Patent similarity evaluation is essential for intellectual property analysis, yet existing methods struggle to capture the multifaceted structure of patent documents encompassing technical specifications, legal boundaries, and application contexts. We propose PatentMind, a framework that performs patent similarity assessment through a Multi-Aspect Reasoning Graph (MARG). PatentMind decomposes patent documents into three dimensions: technical features, application domains, and claim scopes, and computes dimension-specific similarity scores, which are then integrated via a context-aware dynamic weighting mechanism that emulates expert-level judgment. To facilitate evaluation, we introduce PatentSimBench, an expert-annotated benchmark comprising 500 patent pairs. Experiments show that PatentMind achieves a Pearson correlation of r=0.938 with expert annotations, substantially outperforming embedding-based, patent-specific, and prompt engineering baselines. Our framework offers interpretable, multi-dimensional assessment applicable to downstream tasks such as prior art search and infringement risk analysis.

High-stakes texts such as patent claims, medical records, and technical reports are structurally complex and demand a high degree of reliability and precision. While large language models (LLMs) have recently been applied to automate their generation in high-stakes domains, reliably evaluating such outputs remains a major challenge. Conventional natural language generation (NLG) metrics are effective for generic documents but fail to capture the structural and legal characteristics essential to evaluating complex high-stakes documents. To address this gap, we propose PatentScore, a multi-dimensional evaluation framework specifically designed for one of the most intricate and rigorous domains, patent claims. PatentScore integrates hierarchical decomposition of claim elements, validation patterns grounded in legal and technical standards, and scoring across structural, semantic, and legal dimensions. In experiments on our dataset which consists of 400 Claim1, PatentScore achieved the highest correlation with expert annotations (r = 0.819), significantly outperforming widely used NLG metrics. This work establishes a new standard for evaluating LLM-generated patent claims, providing a solid foundation for research on patent generation and validation.