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Filing Information
| Field | Detail |
|---|
| Title of Invention | Systems and Methods for Non-Custodial Institutional Digital Asset Settlement with Integrated Protection Coverage, Multi-Jurisdictional Validator Consensus, Entity-Level Adversarial Trade Detection, Compliance-Gated Token Deployment, Self-Healing Smart Contract Lifecycle Management, Autonomous Fleet Monitoring, and Predictive Liquidity Management |
| Applicant/Assignee | JIL Sovereign Holdings, LLC (Wyoming) |
| Correspondence Address | 661 East Main Street, Suite 200-245, Midlothian, TX 76065 |
| Filing Type | Provisional Patent Application under 35 U.S.C. §111(b) |
| Priority Date | February 1, 2026 |
| Number of Claims | 48 Claims (10 Independent, 38 Dependent) |
| Deadline | Non-provisional or PCT filing required by February 1, 2027 |
Summary: 48 Claims (10 Independent + 38 Dependent)
Each independent claim protects a novel combination of techniques. Dependent claims protect specific sub-innovations within each system.
Individual components (MPC, BFT, LSTM, hysteresis) exist in prior art. What is patentable is the specific integration that no existing system implements.
Custody & Coverage (Claim A)
MPC 2-of-3 threshold signing where the user holds the primary shard, combined with automatic coverage payout triggered by validator consensus -- no manual claims.
Bridge & Settlement (Claim B)
14-of-20 BFT validators across 13+ legal jurisdictions with zone-partitioned P2P settlement and fail-closed compliance gates.
Validator Security (Claims C, D)
7-gate bootstrap requiring code integrity before identity verification, plus dual-policy fleet remediation that overrides quorum for security threats.
Anti-Whale Defense (Claim E)
Entity-level toxicity scoring across 5 behavioral dimensions with sybil cluster detection and retroactive score attribution defeating clean-wallet evasion.
Compliance (Claims F, G)
Ledger-level compliance with ATCE anti-concentration controls, plus KYB-gated token factory embedding immutable compliance rules in every transfer.
Smart Contracts (Claim H)
Complete on-chain self-healing lifecycle: detect anomaly, auto-pause, propose fix, auto-approve known patterns or vote on novel ones, timelock, execute, resume.
Fleet Intelligence (Claim I)
AI fleet inspector with fail-open heartbeat telemetry, stateful observation windows with security exception override, and trend-aware composite threat scoring.
Predictive Finance (Claim J)
LSTM-driven liquidity forecasting with tax-optimized position selection, gradual TWAP execution with volatility-aware scheduling, and self-correcting allocation rebalancing.
Detailed Technical Claims
Full technical deep-dives for each patented innovation, with architecture diagrams, competitive analysis, and implementation specifications.
Claim A: Non-Custodial Threshold Signing with Integrated Protection Coverage
Custody & Coverage
Strongest
The Problem
Existing MPC custody platforms (Fireblocks, ZenGo, Coinbase) distribute key shares but retain operational control: the platform can freeze assets, the user cannot transact without platform cooperation, and loss coverage requires separate insurance policies with manual claims taking weeks to months. No system combines user-primary signing with automatic coverage payout.
The Innovation
Three elements combined for the first time: (1) User-primary shard model -- the user holds Shard A on their device (biometric/TEE secured), the platform holds Shard B in an HSM in a different jurisdiction, Shard C is in cold storage in a third jurisdiction. The user's shard is required for every transaction -- the platform cannot sign alone. (2) Protocol-embedded coverage payout -- when 14-of-20 validators attest to a qualifying loss event, coverage payout executes automatically through consensus with no manual claims filing. (3) Cross-jurisdictional shard distribution -- no single legal system can compel access to 2-of-3 shards.
Prior art differentiation: Fireblocks MPC-CMP (2019) -- platform retains operational authority. ZenGo 2-of-2 (2019) -- no recovery shard. BitGo multi-sig (2013) -- separate insurance with manual claims. Shamir SSS (1979) -- mathematical primitive only. None combines user-primary signing + automatic coverage + jurisdictional shard distribution.
Business Justification & Benefit
Market gap: Institutional investors require both self-custody (regulatory trend post-FTX) and loss protection (fiduciary requirement). Today these are mutually exclusive -- you either surrender keys for custody insurance, or self-custody with zero protection.
Revenue model: 100 bps/year on AUM for Premium tier ($250K+ coverage), creating recurring revenue scaled to assets under management. At $1B AUM, this generates $10M/year in coverage premium revenue alone.
Competitive moat: Patent prevents competitors from offering the same user-primary + auto-coverage combination. Any competitor must either require platform-primary signing (inferior UX) or offer coverage as a separate product (inferior integration).
