Barnicle-AI-Systems

Shawn Barnicle | AI Systems Inventor

Patent portfolio: Universal failure prediction across 12+ domains + reducing wasted AI compute

Patents Filed: 15 provisionals

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Patent Portfolio Summary

GitHub Repo	Patent Title	Application #	Filed	Status
phi-objective-controller	Stability-Guided Control Using Predictive Action Selection with Interchangeable Measurement Adapters	63/984,704	Feb 17, 2026	Filed
phi-bio-stability	Real-Time Physiological Instability Detection	63/978,132	Feb 9, 2026	Filed
phi-controller-quantum	Real-Time Quantum Circuit Intervention	63/973,723	Feb 2, 2026	Filed
llm-phi-stability	LLM Behavioral Drift & Safety Guardrail Detection	63/973,673	Feb 2, 2026	Filed
universal-stability-engineering	Universal Stability Engineering	63/960,829	Jan 15, 2026	Filed
neural-phase-transition-detection	Neural Phase Transition Detection	63/960,091	Jan 14, 2026	Filed
thermodynamic-stability-prediction	Thermodynamic Stability Prediction	63/959,205	Jan 13, 2026	Filed
universal-phi-ml	Machine Learning Using Training-Free Stability Metrics	63/956,800	Jan 9, 2026	Filed
phi-hybrid-allocation	Quantum-Classical Hybrid Resource Allocation	63/956,752	Jan 9, 2026	Filed
quantum-phi-validation	Quantum Sensor Stability Monitoring	63/952,883	Jan 2, 2026	Filed
phi-controller	Trajectory-Aware Architecture Termination	63/938,279	Dec 11, 2025	Filed
system-degradation-framework	Adaptive Threshold Degradation Detection	63/921,348	Nov 20, 2025	Filed
identity-framework-extensions	Transfer Learning Performance Prediction	63/920,092	Nov 18, 2025	Filed
identity-formation-detection	Training Efficiency from Early Identity Formation	63/914,409	Nov 18, 2025	Filed
task-identity	Behavioral Drift Detection for ML Classification	63/906,072	Oct 27, 2025	Filed
task-identity	Behavioral Drift Detection (Expanded Refiling)	63/981,437	Feb 12, 2026	Filed

15 provisional patents filed | Available for exclusive or non-exclusive licensing

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Scientific Papers

Paper	Key Result	DOI
Φ = I × ρ − α × S: A Domain-Agnostic Stability Metric and Autonomous Controller	8 domains, 42 systems, autonomous controller with anti-Goodhart safeguards	10.5281/zenodo.18684052
A Stability Index for Cross-Domain Degradation Detection	31 systems, 6 domains, 100% separation — one formula, no tuning	10.5281/zenodo.18523292
Thermodynamic Stability Metric Provides Early Warning of Qubit Degradation on IBM Quantum Hardware	445 qubits, 6.8 days average early warning, 100% detection rate	10.5281/zenodo.18522745

Peer-reviewable mathematical proofs underlying the stability metric used across all 15 patents. Real data. Fixed parameters. No per-domain tuning.

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Stability-Guided Control with Predictive Action Selection

Autonomous Intervention Engine with Anti-Goodhart Safeguards

Status: Patent Filed — Application #63/984,704 (Feb 17, 2026)

The Problem:
Monitoring degradation is only half the problem. Once you detect instability, a human still has to decide what to do. Worse, automated controllers that directly optimize a proxy metric fall prey to Goodhart's Law — the proxy improves while real performance gets worse.

Our Solution:
A domain-agnostic controller that maximizes task performance while using Φ as a safety constraint, not an optimization target. Interchangeable measurement adapters let the same control engine operate across neural networks, quantum circuits, industrial equipment, and physiological signals without changing the decision logic.

Three Anti-Goodhart Safeguards:

• Performance Floor: Candidate actions must maintain performance within ε of baseline
• Step-Wise Rejection: If Φ improves but performance worsens, the action is rejected immediately
• History-Based Correlation Monitor: Tracks Φ-performance correlation over time; becomes conservative when correlation breaks down

Validation:

• 8 domains validated: bearings, turbofans, power grids, seismic, neural networks, quantum circuits, cardiac signals, LLMs
• Neural training (CIFAR-10, ResNet-18): 60–65% win rate under strict 2-intervention budget
• Kill-only monitoring: 99.7% precision (2 false kills out of 660+ runs)
• Early stopping comparison failed on 35% of seeds — Φ monitoring matched baseline on 100%
• 85.1% quantum error reduction on IBM hardware (445 qubits, 3 backends)
• Cardiac arrhythmia detection AUC 0.90 without training on cardiac data
• Conservative fallback to no-op when uncertainty is high — "do no harm" by default

Commercial Applications: Autonomous ML training optimization | Quantum circuit execution control | Industrial closed-loop predictive maintenance | Real-time physiological intervention systems | Multi-domain enterprise control platforms

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Physiological Instability Detection

Universal Formula Validated on Biological Systems

Status: Patent Filed — Application #63/978,132 (Feb 9, 2026)

The Problem:
Medical monitoring uses domain-specific algorithms for each condition. Cardiac monitors use HRV metrics. EEG systems use spectral analysis. No universal method exists to detect physiological instability across organ systems.

