Transforming academic papers into patent descriptions challenges LLMs with extreme length, strict factuality, and rigorous stylistic norms. Existing supervised methods suffer from pathological repetition, while static preference optimization causes style degradation by erroneously penalizing necessary structural repetitions. To address this, we propose PatAlign, a framework utilizing Iterative Preference Optimization to dynamically sample negative instances from the evolving policy, effectively distinguishing pathological repetition from the structural repetition required to ensure patent rigor. We also introduce the Paper-to-Patent Score (P²S) metric to evaluate the overall quality of generated patents. Experiments demonstrate that PatAlign effectively mitigates pathological repetition and style degradation, achieving state-of-the-art performance in both factual consistency and language style. Notably, our 8B model significantly outperforms 32B general-purpose models, validating the efficacy of iterative alignment strategies in complex long-form generation tasks. Our code and data are available at https://github.com/SUSTech-GenAI/PatAlign.