Cloud Wi-Fi Monitoring for Predictive Customer Support
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Solution Overview
Problem
Conventional Wi-Fi networks lack visibility and proactive monitoring, leading to inefficient issue resolution, increased customer churn, and poor customer satisfaction due to the inability of service providers to troubleshoot beyond the gateway.
Innovation Solution
Implementing cloud-based intelligent monitoring systems that provide end-to-end network visibility, proactive issue identification, and autonomous assistance to service providers, utilizing machine learning models for predictions and alerts, and a Network Operations Center dashboard for comprehensive network management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If service providers implement conventional Wi-Fi network monitoring, then network visibility is improved, but system complexity and operational overhead increase
Solution Approach 1:
The patent introduces a cloud-based intermediary platform that sits between service providers and customer Wi-Fi networks. This cloud platform aggregates and analyzes network data from multiple sources, providing centralized visibility without requiring complex monitoring infrastructure at each customer premises. The intermediary handles data collection, processing, and analysis, thereby reducing the complexity burden on individual service provider systems.
Solution Approach 2:
The system implements autonomous self-service capabilities through automated troubleshooting workflows and AI-driven diagnostics. The monitoring system automatically detects issues, diagnoses root causes, and executes remediation actions without requiring manual intervention from service providers. This self-service approach reduces operational overhead and simplifies the monitoring system by eliminating the need for complex manual troubleshooting procedures.
2Measurement precision
If service providers manually troubleshoot network issues, then issue resolution accuracy is improved, but time consumption and operational costs increase
Solution Approach 1:
The system performs preliminary actions by proactively detecting and diagnosing network issues before they impact customer experience. Automated workflows continuously monitor network parameters, identify anomalies, and execute preliminary troubleshooting steps in advance. This preliminary action reduces the time required for manual intervention while maintaining high resolution accuracy through pre-configured diagnostic algorithms and remediation playbooks.
Solution Approach 2:
The patent implements closed-loop feedback mechanisms where the system continuously monitors network performance, compares actual results against expected outcomes, and automatically adjusts troubleshooting actions. The feedback loop enables the system to learn from resolved issues and improve future diagnostic accuracy. This automated feedback-driven approach maintains high resolution accuracy while significantly reducing troubleshooting time compared to manual methods.
3Reliability
If service providers implement proactive monitoring, then customer satisfaction is improved, but system resource consumption and operational complexity increase
Solution Approach 1:
The system implements partial monitoring by focusing computational resources on detecting only the most critical network parameters and anomalies that directly impact customer experience. Rather than continuously analyzing all network data at full depth, the system applies selective monitoring thresholds and prioritizes analysis of high-impact metrics. This partial action approach maintains high customer satisfaction through proactive detection of significant issues while reducing overall system resource consumption.
4Reliability
If service providers increase network monitoring capabilities, then network reliability is improved, but implementation cost and infrastructure requirements increase
Solution Approach 1:
The patent implements a universal cloud-based monitoring platform that serves multiple service providers and network types through a single infrastructure. The system is designed to handle diverse Wi-Fi network configurations, customer premises equipment, and network topologies through standardized interfaces and adaptive analysis algorithms. This multi-functional approach enables network reliability improvement across different service provider environments without requiring separate implementation systems for each provider, thereby reducing overall implementation cost and complexity.
Data Source
AI summary
System and methods include obtaining Wi-Fi network data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers and obtaining customer data for each customer associated with the plurality of Wi-Fi networks, the customer data including call-ins made by customers; aggregating and filtering the data; analyzing the aggregated and filtered data including correlating the call-ins made by customers to the Wi-Fi network data; predicting customer call-ins based on correlations made between the call-ins made by customers and the Wi-Fi network data; and initiating a customer outreach workflow prior to a predicted customer call-in.