Our Solution:
Apply the same stability metric validated on bearings, power grids, earthquakes, and quantum hardware to biological signals. Cardiac arrhythmia detection achieves AUC 0.90 — competitive with specialist HRV metrics that took decades to develop.

Validation:

• 8 tests across 3 PhysioNet datasets (MIT-BIH Arrhythmia, MIT-BIH AFib, CHB-MIT EEG)
• Cardiac arrhythmia: AUC 0.90, within 0.07 of specialist metrics
• Resolution scaling: AUC 0.88 at 30s, 0.64 at 5s (wearable-ready)
• EEG seizure prediction: 67% sensitivity, 0.74 false alarms/hr
• System-type adaptation validated (cardiac vs neural)
• 69 patent claims

Commercial Applications: Arrhythmia monitoring wearables | ICU early warning systems | Remote cardiac monitoring | Seizure warning devices | Universal health monitoring platforms

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Real-Time Quantum Circuit Intervention

Closed-Loop Control During Quantum Execution

Status: Patent Filed — Application #63/973,723 (Feb 2, 2026)

The Problem:
Current quantum execution is open-loop: select qubits, run circuit, hope for the best. If a qubit degrades mid-circuit, you don't know until final measurement.

Our Solution:
Monitor stability continuously during execution and intervene when quality degrades. Five intervention actions: checkpoint, migrate, classical fallback, restart, or continue degraded.

Validation:

• 10 tests across 3 IBM backends (ibm_fez, ibm_torino, ibm_marrakesh)
• 445 qubits validated
• 85.1% error reduction (high-quality vs low-quality selection)
• 68.7% improvement over raw T2 metric
• 98.78% mid-circuit conditional consistency
• 56 patent claims

Commercial Applications: Quantum workflow optimization | Mid-circuit error mitigation | Quantum resource cost reduction | Cross-platform quantum control

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LLM Behavioral Drift Detection

Black-Box Safety Monitoring for Language Models

Status: Patent Filed — Application #63/973,673 (Feb 2, 2026)

The Problem:
Language models change behavior invisibly. Fine-tuning, temperature changes, quantization, or adversarial injection can degrade quality and weaken safety guardrails. Existing approaches require access to model internals.

Our Solution:
Black-box drift detection using external embeddings only. No access to logits, hidden states, or attention weights required. Works with any LLM API.

Validation:

• 8 tests + scale validation on models up to 2.7B parameters
• Temperature correlation: r=-0.97
• Safety drift, fine-tuning drift, and jailbreak detection all validated
• 42 patent claims (7 independent)

Commercial Applications: LLM safety monitoring | Fine-tuning QA validation | Adversarial prompt detection | Model deployment guardrails | API provider quality assurance

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Universal Stability Engineering

Design Stable Systems, Maintain Stability, Monitor Everything

Status: Patent Filed — Application #63/960,829 (Jan 15, 2026)

The Approach:
Previous methods predict failure. This method prevents it. Three capabilities in one framework: design systems with stability constraints before construction, maintain stability through real-time control, and monitor any system type through a single unified platform.

Three Methods, One Framework:

• Inverse Design: Calculate stability constraints at design time
• Closed-Loop Control: Continuous monitoring with 73-90% advance warning
• Universal Monitoring: Single dashboard for mechanical, electrical, aerospace, AI, seismic, NLP, medical, audio, and financial systems

Validation:

• 42 systems across 9 domains (100% accuracy)
• 10 bearings with 73-90% advance warning (avg 86.1%)
• Real catastrophic events: Tohoku M9.1, UK Blackout, Parkfield M6.0, San Simeon M6.5
• 8.4M+ real measurements analyzed

Commercial Applications: Industrial predictive maintenance | Power grid stability control | AI/ML production monitoring | Multi-domain enterprise monitoring | Safety-critical system design

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Neural Phase Transition Detection

Predict Architecture Viability in One Epoch

Status: Patent Filed — Application #63/960,091 (Jan 14, 2026)

The Problem:
Neural architecture search wastes 80-95% of compute on architectures that will never work. Teams train hundreds of candidates for days, only to discover most were doomed from the start.

Our Solution:
Predict whether any architecture will succeed or fail after just one training epoch. Binary go/no-go decision in minutes instead of days.

