Fleet Analytics
Privacy-preserving fleet monitoring from fewer than 20 scalar values. An 8-component identity fingerprint. Privacy ratio 1,110:1. No raw sensor data leaves the device. Environmental classification, relocation detection, deployment verification, and drift monitoring—all without cameras, GPS, or telemetry.
What it is
A fleet of 500 robots is deployed across eldercare facilities. How do you monitor them without cameras, GPS, microphones, or any raw sensor data? Prov 3 answers: each robot computes an 8-component identity fingerprint from its accumulated CCF state—trust landscape shape, vocabulary density, phase distribution, temporal rhythm. The fingerprint is fewer than 20 scalar values. The raw sensor data stays on-device.
The privacy ratio is 1,110:1. The robot’s internal state space has 22,201 dimensions. The fingerprint has 20. Everything between is irreversibly compressed. You cannot reconstruct what the robot saw, heard, or who it interacted with from the fingerprint. But you can tell which ward it’s in, whether it’s been moved, whether its deployment matches its training, and whether the environment has changed.
The fleet server clusters fingerprints to discover environment taxonomies, detects outliers, compares cohort trajectories, and computes quality scores. Adaptive thresholds adjust per environment type. Store-and-forward handles intermittent connectivity—robots buffer fingerprints locally and transmit on reconnect. Time series reconstruct without gaps.
Problems it solves
Monitoring 500 robots without surveillance
Traditional fleet monitoring uses cameras, GPS, and full telemetry. In eldercare, that's a HIPAA/GDPR minefield. CCF fingerprints contain fewer than 20 numbers with no individual identifiers, no interaction content, and no raw sensor data. You get full operational visibility with zero privacy exposure.
Detecting relocation without location sensors
A robot is moved from Ward A to Ward B. It has no GPS. But its fingerprint drifts against its adaptive threshold (μ + 3σ). The fleet server flags relocation within hours. The signal comes from the robot's own coherence landscape changing—different people, different routines, different ambient conditions. Location is inferred from behavioral change.
Detecting staffing changes from robot behavior
A care home reduces overnight staffing. The robot's fingerprint shows sustained sub-threshold drift in its temporal rhythm component and phase distribution. The fleet server flags environmental drift. Nobody told the system about the staffing change—the robot's trust landscape revealed it.
HIPAA/GDPR compliance by construction
The privacy is not a policy—it's a mathematical property. With 1,110:1 compression ratio, the reconstruction problem is provably ill-posed. An attacker with access to every fingerprint ever transmitted cannot recover who the robot interacted with, what was said, or what was observed.
Real-world scenarios
Eldercare fleet: 50 rooms, correct ward verification
A care home chain deploys companion robots across 50 rooms in three buildings. Each robot’s fingerprint encodes the signature of its environment: lighting rhythm, noise patterns, interaction frequency, temporal routine. The fleet server clusters fingerprints and automatically discovers that Building A has three environment types (day ward, night ward, common room). When a robot is mistakenly moved to a different wing after cleaning, the fingerprint drift triggers a relocation alert within 4 hours—before any resident notices their companion is missing.
Agriculture: 200 fields, equipment relocation detection
Autonomous monitoring stations in agricultural fields collect CCF fingerprints from their environmental interactions. When a station is moved to a different field (different soil, different crop, different microclimate), its fingerprint diverges. The fleet server detects relocation without any GPS signal—the environmental signature alone is sufficient. Seasonal drift monitoring tracks gradual changes in field conditions across growing cycles.
Hospital: deployment health scoring
A hospital deploys service robots across departments. The fleet analytics module computes deployment health scores by comparing each robot’s fingerprint trajectory to its cohort baseline. A robot in the emergency department whose fingerprint shows declining interaction quality (rising tension, falling coherence) signals that its environment has become more stressful. The operations team investigates and discovers a layout change that puts the robot in a high-traffic bottleneck. The fingerprint caught it before the staff complained.
What the claims cover
Claims 1--9 -- Fingerprint Computation and Use
8-component identity fingerprint computed locally from accumulated operational state. Fewer than 20 scalars transmitted. No raw sensor data for any monitoring function. Composite identity distance across vocabulary, familiarity landscape, and phase distribution.
Claims 10--17 -- Fleet Monitoring Applications
Environmental classification, relocation detection without GPS (fingerprint drift against μ + 3σ threshold), deployment verification, environmental drift monitoring. Adaptive thresholds from fleet-wide statistics.
Claims 18--27 -- Fleet Analytics and System
Environment taxonomy discovery via clustering. Outlier detection. Cohort trajectory comparison. Quality scoring. Fleet server with fingerprint database, reference library, and analytics module. Store-and-forward for intermittent connectivity with gap-free time series reconstruction.
Applications
Licensing enquiries
CCF is released under BSL-1.1 — free for evaluation and non-commercial use. Commercial licensing is available from Flout Labs.
cbyrne@floutlabs.com