Abstract
Artificial intelligence systems increasingly rely on persistent memory to improve personalization, long-term reasoning, enterprise assistance and autonomous decision making. Existing memory architectures primarily store information as static embeddings or conversational history without mechanisms for structured lifecycle management, gradual forgetting, temporal relevance evaluation, or policy-aware governance. This invention introduces a computational framework for intelligent memory lifecycle management in AI systems. Information generated or ingested by the AI is transformed into structured claim-level semantic units enriched with contextual, temporal and risk metadata. A scoring engine continuously evaluates each claim using relevance, usage frequency, recency, sensitivity and policy signals to dynamically determine retrieval priority, reinforcement, attenuation, or removal. The framework incorporates adaptive decay functions, obsolescence detection, policy-driven control mechanisms and auditable lifecycle tracking to enable reliable, compliant and continuously evolving AI memory behaviour.
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Recommended Citation
INC, HP, "Adaptive Multi-Factor Knowledge Prioritization and Temporal Control in Artificial Intelligence", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10888