Rag and reminder rings: surfacing memories for humans and llms
AssigneeConversation Processing Intelligence Corp.
InventorsRICHARD LEEDS, ELON GASPER
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Abstract
A system and method for collaborative Al, machine learning units, macrocellular automata, and experience chain processing integrated with RAG and ReminderRing technology. The invention seeks to make Collaborative Conversational AIs (CCAIs), such as those used in conversational digital personal assistants, more productive, collaborative, and useful in Notification Selection based on context and experience. The invention includes further methods and apparatus relating to topics within the context of intelligent software agents, including modern conversational LLMs, as well as persuadable AIs, machine learning units, and machine learning by experience.
Claims27
A system for collaborative conversational artificial intelligence enabled notification selection, the system comprising:
- a plurality of data streams providing content and context input;
- a plurality of recognizers, each coupled to at least one of the plurality of data streams;
- a pre-processing module comprising a plurality of pre-processors for selection and scoring, each coupled to at least one of the plurality of recognizers;
- an integration module coupled to the plurality of pre-processors for integrating the inputs and scoring,
- a selection module coupled to the integration module for selecting a notification based on the integrated inputs;
- a feedback management system coupled to the selection module, said feedback management system comprising a CCAI including a facilitator and a plurality of subminds;
- and
- an extraction module coupled to the feedback management system for extracting and transmitting the selected notification.
A system for collaborative conversational artificial intelligence (CCAI) ensembles, the system comprising:
- a plurality of machine learning units (MLUs), each configured for collaboration through a common protocol;
- at least one CCAI configured to incorporate the plurality of MLUs as subminds;
- wherein the incorporation of the MLUs in the at least one CCAI forms a collaborative ensemble in which expertise is aggregated across the MLU subminds and the at least one CCAI.
A system for improving machine learning performance in a distributed environment, comprising:
- a plurality of machine learning units (MLUs), each trained on a subset of data;
- and
- at least one collaborative conversational artificial intelligence (CCAI) configured to evolve its structure by selectively incorporating the plurality of MLUs by rearranging and recruiting MLU subminds based on an analysis of expertise aggregated across the distributed environment;
- wherein the evolution of the CCAI by rearrangement and recruitment of MLU subminds drives machine learning improvements across the distributed environment.
A method for integrating collaborative conversational artificial intelligence (CCAI) with external systems for improved functionality, the steps comprising:
- interfacing a plurality of data streams providing content and context input to at least one CCAI;
- coupling at least one selection module for choosing a notification to at least one submind of the CCAI;
- connecting the at least one selection module to a plurality of pre-processing modules each coupled to at least one data stream;
- and
- enabling the CCAI to incorporate one or more machine learning units (MLUs) as subminds to aggregate expertise,
- whereby the interconnected modules, MLUs and CCAI form an integrated system enabling optimized collaborative processing.
A system for transparent analysis in a collaborative conversational AI (CCAI), comprising:
- at least one CCAI incorporating a plurality of members including machine learning units (MLUs), the CCAI configured to conduct a collaborative discussion on a topic and determine an outcome;
- and
- a visualization module configured to render a graphic representation of individual contributions and decision-making influence for each of the plurality of members in determining the discussion outcome;
- whereby rendering transparent member participation enables improved accountability, participant and facilitator performance metrics, and facilitator insight for automated analysis.
A system for simulating collaborative intelligence, comprising:
- at least one large language model (LLM) configured via at least one of preconfiguration, context and prompting to enact a plurality of personalities as members in a forum;
- wherein the LLM simulated conversation enables exploration of collective and collaborative intelligence for research purposes without necessitating real-time human participation.
A system for simulating a collaborative intelligence system to enable accelerated compilation, comprising:
- an instructor module configured via prompting to direct a large language model (LLM) to simulate a plurality of components in a virtual implementation of a target collaborative intelligence system;
- and
- a compiler configured to translate all or a portion of the simulation into an executable, optimized version of the target collaborative intelligence system.
A system for improving the interpretability and auditability of AI-driven decision-making, comprising:
- a) a CCAI architecture incorporating a plurality of subminds;
- b) a conversation tracking module configured to record and store the interactions and decisions made by the subminds;
- c) a visualization module configured to render at least one of a graphical, auditory and other sensory, somatic and other recorded representation of the decision-making process, including the individual submind contributions and the influence of each submind.
A method for enhancing the performance of a collaborative conversational AI (CCAI) system, comprising:
- a) integrating one or more large language models (LLMs) as subminds within the CCAI;
- b) leveraging the LLMs to generate the most natural and contextually relevant responses during collaborative decision-making;
- c) utilizing the LLMs to facilitate cross-domain reasoning and adaptability within the CCAI.
The method 24 where functionality includes a failsafe step including at least one of catastrophic failure prevention by bounds checking, maintenance requirements, out of tolerance, error checking utilizing US government data, error checking using government intellectual property registrations.
The method 24 where functionality includes a failsafe step including at least one of prevention of damage, dangerous situations, bad actor entry, emergencies using at least one of Experience Chain Data Structure analysis, prediction and posited responses.
Dependencies are inferred from the claim text.