Independent Claim 1: A computer-implemented system for non-custodial digital asset custody comprising: a threshold signing protocol distributing cryptographic key shares across three independent legal jurisdictions where a user-held primary shard is required for every transaction; a coverage payout mechanism triggered by validator consensus attestation of qualifying loss events without manual claims filing; and a signing session manager that aggregates partial Schnorr signatures from at least two of three shard holders using Lagrange interpolation to produce a standard single-signer-indistinguishable signature.
Dependent Claims (2-5)
Claim 2. The system of claim 1, wherein the signing session manager generates partial Schnorr signatures using nonce commitments exchanged in a two-round protocol, and aggregates them via Lagrange interpolation coefficients computed from participant indices to produce a single-signer-indistinguishable signature verifiable against the group public key.
Claim 3. The system of claim 1, wherein the coverage payout mechanism requires attestation from at least 14 of 20 validators independently confirming a qualifying loss event, with coverage amount determined by tier ($250K+ per incident) and payout executed automatically within 24 hours.
Claim 4. The system of claim 1, further comprising a WebAuthn authentication layer wherein transaction signing requires biometric verification via passkey (P-256 ECDSA assertion) before threshold signing session initialization.
Claim 5. The system of claim 1, wherein the three independent legal jurisdictions comprise: user device in user's jurisdiction (Shard A), platform HSM in Switzerland (Shard B), and AES-256-GCM encrypted cold storage in Singapore (Shard C), ensuring no single legal system can compel access to 2-of-3 shards.
Claim B: Multi-Jurisdictional Validator Bridge with Zone-Authorized P2P Settlement
Bridge & Cross-Chain
Strongest
The Problem
Cross-chain bridges are the highest-value attack surface in blockchain: Ronin ($625M, 5-of-9 where one entity controlled 5 keys), Wormhole ($320M), Nomad ($190M). Common failures: insufficient validator diversity, no jurisdictional distribution, no compliance-zone partitioning, fail-open compliance.
The Innovation
Three novel elements: (1) Regulatory jurisdiction diversity as a consensus requirement -- 14-of-20 BFT threshold distributed across 13+ independent legal jurisdictions (CH/FINMA, US/FinCEN, SG/MAS, GB/FCA, DE/BaFin, JP/JFSA, AE/FSRA, EU/ESMA, BR/CVM). No single regulator can compel a majority. (2) Zone-authorized settlement partitioning -- each validator subscribes only to settlement topics for its authorized compliance zones. A DE_BAFIN validator cannot process SG_MAS settlements. (3) Fail-closed compliance gates -- when the compliance service is unreachable, settlement messages are REJECTED, not approved.
Prior art differentiation: Cosmos IBC (2019) -- relayer-based, no BFT committee, no zone authorization. Wormhole (2021) -- 13-of-19 guardians but no jurisdictional diversity or zone partitioning. Polkadot (2020) -- GRANDPA/BABE consensus, no per-zone model. No existing bridge combines jurisdictional diversity + zone-authorized settlement + fail-closed compliance.
Business Justification & Benefit
Market gap: Institutional bridge users (exchanges, funds, custodians) require regulatory compliance and cannot use bridges where a single jurisdiction could compel key compromise. The $2T+ cross-chain market has no bridge meeting institutional compliance requirements.
Revenue model: 4 bps settlement fee on all bridged volume. At $100M daily bridged volume, this generates $14.6M/year in settlement fees.
Competitive moat: Replicating the 13-jurisdiction validator network requires establishing legal entities, regulatory relationships, and validator infrastructure across 13 countries. The patent protects the specific zone-authorized settlement architecture that makes this viable.
Independent Claim 2: A computer-implemented system for cross-chain digital asset bridging comprising: a Byzantine fault tolerant validator set distributed across at least thirteen independent regulatory jurisdictions with a supermajority threshold; a zone-partitioned settlement processing architecture where each validator subscribes exclusively to settlement message topics corresponding to its cryptographically authorized compliance zones; and a fail-closed compliance evaluation gate that rejects settlement messages when the compliance evaluation service is unreachable.
Dependent Claims (3-6)
Claim 3. The system of claim 2, wherein the supermajority threshold is computed as ceil(TOTAL_VALIDATORS * 0.70), requiring at least 14 of 20 validators, each independently verifying withdrawals on the source chain before signing with Ed25519.
Claim 4. The system of claim 2, wherein zone authorization is verified during validator bootstrap and enforced at the message consumption layer via cryptographic zone subscription tokens.
Claim 5. The system of claim 2, wherein the compliance evaluation gate applies a 3-gate sequential pipeline per message: validator signature verification, zone authorization check, and full compliance check, with 8 distinct permanent rejection patterns distinguished from transient errors eligible for exponential backoff retry.
Claim 6. The system of claim 2, further comprising manual offset commit management wherein message offsets are committed only after database persistence is confirmed, preventing message loss on crash between processing and commit.
