Biological State Inference From Continuity Baseline

Mechanism

The state-inference subsystem operates as a downstream consumer of the biological-identity continuity slope. Where the continuity slope itself is responsible for confirming that the individual presenting at time t is the same individual whose baseline was constructed at times t-n, the state-inference subsystem is responsible for characterising the manner in which that individual currently differs from their own historical pattern. The inference is strictly intra-individual: there is no population reference, no clinical threshold, and no normative scale against which the present observation is compared. The reference is the individual's own accumulated baseline distribution, and the only quantity computed is the multidimensional deviation vector between the current observation window and that baseline.

Within the subsystem, a running statistical model of the individual's biological-signal characteristics is maintained as part of the trust slope itself. The model captures, at minimum, the central tendencies and dispersion of each tracked signal channel, the cross-channel covariance structure, and the temporal autocorrelation pattern that characterises the individual's normal variability. When a new observation window is consumed, the subsystem computes a deviation vector in the space defined by these statistics. If the deviation vector falls within noise-tolerant thresholds the observation is treated as nominal and folded into the running baseline. If the deviation exceeds threshold the vector is passed to a classifier that maps it onto a pre-enumerated set of physiological-state hypotheses.

The classifier itself does not see raw signals. It sees only the deviation vector and a small set of contextual covariates (time of day, time since last observation, recent activity class). The mapping from deviation vector to state hypothesis is constructed once, during system characterisation, against a population of consenting subjects under controlled state induction; once deployed it operates entirely on the individual's own deviation, with no further population reference required. The output of the classifier is a bounded score per hypothesis, not a categorical determination. A score above a tier-specific confidence floor is reported to the consuming policy as an indication; below that floor the indication is suppressed.

Storage is governed by the same minimisation discipline that governs the continuity slope. The raw biological signal is consumed in a streaming window and is not retained beyond the window length required to compute the deviation. The deviation vector is retained only long enough to update the baseline and emit any indication. The baseline statistics themselves are retained, but they are sufficient statistics — moments and covariances — not raw traces, and they cannot be inverted to reconstruct the underlying signal.

Operating Parameters

The subsystem operates within a defined parameter envelope that is itself tier-gated. Window length, noise threshold, classifier confidence floor, and the set of hypotheses that may be evaluated against a given individual are all parameters whose values are set by the credential under which the consuming application is operating. A consumer-grade alertness application might operate with a long window, a permissive noise threshold, a low confidence floor, and a hypothesis set restricted to alertness and fatigue. A safety-critical operator-monitoring application in an aviation or surgical context operates with a shorter window, tighter thresholds, a higher confidence floor, and a broader hypothesis set that includes acute stress and impairment.

The hypothesis set is enumerated rather than open. The subsystem will not report a state for which it has not been characterised, and it will not extrapolate from one hypothesis to another. This is a deliberate constraint: it bounds the inference to claims for which the deviation-to-state mapping has been established empirically, and it prevents the system from manufacturing speculative diagnoses. A policy that needs a hypothesis the subsystem cannot evaluate must either accept the absence of that signal or commission an extension of the characterisation against the appropriate consenting population.

Latency from observation to indication is bounded by the window length plus a small constant for classifier evaluation. The subsystem is designed for the indication to be available before any actuation that depends on it, which in practice means windows on the order of seconds for operator-monitoring applications and on the order of minutes for ambient wellness applications. Indications are emitted as bounded scores with explicit confidence and explicit time-stamp, never as untimed assertions.

Alternative Embodiments

The mechanism admits several embodiments differing in sensor modality, deployment topology, and the locus of the classifier. In a wearable embodiment the entire pipeline — baseline maintenance, deviation computation, classifier evaluation — runs on-device, and only the bounded indication is emitted to a host application. In a vehicle embodiment the sensing is distributed across cabin instrumentation (camera, steering torque, seat-pressure, in-cabin microphone) and the pipeline runs on the vehicle compute, with the baseline tied to the operator's licence credential rather than to the vehicle. In a facility embodiment the sensing is performed at access points and at workstations, the baseline travels with the operator's credential, and the indication is consumed by the access-control policy.

Sensor modality is itself an embodiment axis. Cardiovascular variability, respiratory pattern, ocular dynamics, vocal prosody, micro-postural drift, and fine-motor tremor have each been demonstrated to carry state information when referenced against an individual baseline. A given deployment may use any subset of these channels; the mechanism is invariant under sensor substitution provided the baseline-and-deviation discipline is preserved. A deployment that adds a sensor channel after baseline establishment must either re-establish the baseline against the augmented channel set or treat the new channel as advisory until sufficient baseline has accumulated.

