Automated assessment of patent quality is increasingly important given the growth of patent filings and the adoption of AI-assisted drafting. Existing methods often rely on modular pipelines or generic detectors, resulting in fragmented decisions and limited integration across quality dimensions. We propose P-QuASAR (Patent Quality Assurance via Structured Assessment and Refinement), a unified probabilistic framework that represents patent specifications as Quality Graphs. Multiple interdependent quality dimensions—such as regulatory compliance, technical coherence, and figure–text consistency—are jointly modeled using uncertainty-aware Quality Assessment Functions with learned edge potentials. Cross-dimensional evidence propagation via loopy belief propagation enables calibrated defect detection, while Optimal Intervention Paths translate inferred quality states into prioritized and actionable refinement recommendations. Evaluated on 500 patents across eight IPC domains against seven state-of-the-art baselines, P-QuASAR achieves substantial improvements: 99.86% balanced accuracy on regulatory compliance, 88.91% on technical coherence, and 94.70% on figure consistency, outperforming the strongest baselines by 3.0%, 9.0%, and 7.1%, respectively. Ablation studies confirm that joint graph reasoning contributes 3.66 points to average performance. When applied for refinement, P-QuASAR reduces average defects in AI-generated patents from 9.04–12.15 to 3.21 per document, surpassing human-authored patents.