AI Image Processing for Remote Technical Support
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Solution Overview
Problem
Current technical support systems face challenges in providing efficient and customer-satisfying services due to complex customer needs, communication gaps, limited problem-solving rates, and customer frustration, often requiring technician dispatches.
Innovation Solution
An image processing system that uses artificial intelligence to analyze real-time video streams from mobile devices, identifying issues with electronic appliances and guiding users through sequential instructions, with movable augmented indicators to assist in connecting cables and troubleshooting, and automatically suspending or resuming instructions based on the appliance's visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional technical support systems are used, then customer service can be provided, but efficiency is low and customer satisfaction deteriorates due to complex needs and communication gaps
Solution Approach 1:
The system enables self-service through automated image analysis and AI-driven diagnosis. The mobile device captures images of the appliance, and the system automatically analyzes them to identify problems, eliminating the need for customers to manually describe issues or wait for technician assessment. This self-service approach significantly reduces wait time while maintaining support efficiency.
Solution Approach 2:
The patent replaces traditional mechanical support systems (technician dispatch, manual diagnosis) with an automated digital system. The image processing system uses computer vision and AI algorithms to automatically diagnose appliance issues, substituting human technicians with an automated digital diagnostic platform that provides immediate results without requiring physical presence or manual intervention.
2Reliability
If technician dispatch is used to resolve complex issues, then problem-solving can be achieved, but cost and time consumption increase
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing images of the appliance and identifying problems without human intervention. The AI algorithm processes the captured images, determines the nature of the issue, and provides diagnostic results immediately, eliminating the need to wait for technician arrival and assessment while maintaining high problem-solving accuracy.
Solution Approach 2:
The system performs preliminary diagnosis and problem identification before any technician dispatch is considered. By automatically analyzing appliance images and identifying issues in advance, the system determines whether technician intervention is actually needed, reducing unnecessary dispatches and accelerating the overall resolution process for cases that can be handled remotely.
3Ease of operation
If self-service technologies are implemented, then customer independence improves, but adoption rate remains low due to complexity and frustration
Solution Approach 1:
The patent replaces complex manual self-service procedures with an automated image-based system. Instead of requiring customers to navigate complex menus, input detailed descriptions, or perform diagnostic steps themselves, the system simply captures images and automatically processes them, making self-service as easy as taking a photo while maintaining high independence and reducing frustration.
Solution Approach 2:
The system uses image copying and analysis to understand the appliance issue. By capturing visual copies of the appliance and its components through the mobile device camera, the system translates physical appliance states into digital data that can be automatically analyzed, providing an intuitive and simple self-service experience that naturally increases adoption rates.
4Loss of information
If manual diagnosis and communication are used, then customer needs can be understood, but communication gaps and frustration increase due to complex needs
Solution Approach 1:
The system replaces manual communication and diagnosis with automated image processing. Instead of relying on customers to accurately describe problems or technicians to interpret verbal descriptions, the system directly analyzes images of the appliance to identify issues, eliminating communication gaps entirely while keeping the interface simple (just capture and send images).
Data Source
AI summary
An image processing system including at least one processor configured to receive real-time image data captured by an image sensor of a mobile device. The real-time image data includes at least one image of an inoperative product. The processor is further configured to perform image recognition on the real-time image data to identify a likely source product inoperability; cause the mobile device to display sequential instructions for mitigating the inoperability; determine that an error was made when a particular one of the instructions is not complied with; cause the mobile device to display an error notification when the particular instruction is not complied with and before a subsequent instruction is displayed; determine that the particular instruction was subsequently complied with based on real-time image data captured following the notification; and cause the mobile device to display the subsequent instruction after the particular instruction is determined to have been complied with.