US 63/988,438 · Patent Pending

CCF Patent Information

US Provisional Patent Application 63/988,438 was filed 23 February 2026 by Flout Labs. The application covers the CCF framework — a novel method for emergent social behaviour in autonomous systems via context-keyed coherence accumulation.

Claim map — 34 claims across 6 modules

Each module is implemented as a separate Rust crate in ccf-core. The claim numbers below correspond to the patent application.

Claims 1, 8

SensorVocabulary / ContextKey

The fundamental encoding: any sensor set implementing SensorVocabulary<N> produces a deterministic, hashable ContextKey. This is the identity layer — every subsequent computation is keyed to it.

#1
A method for encoding multi-dimensional sensor state into a deterministic context identity using a SensorVocabulary trait with const-generic feature vector length N.
#8
A ContextKey structure combining a vocabulary instance with a pre-computed FNV-1a hash and normalised feature vector, enabling O(1) context lookup and cosine similarity comparison.
Claims 2–5

CoherenceAccumulator

The accumulation layer: dual exponential moving averages (short-run α_s and long-run α_l) per context key. Positive and negative interactions advance or decay each EMA independently.

#2
A CoherenceAccumulator maintaining two EMAs per context with configurable decay constants λ_s and λ_l.
#3
A method for computing instantaneous coherence from α_s scaled by Personality.reactivity.
#4
A method for computing context coherence from α_l representing accumulated long-run evidence.
#5
Decay-on-tick: both EMAs decay toward zero on every tick regardless of interaction event, providing natural forgetting.
Claims 6–7, 13

CoherenceField

The field layer: a map from ContextKey to CoherenceAccumulator. Supports positive_interaction(), effective_coherence(), and report_context_with_key() across all known contexts.

#6
A CoherenceField maintaining a bounded map of CoherenceAccumulators keyed by context hash, with O(1) lookup and insert.
#7
effective_coherence(): combines instantaneous and context coherence via the minimum gate condition.
#13
report_context_with_key(): produces a ranked context report across all known keys, enabling dashboard and audit output.
Claims 9–12

MinCutBoundary

The partition layer: Stoer–Wagner global min-cut on the context similarity graph. Separates the context space into two partitions; cross-partition influence is bounded by cut weight.

#9
A method for constructing a weighted context graph where edge weights are cosine similarities of context feature vectors.
#10
The Stoer–Wagner algorithm applied to the context graph to find the global minimum cut in O(|V|^3).
#11
A MinCutResult structure reporting partition_s, partition_complement, and min_cut_value.
#12
Cross-partition coherence blending proportional to min_cut_value, bounded to [0, 1].
Claims 14–18

SocialPhase

The output layer: four discrete social phases (ShyObserver, StartledRetreat, QuietlyBeloved, ProtectiveGuardian) determined by the minimum gate. Each phase maps to LED tint, motor scale, and voice tone.

#14
Four SocialPhase variants derived from the Cartesian product of (α_s threshold) × (α_l threshold).
#15
led_tint(): a method returning a 24-bit RGB colour per phase as a visual output signal.
#16
expression_scale(): a method returning a f32 motor/movement scale per phase.
#17
The minimum gate: phase is ShyObserver unless both α_s > θ_instant AND α_l > θ_context.
#18
Personality struct with bounded warmth, reactivity, caution fields modulating all thresholds.
Claims 19–23

SinkhornKnopp

The manifold layer: iterative row-column normalisation that projects the cross-context coherence matrix onto the doubly-stochastic manifold, preserving the conservation of social budget.

#19
A SinkhornKnopp projector taking an N×N coherence matrix and returning a doubly-stochastic matrix.
#20
Iterative alternating normalisation: D_r · A · D_c converging to A*.
#21
Convergence criterion: max absolute row/column sum deviation < ε.
#22
Integration with CoherenceField: SinkhornKnopp applied after cross-partition blending.
#23
no_std compatibility: projector uses fixed-size arrays, no heap allocation.

Licensing enquiries

CCF is released under BSL-1.1 — free for evaluation and non-commercial use. Commercial licensing is available. Contact us to discuss integration, white-labelling, or custom deployment agreements.

cbyrne@floutlabs.com
API docs: docs.rs/ccf-core