Machine Vision Error Diagnosis for Electronic Device Remediation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Diagnosing and remediating errors in electronic devices is challenging for users with little technical background, as existing methods require complex documentation navigation or inadequate phone-based support, leading to resource-intensive and time-consuming solutions.
Innovation Solution
A machine vision and machine learning-based system that uses trained models to identify device categories, diagnose errors, and provide remediation steps through augmented reality overlays, enabling users to quickly address issues without specialized training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional documentation and phone support methods are used for error diagnosis, then users can access error information, but the process is time-consuming and complex requiring specialized training
Solution Approach 1:
The patent replaces manual mechanical processes (reading dense documentation, navigating complex support systems) with an automated machine vision and machine learning system. The system captures images of error displays, automatically recognizes error codes through computer vision, diagnoses issues using trained neural networks, and provides remediation steps without requiring user technical expertise or time investment.
Solution Approach 2:
The system enables self-service error diagnosis by automatically performing what would otherwise require user expertise. The machine learning models independently analyze error displays, interpret error codes, determine device categories, and generate solutions without requiring users to manually search documentation or communicate with support representatives.
2Productivity
If machine vision and machine learning models are deployed for automated diagnosis, then diagnosis speed and effectiveness improve, but system complexity increases
Solution Approach 1:
The diagnostic system is segmented into distinct functional modules: image capture, error code recognition, device category identification, error diagnosis, and remediation generation. Each module is handled by specialized components (image processing unit, machine learning models for specific tasks), making the overall complex system manageable through functional decomposition.
Solution Approach 2:
The patent introduces an intermediary layer of machine learning models that mediate between the simple image input and the complex diagnostic output. These models act as translators that convert visual error display data into structured diagnostic information and remediation steps, simplifying the interaction between users and the complex underlying diagnostic logic.
3Measurement precision
If multiple trained models are used for comprehensive error analysis, then diagnostic accuracy improves, but training data requirements and computational resources increase
Solution Approach 1:
The system employs multiple specialized machine learning models, each trained on specific subsets of data (error code recognition, device category identification, error diagnosis, remediation generation). This partial action approach allows each model to be highly accurate for its specific function without requiring all models to process all data types, reducing overall data requirements compared to a single universal model.
Data Source
AI summary
Machine vision-based technical support is provided herein. An example method includes receiving an image of an electronic device, the image including an output of the electronic device indicative of an error code associated with an operation of the electronic device, executing a first trained model to recognize the error code from the output included in the image, executing a second trained model to output an error diagnostic based on the error code recognized using the first trained model, in response to verification of the error diagnostic output by the second trained model, executing a third trained model to output at least one solution to remedy an error of the electronic device corresponding to the error code recognized using the first trained model, and retraining the third trained model based on whether the at least one solution was effective in remedying the error.