US 2026/0087486 A1Published application

Ai co-pilot platform for generating computational awareness and autonomous operational guidance

AssigneeRMINT Inc

InventorBalaji Kannaiyan

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Abstract

A system and method for enabling autonomous restaurant operations are disclosed. The system is executed by an AI co-pilot server platform comprising a multi-agent AI architecture operating on an Aggregated Domain Intelligence Layer. A Discovery Intelligence Agent interacts with users via a multi-modal interface to generate personalized “Menu Directives.” An Operational Intelligence Agent then orchestrates a suite of specialized, sLLM-powered Task Agents to autonomously generate a complete operational plan. This plan includes a discovered price point, a time-aware schedule, and skill-based execution guidance. The system's intelligence is built and maintained through a continuous train-evaluate-inference loop, employing techniques such as hierarchical fine-tuning of foundational LLMs and Reinforcement Learning from Human Feedback (RLHF). The platform transforms multi-modal culinary content into dynamic, personalized experiences and provides deep audience awareness to creators and restaurants, with content usage metered via a secure attribution system.

Claims20

Dependent claims

A computer-implemented method executed by an artificial intelligence (AI) co-pilot server platform, the method for enabling autonomous restaurant operations by computationally transforming user-defined culinary co-creation requests into a scheduled, priced, and actionable operational workflow, the method comprising:

by a Discovery Engine:
a. receiving user input data, the user input data comprising user preference information for multi-modal culinary content;
b. analyzing the received user input data using one or more machine learning models to determine structured user preference characteristics and a predicted number of customers for specific multi-modal culinary content;
and
c. generating, based on the determined structured user preference characteristics and the predicted number of customers, a demand-informed menu directive for a target restaurant;
by an Operational Engine:
d. retrieving one or more multi-modal content elements from a content database, wherein the one or more content elements are computationally linked to items in the generated menu directive;
e. autonomously discovering, based on the predicted number of customers and an analysis of operational metrics, a menu-directive-informed price point determined to optimize an operational profitability metric;
and
f. autonomously creating, based on a generated data package of multi-modal execution content and contextual data, a schedule for the target restaurant;
by the AI co-pilot server platform:
g. transmitting the generated data package, the created schedule, and the determined price point, said transmitted data together comprising an autonomous operational workflow, to one or more restaurant computing devices;
and
h. recording, in a database, a transaction record when creator-sourced content is included in the autonomous operational workflow, wherein the transaction record identifies a source of the creator-sourced content to create a data trail for metering said content usage.

11 more elements

A computer-implemented artificial intelligence (AI) Co-Pilot server system for enabling autonomous restaurant operations by computationally transforming user-defined culinary co-creation requests into a scheduled, priced, and actionable operational workflow, the system comprising:

a. a Multi-Modal Data Ingestion Layer configured to receive heterogeneous data streams, including User Multi-Modal Input and Creator Content Input;
b. an Aggregated Domain Intelligence Layer comprising a non-transitory memory storing a multi-component memory system including:
i. an Experiential Memory for storing time-ordered operational experience data;
and
ii. a Skill Memory storing Compact Latent Representations (CLRs) of multi-modal execution content;
c. a Deep Learning Processing Infrastructure comprising a plurality of computing devices including one or more Graphics Processing Units (GPUs) or specialized Tensor Processing Units (TPUs), the infrastructure configured to execute a multi-agent AI architecture, the architecture including:
i. a Model Training Sub-System configured to perform a hierarchical fine-tuning process to generate a Foundational Multi-Modal Culinary LLM and a plurality of specialized, Hyper-Local small language models (sLLMs);
and
ii. a Distributed Orchestration Sub-System configured to dispatch specialized tasks to the Hyper-Local sLLMs executing on distributed Local Execution Layers;
and
d. an Operational Intelligence Agent Hub executing on the processing infrastructure, the Hub being configured to execute a generative flow field, said field defining a continuous transformation between a Discovery Agent's Person-Aware Latent Space and an Operational Agent's Constraint-Aware Latent Space to autonomously discover a topologically-optimized price point and create a schedule based on the execution of the multi-agent AI architecture.

8 more elements

Dependencies are inferred from the claim text.

Facts

Application: 19/397,892Filed 22 Nov 2025
Priority: 8 Mar 2022
Published: 26 Mar 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification12 codes

G06Q 50/12
Services: hotels or restaurants
G06N 20/00
Machine learning
G06Q 20/363
Using electronic wallets or electronic money safes: with the personal data of a user
G06Q 20/389
Payment protocols; Details thereof: keeping log of transactions for guaranteeing non-repudiation of a transaction

Family

Application63/317,664Us provisional application · filed 8 Mar 2022
Application18/116,881Continuation · filed 3 Mar 2023
Application19/397,892Continuation
US 2023/0289776 A1Published application
US 2026/0087486 A1This patent

From the US filing history. Foreign family members aren’t listed.

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