CROSS-REFERENCES
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of and priority to U.S. Provisional Patent Applications No. 63/967,576, No. 63/974,083, No. 63/974,099, No. 63/988,460, No. 63/988,475, No. 63/988,480, No. 63/991,254, and No. 63/991,263. All prior provisional applications are incorporated herein by reference in their entireties.
I.FIELD OF THE INVENTION
[0001]The present invention relates generally to machine learning operations (MLOps), computer security, and software supply chain integrity. More specifically, it relates to systems and methods for the automated validation, calibration, and cryptographically enforced execution of neural network-based inference services, particularly constrained dynamical-system predictors such as clinical digital twins.
II.BACKGROUND OF THE INVENTION
1.Configuration Drift in ML Deployment
[0002]The deployment of machine learning (ML) models in high-stakes environments presents unique technical challenges that extend beyond standard predictive accuracy. A critical vulnerability in the lifecycle of such models is “configuration drift” — the decoupling of model weights from their associated safety policies and calibration artifacts. For example, a model checkpoint may be updated while its associated calibration curves remain stale, resulting in dangerously miscalibrated outputs.
2.Limitations of Champion-Challenger Frameworks
[0003]Standard “Champion-Challenger” evaluation frameworks typically rely on aggregate metrics (e.g., global Area Under the Curve or Concordance Index). These frameworks fail to detect granular safety violations, such as a challenger model that improves global metrics while silently degrading performance on a specific, underrepresented subgroup, or a model that achieves high accuracy by learning physically impossible shortcuts (e.g., predicting negative volumes or infinite rates in dynamical systems).
[0004]Furthermore, current MLOps practices lack a technical enforcement boundary to guarantee that a model is only executed in a mode commensurate with its validated calibration fidelity. There is an unmet need for a secure, fail-closed model-policy loader and output-mode enforcement mechanism that cryptographically binds model weights, calibration curves, and routing rules into a single verified manifest.
III.SUMMARY OF THE INVENTION
[0005]The invention provides the CohortShield system for cryptographically enforced execution of calibrated machine learning pipelines. The system: (1) implements a multi-gate champion-challenger evaluation framework with physics constraint verification, subgroup non-inferiority testing, calibration assessment, and external generalization validation; (2) binds model weights, calibration curves, safety policies, and API routing rules into a cryptographically signed manifest; (3) enforces fail-closed boot-time loading that prevents model instantiation when the manifest signature is invalid or policies are stale; and (4) provides non-bypassable route-table enforcement that structurally prevents unauthorized API endpoints from serving uncertified model outputs.
[0006]The system automatically determines whether a challenger model should be PROMOTED, REJECTED, or held for manual review based on a 5-gate evaluation: Gate 1 (physics constraint compliance), Gate 2 (global performance non-degradation), Gate 3 (subgroup non-inferiority with bootstrap testing), Gate 4 (calibration quality via Integrated Calibration Index), and Gate 5 (external cohort generalization). GPU allocation interlocks prevent execution of models that fail any gate.
IV.CLAIMS
20 claims covering the CohortShield system, multi-gate champion-challenger evaluation, cryptographically signed model-policy manifests, fail-closed boot-time loading, non-bypassable route-table enforcement, GPU allocation interlocks, and automated safety assurance for calibrated ML inference pipelines.
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