Sample Walkthrough: Recognizing Patterns Before Structure

This is a high-level walkthrough of how a pattern engine might begin to operate before any formal database or graph structure is in place.

Step 1: Initial Observation

A child sees a brown, four-legged animal with a wagging tail.

Pattern Noticed

• Shape: 4 legs, small head, moving tail
• Color: brown
• Motion: wagging tail
• Sound: bark

These are captured as raw patterns.

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Step 2: Label Association

Someone points and says: “Dog.”

Result

• The system labels the observed pattern as Dog.

Now a soft association begins to form between the multi-sensory pattern and the word.

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Step 3: Reinforcement

Over time, the child sees other animals with similar patterns and hears the word “Dog” again.

Reinforced Pattern

• Pattern group: {4 legs + wagging tail + bark} → likely Dog

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Step 4: Rule Formation (Implicit)

The system now begins to form generalizations like:

• “Dog” usually has 4 legs.
• “Dog” may bark.
• “Dog” is not a cat (difference pattern emerging).

These aren’t rules in logic—they’re accumulated probability-weighted patterns.

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Step 5: Anomaly or Correction

One day the child sees a statue of a dog.

System Reaction

• Visually matches “Dog” pattern
• No sound, no movement

Possible outcomes:

• Adjust pattern confidence: Not all “Dogs” move or bark.
• Add new sub-pattern: “Some dogs are not alive.”

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Step 6: Conflict

The child is bitten by a dog.

New pattern:

• Dog → Pain

This introduces a new pattern link:

• Dog may be Dangerous

But this conflicts with:

• Dog → Friendly

The system now starts to track contradictory patterns and context (e.g., not all dogs are dangerous).

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What This Walkthrough Shows

• The system accumulates pattern links, not hard truths.
• Associations are formed from input and feedback.
• Contradictions are handled by splitting contexts or lowering confidence.
• No graph structure is needed yet—just recursive pattern registration and comparison.

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Next: Explore the Schema to see how this is formalized in a Graph and KeyPair database.