Abstract
Contemporary personal-AI systems preserve continuity in the weakest possible way: the provider owns the model, the memory, and the interface, and the user retains accumulated context only so long as the service remains available and willing to keep it. When a model changes, a platform resets memory, or a provider alters policy, the user's interpretive history can vanish. This disclosure presents the Continuity Node Framework, a user-owned, local-first, longitudinal memory-and-interpretation architecture in which the durable asset is not the AI model but the preserved, provenance-tagged map of a person's reasoning, values, patterns, and tested interpretations over time, and in which inference engines remain interchangeable while the user-owned continuity layer persists.
The framework composes ten layers — raw archive, search index, interpretive ledger, pattern register, witness federation, dissent and memory-rights controls, reflective surfacing, continuity operations, continuity endowment, and a coordination interface — over four cross-cutting concerns: validation, capture and third-party consent, security, and over-reliance. It rests on three invariants: raw memory is separated from interpretation and kept immutable and content-addressed; every derived layer is rebuildable from raw; and continuity is maintained through lineage rather than mere storage. Every interpretation is recorded with its engine, prompt, lens, evidence, confidence, and the user's response, with conflicting interpretations coexisting rather than overwriting.
Released as defensive prior art, the framework claims novelty not in any single component — most are individually known — but in their governed arrangement into a durable, user-owned continuity system that preserves dissent and revision rights, supports witness-attested validation, maintains lineage across engine and generational change, and exports only permissioned, provenance-tagged abstractions to higher-order coordination layers.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Walker, Joseph JM, "System and Method for User-Owned Longitudinal Memory and Interpretation System with a Provenance-Tagged Interpretive Ledger, Engine-Interchangeable Inference, Federated Validation, and Generational Maintenance", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10374