Classification: CrownThrive Internal • Trade Secret • Attorney–Client Draft

Scope: End‑to‑end algorithms, scoring formulas, model specs, data schemas, rule DSL, engine logic, proofs/invariants, and implementation guides for all core subsystems powering CHLOM™, LEX, DLA, TLaaS, ADE, ACE, CrownLytics, CrownPulse, FindCliques / NFTCliques / ChainCliques, ThriveSeat, AdLuxe Network, CrownRewards, CrownFluence, Kamora360, NeuralCraft AI Studio, and related services.

Use this document to (a) generate provisional applications; (b) brief counsel; (c) guide engineering builds. Keep off public networks.

0) Architecture at a Glance

• Layer‑1 Metaprotocol: Compliance‑at‑Consensus; custom VM (ComplianceVM), State (License Registry + Audit Log), Privacy (ZK), Execution (Rule DSL).
• Control Plane: TLaaS, LEX, DLA, ADE, ACE, Oracles & Governance Scribes (OGS).
• Data Plane: Transaction Graph, Identity Graph, License State Machine, Payout Graph.
• AI Plane: Risk Scoring, Fraud/Anomaly Detection, Reputation & Sentiment (CrownPulse), Matching (Cliques), Forecasting (AdLuxe & CrownLytics).

Key invariants (must always hold):

• I1 Compliance Finality: A block is final only if every included transaction passes
• I2 Value Conservation: ∑Inputs = ∑Outputs + Fees + Taxes + Insurance + (Escrow Δ) for each settlement window.
• I3 License Liveness: Every active license NFT is either VALID, SUSPENDED, or REVOKED and transitions via authenticated events only.
• I4 Privacy Soundness: Any private predicate used in consensus has a valid ZK proof verified by on‑chain verifier.

1) Rule DSL (Metaprotocol) — ACE/ComplianceVM

Goal: Convert regulations into executable logic.

1.1 Grammar (EBNF)

POLICY      := 'policy' IDENT '{' RULE+ '}'
RULE        := 'require' PREDICATE ('else' ACTION)? ';'
PREDICATE   := COMPARISON | LOGIC
COMPARISON  := FIELD OP VALUE | FUNC '(' ARGS ')' OP VALUE
LOGIC       := PREDICATE (('and'|'or') PREDICATE)+ | 'not' PREDICATE
OP          := '==' | '!=' | '<' | '<=' | '>' | '>=' | 'in' | 'subset'
ACTION      := 'deny' | 'suspend' | 'route_tax' '(' JURIS ',' RATE ')' | 'notify' '(' ROLE ')'
FIELD       := IDENT ('.' IDENT)*

1.2 Example — Export Control

policy EAR_742 {
  require exporter.kyc.level >= 2;
  require not buyer.country in {"IR","KP","SY"} else deny;
  require item.eccn in whitelist else suspend;
  require tx.value_usd <= limits.by_country(buyer.country);
  require zkp_attest("sanctions_clean") == true;
}

1.3 Execution Semantics

• Parse → build AST → compile to ComplianceVM bytecode.
• Deterministic evaluation across validators (pure functions + provided oracle snapshots).
• Side effects limited to

2) Identity & Privacy — ZK + Fingerprint‑Backed DID

2.1 Data Model

• DID
• BioHash v2
• Selective Disclosure

2.2 ZK Circuits (high‑level)

• Proof A — BioMatch: Prove
• Proof B — SanctionsClean: Merkle‑membership proof over compliance dataset snapshot.
• Proof C — LicenseTier: Prove possession of credential with tier ≥ required threshold.

2.3 Identity FSM

NEW → VERIFIED → (VALID | SUSPENDED | REVOKED)
Transitions gated by ZK proofs + DLA authority signature.

2.4 Implementation (pseudocode)

# zk_prove_biomatch(fp_template, biohash, salt): returns proof π
# on-chain verifier verifies vk, π against public inputs

def pre_trade_check(tx):
    assert verify_zk(tx.zk_biomatch)
    assert verify_zk(tx.zk_sanctions)
    assert verify_cred(tx.cred_vc, schema="KYC_TIER")
    run_policy(tx, compiled_policy)
    return True

3) Risk & Compliance Scoring — AI/ML Stack

3.1 Features (canonical)

• KYC/Behavioral: account_age, doc_consistency_score, device_entropy, velocity.
• Graph: pagerank(entity), betweenness(tx_graph), community_overlap(sanction_clusters).
• Transaction: amount_z, geo_distance, time_of_day, method, merchant_mcc.
• Text/Semantic: invoice_ocr_similarity, purpose_embedding_distance.

