Abstract: This paper proposes a training framework termed LACC(Large Language Model-Augmented Consistency-Constrained),designed to address key challenges in patent abstract generation,including incomplete coverage of technical features,insufficient legal compliance,and inefficiency in edge deployment.The LACC framework constructs a bidirectional reversible task structure between abstract generation and claim expansion,incorporating a cycle-consistency constraint to jointly optimize technical expression and legal formulation.On this basis,LACC integrates a controllable data augmentation strategy powered by large language models(LLMs) to automatically generate high-quality patent text pairs.A dynamic verification mechanism is further introduced to enhance the technical accuracy and regulatory reliability of generated content.Experimental results on the Chinese patent dataset CPTD demonstrate that LACC achieves a ROUGE-L score of 56.74,outperforming the baseline by 8.99 percentage points,and shows significant improvements in the recurrence consistency score(RCS).Moreover,the framework supports efficient edge deployment,with inference latency controlled within 420 ms and single-GPU memory usage limited to 4.5 GB.Overall,LACC offers a practical and scalable solution for downstream tasks such as patent drafting assistance,legal text generation,and intelligent intellectual property(IP) management,and shows strong potential in enabling the automation of the full lifecycle of IP processing.

Key words: Natural language processing, Large language model, Cycle consistency, Data augmentation, Knowledge distillation, Collaborative training