Claim C: Multi-Gate Cryptographic Validator Bootstrap with Integrity-First Sequencing
Cryptography & Security
Strong
The Problem
Existing validator bootstrap protocols (Kubernetes node join, AWS SSM, Tendermint) verify identity before verifying code integrity. This allows a node running tampered software to authenticate successfully and participate in consensus with compromised code.
The Innovation
A 7-gate ordered bootstrap protocol where code integrity verification (Gate 3: image digest matching against pinned manifest) is a mandatory prerequisite before identity verification (Gate 4: 5-key-type challenge-response) can begin. Combined with 24-hour consensus authorization tokens (Gate 5) forcing daily re-verification of both integrity and identity. A node with tampered images cannot even attempt authentication.
Prior art differentiation: Kubernetes bootstrap -- identity first, no image verification gate. Docker Content Trust (2015) -- independent of node authentication, not sequenced. AWS SSM -- IAM role verification, no code integrity gate. Tendermint -- staking tx, no pre-auth verification. No existing protocol mandates code integrity before identity verification.
Business Justification & Benefit
Security value: Prevents supply-chain attacks where a compromised node binary authenticates and joins consensus. In a $1B+ bridge network, a single compromised validator could enable theft of bridged assets.
Defensive patent: Prevents competitors from claiming this specific protocol ordering. Forces alternative bootstrap designs that are inherently less secure (identity-first leaves a window where compromised code can participate).
Independent Claim 3: A computer-implemented method for bootstrapping a validator node in a distributed network, the method comprising an ordered sequence of gates wherein: a code integrity verification gate comparing local container image digests against a centrally pinned manifest is executed as a mandatory prerequisite before an identity verification gate using multi-key-type challenge-response authentication; and a time-limited consensus authorization token with a maximum 24-hour validity period is issued upon successful completion of both gates, requiring daily re-execution of the integrity and identity verification sequence.
Dependent Claims (8-10)
Claim 8. The method of claim 7, wherein the code integrity verification gate computes SHA-256 digests for each of 17+ container images and compares each against a centrally pinned manifest maintained in a hq_image_digests table, with any single mismatch halting bootstrap.
Claim 9. The method of claim 7, wherein the identity verification gate uses a 5-key-type challenge-response protocol comprising: ed25519 consensus key signing, HMAC-SHA256 shared secret computation, API key verification, SSH key verification, and HSM attestation.
Claim 10. The method of claim 7, further comprising a configuration bundle gate wherein the validator pulls a configuration bundle that is HMAC-SHA256 signed by the fleet controller, with hash validation prior to service initialization.
Claim D: Autonomous Fleet Monitoring with Quorum-Protected Dual-Policy Remediation
Fleet Management
Strong
The Problem
Automated remediation systems face a fundamental tension: acting on threats risks cascading failures (taking too many nodes offline), while not acting risks undetected compromise. All existing systems use a single policy -- either always respect availability (missing security threats) or always prioritize security (risking availability cascades).
The Innovation
A dual-policy remediation model: (1) For operational issues (container down, performance degradation), auto-remediation is permitted ONLY IF the action would not reduce healthy nodes below quorum minimum: max(7, ceil(total * 0.70)). (2) For cryptographic integrity violations (image digest mismatch = possible tampering), auto-remediation overrides quorum protection and executes immediately. A compromised node inside the network is a greater threat than the availability cost of removing it.
Prior art differentiation: Prometheus + Alertmanager (2012) -- no auto-remediation, no quorum awareness. Kubernetes PDB (2017) -- single policy, always respects budget. AWS Auto Scaling (2009) -- no composite threat model, no category-dependent policy. No system implements different quorum policies for different threat categories.
Business Justification & Benefit
Operational value: Reduces incident response time from hours (human-in-the-loop) to seconds (autonomous) for 80% of fleet issues while preventing cascading failures that would take the entire network offline.
Security value: Image tampering (supply-chain attack) triggers immediate node isolation regardless of quorum impact. This is critical for a network securing $1B+ in bridged assets -- a tampered validator could sign fraudulent withdrawals.
Independent Claim 4: A computer-implemented system for autonomous fleet monitoring and remediation comprising: a threat scoring engine evaluating a configurable set of rules across security, performance, availability, and fleet categories to produce per-node composite threat scores; and a dual-policy remediation controller wherein: for operational threat categories, auto-remediation is permitted only when the resulting healthy node count would remain at or above a quorum minimum; and for cryptographic integrity threat categories, auto-remediation overrides the quorum minimum and executes immediately.
Dependent Claims (12-15)
Claim 12. The system of claim 11, wherein the threat scoring engine evaluates 20 configurable rules across four categories: security (digest mismatch, key expiry), performance (CPU, memory, latency), availability (container health, service uptime), and fleet (version drift, config divergence).
Claim 13. The system of claim 11, wherein the quorum minimum is computed as max(7, ceil(total_validators * 0.70)), and auto-remediation for operational threats is blocked when the resulting healthy count would fall below this minimum.