Validation:

• 22 architectures tested across 3 datasets
• 95% prediction accuracy (21/22 correct)
• Works on grayscale AND RGB images
• Validated on both CNN and MLP architectures

Commercial Applications: Neural architecture search acceleration | Hyperparameter optimization | Cloud ML cost reduction | Research lab efficiency

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Thermodynamic Stability Prediction

Universal Failure Prediction Across 5 Domains

Status: Patent Filed — Application #63/959,205 (Jan 13, 2026)

The Approach:
One physics-based method evaluated for catastrophic failure prediction across computational, mechanical, electrical, aerospace, AND geophysical systems. Same method. Same threshold.

Validation:

• 28 systems across 5 domains evaluated
• UK blackout (Aug 9, 2019) — pre-event instability detected
• 3 major earthquakes evaluated (Tohoku M9.1, Parkfield M6.0, San Simeon M6.5)
• 1 stable period correctly identified (2010 quiet year)
• 5.8M+ real measurements analyzed

Commercial Applications: Earthquake early warning | Power grid blackout prediction | Aerospace predictive maintenance | Industrial equipment monitoring | AI/ML operations

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Machine Learning Using Training-Free Stability Metrics

Train Once, Deploy Anywhere with 98%+ Accuracy

Status: Patent Filed — Application #63/956,800 (Jan 9, 2026)

The Problem:
Predicting system quality requires retraining ML models for each new platform. Models trained on Platform A don't work on Platform B.

Our Solution:
Use training-free stability components as universal input features. Models trained on one quantum computer achieve 98.4% accuracy on a completely different one — zero retraining.

Validation:

• 445 qubits from 3 IBM Quantum backends
• 98.4% balanced accuracy on cross-backend transfer
• All 4 ML model types work (Random Forest, Gradient Boosting, Neural Network, SVM)
• Strict methodology: backend-split validation, circularity addressed

Commercial Applications: Cross-platform quality prediction | Automated system monitoring | Transfer learning without retraining

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Quantum-Classical Hybrid Resource Allocation

Route Computations Based on Real-Time Qubit Quality

Status: Patent Filed — Application #63/956,752 (Jan 9, 2026)

The Problem:
Quantum isn't always better. A circuit run on low-quality qubits may produce higher error than classical simulation. Current systems run everything on quantum and hope for the best.

Our Solution:
Dynamically route computations between quantum hardware and classical simulation based on real-time qubit quality assessment.

Validation:

• 23 tests across 7 quantum algorithms
• 30.47x error discrimination between high-quality and low-quality execution
• Classical simulation (0% error) beats low-quality quantum (up to 91.60% error)
• 3 IBM backends validated

Commercial Applications: Cloud quantum platform optimization | Quantum computing cost reduction | Hybrid workflow orchestration

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Quantum Sensor Stability Monitoring

Same Formula Works on Qubits

Status: Patent Filed — Application #63/952,883 (Jan 2, 2026)

The Problem:
Quantum hardware exhibits variable qubit quality. Current calibration systems use proprietary ML requiring extensive training data.

Our Solution:
Apply the same stability metric that works on bearings, turbofans, and power grids to quantum hardware. Same threshold validated across all domains.

Validation:

• 445 qubits, 1004 two-qubit gates, 3 IBM backends
• 83% error reduction using stability-based qubit selection
• 8-63x higher error for low-quality qubits across all circuit depths
• 100% dead qubit detection

Commercial Applications: Quantum hardware calibration | Qubit selection optimization | Cross-platform quality assessment

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Trajectory-Aware Architecture Termination

Kill Unviable Neural Architectures with 99.7% Precision

Status: Patent Filed — Application #63/938,279 (Dec 11, 2025)

The Problem:
Neural architecture search wastes significant compute training hundreds of candidates. Standard early stopping kills 83% of viable architectures experiencing temporary setbacks.

Our Solution:
Trajectory-aware termination tracks best progress from training start, not just recent epochs. Prevents false kills from dropout, batch normalization, or learning rate schedules.

Validation:

• 660 architectures tested (MLPs and CNNs)
• 99.7% kill precision (2 false kills total)
• Standard early stopping comparison: 16.7% precision
• Validated on MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, Breast Cancer
• Works across all hyperparameters

Commercial Applications: Neural architecture search acceleration | Cloud ML cost reduction | Hyperparameter optimization | Enterprise ML training pipelines

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Adaptive Threshold Degradation Detection

Predictive Maintenance for Industrial Equipment

Status: Patent Filed — Application #63/921,348 (Nov 20, 2025)

The Problem:
Industrial equipment monitoring uses fixed thresholds that miss failures while generating excessive false alarms.

The Solution:
Adaptive detection system that automatically adjusts sensitivity based on equipment degradation patterns. Validated on mechanical and electrochemical systems.