3.2 Models

• Compliance Risk Score (CRS): Logistic regression with monotonic constraints for auditability.
• Calibration via isotonic regression; bins 0‑1.
• Anomaly Detection: Robust autoencoder + Isolation Forest ensemble; voting threshold τ.
• Fraud Graph: Heterogeneous GNN (HAN) over Entity–Wallet–Device–IP graph; edges typed.
• Document Integrity: Vision Transformer fine‑tuned for tamper cues; output

3.3 Decision Layer

if S_CRS ≥ 0.85 or anomaly_vote≥2: deny
elif 0.65 ≤ S_CRS < 0.85: hold + ZK reverify + human review SLA=2h
else: allow

3.4 Drift & Governance

• PSI > 0.25 or AUC drop > 5% triggers retrain proposal → DLA vote.
• All models signed and registered in Model Registry on-chain (hash + license).

3.5 Training Data Schema (sanitized)

entities(entity_id, did, kyc_tier, risk_segment, created_at)
transactions(tx_id, entity_id, amount, currency, geo, mcc, method, ts, label)
artifacts(tx_id, doc_hash, ocr_text, doc_score)
graph_edges(src_id, dst_id, edge_type, weight, ts)
labels(tx_id, y_fraud, y_violation)

3.6 Evaluation

• Metrics: AUC, PR‑AUC, Recall@FPR=1%, Detected$ per 1k tx, Avg Review Time.
• Fairness: ΔFPR across segments ≤ 2%.

4) ADE — Automated Distribution Engine (Royalties/Tax/Insurance/Escrow)

4.1 Payout Graph

• DAG

4.2 Settlement Formula

P_i(t) = \min\{ cap_i(t), \max\{ floor_i(t), s_i \cdot (A - T - F - E) + adj_i(t) \} \}

• Rounding: Bankers rounding; dust → community pool; determinism ensured by fixed order.

4.3 Tax Routing

T = Σ_j amount * tax_rate(jurisdiction_j, mcc, nexus)
route(T, authority_wallet)

4.4 Yield Allocation (DeFi)

• Risk‑budget optimizer with constraints
• Greedy knapsack by Sharpe proxy when latency‑constrained.

4.5 Smart Contract Skeleton (Solidity‑like)

struct Split {address to; uint32 bps; uint128 cap; uint128 floor;}
function settle(bytes32 txid, Split[] memory s) external verifies(txid) {
  uint256 A = getGross(txid);
  uint256 T = computeTax(txid);
  uint256 base = A - T - fee(txid) - escrow(txid);
  for (Split memory sp: s) {
    uint256 amt = clamp(base * sp.bps / 10000, sp.floor, sp.cap);
    pay(sp.to, amt);
  }
  routeTax(T); emit Settled(txid);
}

5) LEX — License Exchange (Compliance‑Before‑Trade)

5.1 Pre‑Trade Algorithm

def can_trade(a, b, asset):
    assert has_valid_license(a, asset)
    assert has_valid_license(b, asset)
    assert run_policy(asset.policy, a, b)
    assert price_oracle_sane(asset)
    return True

5.2 Order Matching with Compliance Hooks

• Orders enter book only if
• On match,

5.3 Dispute & Rollback

• Challenge window

6) DLA — Decentralized Licensing Authority (Dual DAO)

6.1 Voting

• House‑I (Identity/Consumer): one‑person‑one‑vote via soulbound credential + Proof‑of‑Humanity.
• House‑II (Regulators/Enterprise): stake‑weighted with quadratic cap; slashing for malicious proposals.
• Proposal passes if:

6.2 Governance Scribe

• Append‑only log:

7) Oracles & Scribes (OGS)

7.1 Data Integrity

• Aggregator: median‑of‑n with trimmed 20%; fault tolerance f = ⌊(n−1)/2⌋.
• Attestation: Ed25519 signatures; feed IDs; slash misreporters by bonded stake.

7.2 Snapshotting

• Each policy references oracle snapshot

8) CrownLytics — Analytics & Forecasting

8.1 Heatmaps & Conversion Lift

• Bayesian hierarchical model for multi‑property CTR; partial pooling per property, device, segment.
• Uplift modeling using causal forests for treatment (placement A/B).

8.2 Metrics

• Session‑to‑Book (ThriveSeat), View‑to‑Claim (Locticians), Add‑to‑Cart (XENthrive), Ad Spend ROAS (AdLuxe).

9) CrownPulse — Sentiment & Reputation Index

9.1 Signals

• Text embeddings (domain‑specific), review star trend, dispute rate, refund rate, moderation flags, network centrality.