Claim 14. The system of claim 11, further comprising stateful observation windows maintaining per-rule, per-node trigger counters, requiring 3 consecutive 60-second inspection cycles of sustained anomaly before generating a remediation recommendation, with counter reset on non-triggering cycles.
Claim 15. The system of claim 11, further comprising multi-level rate limiting on remediation actions: per-node cooldown, per-action-type burst limit, and global fleet-wide action cap per inspection cycle.
Claim E: Entity-Level Toxicity Scoring with Sybil Cluster Detection
Anti-Whale & Pump/Dump Defense
Strong
The Problem
DEX liquidity providers lose billions annually to adversarial traders who extract value through informational advantages, coordinated manipulation, and sybil attacks. All existing protections (per-wallet rate limits, MEV protection, sandwich detection) operate at the wallet level and are trivially defeated by creating new wallets. No system evaluates traders at the entity level or detects correlated wallets as a single actor.
The Innovation
Three novel elements: (1) Five-dimensional entity-level toxicity scoring computed over a 7-day rolling window: edge extraction vs. mid-market (30% weight), win rate excess (20%), one-way flow ratio (20%), inventory skew trend (15%), and LP spread capture (15%). (2) Four-signal sybil cluster detection: temporal correlation (synchronized submissions), directional correlation, funding chain analysis, and behavioral fingerprinting. Identified clusters are treated as a single entity. (3) Retroactive cluster attribution: when a new wallet is linked to an existing cluster, all historical trades are reassigned and the entity score is recalculated with merged data -- defeating "clean wallet" evasion instantly.
Prior art differentiation: Flashbots MEV-Share (2023) -- wallet-level sandwich protection only. CowSwap (2021) -- batch auctions, no toxicity scoring. Chainalysis Reactor (2019) -- off-chain analytics, not real-time enforcement. Per-wallet rate limiting -- trivially defeated by new wallets. No DEX implements entity-level scoring with sybil detection driving real-time execution routing.
Business Justification & Benefit
LP protection: Liquidity providers on Uniswap and other DEXs lose an estimated 20-50% of their fee income to toxic flow. Entity-level detection prevents the majority of adversarial extraction, making LP positions significantly more profitable and attracting deeper liquidity.
Whale defense: Prevents coordinated pump-and-dump schemes where multiple wallets controlled by the same entity accumulate positions, pump the price, then dump. Sybil detection treats the entire cluster as one actor, enforcing aggregate position limits and toxicity scoring.
Competitive moat: The retroactive attribution mechanism is technically difficult to replicate and creates an ever-improving detection capability -- the longer the system runs, the larger its cluster graph and the harder evasion becomes.
Independent Claim 5: A computer-implemented system for detecting and mitigating adversarial trading in a digital asset exchange comprising: an entity-level toxicity scoring engine that computes a composite score across at least five behavioral dimensions including edge extraction relative to mid-market price, win rate excess above random baseline, one-way flow ratio, cumulative inventory skew trend, and LP spread capture frequency, each computed over a rolling time window with exponential decay weighting; a sybil cluster detection algorithm that identifies correlated wallets as belonging to a single trading entity via at least temporal correlation, directional correlation, funding chain analysis, and behavioral fingerprinting; a retroactive cluster attribution mechanism that, upon detection of a new wallet belonging to an existing cluster, reassigns all historical trades of the new wallet to the cluster entity and recalculates the composite toxicity score with the merged data; and an adaptive enforcement system that applies graduated trading restrictions based on the composite score.
Dependent Claims (17-21)
Claim 17. The system of claim 16, wherein the five behavioral dimensions are weighted: edge extraction (30%), win rate excess (20%), one-way flow ratio (20%), inventory skew trend (15%), and LP spread capture (15%), with configurable weight adjustment.
Claim 18. The system of claim 16, wherein the sybil cluster detection uses a union-find data structure requiring at least 2 of 4 correlation signals (temporal, directional, funding, behavioral) to merge wallets into a cluster entity.
Claim 19. The system of claim 16, wherein the adaptive enforcement system applies four graduated tiers: LOW (monitoring only), MODERATE (spread widening), HIGH (position size reduction + RFQ-only routing), CRITICAL (complete blocking).
Claim 20. The system of claim 16, wherein the rolling time window uses exponential decay weighting over a 7-day period, giving higher weight to recent trading behavior while retaining historical context.
Claim 21. The system of claim 16, wherein the retroactive cluster attribution, upon merging a new wallet, recalculates the entity score using the combined trade history, instantly defeating clean-wallet evasion strategies.
Claim F: Ledger-Level Compliance Enforcement with Anti-Concentration Controls
Compliance & Regulation
Moderate
The Problem
Compliance in digital assets is typically bolted-on via external services that can be bypassed, disabled, or fail-open. Token vesting schedules prevent immediate selling but enforcement is contractual (not technical) -- a holder can violate terms and face only legal consequences after the fact.
