Phocoustic’s 20-patent family forms a stacked, mutually dependent
architecture in which each layer reinforces the one below it.
Together, they create a defensible
end-to-end pipeline from sensor input → physics drift
extraction → semantic development → cognitive stability → cross-object
lineage.
Goal:
Extract real, physically admissible change from optical,
structured-light, and related physical measurements.
Scope:
This foundational layer establishes deterministic methods for separating
genuine physical change from noise, glare, transient artifacts, and
statistical fluctuation. It enforces persistence across time, continuity
across space, and material plausibility before any change is allowed to
propagate further into the system.
Function:
By preventing hallucinated or unstable change from entering downstream
stages, this layer forms the physical truth anchor for every subsequent
semantic or cognitive capability.
Goal:
Convert raw physical change into stable, traceable internal
representations.
Scope:
This layer defines how the system maintains both a fixed, high-precision
reference baseline and a cautiously adaptive reference that updates only
when change is physically validated. Deterministic rules govern when
each reference is used and how they may be combined without corruption.
Function:
These mechanisms prevent slow defects or gradual instabilities from
being absorbed into the reference itself—a failure mode common in
adaptive vision systems—and provide a reliable internal standard for
inspection, navigation, and reasoning.
Goal:
Translate validated change and geometry into meaningful physical
interpretations.
Scope:
This layer focuses on how structured-light patterns deform across real
surfaces and how geometric variation reveals physical phenomena such as
warpage, micro-cracks, deformation, or subtle surface irregularities.
Semantic interpretation is constrained by physical behavior rather than
visual appearance alone.
Function:
These capabilities enable applications such as circuit board and
semiconductor inspection, fine geometric analysis, and navigation under
fog or glare—without requiring neural-network training.
Goal:
Enable physics-anchored formation, maturation, and pruning of semantic
structures.
Scope:
This layer establishes deterministic rules governing how meaning emerges
from physically validated change. Semantic structures evolve over time
based on stability, recurrence, and coherence, rather than statistical
exposure or retraining cycles.
Function:
This approach differentiates the system from purely data-driven models
by ensuring that semantic development remains grounded in physical
evidence.
Goal:
Allow environmental conditions to influence semantic development without
training.
Scope:
This layer introduces mechanisms by which context—such as operating
domain, environmental exposure, or repeated physical conditions—can
reinforce, suppress, or shape semantic interpretation over time.
Function:
It enables explainable specialization across domains (for example,
circuit boards versus wafers versus robotics), while preserving
deterministic behavior and avoiding opaque learning processes.
Goal:
Bind physical change, semantic interpretation, and environmental
influence to individual physical objects.
Scope:
Rather than treating images independently, this layer tracks how
specific objects evolve across time, preserving history, progression,
and auditability.
Function:
This capability is essential for manufacturing lines, conveyor systems,
semiconductor lots, robotics, and any environment where traceability and
long-horizon reasoning are required. It represents a significant
competitive barrier to imitation.
Goal:
Govern the entire pipeline using physics-constrained logic to prevent
unsupported inference.
Scope:
At the highest level, the system enforces multiple independent
consistency checks before allowing higher-level reasoning or alerts.
Cognitive activation is permitted only when physical evidence, temporal
stability, and contextual agreement converge.
Function:
This layer establishes a safe, physics-grounded cognitive framework that
never escapes verified evidence and avoids the uncontrolled behavior
often associated with unconstrained statistical models.
The patent portfolio defines a vertically integrated architecture:
Physical sensing and measurement
Verified detection of real change
Stable internal references
Structured representations of change
Physics-grounded semantic interpretation
Environment-conditioned semantic development
Object-level historical lineage
System-level cognitive supervision
Each layer depends on the integrity of the one below it. Removing or weakening any layer collapses the system’s ability to reason safely and explainably.
This page provides high-level summaries of selected United States and international patent filings related to the Phocoustic platform. These descriptions are intended for general informational and investor-relations purposes only. They do not reproduce claim language, algorithms, parameter values, or execution logic.
All specific methods, data structures, and functional relationships are defined exclusively in the filed patent applications and granted patents. Nothing on this page should be interpreted as limiting claim scope or providing an enabling technical disclosure beyond those filings.