Conversational Order Automation Using Multi-LLM Validation
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Solution Overview
Problem
Current large language models (LLMs) struggle to transform natural conversations into structured business operations and transactions, such as orders, reservations, and schedules, without human intervention, particularly in industries like restaurants, hotels, and retail, and face challenges with multilingual support.
Innovation Solution
A system utilizing LLMs with a dialogue input box and order/reservation/schedule preview panel, combined with knowledge corpus vectors and relational databases, to enable intelligent and autonomous services, including intent analysis and real-time translation, to convert conversations into structured data for business systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional customer service systems are used, then human intervention is required to process orders and bookings, but this increases operational costs and reduces efficiency
Solution Approach 1:
The system enables self-service through AI agents that autonomously process customer orders and bookings without human intervention. The AI agents independently interact with customers, extract order information, validate details, and submit orders to the order processing system, allowing the system to serve itself rather than relying on human operators.
Solution Approach 2:
The patent replaces the mechanical system of human operators with an automated AI-based system. Large language models and natural language processing technologies substitute for human cognitive and manual work in order processing, transforming a human-dependent mechanical process into an automated digital system.
2Reliability
If manual order processing is used, then human employees can handle customer requests, but this increases error rates and reduces accuracy
Solution Approach 1:
The system incorporates feedback mechanisms where AI agents validate extracted order information against the order processing system's requirements. The system checks for completeness and accuracy of order details, and can request clarification from customers if information is insufficient, ensuring high reliability before final submission.
Solution Approach 2:
The AI agent acts as an intermediary between the customer and the order processing system. It translates natural language customer requests into structured order data, validates the information, and ensures compatibility with the order processing system's format requirements, thereby improving accuracy while maintaining speed.
3Reliability
If complex validation processes are implemented to ensure order accuracy, then error rates decrease, but system complexity increases
Solution Approach 1:
The system performs preliminary validation of order information during the conversation process itself, rather than as a separate post-processing step. The AI agent checks for required fields, validates data formats, and confirms order details with the customer in real-time, ensuring accuracy before final submission without adding complex post-validation infrastructure.
4Productivity
If multiple LLMs are used for different tasks (dialogue management, order extraction, validation), then task performance improves, but system complexity increases
Solution Approach 1:
The system segments the order processing task into distinct functional components handled by different LLMs: one LLM for dialogue management and customer interaction, another for order information extraction, and a third for validation. This segmentation allows each model to specialize in specific tasks, improving overall efficiency while managing complexity through modular architecture.
Data Source
AI summary
The present disclosure relates to systems, software, and computer-implemented methods for autonomous conversational ordering using AI technologies. An example method includes receiving one or more input messages from a user via a dialogue user interface (UI). The method further includes generating, by a first large language model (LLM), one or more output messages based on the one or more input messages and information of a provider. The method further includes transmitting the one or more output messages to the user via the dialogue UI. The method further includes determining, by a second LLM, that the user has submitted a request associated with an order with the provider. The method further includes generating a description of the request in a format in compliance with an order processing system of the provider and transmitting the description to the order processing system.