Large Language Models (LLMs) offer potential for patent analysis but are challenged by the length, complexity, and specialized language of patent documents, hindering systematic analysis. This study introduces an LLM-based approach to generate structured technical summaries of patents, aiming to improve classification efficiency and accuracy. The methodology involved comparing LLM-based patent classification performance using these summaries against full-text and claims-only representations across various test cases. Results demonstrate that summaries significantly improve classification: precision increased by approximately 11% over full-text and 20% over claims, while accuracy rose by 10% and 14%, respectively. F1-scores also showed substantial gains, with comparable recall, indicating effective retention of crucial information. A case study on wind turbine patents validated the method's practical utility. The study concludes that LLM-generated structured technical summaries offer a robust and efficient input for patent classification, providing a promising pathway for scalable and reliable patent intelligence.