AI Image Validation for Fiber CPE Installation Errors
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Solution Overview
Problem
Field technicians often fail to follow proper installation procedures for equipment, leading to network connectivity issues and requiring multiple technician visits, which is inefficient and costly due to the limitations of manual quality assurance processes.
Innovation Solution
Utilizing artificial intelligence to analyze images and videos captured by technicians during installation to identify and rectify installation errors in real-time, providing immediate feedback through a mobile application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual quality assurance specialists review photographs provided by technicians, then installation quality can be improved, but the process is not scalable and costs increase
Solution Approach 1:
The system enables self-service quality assurance by equipping technicians with mobile devices that automatically capture installation photographs and transmit them to a server. The server then automatically analyzes these photographs using image recognition technology to identify installation issues, eliminating the need for manual review by quality assurance specialists while maintaining installation quality standards.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated electronic system. Instead of human specialists manually examining photographs, the system uses server-based image recognition algorithms to automatically detect installation defects. This substitution of mechanical human labor with electronic automation resolves the contradiction between maintaining quality and achieving scalability.
2Reliability
If manual quality assurance review is used, then installation issues can be detected, but feedback is delayed and may be provided after the technician has left
Solution Approach 1:
The system implements real-time feedback by automatically analyzing installation photographs as they are transmitted to the server during the technician's visit. The image recognition system immediately identifies installation issues and communicates results back to the technician's mobile device, enabling same-visit corrections without the time delays inherent in manual review processes.
Solution Approach 2:
The system performs preliminary quality assurance by analyzing installation photographs in real-time during the technician's visit, before the technician leaves the customer location. This preliminary detection and feedback mechanism allows issues to be corrected during the initial visit, preventing the need for follow-up visits and reducing time loss.
3Manufacturing precision
If multiple technician visits are required to resolve installation issues, then thorough quality control can be achieved, but efficiency decreases and costs increase
Solution Approach 1:
The system enables technicians to independently perform quality assurance by capturing their own installation photographs and receiving immediate automated analysis results. This self-service approach allows technicians to identify and correct their own installation errors during the initial visit, eliminating the need for multiple visits by second technicians and thereby improving efficiency while maintaining quality control.
Solution Approach 2:
The real-time feedback mechanism provides technicians with immediate information about installation quality, enabling them to make corrections during the same visit. This continuous feedback loop ensures thorough quality control is achieved in a single visit rather than requiring multiple visits, thus resolving the contradiction between quality control and efficiency.
Data Source
AI summary
The disclosure is directed to, among other things, systems and methods for troubleshooting equipment installations using machine learning. Particularly, the systems and methods described herein may be used to validate an installation of one or more devices (which may be referred to as “customer premises equipment (CPE)” herein as well) at a given location, such as a customer's home or a commercial establishment. As one non-limiting example, the one or more devices may be associated with a fiber optical network, and may include a modem and/or an optical network terminal (ONT). However, the one or more devices may include any other types of devices associated with any other types of networks as well.