The Innovation
(1) Compliance as a mandatory transaction gate -- every transaction passes through an 8-check pipeline (sanctions, jurisdiction, asset, corridor, velocity, amount, zone, ATCE). If unreachable, the transaction is REJECTED (fail-closed). (2) 10 zone-specific compliance configurations (CH/FINMA, US/FinCEN, SG/MAS, etc.) with distinct limits, sanctions lists, and corridor rules. (3) ATCE (Anti-Token Concentration Exploitation): after a vesting unlock, a daily sell limit (10% of unlocked amount per day) is technically enforced for 30 days, preventing large holders from dumping their entire position immediately.
Business Justification & Benefit
Regulatory requirement: MiCA (EU), MAS (Singapore), and other regulatory frameworks increasingly require transaction-level compliance enforcement. Platforms without ledger-level compliance face regulatory exclusion from key markets.
Token price stability: ATCE prevents the "vesting cliff dump" pattern that destroys token value. For JIL's $10B token supply, uncontrolled vesting dumps could crater price 30-50% in a single day. ATCE limits this to ~3% daily maximum impact.
Independent Claim 6: A computer-implemented method for enforcing transaction compliance comprising: a mandatory compliance evaluation gate in a transaction processing pipeline that rejects transactions when the compliance evaluation service is unreachable; zone-specific compliance configurations corresponding to distinct regulatory jurisdictions applied based on transaction origin; and an anti-token-concentration enforcement mechanism that, following a vesting unlock event, applies a percentage-based daily sell limit for a configurable enforcement window, rejecting sell transactions that would exceed the remaining daily quota.
Dependent Claims (23-25)
Claim 23. The method of claim 22, wherein the compliance evaluation gate applies 8 sequential checks: sanctions screening, jurisdiction validation, asset class check, corridor authorization, velocity limit, amount threshold, zone compliance, and ATCE anti-concentration check.
Claim 24. The method of claim 22, wherein the anti-token-concentration mechanism enforces a 10% daily sell limit of unlocked tokens for a 30-day enforcement window following each vesting unlock event, with daily quota tracking and automatic window expiration.
Claim 25. The method of claim 22, wherein zone-specific configurations for 10+ regulatory jurisdictions (CH/FINMA, US/FinCEN, SG/MAS, GB/FCA, DE/BaFin, JP/JFSA, AE/FSRA, EU/ESMA, BR/CVM, FATF) define distinct transaction limits, velocity caps, and travel rule thresholds.
Claim G: Compliance-Gated Token Deployment with Immutable Transfer-Level Policy
Token Factory & Smart Contracts
Strong
The Problem
Token creation on existing platforms (ERC-20, SPL) is permissionless: anyone can deploy with no identity verification and no mandatory compliance rules. Compliance is bolted-on via external contracts that can be bypassed. Fraudulent tokens (rug pulls, unregistered securities) cannot be recalled once circulating.
The Innovation
(1) KYB-gated deployment -- token creation requires verified Know Your Business credentials. No anonymous entity can create a token. (2) Immutable compliance in every transfer -- five rules set at creation and enforced inside do_transfer() on every call: zone restrictions, transferability flag, humanity fee (basis-point deduction routed to designated pool), account freezing, and certification ID. These cannot be disabled or upgraded away. (3) Cooldown activation -- tokens enter Pending state for 24 hours (1 hour fast-track for certified issuers) before becoming Active, preventing immediate post-deployment pump-and-dump.
Prior art differentiation: ERC-20 (2015) -- no compliance hooks, permissionless deployment. ERC-1400 (2018) -- optional compliance modules, not immutable. Token factories (TokenMint) -- no KYB, no embedded compliance. OpenZeppelin Pausable -- opt-in per contract. No platform combines KYB-gated deployment + immutable transfer-level compliance + cooldown activation.
Business Justification & Benefit
Fraud prevention: Eliminates rug pulls at the deployment layer. In 2023, crypto rug pulls caused $2.6B in losses. KYB-gated deployment with cooldown activation prevents the entire attack class.
Revenue model: Humanity fee (configurable per token, default 10 bps) on every transfer creates a continuous social impact revenue stream. At $1B daily transfer volume across all JIL20 tokens, this generates $365K/year per basis point.
Regulatory advantage: Tokens launched on JIL are born compliant -- zone restrictions, KYB verification, and transfer-level rules are embedded from day one, satisfying MiCA tokenization requirements without additional infrastructure.
Independent Claim 7: A computer-implemented system for compliance-gated token deployment comprising: a token factory that requires verified Know Your Business (KYB) credentials for the issuing entity as a prerequisite for token creation; immutable compliance parameters embedded in the token contract at deployment including geographic zone restrictions, transferability controls, and a mandatory per-transfer fee deduction routed to a designated pool, wherein said compliance parameters are enforced within the token's core transfer function on every transfer call without exception; and a cooldown-based activation mechanism wherein newly deployed tokens enter a pending state and transition to active status only after a configurable delay period, combined with per-issuer rate limiting on deployment frequency.
