The Regulatory Surface Treats Training as Manufacturing

For a software-as-a-medical-device model, training is the step that produces the device. The regulatory regimes that govern medical AI therefore reach inside the training loop rather than stopping at the deployed artifact. The FDA's AI/ML SaMD Action Plan and its Predetermined Change Control Plan framework expect a manufacturer to specify, in advance, what may change in the model and how that change is controlled and verified. 21 CFR Part 11 demands trustworthy, attributable, tamper-evident electronic records for any data that supports a regulated claim. IEC 62304 governs the software lifecycle, ISO 13485 the quality system, and ISO 14971 the risk-management file that must trace residual risk to mitigations. In the European market, EU MDR Annex VIII read with Article 27 imposes classification and unique-device-identification traceability, while EU AI Act Article 10 sets explicit data-governance obligations for high-risk systems: relevant, representative, error-examined training data with documented provenance. The NIST AI Risk Management Framework and ISO/IEC 42001 wrap these in a govern-map-measure-manage management system.

The common thread across all of these is a demand the conventional training stack cannot answer: show what the model learned, from which evidence, with what verification, and demonstrate that change is controlled. A pipeline that aggregates every example uniformly into every parameter produces a model whose knowledge is an undifferentiated mass. Such a model cannot tell an auditor which records influenced which behaviors, cannot confine a rights-restricted or time-limited record to a recoverable region, and cannot prove that protected health information was not durably memorized. The gap is architectural, not procedural, and procedural documentation layered on top of an ungoverned optimizer does not close it.

What the Inventive Step Supplies

The Training Governance step of United States Patent Application 19/647,395 reframes the training loop so that the answers the regulator demands are produced as a byproduct of training rather than reconstructed after the fact. Five primitives from that disclosure carry the clinical application.

First, each training example is treated as a proposed semantic mutation to the model's knowledge state, evaluated for admissibility before it is allowed to update parameters. An example that carries no semantic metadata, or whose policy scope prohibits integration, is inadmissible by default, and a refusal to learn from an example is a valid recorded result rather than an error. Each example must carry, at minimum, an entropy-band classification, a slope position in the platform's trust hierarchy, a content-provenance record describing source and chain of custody, and a policy scope describing licensing terms, usage restrictions, temporal-validity bounds, and exclusion mandates.

Second, admissibility is graded rather than binary. The substrate renders a depth profile: a per-layer or per-block contribution-weight vector that governs how much of each example's gradient signal is permitted to reach each region of the model. A weight of zero blocks a layer entirely, a weight between zero and one attenuates, a weight of one permits full flow. An example may be admitted for shallow integration and excluded from deep integration.

Third, depth profiles are indexed to entropy bands. The platform's entropy-extraction pipeline classifies each example by its semantic divergence from the model's current representational state. Low-entropy, routine content is weighted toward shallow layers; high-entropy, novel content is directed toward deeper layers where multi-step abstraction is encoded. A profile-adaptation engine monitors the model's internal entropy distribution at checkpoints and adjusts the mapping as representations stratify during training.

Fourth, the depth profile is enforced on the gradient during the backward pass through one or more of three disclosed mechanisms: gated residual connections that attenuate flow through a residual pathway, attention-based depth selection that scales the gradient reaching attention and value projections in transformer layers, and architecture-agnostic layer-specific scaling factors that multiply the gradient at each layer boundary. The forward pass is unchanged, so inference behavior is not perturbed by the depth-selective training mechanism. Profiles may be specified at block granularity to remain tractable in deep networks.

Fifth, every decision is written to a training-provenance log: a chronologically ordered, append-only, sequentially numbered, timestamped record that captures, for each example or batch, the entropy band, slope position, depth-aggregation profile, per-layer contribution weights actually applied, the governing policy objects, the content-provenance record, and the admit, reject, or modify determination with its reason. The log is tamper-resistant by construction and may be periodically sealed with the cryptographic sealing infrastructure of the cross-referenced governance disclosure to produce checkpoints a third party can verify.

Depth-Selective Routing of Clinical Content

In a clinical training corpus, different classes of content carry different governance requirements, and depth-selective aggregation lets those requirements be expressed structurally rather than by hand-curated dataset partitioning. The disclosure's treatment of therapeutic-agent training is directly on point and supplies the embodiment pattern.

General clinical principles that are stable and broadly applicable are integrated broadly across the model's depth. Specific protocols that vary across jurisdictions, that are subject to ongoing regulatory revision, or that involve contested approaches are encoded with suppressed depth profiles that confine them to intermediate layers, so that they can be updated or replaced without full retraining. This is the architectural basis for a Predetermined Change Control Plan: the manufacturer can specify in advance that the protocol-bearing intermediate blocks are the locus of permitted change, leaving the deeply encoded general competence stable, and the depth profiles make that boundary real in the parameter space rather than aspirational in a document.