The classifier itself admits embodiment variation. A linear-discriminant classifier suffices for low-dimensional deviation vectors and small hypothesis sets and has the advantage of being directly inspectable. A small neural classifier is appropriate for higher-dimensional deviation vectors and larger hypothesis sets and admits per-individual fine-tuning against the running baseline. The choice is governed by the tier and the deployment; both embodiments preserve the bounded-score, no-raw-signal-storage discipline.

Composition

State inference does not stand alone. Its purpose is to be composed with downstream policy, and the composition is structured so that the inference and the policy that consumes it remain separable. The inference emits an indication; the policy decides what to do with it. An autonomous-vehicle policy might decelerate and request operator confirmation when fatigue indication crosses threshold. A secure-facility policy might require a second factor when stress indication is elevated at the access boundary. A therapeutic-agent policy might modulate the cadence and tone of its interaction when cognitive-load indication is high. In each case the inference is the same primitive; the policy is domain-specific.

The composition is also tier-gated. A given policy is permitted to consume only those hypotheses for which it holds the appropriate credential. A consumer wellness application is not permitted to consume an impairment indication, even if the underlying classifier can evaluate it, because the credential under which the application is operating does not extend that far. A safety-critical operator-monitoring application holds a credential that does extend that far, but it consumes the indication under audit. This structure prevents the inference from being repurposed by an application whose credential does not match the sensitivity of the hypothesis being evaluated.

Bounded Inference and Tier-Gated Access

The bounded character of the inference is a substantive constraint, not a stylistic one. The subsystem produces a score on an enumerated hypothesis with explicit confidence and explicit time-stamp, and it does not produce anything else. It does not produce a free-form characterisation of the operator. It does not produce a probability distribution over an open-ended diagnostic space. It does not produce a derivative inference about traits, propensities, or future states. The bound is enforced at the classifier interface: the consuming policy is permitted to consume the score on the hypotheses for which it is credentialed and is not permitted to consume the underlying deviation vector or the baseline statistics that produced it. A consumer that wishes to perform additional inference must commission its own characterisation under its own credential and against its own consenting population.

Tier-gated access is implemented as a credential-bound capability rather than as a runtime check. The credential under which a consuming application is admitted to the subsystem encodes the hypothesis set the application may evaluate, the confidence floor it may operate at, and the audit obligations it incurs by consumption. A consumer cannot widen its hypothesis set by argument; it can do so only by presenting a credential of greater scope, and the subsystem will not honour a credential that has not been signed by the appropriate authority. The credential structure follows the same authority chain that governs the rest of the biological-identity stack, so that a consumer's access to state inference is congruent with its access to the underlying continuity slope.

The inference is also bounded in time. Each indication carries an explicit validity window beyond which it must not be relied upon, and the subsystem does not extrapolate an indication forward in time. A policy that needs a current indication must request one; the subsystem will compute it against the current observation window and emit it with a fresh time-stamp. This discipline prevents stale indications from accumulating influence and prevents an early-shift fatigue indication from being relied upon late in a shift after the operator's state has changed.

Prior-Art Distinction

Existing physiological-monitoring practice falls into two camps. The clinical camp uses dedicated sensors, explicit enrolment, and population-referenced thresholds; it is accurate within its operating envelope but it requires the subject to participate actively in the monitoring, and it produces measurements that must be interpreted against norms that may not fit the individual. The consumer camp uses ambient sensors and population-referenced heuristics; it is unobtrusive but it is noisy at the individual level because it lacks an individualised baseline.

The mechanism described here differs from both. The reference is the individual's own accumulated baseline rather than any population norm; the sensing is shared with the identity-continuity subsystem rather than dedicated to physiological monitoring; the output is a bounded, tier-gated indication rather than an absolute measurement; and the storage discipline keeps the underlying signal out of persistent record. The combination is not present in the clinical literature and is not present in the consumer wearable literature.

Disclosure Scope

The disclosure covers the mechanism by which an individual-referenced biological baseline is used to support bounded inference of internal physiological state, the tier-gated structure that governs which consumers may consume which hypotheses, the enumerated-hypothesis discipline that prevents speculative extension, and the storage discipline that keeps raw signals out of persistent record. It covers the embodiments above and any embodiment that preserves the baseline-and-deviation, bounded-score, tier-gated-consumption, and minimised-storage disciplines together. It does not cover any embodiment that retains raw biometric traces beyond the streaming window, that uses population thresholds in lieu of an individual baseline, or that exposes hypothesis indications to consumers whose credentials do not extend to the hypothesis in question.