Implement pattern learning engine

[BillChirico]

🧠 Implement Pattern Learning Engine

Overview

Build the intelligence layer that learns from user behavior and adapts messaging accordingly.

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Goals

• Learn check-in patterns (when user is active)
• Identify trigger patterns (risky moments)
• Remember effective coping strategies
• Adapt tone to user's style
• Respect streak sensitivity preferences

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Pattern Types

Pattern	Data Source	Learning Method
Check-in times	Daily check-in timestamps	Time-series clustering
Trigger patterns	Trigger logs + check-in mood	Correlation analysis
Coping strategies	Tool usage + follow-up mood	Success rate tracking
Communication style	Message response patterns	Tone matching
Streak sensitivity	Milestone reactions	Sentiment analysis

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Technical Architecture

User Activity → Pattern Detection → mem0 Storage → Inference → Personalization

Components

1. Data Ingestion — Collect events (check-ins, triggers, tool usage)
2. Feature Extraction — Convert events to learnable features
3. Pattern Detection — Identify recurring behaviors
4. mem0 Storage — Persist learned patterns (Implement mem0 user memory system #423)
5. Inference Engine — Apply patterns to messaging decisions

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Pattern Examples

Check-in Time Learning

User checks in at:
- Day 1: 8:15 AM
- Day 2: 8:05 AM
- Day 3: 8:20 AM
- Day 4: 8:12 AM

→ Pattern: Morning person, ~8 AM availability
→ Action: Schedule messages for 8-10 AM window

Trigger Anticipation

User reports cravings:
- Friday 6 PM (3 weeks in a row)
- After work stress mentions

→ Pattern: Friday evenings are high-risk
→ Action: Proactive check-in Friday 5 PM

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Implementation Tasks

• Design pattern data models
• Implement event ingestion pipeline
• Build feature extraction layer
• Create pattern detection algorithms
• Integrate with mem0 (Implement mem0 user memory system #423)
• Build inference engine for messaging
• Add pattern confidence scoring
• Create pattern visualization for users
• Write tests for pattern detection

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Acceptance Criteria

• Detects check-in time patterns within 7 days
• Identifies trigger patterns with >70% confidence
• Coping strategy recommendations improve over time
• Tone adaptation reflects user responses
• Patterns persist across app reinstalls
• User can view/delete learned patterns

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Privacy Considerations

• Patterns stored locally first, encrypted sync
• User can export/delete all learned data
• No raw recovery details in pattern logs
• Anonymized IDs for cloud storage

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Related

• Parent: [Epic] Sobers Buddy Implementation #412
• Blocks: Build message generation and scheduling system #417
• Depends on: Implement mem0 user memory system #423 (mem0)

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Part of Sobers v2

[BillChirico]

Updated: Pattern learning should leverage mem0 (#423) for long-term memory storage and retrieval.

[Mnehmos]

May I recommend a DAG and typed node based chat histories and user meta data instead of flat chat history array. DAG nodes can be semantically searched to incorporate contextual rag experience.

The agent will build its own context history by attaching meta data enriched nodes to its context window for database backed persistent memory management