Language Model Guidance for System Installation and Troubleshooting
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Solution Overview
Problem
Complex systems require significant technical expertise for installation, troubleshooting, and maintenance, leading to high operational costs and downtime due to the need for expert assistance.
Innovation Solution
Leveraging language models to provide real-time guidance and instructions to non-expert users through natural language interactions, utilizing system-specific documentation and diagnostic tools for installation, troubleshooting, and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expert technical assistance is used for system installation and troubleshooting, then system reliability and proper functionality are ensured, but operational costs increase and downtime occurs due to expert availability constraints
Solution Approach 1:
The system enables non-expert users to independently perform installation, troubleshooting, and maintenance tasks through AI assistant guidance. The AI analyzes system logs, identifies issues, and provides step-by-step instructions, allowing the system to serve itself without external expert intervention.
Solution Approach 2:
An AI assistant acts as an intermediary between non-expert users and complex system diagnostics. The AI translates technical system states into understandable guidance, bridging the gap between user capability and system complexity without requiring direct expert involvement.
2Reliability
If expert assistance is required for system servicing, then technical accuracy is maintained, but time loss increases due to scheduling and travel requirements
Solution Approach 1:
The AI assistant performs preliminary diagnostics by analyzing system logs and identifying issues before they escalate. This early detection and guidance enable users to address problems immediately, preventing further downtime and eliminating the need for delayed expert intervention.
Solution Approach 2:
Users independently resolve issues through AI-guided troubleshooting steps, eliminating waiting time for expert availability. The system provides immediate analysis and instructions, allowing users to perform repairs during normal operational windows rather than during scheduled expert visits.
3Measurement precision
If complex diagnostic tools and documentation are used, then accurate system analysis is achieved, but ease of operation decreases for non-expert users
Solution Approach 1:
The AI assistant serves as an intermediary that accepts complex diagnostic data from system tools and translates it into simple, actionable guidance for users. It processes technical logs and error codes, then presents results in user-friendly language with step-by-step instructions.
Solution Approach 2:
The diagnostic process is segmented into manageable steps with clear objectives. The AI breaks down complex troubleshooting into sequential tasks, allowing users to progress through diagnostics systematically without being overwhelmed by system complexity.
Data Source
AI summary
Disclosed are apparatuses, systems, and techniques that train and use trained language models to assist users with complex systems installation, troubleshooting, and/or maintenance. The techniques include receiving, via a user interface (UI), a natural language (NL) query associated with one or more malfunction indicators indicative of a malfunction state of a system, providing, to a language model (LM) trained using a documentation associated with the system, an input having a prompt that is based at least on the NL query. The techniques further include receiving, from the LM, a response to the NL query, the response having one or more instructions associated with resolution of the malfunction state of the system and causing the UI to display the response.