Enhancing Patent Document Similarity Evaluation and Classification Precision Through a Multimodal AI Approach

With the global surge in patent filings, accurately evaluating similarity between patent documents has become increasingly critical. Traditional similarity assessment methods—primarily based on unimodal inputs such as text or bibliographic data—often fall short due to the complexity of legal language and the semantic ambiguity that is inherent in technical writing. To address these limitations, this study introduces a novel multimodal patent similarity evaluation framework that integrates weak AI techniques and conceptual similarity analysis of patent drawings. This approach leverages a domain-specific pre-trained language model optimized for patent texts, statistical correlation analysis between textual and bibliographic information, and a rule-based classification strategy. These components, rooted in weak AI methodology, significantly enhance classification precision. Furthermore, the study introduces the concept of conceptual similarity—as distinct from visual similarity—in the analysis of patent drawings, demonstrating its superior ability to capture the underlying technological intent. An empirical evaluation was conducted on 9613 patents in the manipulator technology domain, yielding 668,010 document pairs. Stepwise experiments demonstrated a 13.84% improvement in classification precision. Citation-based similarity assessment further confirmed the superiority of the proposed multimodal approach over existing methods. The findings underscore the potential of the proposed framework to improve prior art searches, patent examination accuracy, and R&D planning.