Dependent Claims (27-29)
Claim 27. The system of claim 26, wherein the KYB verification requires verified business entity credentials including legal name, jurisdiction of incorporation, and authorized representative identity as a mandatory prerequisite before the token factory processes a deployment request.
Claim 28. The system of claim 26, wherein the cooldown-based activation enforces a 24-hour standard delay for unverified issuers and a 1-hour fast-track delay for certified issuers, combined with per-issuer rate limiting of maximum 10 deployments per day.
Claim 29. The system of claim 26, wherein token identifiers are generated via blake3 cryptographic hash of deployment parameters, ensuring deterministic and collision-resistant token identification.
Claim H: Self-Healing Smart Contract Lifecycle Management
Smart Contracts & Security
Strongest
The Problem
Smart contract vulnerabilities cause catastrophic losses ($3.8B in 2022). Current response is fragmented: Forta detects, Pausable pauses, proxies upgrade, Governor votes. No system manages the complete lifecycle -- detect, pause, diagnose, fix, approve, execute, resume -- through coordinated on-chain contracts with a unified state machine.
The Innovation
A complete on-chain healing lifecycle through two coordinated contracts: (1) On-chain anomaly registry with 6-state machine (NOT_ENROLLED → ACTIVE → MONITORING → EMERGENCY_PAUSED → HEALING → RECOVERED) and 8 anomaly types. Four critical types (reentrancy, flash loan, oracle manipulation, invariant violation) trigger automatic emergency pause without human intervention. Gas baseline monitoring with 1.5x threshold triggers alerts. (2) On-chain fix governance with 7 vulnerability classes and pre-validated fix templates. Known patterns (reentrancy guard, safe math, access control) are auto-approved without voting. Novel patterns require 3+ voter approvals, 24-hour voting period, 1-hour timelock, then execution. Admin veto provides emergency override.
Prior art differentiation: Forta (2021) -- detects, doesn't remediate. Pausable (2017) -- single toggle, no detection, no healing. UUPS Proxy (2019) -- upgradeable, no detection or governance. Governor (2021) -- voting, no detection or auto-pause. No system manages the complete on-chain vulnerability lifecycle from detection through healing.
Business Justification & Benefit
Asset protection: Reduces exploit response time from hours/days (manual Forta alert → human review → governance proposal → timelock → fix) to seconds (auto-detect → auto-pause → auto-approve known fix → timelock → execute). In the 2022 Euler Finance hack ($197M), the exploit window was 3+ hours. Auto-pause would have limited losses to the first transaction.
Insurance qualification: On-chain self-healing with auditable state transitions satisfies the "automated risk mitigation" requirement increasingly demanded by crypto insurance underwriters. Platforms with provable self-healing qualify for 40-60% lower premiums.
Developer ecosystem: Template-based auto-approval for known patterns (reentrancy, overflow, access control) means that 70%+ of vulnerabilities are fixed in minutes, not days. This makes building on JIL significantly safer than any competing L1.
Independent Claim 8: A computer-implemented system for self-healing smart contract lifecycle management comprising: an on-chain anomaly registry that tracks enrolled smart contracts through a multi-state lifecycle including active, monitoring, emergency-paused, healing, and recovered states, with automatic emergency pause triggered by detection of critical vulnerability classes including reentrancy, flash loan attacks, oracle manipulation, and invariant violations; an on-chain fix governance protocol comprising pre-validated fix templates for known vulnerability classes that receive automatic approval without voting and a voting-based approval process for novel vulnerability patterns requiring a minimum number of approvals within a voting period followed by a mandatory timelock before execution; and a gas anomaly detection mechanism using an exponential moving average baseline with automatic anomaly reporting when measured gas consumption exceeds a configurable multiplier of the baseline.
Dependent Claims (31-34)
Claim 31. The system of claim 30, wherein the on-chain anomaly registry supports 8 anomaly types, with 4 critical types (reentrancy, flash loan, oracle manipulation, invariant violation) triggering automatic emergency pause without human intervention.
Claim 32. The system of claim 30, wherein the fix governance protocol maintains pre-validated fix templates for 7 known vulnerability classes (reentrancy guard, safe math, access control, oracle validation, flash loan defense, invariant restoration, gas optimization) that receive automatic approval without voting.
Claim 33. The system of claim 30, wherein the gas anomaly detection uses an exponential moving average baseline with a configurable multiplier threshold (default 1.5x), triggering automatic anomaly reporting when measured consumption exceeds the threshold.
Claim 34. The system of claim 30, wherein novel vulnerability patterns require a minimum of 3 voter approvals within a 24-hour voting period, followed by a mandatory 1-hour timelock before execution, with admin veto capability at any stage.