Patient-specific content, where it is used in training at all, receives a maximally suppressed depth profile and is placed under time-limited policy governance that drives automatic exclusion on policy expiration. Several deployment variations follow from this same primitive. A consent-scoped variation ties the temporal-validity bound of each record's policy scope to the patient's consent term, so that withdrawal of consent corresponds to an expiration the substrate enforces at the next training iteration and records as a governed non-training event. A jurisdictional variation attaches distinct policy scopes to records governed by different data-protection regimes, allowing a single corpus to be trained under per-record constraints rather than the most restrictive global setting. A device-family variation reuses the deeply encoded general competence across cleared device variants while differentiating the intermediate, protocol-bearing layers per indication.

Memorization Detection and PHI Confinement

The obligation that most directly resists conventional pipelines is proving that protected health information was not memorized. Two disclosed mechanisms address it together.

Differential privacy via depth-selective gradient routing makes the privacy guarantee structural rather than statistical. Rather than calibrating Gaussian or Laplacian noise to the worst-case record across the whole corpus, which degrades accuracy for content that needs no protection, the substrate routes a privacy-sensitive record's gradient primarily to shallow layers, where representations are generic and distributed and inherently less memorizable, and suppresses its contribution to the deep layers where representations are specific and retrievable. The depth profile for such content specifies high gating coefficients at shallow blocks and low or zero coefficients at deep blocks. The model cannot memorize what it was not permitted to encode in memorizable layers, and because the guarantee is per-record, non-sensitive clinical content can still be integrated at full depth without paying the accuracy cost of a global privacy budget.

Memorization detection then provides the audit-time proof. When model output at inference exhibits high similarity to a known training artifact, the detection module issues a reverse-provenance query against the training-provenance log and classifies the result as shallow, deep, or absent memorization. Shallow memorization, where the content was trained with a suppressed depth profile that confined its influence to shallow layers, is the expected and acceptable outcome for time-limited or rights-restricted clinical content. Deep memorization of content that should have been depth-restricted is a flagged condition the inference substrate can act on. Absent memorization, where the log contains no record of the similar content, confirms the content was not in the corpus. For 21 CFR Part 11 and EU AI Act data-governance review, this turns a hand-waving assurance into a query with a defined, three-valued answer grounded in an append-only record.

Provenance, Fine-Tuning Lineage, and the Audit Record

The training-provenance log answers the data-governance questions directly. A forward query begins with a record or a class of records and traces the depth profile, contribution weights, and governance decisions that controlled its integration. A reverse query begins with an observed behavior and identifies the bounded set of training content that was structurally permitted to influence the active layer blocks. The disclosure is careful that reverse attribution is bounded rather than exact, because gradient-based optimization is non-linear, but a bounded attribution set far narrower than the full corpus is precisely what a regulator needs to investigate a challenged output. When a content owner or data-protection authority asks whether specific content was used, the log gives a definitive yes-with-records or no answer. When an auditor requires evidence that restricted content was not deeply integrated, the log supplies the depth-profile records showing the confined contribution weights.

Governed fine-tuning provenance carries this into post-market reality, where a base model is adapted to a hospital network, a device indication, or a regional standard of care. The semantic metadata, policy constraints, depth profiles, and admissibility determinations of the fine-tuning corpus are recorded as a fine-tuning provenance record structurally distinct from the pre-training record. Because pre-training and fine-tuning content are integrated through distinct depth profiles, their contributions occupy distinguishable layer-block regions, so a challenged output can be traced to whether it arose from pre-training competence or a post-market adaptation. For SaMD liability allocation and post-market surveillance under MDR and the FDA framework, that distinction assigns responsibility between the base-model manufacturer and the deploying institution.

Two further disclosed capabilities suit clinical deployment. On-device training without network dependency freezes the base model and confines all updates to a small parameter-efficient adaptation layer that stays on the local device, so that a hospital can adapt a model on interaction histories, corrections, and local records without that data ever leaving its network, a structural privacy property that does not depend on a data-processing agreement. Real-time interactive training from individual interactions processes each clinician acceptance or correction through the same depth-selective routing, metadata evaluation, and provenance recording as batch training, with corrections carrying elevated contribution weight, so continuous post-market learning remains inside the governed loop rather than escaping it.

Disclosure Scope

This article is a domain application of the Training Governance inventive step disclosed in United States Patent Application 19/647,395. The medical-AI framing, the mapping to specific regulatory regimes, and the enumerated deployment variations are application context. The underlying technology, including treating training examples as admissibility-evaluated semantic mutations, entropy-indexed training depth profiles, depth-selective aggregation via gated residual connections, attention-based depth selection and layer-specific scaling factors, differential privacy by depth confinement, training-level memorization detection with reverse-provenance classification, governed fine-tuning provenance, on-device parameter-efficient adaptation, and the append-only, cryptographically sealable training-provenance log, is disclosed in that application. No training metrics, dataset sizes, or benchmark numbers are asserted here beyond what that disclosure supports.