The Pattern Recognition Specification is a modular framework for identifying, correlating, and understanding meaningful patterns across personal information. It provides a common foundation for systems that analyze recurring events, trends, relationships, and changes over time while keeping users in control of how discoveries are interpreted. The specification is designed to distinguish between raw observations, potential patterns, and user-confirmed insights rather than automatically treating detected relationships as facts.
The core modules provide pattern detection, personal data correlation, insight management, user confirmation, timelines, visualization, privacy boundaries, and explainability. These modules allow an implementation to identify recurring events, analyze temporal relationships, track discoveries, maintain evidence, visualize trends, and provide users with enough context to understand why a particular pattern was detected. The architecture also supports local-first processing, explicit data permissions, data minimization, and user-controlled retention and deletion.
Optional plugin modules extend the specification into specialized areas of personal pattern analysis. Journal Analysis can identify recurring themes and topics, while Dream Correlation can identify potential relationships between dream records and other events. Emotional Trend Detection tracks changes and recurring emotional indicators, Cycle Relationship Mapping examines relationships between recurring cycles and events, and Behavioral Pattern Discovery identifies repeated behaviors and contextual relationships. Additional plugins support Personal Insight Timelines, User-Confirmed Discoveries, and Visualization Generation.
A central feature of Pattern Recognition is its human-in-the-loop discovery model. The system can identify a potential pattern, explain the evidence behind it, and present it to the user for review. The user can then confirm, reject, rename, annotate, or continue tracking the discovery. This creates a system where automated analysis assists personal understanding without replacing the user’s judgment, making Pattern Recognition suitable as a foundation for privacy-conscious journaling, reflection, self-tracking, and personal knowledge systems.
This specification is released under the AGPL-3.0+ license and is free to use with required attribution. Attribution-free deployments may obtain a Specification Branding License, with fees based on usage, deployment scope, and environment size.
Specification Repository:
- HerOneiros – An open-source, AI-powered specification framework for women-centered inner awareness systems that support journaling, emotional intelligence, dream exploration, communication, and personal reflection.
Premium Module Specifications:
Pattern Recognition Module Specification Pricing:
| Network Size | # of Users | One-Time Price | Duration |
| Small | 1 – 20 | $40,000 | Perpetual License |
| Medium | 21- 1000 | $80,000 | Perpetual License |
| Large | 1001 + | Custom Quote | Custom Quote |