Validation:

• 13 real-world systems tested
• 10 mechanical bearing systems (XJTU-SY dataset)
• 3 electrochemical battery systems (NASA dataset)
• F1 scores: 0.550-0.975 across validated systems
• Runs on embedded systems (<1KB memory)
• Zero training data required

Commercial Applications: Industrial predictive maintenance | Electric vehicle battery monitoring | Renewable energy system monitoring | Manufacturing equipment health monitoring

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Transfer Learning Performance Prediction

Reduce Wasted Pre-Training Experiments

Status: Patent Filed — Application #63/920,092 (Nov 18, 2025)

The Problem:
Enterprise teams test many pre-trained models with hours of fine-tuning each. Most experiments fail, wasting significant compute.

The Solution:
Predict transfer learning success across different domains before committing compute.

Validation:

• Binary prediction: Which models will help vs. hurt (p<0.003)
• Magnitude prediction: Performance gain/loss correlation (r=-0.941, p<0.00001)
• Cross-domain: Computer vision AND financial services
• Real-world scale: 852,607 financial transactions + 247 image scenarios

Commercial Applications: Pre-trained model marketplaces | Medical imaging | Financial ML | Cloud ML platforms | Enterprise AI teams

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Training Efficiency from Early Identity Formation

Predict Training Cost in 1 Epoch

Status: Patent Filed — Application #63/914,409 (Nov 18, 2025)

The Problem:
Architecture search = test 100 candidates × 50 epochs = 5,000 training runs = weeks of compute

Our Solution:
Predict total training requirements after 1 epoch = 100 runs instead of 5,000

Validation:

• Universal correlation (r = -0.78) across simple and complex datasets
• Identical pattern on MNIST and CIFAR-10 (supports universality)
• Works for MLPs and CNNs

Commercial Applications: Neural architecture search | Hyperparameter optimization | Transfer learning validation | Cloud ML services

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Behavioral Drift Detection for ML Classification

Catches Failures That Traditional Monitoring Misses

Status: Patent Filed — Original #63/906,072 (Oct 27, 2025) | Expanded Refiling #63/981,437 (Feb 12, 2026)

The Critical Gap:
A production model collapsed from 99.3% → 0.0% accuracy. Traditional monitoring showed 0.583 ("moderate, looks stable"). Our method showed 0.000 (catastrophic failure).

Detection Gap: 58.3 percentage points better than comparison method

Validation:

• 12 comprehensive tests across 5 domains
• Computer Vision, NLP, Medical AI, Audio, Financial Services
• 95%+ coverage of production ML workloads
• Zero training required

Commercial Applications: Production ML monitoring | Autonomous vehicles | Medical AI | Content moderation | Voice assistants

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Target Companies

Critical Infrastructure & Safety:
Earthquake early warning systems | Power grid operators | Aerospace manufacturers

Industrial/Manufacturing:
Predictive maintenance platforms | Electric vehicle manufacturers | Industrial equipment OEMs | Battery management systems | Renewable energy operators

AI/ML Industry:
Production ML monitoring platforms | Neural architecture search optimization | Pre-trained model evaluation | Enterprise MLOps | LLM safety monitoring

Medical/Healthcare:
Cardiac monitoring device manufacturers | Wearable health technology companies | ICU monitoring system providers | Seizure detection device companies | Remote patient monitoring platforms

Quantum Computing:
Cloud quantum platforms (IBM, Google, IonQ, Rigetti) | Quantum software development kits | Hybrid quantum-classical systems

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Validation Standards

✓ Real Data Only — No synthetic data generation

✓ Published Datasets — MNIST, CIFAR-10, Fashion-MNIST, 20 Newsgroups, Wisconsin Breast Cancer, Free Spoken Digit Dataset, Lending Club Loans, NASA C-MAPSS, XJTU-SY Bearings, USGS Strainmeter, UK National Grid, IBM Quantum, PhysioNet MIT-BIH, PhysioNet CHB-MIT EEG

✓ Statistical Rigor — P-values, significance testing, correlation analysis

✓ Cross-Domain Testing — Multiple domains per method to evaluate universality

✓ Real Catastrophic Events — UK blackout, 3 major earthquakes validated

✓ Honest Reporting — Failed experiments documented, no cherry-picking

✓ Reproducible — All validation code available in respective repositories

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Commercial Inquiries

All innovations available for licensing or acquisition.

Licensing Options: Exclusive or non-exclusive arrangements available.

Contact:
Email: ShawnBarnicle.ai@gmail.com
Email: ShawnBarnicle@proton.me
LinkedIn: linkedin.com/in/shawn-barnicle-811887390
GitHub: Patent Portfolio

Response Time: 24-48 hours for licensing inquiries

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Last Updated: February 2026
Patents Filed: 15 of 15
Validation Status: Complete across all filed innovations