- Add extract_conversation_patterns method to PatternExtractor class
- Extract all pattern types (topic, sentiment, interaction, temporal, style)
- Calculate overall confidence score across all pattern types
- Close personality learning pipeline integration gap
- Implement PersonalityAdaptation class with time-weighted learning and stability controls
- Integrate PersonalityLearner with MemoryManager and export system
- Create memory-integrated personality system in src/personality.py
- Add core personality protection while enabling adaptive learning
- Close personality learning integration gap from verification report
- Created src/memory/personality/__init__.py module structure
- Implemented PatternExtractor class with multi-dimensional analysis:
- Topics: Track frequently discussed subjects and user interests
- Sentiment: Analyze emotional tone and sentiment patterns
- Interaction: Response times, question asking, information sharing
- Temporal: Communication style by time of day/week
- Response styles: Formality level, verbosity, emoji/humor use
- Pattern extraction methods for all dimensions with confidence scoring
- Lightweight analysis techniques to avoid computational overhead
- Pattern validation with stability tracking and outlier detection