Claim I: Autonomous Validator Fleet Monitoring with Fail-Open Telemetry and Trend-Aware Scoring
AI Fleet Inspector
Strong
The Problem
Distributed validator networks require monitoring across multiple dimensions (consensus, settlement, containers, disk, memory, message queues, crypto integrity). Existing tools collect metrics but don't remediate. Existing remediation tools trigger on single thresholds without composite scoring. No system combines fail-open telemetry, transient-issue filtering, and trend-aware scoring.
The Innovation
Four novel elements: (1) Fail-open heartbeat telemetry -- 5 independent metric collectors (RedPanda, settlement, system, consensus, security) each with isolated 3-second timeouts. If any collector fails, it returns fallback values without blocking others. The heartbeat always publishes, even if all 5 collectors timeout. (2) Stateful observation windows -- per-rule, per-node trigger counters persist across 60-second inspection cycles. A rule must trigger on 3 consecutive cycles (~3 minutes sustained) before firing. Exception: SEC_DIGEST_MISMATCH (image tampering) fires immediately. (3) Majority-consensus fleet baselines -- fleet "normal" is computed from the statistical mode of all node values (version, config hash, throughput). Individual nodes are evaluated for drift against these dynamic baselines. (4) Trend-aware composite scoring -- confidence-weighted threat accumulation with 1.2x health penalty multiplier and delta-based momentum detection (spike/rising/falling/stable).
Prior art differentiation: Prometheus (2012) -- stateless rule evaluation, no composite scoring. Datadog (2010) -- no fail-open architecture, no quorum-aware remediation. Kubernetes probes (2014) -- binary pass/fail, no category exceptions. AWS CloudWatch Composites (2020) -- boolean logic, no weighted scoring or trend detection. No system combines fail-open collection + observation windows with security exceptions + trend-aware trajectory tracking.
Business Justification & Benefit
Operational cost reduction: Autonomous remediation handles 80%+ of fleet issues without human intervention, reducing on-call engineering costs by an estimated $200K-$500K/year for a 20-validator network.
Uptime guarantee: Observation windows prevent false-positive remediation (which itself causes outages). The 3-cycle requirement means transient network blips don't trigger unnecessary restarts. Security exceptions ensure real threats are addressed immediately.
Trend-based preemption: Delta-based scoring detects degradation trends before they reach critical thresholds. A node with "rising" threat trajectory can be proactively cycled before it fails catastrophically. This shifts the operational model from reactive firefighting to predictive maintenance.
Independent Claim 9: A computer-implemented system for autonomous validator fleet monitoring comprising: a fail-open heartbeat telemetry protocol wherein each validator node collects metrics across multiple independent categories with each collector operating under an isolated timeout such that failure of any individual collector returns a predetermined fallback value without blocking other collectors and the heartbeat is always published regardless of individual collector failures; a stateful observation window mechanism maintaining per-rule, per-node trigger counters across consecutive inspection cycles, requiring a configurable number of consecutive triggering cycles before generating a remediation recommendation, with a counter reset upon non-triggering cycles, and an exception override for critical security rules that bypass the observation window and fire immediately; a majority-consensus fleet context computation that derives fleet-wide baselines from the statistical mode of node-reported values and evaluates individual nodes for drift against those dynamic baselines; and a trend-aware composite threat scoring system computing confidence-weighted per-node threat scores with a non-linear health penalty multiplier and delta-based trend classification.
Dependent Claims (35-38)
Claim 35. The system of claim 34, wherein the fail-open heartbeat telemetry protocol comprises 5 independent metric collectors (message queue, settlement throughput, system resources, consensus state, and cryptographic security) each operating under an isolated 3-second timeout, returning predetermined fallback values upon timeout without blocking other collectors.
Claim 36. The system of claim 34, wherein the stateful observation window requires 3 consecutive triggering cycles of 60-second intervals (approximately 3 minutes sustained anomaly) before generating a remediation recommendation, with a counter reset to zero upon any non-triggering cycle, and a critical security exception for SEC_DIGEST_MISMATCH rules that bypass the observation window and fire immediately upon first detection.
Claim 37. The system of claim 34, wherein the trend-aware composite scoring classifies per-node threat score momentum into 4 categories (spike, rising, falling, stable) using delta-based comparison between consecutive inspection cycles, applying a 1.2x health penalty multiplier that amplifies threat score contributions for nodes with degraded health metrics.
Claim 38. The system of claim 34, wherein the majority-consensus fleet context computes dynamic baselines from the statistical mode of all reporting node values including software version, configuration hash, and throughput, evaluating individual nodes for drift against these baselines without requiring static threshold configuration.
