Prompt Engineering Interface for Context-Aware Automation Troubleshooting
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Solution Overview
Problem
Existing AI models in industrial automation systems lack human intuition, understanding of system context, and data accuracy, leading to misdiagnosis and incorrect recommendations, posing safety hazards and financial losses.
Innovation Solution
A prompt engineering interface service that integrates large language models with industrial automation environments to generate accurate and relevant troubleshooting prompts, leveraging past workflows and natural language processing to identify anomalies and provide solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional machine learning algorithms are used for troubleshooting, then automation is achieved, but accuracy and reliability of diagnosis deteriorate due to lack of human intuition and system context understanding
Solution Approach 1:
The patent introduces a large language model as an intermediary between traditional ML algorithms and troubleshooting tasks. The LLM processes natural language descriptions of system contexts, anomalies, and error messages, transforming unstructured data into structured diagnostic insights that enhance the accuracy of automated troubleshooting while maintaining automation benefits
Solution Approach 2:
The patent replaces traditional rule-based and statistical ML mechanisms with a neural language model that can understand and reason about system contexts, anomalies, and solutions in natural language. This substitution enables the system to comprehend nuanced system states and provide more accurate diagnoses without sacrificing automation
2Reliability
If complex AI models are deployed to improve troubleshooting accuracy, then diagnosis reliability improves, but system complexity and computational resources increase
Solution Approach 1:
The patent implements a self-service approach where the LLM autonomously analyzes system contexts, identifies anomalies, generates diagnostic conclusions, and provides troubleshooting recommendations without requiring complex external processing systems. The model serves itself by directly interpreting input data and generating actionable insights, reducing overall system complexity
Solution Approach 2:
The patent changes the fundamental parameters of the AI approach by using a pre-trained large language model with natural language processing capabilities instead of traditional classification algorithms. This parameter change enables the system to handle unstructured data and complex reasoning tasks with improved reliability while maintaining manageable system architecture
3Measurement precision
If comprehensive system analysis is performed to improve troubleshooting accuracy, then diagnosis quality improves, but time required for troubleshooting increases
Solution Approach 1:
The patent leverages the LLM's pre-training on vast amounts of technical documentation, system manuals, and troubleshooting knowledge bases to perform preliminary analysis of system contexts and anomalies. This preliminary action enables the model to quickly generate accurate diagnostic conclusions without requiring time-consuming manual analysis or complex multi-step processing
Solution Approach 2:
The patent implements continuous natural language processing where the LLM continuously analyzes system data streams, error messages, and contextual information in real-time. This continuous analysis maintains high troubleshooting accuracy while minimizing downtime by providing immediate diagnostic feedback without interrupting system operations
Data Source
AI summary
The present technology relates to artificial intelligence assisted device troubleshooting. In an implementation, an interface service of a human machine interface application trains a machine learning model on the content of an embeddings database. The interface service then receives an input comprising a context of an automation system design. The interface service generates a prompt that includes an instruction for the ML model to identify an anomaly type associated with the context of the automation system design and to generate a solution that addresses the anomaly type. The interface service transmits the prompt to the ML model and receives a response from the ML model that includes the anomaly type and the requested solution. After receiving a response, the interface service may modify the automation system design based on the content of the response and surface a graphical user interface that includes the modified design.