Formally Verified Patent Analysis via Dependent Type Theory
Summary: arXiv:2604.18882v1 Announce Type: new
Abstract: We present a formally verified framework for patent analysis as a hybrid AI + Lean 4 pipeline. The DAG-coverage core (Algorithm 1b) is fully machine-verified once bounded match scores are fixed. Freedom-to-operate, claim-construction sensitivity, cross-claim consistency, and doctrine-of-equivalents analyses are formalized at the specification level with kernel-checked candidate certificates.
Existing patent-analysis approaches rely on manual expert analysis, which tend to be slow and non-scalable, or on machine learning (ML) and natural language processing (NLP) methods that are probabilistic, opaque, and non-compositional. To our knowledge, this is the first framework that applies interactive theorem proving based on dependent type theory to intellectual property analysis.
Key Innovations
The framework introduces several key innovations:
- Claims Encoding: Claims are encoded as directed acyclic graphs (DAGs) in Lean 4.
- Match Strengths: Match strengths are treated as elements of a verified complete lattice.
- Confidence Score Propagation: Confidence scores propagate through dependencies using proven-correct monotone functions.
Formalization of Intellectual Property Use Cases
We formalize five key intellectual property use cases through six algorithms:
- Patent-to-product mapping
- Freedom-to-operate analysis
- Claim construction sensitivity
- Cross-claim consistency
- Doctrine of equivalents
Structural lemmas, the coverage-core generator, and the closed-path identity coverage = W_cov are verified in Lean 4. While higher-level theorems for the other use cases remain informal proof sketches, their proof-generation functions have been architecturally mitigated. This involves using untrusted generators whose outputs are kernel-checked and audited to ensure they are free of unsound axioms.
Guarantees and Limitations
The guarantees provided by this framework are conditional on the machine learning layer. Specifically, the framework certifies the mathematical correctness of computations that occur downstream of the ML scores, but it does not certify the accuracy of the scores themselves. This creates a critical balance between leveraging modern AI techniques and ensuring the reliability of patent analysis.
Case Study and Future Work
A case study on a synthetic memory-module claim demonstrates the effectiveness of weighted coverage and construction-sensitivity analysis. However, validation against adjudicated cases remains a topic for future research. The aim is to further enhance the framework’s applicability and reliability in real-world patent analysis scenarios.
In conclusion, this research represents a significant advancement in the field of intellectual property analysis, providing a robust framework that marries the rigor of formal verification with the flexibility of AI methodologies.