correlation ($r=0.938$) with expert annotations, significantly outperforming embedding-based models, patent-specific models, and advanced prompt engineering methods. Beyond computational linguistics, our framework provides a structured and semantically grounded foundation for real-world decision-making, particularly for tasks such as infringement risk assessment, underscoring its broader impact on both patent analytics and evaluation.