Automated Patent Claim Prior Art Search & Novelty Assessment using Multi-Modal Data Fusion and Recursive Filtering Networks

**Abstract:** This paper introduces a novel framework, the Multi-Modal Data Fusion and Recursive Filtering Network (MMDF-RFN), for automating patent claim prior art search and novelty assessment. Traditional methods are often time-consuming and subjective, leading to inconsistent results. MMDF-RFN leverages advanced natural language processing, computer vision, and code analysis techniques to comprehensively assess patent claims against a vast corpus of existing technical literature, significantly improving efficiency and accuracy while reducing reliance on manual review. Its recursive filtering network dynamically optimizes the search process, identifying potentially relevant prior art with unprecedented precision. The system exhibits immediate commercializability as an integral component of patent prosecution workflows and knowledge management systems, poised to streamline innovation and accelerate the patent process.