Glossary
Pattern Recognition Layer
Layer 5 of the 5-Layer Architecture - unsupervised learning that surfaces 7 validated behavioral archetypes from vector spaces.
Definition
Pattern Recognition is the fifth and final layer of the Sentient 5-Layer Architecture. It uses unsupervised learning to discover behavioral clusters in the DNA layer's vector spaces - finding patterns that demographics miss. Instead of 'Males, 30-40,' the system surfaces archetypes like 'Skeptical Innovators' and 'Value Optimizers.' These archetypes are not theoretical - they are continuously validated against hard outcome data (revenue, margin, conversion rate). 7 behavioral archetypes have been validated in production. Archetypes emerge from data, not assumptions, and new archetypes can emerge as behavior shifts. Every archetype feeds all 8 Command Center modules.
Why It Matters
Pattern Recognition turns vector spaces into actionable segments. These are the behavioral archetypes that demographics cannot find.
Related Pages
Related Terms
5-Layer Architecture
The Sensor, Translator, Logic Engine, DNA, and Pattern Recognition pipeline that transforms raw signals into deterministic decisions.
DNA Layer
Layer 4 of the 5-Layer Architecture - every actor encoded as a point in 48-dimensional vector space. Mathematics instead of databases.
Behavioral Archetypes
Data-driven behavioral segments like 'Skeptical Innovators' and 'Value Optimizers' - found by unsupervised learning, validated against outcomes.
Archetypal Clustering
Unsupervised learning to identify behavioral clusters like 'The Skeptical Innovators' rather than demographic groups.
Unsupervised Learning
ML approach that discovers hidden patterns without labeled data. Used in Layer 5 for behavioral archetype identification.
Explore the Full Platform
See how these concepts come to life inside Sentient OS.