Integrated Patent Prior Art Search with Claim-Aware Retrieval and Novelty Assessment

Existing patent examination approaches face fundamental limitations: they struggle with comprehensive prior art coverage due to maximum similarity scoring without considering all claim elements, provide limited ranked retrieval through binary classification without confidence scoring, and incur substantial computational overhead while generating generic outputs that miss claim-specific details. To address these challenges, we introduce \textbf{Integrated Patent Prior Art Search with Claim-Aware Retrieval and Novelty Assessment} (IPAS-CARNA), a novel three-stage pipeline combining enhanced claim-document matching, continuous novelty assessment, and claim-aware summarization. Our approach models element-wise claim coverage through adaptive chunking and weighted aggregation, integrates continuous novelty scoring with confidence assessment, and introduces claim-aware summarization with dynamic length control. Extensive experiments on CLEF-IP 2013, USPTO examination records, and HUPD validation sets demonstrate significant improvements: MAP@100 of 0.342 with 14.8\% improvement in retrieval recall, 18.2\% improvement in NDCG@10 for novelty ranking, technical accuracy above 0.85, and ROUGE-L scores of 0.456 for summarization. Our work establishes an effective integrated solution for automated patent prior art analysis.