Title: Layer‑1 Blockchain Metaprotocol with Integrated Compliance‑at‑Consensus Virtual Machine (ComplianceVM)
Field: Distributed ledger technology; programmable compliance enforcement; blockchain consensus protocols.
Background: Current blockchain platforms require off‑chain compliance checks or application‑layer enforcement, leading to fragmentation, non‑determinism, and weak regulatory alignment. No protocol integrates a compliance VM into consensus rules, preventing block finality without verified regulatory predicates.
Summary: CHLOM Layer‑1 introduces a native ComplianceVM with custom bytecode and deterministic execution of encoded regulations. Validators must run pre_trade_check() and post_settlement_check() on all transactions. State includes License Registry, Audit Log, and ZK Proof Store. Compliance rules are expressed in a Rule DSL compiled to VM bytecode, referencing signed oracle snapshots.
Detailed Description:
- Consensus: Modified PoS finality with compliance hook in block proposal phase.
- State: Merkle‑Patricia trie with compliance namespace; License Registry state machine.
- ComplianceVM: WASM‑based with restricted opcodes for deterministic, pure execution.
- Rule DSL: Formal grammar (provided in playbook) compiled to bytecode.
- Privacy: ZKP verification integrated at opcode level; failures halt transaction inclusion.
- Governance: DLA proposals can upgrade rulesets; changes take effect after supermajority approval and epoch delay.
Advantages: Enforces compliance before settlement; fully auditable chain of rules and proofs; regulatory agility with governance.
Figures: Architecture diagram, VM state flow, Rule DSL compilation flow, Consensus timeline.
Checklist: Include VM opcode list, DSL parser specs, test vectors.
Provisional Patent Application Draft — TLaaS Tokenized Licensing & License Registry FSM
Title: Tokenized Licensing as a Service with Blockchain License Registry Finite State Machine
Field: Digital rights management; tokenized licensing; smart contract registries.
Background: Digital licensing suffers from unverifiable issuance, opaque state changes, and siloed registries. TLaaS provides a blockchain‑anchored, state‑machine‑driven license registry.
Summary: TLaaS issues NFT‑based licenses bound to identity credentials. The License Registry FSM enforces lifecycle transitions (NEW→VALID→SUSPENDED/REVOKED) only via authenticated events signed by DLA authority keys. Supports selective disclosure via ZK proofs.
Detailed Description:
- Token Standard: ERC‑721‑compatible with compliance metadata schema.
- FSM: Transition table enforced on‑chain; guarded by compliance checks.
- Issuance: Requires verified KYC tier, sanctions clearance proof, and policy pass.
- Suspension/Revocation: Triggered by rule violation or governance decision.
- Renewal: Auto‑renew rules via policy hooks.
- Integration: Interoperable with LEX and ADE.
Advantages: Unforgeable license state; compliance‑aware lifecycle; cross‑platform verification.
Figures: FSM diagram, issuance/revocation sequence, metadata schema.
Checklist: Include schema JSON, FSM table, sample transactions.
Provisional Patent Application Draft — LEX Compliance‑Before‑Trade & Atomic ADE Settlement
Title: Decentralized Exchange with Pre‑Trade Compliance Enforcement and Integrated Atomic Settlement
Field: Decentralized marketplaces; compliance automation; smart contract settlement.
Background: Existing DEXs lack regulatory compliance enforcement before trade execution, leading to potential legal exposure.
Summary: LEX matches orders only if both counterparties and assets pass compliance checks (can_trade). Upon match, it triggers ADE settlement atomically; failure reverts trade.
Detailed Description:
- Matching Engine: Off‑chain order book or on‑chain AMM; all entries gated by
- Pre‑Trade Checks: License validity, policy evaluation, sanity of price oracles.
- Settlement: Calls ADE with transaction context; success finalizes trade.
- Dispute Resolution: Challenge window for fraud proofs; DLA arbitration.
Advantages: Zero non‑compliant trades; atomic compliance + settlement.
Figures: Trade lifecycle diagram, compliance hook integration, rollback flow.
Checklist: Include can_trade pseudocode, ADE call flow.
