Systems and methods for personalized multi-user interactions in AI communication
Systems and methods for managing AI memory recall, validation, and secure access are disclosed. The invention enables AI to dynamically retrieve and verify stored memory across multiple user interactions while ensuring security and privacy compliance. A hierarchical memory access model restricts unauthorized recall based on user authentication levels, while a trust-weighted retrieval system adjusts memory recall permissions in real time based on evolving user trust scores. AI-driven validation mechanisms ensure that only relevant and accurate stored information is retrieved, cross-checking external validation sources when necessary. Additionally, AI communication-based memory retrieval enables multi-device interaction and adaptive recall filtering based on user preferences and device type. The disclosed system enhances AI conversational integrity, secures memory recall processes, and provides structured access control mechanisms to prevent unauthorized data exposure.
1 . A system for artificial intelligence (AI) memory recall, comprising:
a memory storage module configured to persist context beyond a single interaction, wherein the memory storage module comprises stored contextual data;
a generative artificial intelligence (GenAI) model with integrated memory recall;
a server operatively coupled to the memory storage module and the GenAI model, wherein the server is configured to:
recall, from the memory storage module, contextual data associated with a user profile across a plurality of prior interactions, wherein the contextual data is retrievable for provision to the GenAI model;
authorize, using a hierarchical access control mechanism, a user access to the contextual data, wherein:
the hierarchical access control mechanism restricts the user access based on user authorization levels; and
the recall is dynamically adjusted based on the user authorization levels, preventing unauthorized retrieval;
validate, using a memory validation module, the contextual data, based on whether the contextual data satisfies accuracy or consistency requirements, wherein:
the memory validation module performs real-time cross-referencing of the contextual data against external knowledge sources; and
the server prevents output of unvalidated contextual data by discarding or flagging the contextual data that fails the memory validation module;
filter, using an adaptive recall filtering mechanism, the contextual data according to a dynamically assigned user trust score, wherein the user trust score is refined based on a sentiment analysis of user interactions, and wherein the sentiment analysis performed by the GenAI model; and
output, the contextual data to the GenAI model for generating one or more personalized responses, the output only occurring if the contextual data is validated and the user access to the contextual data is authorized.
2 . The system of claim 1 , wherein the memory storage module is configured to allow selective deletion of the stored contextual data based on predefined retention policies.
3 . The system of claim 1 , wherein the server assigns contextual tags to the stored contextual data, the contextual tags enabling-expedited retrieval.
4 . The system of claim 1 , wherein the GenAI model determines a probability of outdated information and adjusts a memory weighting accordingly before the recall.
5 . The system of claim 1 , wherein the user authorization levels are dynamically assigned and updated based on authentication credentials and interaction history.
6 . The system of claim 1 , wherein the user authorization levels are modified in real-time based on organizational policy changes or user-specific security parameters.
7 . The system of claim 1 , wherein the memory validation module includes a self-verification module that identifies inconsistencies in the stored contextual data before the recall.
8 . The system of claim 1 , wherein the GenAI model reduces access to the contextual data if the user trust score falls below a predefined threshold.
9 . The system of claim 1 , wherein the server is further configured to track, using an auditing mechanism, memory recall history.
10 . The system of claim 9 , wherein the GenAI model generates an audit log for the memory recall history to ensure compliance with data protection regulations.
11 . The system of claim 1 , wherein the server recalls the contextual data when a user switches between devices.
12 . The system of claim 1 , wherein the GenAI model filters the one or more personalized responses based on a device type of the electronic device.
13 . The system of claim 1 , further comprising a user customization interface that allows manual adjustments to memory recall settings.
14 . The system of claim 1 , wherein the server is further configured to resolve, using a conflict resolution mechanism, discrepancies in the AI memory among different access levels, wherein the conflict resolution mechanism includes an escalation protocol upon an unauthorized access attempt.
15 . The system of claim 1 , wherein the GenAI model ranks the contextual data based on user-defined importance levels.
16 . The method of claim 1 , wherein the GenAI model generates proactive memory suggestions based on detected patterns of one or more user inputs.
17 . The system of claim 1 , wherein the server provides users with a transparency reports on how the memory recall is determined.
18 . The system of claim 1 , wherein the hierarchical access control mechanism identifies access anomalies and flags potential security breaches in real-time.
19 . The system of claim 1 , wherein the memory storage module stores multiple user profiles, interaction history, and contextual tags associated with prior communication sessions.
20 . A method for AI memory recall, comprising:
recalling, from a memory storage module, contextual data associated with a user profile across a plurality of prior interactions, wherein:
the contextual data is retrievable for provision to a GenAI model with integrated memory recall; and
the memory storage module is configured to persist context beyond a single interaction;
authorizing, using a hierarchical access control mechanism, a user access to the contextual data, wherein:
the hierarchical access control mechanism restricts the user access based on user authorization levels; and
the recalling is dynamically adjusted based on the user authorization levels, preventing unauthorized retrieval;
validating, using a memory validation module, the contextual data, based on whether the contextual data satisfies accuracy or consistency requirements, wherein:
the memory validation module performs real-time cross-referencing of the contextual data against external knowledge sources; and
the server prevents output of unvalidated contextual data by discarding or flagging the contextual data that fails the memory validation module;
filtering, using an adaptive recall filtering mechanism, the contextual data according to a dynamically assigned user trust score, wherein the user trust score is refined based on a sentiment analysis of user interactions, and wherein the sentiment analysis is performed by the GenAI model; and
outputting the contextual data to the GenAI model for generating one or more personalized responses, the outputting occurring only if the contextual data is validated and the user access to the contextual data is authorized.