LLM-Based AI Agents for Autonomous Drive Parameter Analysis
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Solution Overview
Problem
Data engineers and data scientists face limitations in availability, focus, and expertise, leading to suboptimal execution of ML algorithms in drive analytics applications, with high training requirements and resource intensity, and reliance on human expertise for error handling and parameter identification.
Innovation Solution
A method for obtaining a large language model-based generative AI agent trained on drive operational data to identify drive parameters and relationships, enabling autonomous task completion, optimization, and reduced human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data engineers and data scientists are used to execute ML algorithms, then expertise and system knowledge are improved, but availability and capacity are limited
Solution Approach 1:
The patent creates a digital twin or virtual replica of the data scientist's knowledge and decision-making processes through an AI agent. This agent is trained on historical data, expert knowledge bases, and operational patterns to replicate human expertise in ML algorithm execution, thereby overcoming the availability limitations of human experts while maintaining high measurement precision in parameter identification.
Solution Approach 2:
The system enables self-service automation where the AI agent independently executes ML algorithms, identifies drive parameters, and optimizes drive analytics applications without requiring continuous human intervention. The agent autonomously monitors system state, retrieves relevant data, and implements decisions based on learned patterns, freeing human experts from routine tasks while maintaining system performance.
2Measurement precision
If human expertise is used for parameter identification and error handling, then accuracy is improved, but time consumption and resource intensity increase
Solution Approach 1:
The patent implements preliminary action by pre-training the AI agent on extensive historical data, expert knowledge bases, and operational scenarios before deployment. This pre-training phase captures human expertise and decision-making patterns in advance, enabling the agent to quickly and accurately identify drive parameters and handle errors during operational phases without requiring time-consuming human analysis each time.
Solution Approach 2:
The system replaces the mechanical process of human expert analysis with an automated AI-based system. The AI agent uses machine learning models and pattern recognition algorithms to substitute human cognitive processes in parameter identification and error handling, achieving comparable or superior accuracy while dramatically reducing time consumption and resource intensity.
3Reliability
If data engineers and data scientists are required for every task, then quality control is improved, but operational costs increase
Solution Approach 1:
The patent segments the work into different levels of automation: routine ML algorithm execution and parameter identification are handled autonomously by the AI agent, while human experts focus on high-level oversight, complex decision-making, and exceptional cases. This segmentation maintains quality control through human-in-the-loop validation while reducing operational costs by eliminating the need for human experts to perform every task.
Solution Approach 2:
The AI agent is designed as a universal system capable of performing multiple functions including ML algorithm execution, drive parameter identification, error handling, and system optimization. This multi-functional agent replaces the need for multiple specialized human roles, maintaining comprehensive quality control while significantly reducing operational costs through consolidation of functions into a single automated system.
Data Source
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AI summary
The present invention relates to a method, for obtaining an artificial intelligence, Al, agent applicable on a drive application. The method comprises obtaining (S310) training data indicative of predetermined drive operational data related to predetermined drive applications and/or of predetermined drive parameters related to the predetermined drive applications. The method further comprises training (S320) a large language model-, LLM-, based generative Al agent using the obtained training data to identify at least one of the following: first drive parameters related at least partly to the drive application, and relationships among the first drive parameters.