TRIZ-RAGNER: A Retrieval-Augmented Large Language Model for TRIZ-Aware Named Entity Recognition in Patent-Based Contradiction Mining

Researchers developed TRIZ-RAGNER, a retrieval-augmented large language model framework that improves patent analysis and systematic innovation by extracting technical contradictions from patent documents. The system achieved 84.2% F1-score accuracy, outperforming existing methods by 7.3 percentage points through better integration of domain-specific knowledge.