Hybrid AI and Lean 4 Pipeline Enables Formally Verified Patent Analysis

For the first time, a novel framework utilizes interactive theorem proving grounded in dependent type theory to analyze intellectual property. This Lean 4 and AI hybrid pipeline generates machine-checkable certificates for patent evaluations via formal verification. Patent claims are represented as directed acyclic graphs in Lean 4, with match strengths depicted as elements of a verified complete lattice. Confidence scores are transmitted through dependencies using verified monotone functions. Five intellectual property scenarios are formalized at the specification level, including patent-to-product mapping and freedom-to-operate analysis. The core algorithm for DAG-coverage achieves complete machine verification once match scores are defined. Current patent analysis methods either depend on manual expert reviews, which are not scalable, or on machine learning techniques that are probabilistic and opaque. Kernel-checked candidate certificates ensure formal guarantees for patent analysis results, addressing the shortcomings of existing methods with a verified, compositional approach.