AI Installation Validation for Fiber CPE Troubleshooting
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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 equipment installation, providing real-time feedback to ensure proper installation and configuration, including physical connections and software settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual quality assurance specialists review photographs of equipment installation, then installation quality can be validated, but the process is not scalable and feedback is not immediate
Solution Approach 1:
The patent replaces the manual mechanical review process of quality assurance specialists with an automated image recognition system using machine learning algorithms. The system automatically analyzes photographs of equipment installation to validate proper installation, eliminating the need for human reviewers while maintaining or improving validation accuracy. This substitution enables scalable processing of installation images across multiple markets simultaneously.
Solution Approach 2:
The system enables self-service quality assurance by allowing the installation process itself to generate and submit photographs for automatic validation. The technology performs self-validation of installation quality through automated image analysis, providing immediate feedback without requiring external human intervention. This self-service approach allows the system to validate its own installation quality at scale.
2Reliability
If quality assurance specialists provide feedback after technician departure, then some validation occurs, but multiple technician visits are still required
Solution Approach 1:
The system performs preliminary validation of installation quality by analyzing photographs taken during the initial technician visit. The automated image recognition system evaluates installation correctness before the technician leaves the customer location, identifying any issues that need immediate correction. This preliminary action prevents the need for follow-up visits by ensuring proper installation the first time.
Solution Approach 2:
The system provides immediate feedback to the technician during the installation process by analyzing captured photographs in real-time. When installation issues are detected, the system instantly notifies the technician to correct the problem before completing the visit. This immediate feedback loop ensures installation correctness is verified and corrected on-site, eliminating the need for multiple technician visits.
3Reliability
If multiple technician visits are deployed to resolve installation issues, then installation quality can be ensured, but costs and time consumption increase
Solution Approach 1:
The system performs preliminary validation of installation quality by analyzing photographs taken during the initial technician visit. The automated image recognition system evaluates installation correctness before the technician leaves the customer location, identifying any issues that need immediate correction. This preliminary action prevents the need for follow-up visits by ensuring proper installation the first time.
Solution Approach 2:
The system provides immediate feedback to the technician during the installation process by analyzing captured photographs in real-time. When installation issues are detected, the system instantly notifies the technician to correct the problem before completing the visit. This immediate feedback loop ensures installation correctness is verified and corrected on-site, eliminating the need for multiple technician visits.
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.