Provisional Patent Application Draft — DLA Dual‑House Governance & Governance Scribe
Title: Dual‑House DAO Governance Model with Authenticated Governance Scribe Log
Field: Blockchain governance; decentralized organizations.
Background: Governance often lacks balanced representation and auditability.
Summary: DLA splits governance into House‑I (identity‑based) and House‑II (stake‑weighted), with both needing supermajority approval for proposals. The Governance Scribe logs all proposals, votes, and outcomes in an append‑only Merkle‑rooted log.
Detailed Description:
- Proposal Flow: Submission → review → dual‑house vote → execution.
- Scribe: Anchors Merkle root of signed vote records each epoch.
- Veto/Override: Risk Council may veto within defined window.
- Slashing: Malicious actors lose stake.
Advantages: Balanced governance; tamper‑evident history.
Figures: Proposal lifecycle, Merkle log structure.
Checklist: Include vote weighting formulas, Scribe log schema.
Provisional Patent Application Draft — Identity ZK Proofs, BioHash & Selective Disclosure
Title: Privacy‑Preserving Identity Verification Using BioHash and Selective Disclosure Credentials
Field: Identity verification; zero‑knowledge proofs; privacy technologies.
Background: Identity checks often require PII exposure; BioHash allows verification without revealing raw biometrics.
Summary: System hashes biometric templates with salt to create BioHash, then uses ZK proofs to verify matches. Selective disclosure credentials allow revealing only required predicates.
Detailed Description:
- BioHash Generation: Minhash fingerprint template + salt → hash.
- ZK Proof Circuits: Verify BioHash match, sanctions clearance, and credential tier.
- Selective Disclosure: BBS+ signatures; reveal minimal attributes.
Advantages: Privacy protection; verifiable compliance.
Figures: BioHash process flow, ZK proof interaction.
Checklist: Include circuit spec, credential schema.
Provisional Patent Application Draft — Oracles & Scribes Aggregation + Slashing
Title: Fault‑Tolerant Oracle Aggregation with Slashing and Governance Anchoring via Scribes
Field: Blockchain data feeds; oracle networks.
Background: Oracles can be corrupted without strong fault tolerance.
Summary: Median‑of‑n with trimmed mean aggregation, slashing for misreporting, and Merkle‑anchored feed logs.
Detailed Description:
- Aggregation: Trimmed mean; fault tolerance up to f misreports.
- Attestation: Ed25519 signatures.
- Slashing: On misreport proof.
- Scribe Anchoring: Merkle root of feed values.
Advantages: Secure, auditable oracles.
Figures: Aggregation flow, slashing proof.
Checklist: Include aggregation pseudocode, slashing protocol.
Provisional Patent Application Draft — CrownLytics & CrownPulse
Title: Multi‑Modal Analytics and Reputation Scoring Engine with Causal Uplift Modeling
Field: Data analytics; sentiment analysis.
Background: Businesses need unified insights from behavioral, transactional, and sentiment data.
Summary: CrownLytics applies Bayesian hierarchical models for conversion lift; CrownPulse computes a reputation index from multi‑source signals.
Detailed Description:
- Metrics: CTR, ROAS, sentiment trends.
- Models: Causal forests; embeddings for sentiment.
- Index: Weighted z‑scores; decay over time.
Advantages: Holistic, predictive analytics.
Figures: Model pipeline, index computation flow.
Checklist: Include data schema, model formulas.
Provisional Patent Application Draft — Cliques Matching Engine
Title: Constraint‑Aware Social/Business Matching Engine Using Two‑Tower Embeddings and Trust Signals
Field: Recommendation systems; social networks.
Background: Matching systems often ignore constraints like diversity and trust.
Summary: Two‑tower embedding model with reranker enforcing constraints on group composition, trust score minimums, and time zone coverage.
Detailed Description:
- Embedding: User and group vectors; cosine similarity.
- Reranker: Constraint satisfaction.
- Scoring: Weighted sum of match, trust, compatibility, overlap.
Advantages: Higher‑quality matches with governance alignment.
Figures: Matching flow, constraint solver.
Checklist: Include constraint spec, embedding architecture.
End of Consolidated Provisional Pack