Visual Network Hardware Troubleshooting with Guided Multimodal AI
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Solution Overview
Problem
Existing visual troubleshooting systems for network hardware are hindered by factors such as model hallucination, poor image quality, and the open-ended nature of network issues, limiting the effectiveness of supervised learning and computer vision in automated or semi-automated troubleshooting.
Innovation Solution
Utilizing multimodal generative artificial intelligence (GenAI) to process images of network hardware, extract identifying features, and generate insights for troubleshooting, assisted by device-specific information and visual guides, to facilitate automated or semi-automated troubleshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If supervised learning and computer vision are used for automated troubleshooting, then automation is improved, but accuracy deteriorates due to model hallucination and poor image quality
Solution Approach 1:
The patent introduces a specialized image preprocessing module as an intermediary between image input and the vision language model. This module enhances image quality through denoising, deblurring, and resolution improvement before images are processed by the automated troubleshooting system, thereby maintaining automation while improving accuracy
Solution Approach 2:
The system performs preliminary actions by preprocessing images before they are analyzed by the AI model. This includes enhancing image quality, extracting relevant features, and preparing visual data in advance to prevent model hallucination and improve diagnostic accuracy in the automated troubleshooting process
2Measurement precision
If visual guides and device-specific information are added to enhance troubleshooting accuracy, then accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements a unified vision language model that handles multiple troubleshooting functions simultaneously - image analysis, device identification, and diagnostic reasoning - rather than separate specialized systems. This multi-functionality improves accuracy while managing complexity through a single integrated AI component
Solution Approach 2:
The system employs self-service mechanisms where the AI model automatically retrieves device-specific information and generates visual guides based on the analyzed images, without requiring manual configuration or external intervention for each troubleshooting scenario, thereby improving accuracy while keeping the system architecture manageable
Data Source
AI summary
One or more computing devices, systems, and/or methods for visual troubleshooting a network device setup. Images of the network device setup are provided to the system. A GenAI component processes the images to generate one or more device identifying features. The features are further processed to identify the device. The system utilizes hardware-specific information to prompt the GenAI component to answer troubleshooting-related questions concerning the device setup. The images may be pre-processed to include one or more visual guides to assist the GenAI component.