9.2 Score

S_{Pulse} = w_1 z(emb\_sent) + w_2 z(Δstars) + w_3 z(1-complaint\_rate) + w_4 z(pagerank)

10) Cliques Matching (FindCliques / NFTCliques / ChainCliques)

10.1 Objective

• Maximize mutual‑fit score across purpose, expertise, chain affinity, engagement time, and trust signals.

10.2 Algorithm

• Two‑tower embedding model + cross‑attention reranker.
• Constraint solver to enforce group diversity, time zone coverage, and minimum trust score.

10.3 Score

S_{match} = α\langle u, g \rangle + β\,trust(u) + γ\,compat(u,g) - δ\,overlap(u,g)

11) AdLuxe Network — Bidding & Placement

11.1 Ranking

• eRPM =
• Pacing controller: PID loop to respect daily budgets.

11.2 Creative QC

• Vision/text classifiers for policy violations; OCR rule checks; SHA‑256 dedupe.

12) CrownRewards — Loyalty & Redemption

12.1 Points Engine

• Earn rules via DSL subset: event→points; caps, tiers, decay.
• Redemption smart contract with dynamic exchange rate tied to treasury solvency.

12.2 Anti‑Abuse

• Device entropy + graph link score; cooldowns; negative points for abuse signals.

13) ThriveSeat — Booking & Payments (Stripe Connect Express Ready)

13.1 Availability Engine

• Interval Tree per provider; O( log n ) insert/lookup; atomic reservation with conditional hold TTL.

13.2 Pricing

• Piecewise functions by service + duration + add‑ons; coupon stack validator; tax nexus via ACE.

14) NeuralCraft AI Studio — Usage‑Metered AI

14.1 Quotas & Billing

• Token metering per session; tiered price curves; affiliate splits via ADE.

14.2 Model Ops

• Prompt registry (hash‑addressed); safety filters; audit trail of outputs (hash only, encrypted blob off‑chain).

15) Data Governance, Security & Privacy

• Encryption: PII at rest (AES‑GCM); keys via HSM; envelope encryption.
• Access: ABAC tied to DLA credentials; just‑in‑time grants; dual control for exports.
• Privacy Enhancements: DP training for select models (ε ≤ 4); K‑anonymity for analytics release.

16) Deployment Topology

• Validators: geographically distributed; TEEs optional for private mempool.
• Indexers: read‑only replicas feeding CrownLytics.
• Bridges: light‑client based, message passing with fraud proofs.

17) APIs (Selected)

POST /v1/policy/compile        → {bytecode}
POST /v1/identity/prove        → {proof}
POST /v1/trade/precheck        → {ok, reason}
POST /v1/settlement/execute    → {tx_hash}
GET  /v1/model/registry/:id    → {hash, version, metrics}

18) TLA+ Invariants (sketch)

Invariant ValueConserved == \A tx \in Settlements: SumIn[tx] = SumOut[tx] + Taxes[tx] + Fees[tx] + EscrowDelta[tx]
Invariant LicenseStates   == LicenseState \in {VALID, SUSPENDED, REVOKED}
Invariant ZKSoundness     == \A p \in ZKProofs: Verified(p)

19) Implementation Guides (per subsystem)

19.1 ACE / Rule DSL

• Stack: Rust compiler → WASM → ComplianceVM bytecode.
• Milestones: Grammar parser → bytecode gen → on‑chain verifier tests → 50 canonical policies.

19.2 ADE

• Stack: Solidity/Vyper contracts; TypeScript SDK; graph payouts.
• Tests: Property‑based tests for rounding, caps/floors, vesting; fuzz tax tables.

19.3 LEX/DLA

• Stack: Matching engine in Rust; governance on-chain modules; event sourcing.
• SLA: Match latency < 50ms p95; dispute resolution < 24h.

19.4 AI Models

• Stack: PyTorch/Lightning; Feast feature store; MLflow registry; Kubeflow pipelines.
• Ops: Canary model rollout; shadow eval; backtest harness.

20) Datasets (internal)

• KYC Artifacts: hash pointers only; stored in secure vault.
• Sanctions & Watchlists: periodic snapshots; Merkle roots published by Scribes.
• Behavioral Logs: clickstream, booking, ad events; aggregated to protect privacy.

Note: This section documents schema, not raw PII.

21) Compliance & Legal Notes

• Map each model/engine to provisional filing(s) and white/black/gold paper references.
• Attach figure set: Architecture, Transaction Lifecycle, TLaaS/LEX/DLA flows, ADE map, Privacy/DAO, Metaprotocol stack.