Claim J: Predictive Liquidity Management with Neural Network Forecasting
Predictive Finance
Strong
The Problem
Digital asset holders frequently need fiat liquidity (payroll, rent, margin calls, taxes) but cannot predict needs until urgent, forcing emergency liquidations at unfavorable prices during high volatility. Liquidation is executed without tax awareness, resulting in unnecessary capital gains when loss positions could be sold first or when borrowing would be cheaper than selling.
The Innovation
Four elements in an integrated pipeline: (1) LSTM-based liquidity prediction -- 2-layer LSTM (64 hidden units, 20% dropout) trained on 30 days of per-user on-chain history (10 features including volume, volatility, holder count) produces 7-day forecasts with 95% confidence intervals. Auto-detects recurring obligations from transaction regularity. (2) Tax-optimized position selection -- evaluates every portfolio position across 4 tax strategies: tax-loss harvesting (sell losses first for deductions), long-term capital gains deferral, holding period awareness (defer if 2 weeks from crossing 12-month threshold), borrow-vs-sell analysis (compare 4% APR borrow cost vs. 24% capital gains tax). (3) Gradual TWAP execution -- spreads liquidation over 3-5 days with volatility-aware scheduling that pauses during market stress and MEV-protected routing. (4) Self-correcting rebalancing -- compares predicted vs. actual needs, adjusting allocation limits proportionally with tier-based constraints and daily change caps.
Prior art differentiation: Betterment/Wealthfront (2010s) -- tax-loss harvesting for equities, no prediction, no crypto. Aave/Compound (2020) -- borrow against crypto, no prediction or tax optimization. TWAP execution (standard TradFi) -- not integrated with prediction or tax. LSTM forecasting (academic, 1997+) -- not applied to per-user crypto liquidity needs. No system combines prediction + tax optimization + gradual execution + performance-based rebalancing.
Business Justification & Benefit
Tax savings: For a $10M portfolio, tax-optimized liquidation (selling loss positions first, deferring short-term gains, borrowing when cheaper) can save $200K-$500K annually in tax liability compared to naive FIFO selling. This is a quantifiable, measurable benefit that drives institutional adoption.
Slippage reduction: Gradual TWAP execution over 3-5 days reduces market impact by 60-80% compared to single large sells. For a $1M liquidation in a $50M liquidity pool, single-sell slippage is ~200 bps ($20K); TWAP reduces this to ~40 bps ($4K).
Premium feature revenue: Predictive liquidity management is a premium feature for institutional and high-net-worth users. At $99-199/month Premium tier pricing, this feature alone justifies the subscription cost through tax savings and reduced slippage.
Competitive moat: The integrated pipeline (predict → tax-optimize → gradually execute → self-correct) creates a compound advantage that improves over time as the LSTM model learns user-specific patterns. No competitor offers this integration for non-custodial crypto assets.
Independent Claim 10: A computer-implemented system for predictive digital asset liquidity management comprising: a recurrent neural network trained on per-user historical on-chain transaction data that produces multi-day liquidity need forecasts with confidence intervals, wherein the network automatically detects recurring financial obligations from transaction regularity patterns; a tax-optimized position selection engine that evaluates portfolio positions across at least tax-loss harvesting, long-term capital gains deferral, holding period proximity analysis, and borrow-versus-sell cost comparison to select the most tax-efficient liquidation path; a gradual liquidation executor that spreads asset sales over multiple days using time-weighted average price execution with volatility-aware scheduling that pauses during elevated market stress conditions and resumes when conditions normalize; and a performance-based allocation rebalancer that compares predicted versus actual liquidity needs and adjusts future allocation limits proportionally, creating a self-correcting feedback loop between prediction accuracy and allocation risk.
Dependent Claims (39-42)
Claim 39. The system of claim 38, wherein the recurrent neural network comprises a 2-layer LSTM architecture with 64 hidden units and 20% dropout, trained on 30 days of per-user on-chain history across 10 feature dimensions including transaction volume, asset volatility, and holder count, producing 7-day forecasts with 95% confidence intervals.
Claim 40. The system of claim 38, wherein the tax-optimized position selection engine evaluates 4 strategies: tax-loss harvesting to realize deductible losses first, long-term capital gains deferral for positions approaching 12-month holding thresholds, holding period proximity analysis that defers liquidation when within 2 weeks of crossing the long-term threshold, and borrow-versus-sell cost comparison weighing annual borrow rates against applicable capital gains tax rates.
Claim 41. The system of claim 38, wherein the gradual liquidation executor spreads asset sales over 3-5 days using time-weighted average price execution with volatility-aware scheduling that pauses during periods of elevated market stress as measured by realized volatility exceeding a configurable threshold, and employs MEV-protected routing to prevent front-running of scheduled liquidation orders.
Claim 42. The system of claim 38, wherein the performance-based allocation rebalancer applies tier-constrained proportional adjustments with daily change caps, comparing predicted versus actual liquidity needs and adjusting future allocation limits within configured tier boundaries to prevent overcorrection while maintaining prediction accuracy convergence.