Conversational Interface for Simulation via Language Models
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Solution Overview
Problem
Existing computer-based simulation methods require users to input data in a non-interactive and strictly formatted manner, leading to errors due to incomplete or incorrect inputs, and necessitate high user proficiency.
Innovation Solution
A computer-implemented method that uses natural language processing to determine user intent, interactively request additional input, and configure simulation models, enabling conversational user interfaces for simulation applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users input data in a strictly formatted manner, then simulation execution accuracy is improved, but user operation complexity increases
Solution Approach 1:
The patent introduces a language model as an intermediary between the user and the simulation system. The user provides natural language input, which the language model translates into the strictly formatted data structures required by the simulation engine. This mediator resolves the contradiction by shielding users from format complexity while ensuring accurate simulation execution.
Solution Approach 2:
The patent replaces the traditional mechanical input system (where users directly fill formatted fields) with an AI-based natural language processing system. Instead of manually structuring data according to simulation requirements, users communicate in natural language, and the language model automatically transforms this into the required format, significantly reducing operational complexity.
2Reliability
If users provide complete and correct input, then simulation error rate is reduced, but user time consumption increases
Solution Approach 1:
The language model performs self-service by automatically checking, validating, and completing user input. When a user provides incomplete or ambiguous input, the system autonomously identifies missing information, requests clarifications, and assembles the complete structured data without requiring manual intervention from the user to ensure completeness and correctness.
Solution Approach 2:
The system implements feedback loops where the language model continuously monitors user input for completeness and correctness. When deficiencies are detected, the system provides feedback to the user in natural language, requesting additional information or corrections. This iterative feedback process ensures high-quality input while minimizing the total time users need to spend providing information.
3Reliability
If the system checks user input for completeness and correctness, then simulation reliability is improved, but system complexity increases
Solution Approach 1:
The patent fundamentally changes the input parameter format from structured data fields to natural language text. This parameter transformation allows the system to use flexible language processing techniques instead of rigid validation rules, reducing system complexity while maintaining or improving reliability through the language model's ability to understand and interpret user intent.
Data Source
AI summary
One embodiment of a method for determining user intent includes receiving user input that comprises first natural language text, performing one or more operations to map the user input to one or more classes of intents included in a plurality of classes of intents, and responsive to determining that the user input does not map to any class of intents, generating, via a first trained language model, second natural language text requesting additional user input.