22) Test Vectors (samples)

{
  "tx": {
    "amount": 1500.00,
    "currency": "USD",
    "mcc": "5812",
    "buyer_country": "US",
    "zk": {"biomatch": "0xabc...", "sanctions": "0xdef..."}
  },
  "policy": "policy SALES_US { require tx.amount <= 2000; require zkp_attest(\"sanctions_clean\"); }"
}

23) Rollout Plan (90‑Day)

• Days 0‑30: ACE compiler, ComplianceVM MVP, ADE v0, Identity ZK circuit stubs.
• Days 31‑60: LEX pre‑trade hooks, Model Registry, Risk score v1, Oracles.
• Days 61‑90: DLA governance alpha, Full ZK verifier, Yield router, Bridges PoC.

24) What to File (Provisional Index)

1. CHLOM Layer‑1 Metaprotocol & ComplianceVM
2. TLaaS tokenized licensing, License Registry FSM
3. LEX compliance‑before‑trade, atomic ADE settlement
4. DLA dual‑house governance + Governance Scribe
5. ADE payout graph + tax/yield optimizer
6. Identity ZK proofs + BioHash + selective disclosure
7. Risk/Anomaly/Fraud ML stack & governance triggers
8. Oracles & Scribes aggregation + slashing
9. CrownLytics uplift modeling; CrownPulse reputation index
10. Cliques matching two‑tower + constraints

Appendix A — Mathematical Details

• Isotonic Calibration: piecewise constant mapping optimizing mean squared error of probabilities.
• Sharpe Proxy: with robust estimators (Huber).
• Graph Regularization: .

Appendix B — Security Proof Sketches

• Oracle Safety: With ≥ 2f+1 honest feeds, trimmed mean resists ≤ f Byzantine reporters.
• Value Conservation: ADE settlement is a pure function of inputs; unit tests + formal spec ensure equality.

Appendix C — Data Protection Patterns

• Data minimization; purpose binding; encrypted learning with DP for high‑risk cohorts.

Appendix D — API/ABI Tables

• Function selectors, event topics, role requirements.

End of Document

Provisional Patent Application Draft — ADE (Automated Distribution Engine)

Title: Automated Multi‑Party Royalty, Tax, Insurance, and Escrow Distribution Engine with Deterministic Settlement and Yield Routing

I. Field of the Invention

Financial technology; programmable payments; compliance automation; blockchain smart‑contract settlement systems.

II. Background

Conventional payout systems rely on asynchronous processors, opaque rules, and manual tax workflows. They cannot guarantee value conservation under caps/floors/vesting constraints, nor deterministic behavior across distributed validators.

III. Summary of the Invention

An on‑chain engine (ADE) that transforms a payout graph and transaction attributes into deterministic settlements that route royalties, taxes, insurance, and escrow, with optional time‑based vesting and risk‑bounded yield allocation. The engine exposes: (i) a settlement function that guarantees value conservation and order‑independence; (ii) a tax router with jurisdictional logic; (iii) a yield allocator honoring risk budgets; (iv) audit‑ready events and Merkle‑anchored reports.

IV. Brief Description of Drawings

• FIG. ADE‑1: Payout Graph model and settlement flow.
• FIG. ADE‑2: Tax routing pipeline (jurisdiction → nexus → rate table → authority wallet).
• FIG. ADE‑3: Yield router with risk guardrails and oracle feeds.
• FIG. ADE‑4: Vesting state machine (grant → claim → revoke) with cliff/linear formulas.

V. Detailed Description

1) Data Structures

Node: {id, type in {party, tax, insurer, escrow, pool}, wallet}
Edge: {from, to, split_bps, cap, floor, vesting_id?}
Tx:   {txid, gross_amount, currency, mcc, geo, time, policy_ref}
Vesting: {id, cliff_ts, rate_per_sec, start_ts, end_ts}

2) Settlement Function

For base B = A − T − fee − escrow, pay each edge share with clamps:

amt_i = clamp(B * split_bps_i/10000 + adj_i(t), floor_i, cap_i)

Invariant: sum(amt_i) + T + fee + escrow == A (up to rounding rules). Rounding uses bankers rounding; residual dust sent to community pool with emitted reason code.

3) Tax Router

rate = rate_table.lookup(jurisdiction(tx), mcc, nexus(entity))
T    = A * rate
route(T, authority_wallet)

Tables are versioned; each settlement records {table_hash, jurisdiction_path}.

4) Yield Allocation (Optional)

Let candidates Y = {pool_k} with expected return mu_k, stdev sigma_k, liquidity L_k.

maximize sum(w_k * mu_k)
subject to  sum(w_k) = 1,
            VaR95(portfolio) <= kappa,
            w_k <= L_k

When latency‑constrained, use greedy by mu_k/sigma_k and reject pools with oracle freshness > Delta.

