Physics-Anchored Anomaly Detection • Semantic Drift Analysis • Early-Stage Cognitive Intelligence
VisualAcoustic.ai is the public demonstration portal for Phocoustic, Inc., showcasing technologies that merge physics-based sensing, drift-driven anomaly detection, and early-stage artificial cognitive intelligence (ACI).
The system analyzes how a scene changes over time, not just how it looks in a single image. This shift — from pattern recognition to change recognition — underpins all capabilities of the Visual-Acoustic Semantic Drift Engine (VASDE).
It means the system:
Validates drift or anomaly signals against physical admissibility,
Rejects changes inconsistent with optics, mechanics, or known stability patterns,
Does not rely solely on statistical correlations or learned textures,
Maintains deterministic reasoning pathways that are explainable and inspectable.
The system views instability as a physical phenomenon, not a probability distribution.
Requires large training datasets
Classifies defects by learned visual patterns
Depends on statistical approximations
Can miss early-stage issues
May produce uncertainty or hallucinations without strong priors
Requires zero defect training data
Detects anomalies through drift and instability, not labels
Uses deterministic physics signals to anchor decisions
Identifies pre-defect instabilities before visible failure
Produces outputs traceable to physical causes rather than abstract weights
This is a category shift: from pattern detection → to dynamic stability sensing.
Semantic drift is a measurable form of meaningful change across sequential frames, where:
motion is consistent,
direction is persistent,
magnitude grows or oscillates in interpretable ways.
Unlike raw pixel difference, semantic drift requires:
continuity,
physical feasibility,
temporal repeatability, and
localized distinctiveness.
Only drift meeting these criteria becomes a candidate for anomaly classification.
The system discards:
photon noise
sensor grain
flicker or exposure turbulence
spurious reflections
nonlinear compression noise
nonphysical “jumps” inconsistent with object structure
These are rejected by PADR, TDAL, PQRC, and SEGEN gating.
Often earlier than humans, CNNs, or rule-based inspection systems.
Early drift emerges as:
microtexture instability,
local reflectance inconsistency,
vibration-induced misalignment,
subsurface stress propagation,
delamination precursors, or
solder fatigue micro-movement.
VASDE can amplify these signals well before visible damage develops.
High-gain drift heatmaps
Color-fused microtexture patterns
Magnified directional deltas
PADR-quantized stability layers
PQRC directional arrows
TDAL-aligned region comparisons
Semantic bounding boxes (“emergent_anomaly”)
Older images demonstrate system sensitivity; newer images reflect production semantics and CIP-aligned modules.
Converts raw drift into structured quantized form.
Filters noise, preserves physical change.
Ensures stability by aligning frame sequences.
Compensates for vibration/translation to isolate true anomalies.
Extracts directionality of drift.
Reveals the “source” and “trajectory” of instability.
Ensures deterministic drift interpretation and scenario governance.
Synthesizes multiple drift signals into coherent semantic meaning.
Provides selective amplification/suppression of cognitive events.
Prevents overactivation and hallucination-like behavior.
VISURA / Sensor stack captures imagery.
VASDE computes drift signatures.
PADR quantizes valid physical changes.
TDAL aligns temporal sequences.
PQRC identifies directional patterns.
PAIL / PSYM labels emergent semantic regions.
PHOENIX evaluates context and significance.
SEGEN regulates which conclusions propagate.
The system forms a layered, deterministic reasoning process — not a monolithic neural network.
VASDE identifies:
micro-cracks
slurry non-uniformity
reflectance resonance anomalies
subtle contamination
die-level instability patterns
Because drift is independent of training sets, new wafer patterns require no retraining.
Drift-based detection is highly effective for:
solder fatigue
bond stress propagation
pin warpage
lifted pads
conductive pathway shift
oxidation-induced instability
Even geometrically similar components produce distinct drift fingerprints.
VASDE is robust against:
belt oscillation
variable lighting
throughput speed changes
It highlights physically inconsistent motion such as:
objects bouncing incorrectly,
small fragments beginning to separate,
alignment drift along edges,
precursor vibration to mechanical jams.
XVADA uses drift-based perception to detect:
walkable or drivable boundaries,
unstable objects (tires, debris),
fog-penetration drift,
inconsistent reflectance patterns from headlights.
It is highly stable because it's governed by physics rather than learned texture.
ACI is a physics-governed cognitive model that:
evaluates drift evidence,
forms coherent semantic meaning,
rejects non-physical interpretations,
and escalates only when justified.
ACI does
not emulate human consciousness.
It instead builds a deterministic cognitive loop grounded entirely in
physics.
PHOENIX creates structured semantic relationships between:
drift patterns
object identities
temporal behavior
contextual meaning
It is where raw drift becomes interpretable cognitive content.
SEGEN (Semantic Epigenetic Gating):
regulates cognitive activation,
prevents runaway drift interpretation,
strengthens stable meaning over time,
aligns cognition to physical constraints.
Much like biological epigenetics modulates gene expression, SEGEN modulates semantic activation.
It can incorporate neural modules when appropriate, but:
ACI operates even without them,
most reasoning is deterministic,
drift interpretation is physics-first,
semantic gating follows structured rules.
Neural components enhance, not define, the system.
ASIC avoids hallucinations by enforcing three rules:
Physical admissibility — if it’s not physically possible, it’s rejected.
Temporal consistency — isolated spikes are invalid.
Semantic gating — SEGEN blocks cognitive overactivation.
Unlike free-form LLMs, ACI is highly constrained.
The system uses:
multi-frame consistency
structural pattern verification
directionality convergence
temporal scoring
object-context alignment (OCID / ORDL)
Only drift that persists through multiple filters is surfaced.
Yes.
Each stage leaves an interpretable trace:
drift maps
TDAL alignment offsets
PQRC directional fields
semantic activation logs
SEGEN gating decisions
This transparency is crucial for industrial adoption.
Public filings cover:
physics-based drift extraction (VASDE)
PADR quantization
TDAL alignment
PQRC directional mapping
semantic drift classification
cognitive execution systems (PHOENIX, SEGEN)
developmental semantics (VGER)
and related structured ACI frameworks.
Full legal definitions appear only in official USPTO/WIPO publications.
No.
This FAQ is explanatory only.
The patents themselves define:
claim scope
variations
dependencies
and enforceable rights.
Not necessarily.
The drift engine is lightweight and can run on:
CPUs,
embedded systems,
machine vision hardware,
or GPUs for high-throughput systems.
Drift-based methods require minimal calibration:
baseline frame stability
motion model configuration
optional ROI masking
optional integration with structured light or multi-camera setups
No training cycles are needed.
Yes, via:
camera feeds (RGB, NIR, IR, structured light)
multi-camera arrays
inspection machines (wafer, PCB, SMT)
vehicle-mounted sensors