5) Contract Interface (excerpt)

function settle(bytes32 txid, Graph g, Meta m) returns (bool)
function registerRateTable(bytes32 hash)
function setJurisdiction(address entity, code)
function vest(address to, uint128 amount, Vesting v)

6) Audit & Reports

• Per‑tx Settled event, per‑epoch EpochRoot(root_hash) of CSV digest.
• Off‑chain exporter regenerates CSVs that hash to root_hash.

7) Security & Failure Modes

• Reentrancy‑safe payments; pull‑based claims for vesting.
• Oracle slippage guard; min liquidity; circuit breaker pause_settlement().

VI. Alternative Embodiments

Permissioned chain module; fiat rails via custodial adapters; batch settlement per epoch.

VII. Advantages

Determinism, auditability, compliance‑ready tax routing, risk‑bounded yields, and modular vesting, all at consensus speed.

VIII. Filing Checklist

Include payout graph examples, rate tables (sanitized), test vectors, and proofs of invariants.

Provisional Patent Application Draft — AI Compliance & Risk Stack

Title: Interpretable Risk Scoring and Anomaly Detection for Compliance‑Before‑Trade with Graph‑Aware Fraud Modeling and ZK‑Verified Inputs

I. Field of the Invention

Machine learning for compliance, fraud detection, and risk scoring integrated with blockchain settlement and ZK‑verified credentials.

II. Background

Black‑box models hinder auditability, fairness, and regulatory acceptance. Systems lack strong governance and privacy‑preserving proofs of inputs.

III. Summary of the Invention

A hybrid ML framework combining (i) calibrated logistic models with monotonic constraints for primary Compliance Risk Score (CRS); (ii) deep anomaly detectors; (iii) heterogeneous graph neural networks; (iv) document integrity models; (v) governance triggers bound to distribution drift; and (vi) ZK proofs that inputs (identity tier, sanctions status) are valid without revealing PII.

IV. Brief Description of Drawings

• FIG. CRS‑1: Feature flows into CRS, Anomaly Ensemble, GNN; decision policy.
• FIG. CRS‑2: Drift monitor → governance proposal → model registry update.
• FIG. CRS‑3: ZK‑verified inputs path from identity vault to scorer.

V. Detailed Description

1) Feature Store

Canonical feature groups with point‑in‑time correctness; backfill via event time. Examples: device_entropy, velocity_1h/24h, geo_distance_km, tx_graph_pagerank, ocr_consistency.

2) Models & Formulas

CRS:

S_CRS = sigmoid(w^T x + b)

with isotonic calibration. Anomaly Ensemble: IsolationForest + RobustAutoEncoder; vote if two or more detectors exceed threshold. Fraud GNN: Heterogeneous graph network on typed graph E={entity,wallet,device,ip}; loss L = BCE + lambda * graph_regularizer. Doc Integrity: Vision Transformer detecting manipulations; score doc_score in [0,1].

3) Decision Policy

if S_CRS >= theta_high or anomaly_vote: DENY
elif theta_mid <= S_CRS < theta_high: HOLD + ZK reverification + human review
else: ALLOW

Thresholds set to meet target FPR and SLA.

4) Governance & Registry

• Models are versioned (hash, metrics, data_window, fairness_report) and anchored on‑chain.
• Drift triggers (PSI, AUC drop) auto‑propose retrain; DLA dual‑house vote.

5) Training & Evaluation

• Train/val/test temporal split; leakage guards; nested CV for hyperparams.
• Fairness: delta‑FPR and delta‑TPR across cohorts <= 2%.

6) Privacy and ZK Inputs

Use BBS+ credentials; ZK circuits prove kyc_tier >= k and sanction‑cleared membership without exposing raw PII.

7) Interfaces

POST /risk/score {entity_id, tx_id} -> {score, reasons[], version}
POST /risk/explain {tx_id}           -> {top_contributors, SHAP_like}
GET  /risk/registry/:hash            -> {metrics}

8) Deployment

Online scorer p95 < 50 ms; feature freshness < 5 s; shadow evaluation path for new models.

VI. Alternative Embodiments

Fully local scoring in TEEs; permissioned deployments; federated learning for multi‑institution training.

VII. Advantages

Audit‑ready, privacy‑preserving, governable risk stack aligned with compliance‑before‑trade settlement.

VIII. Filing Checklist

Include feature schema, calibration charts, drift triggers, model cards, and ZK input proofs.