Autoencoder Anomaly Detection for CPE Firmware Revisions
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Solution Overview
Problem
Existing systems for anomaly detection in customer premises equipment (CPE) firmware revisions on DOCSIS networks are inefficient and unable to timely and accurately identify network issues, leading to prolonged downtime and user experience impairments.
Innovation Solution
A system that consolidates metrics using machine-learning models, specifically autoencoders, to detect anomalies in CPE firmware revisions by training on aggregated data and flagging outlier points based on mean absolute error losses, with additional logic to control for transient anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual identification methods are used to determine network issues, then technicians can investigate local locations, but the process is time-consuming and incapable of timely identification
Solution Approach 1:
The patent replaces manual mechanical investigation methods with an automated machine learning system. The anomaly detection system uses trained models to automatically analyze network data, identify problematic firmware revisions, and flag outliers without requiring technician intervention for initial detection. This substitution of mechanical manual processes with automated computational systems directly resolves the contradiction by enabling both timely detection and accurate identification simultaneously.
2Reliability
If technicians are dispatched to local locations to investigate issues, then root cause can be determined, but the system becomes inefficient and unable to scale
Solution Approach 1:
The patent implements a self-service anomaly detection system where the machine learning models automatically perform the investigative function previously requiring technician dispatch. The system autonomously collects network data, trains anomaly detection models, identifies problematic firmware revisions, and generates findings without human intervention. This self-service automation maintains reliable root cause determination while dramatically improving productivity and scalability.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between raw network data and technician investigation. The anomaly detection system acts as a mediator that processes network metrics, identifies patterns indicating firmware issues, and presents filtered, high-confidence findings to technicians. This intermediary layer improves efficiency by eliminating unnecessary technician dispatches while maintaining reliability through automated analysis.
3Adaptability or versatility
If multiple CPE firmware revisions are supported simultaneously, then network service provider can provide diverse services, but it becomes difficult to identify issues attributable to specific firmware revisions
Solution Approach 1:
The patent applies segmentation by training separate anomaly detection models for each CPE firmware revision. The system divides the analysis task into individual models, each specialized in detecting anomalies specific to a particular firmware version. This segmentation enables the system to maintain support for multiple firmware revisions while making it easy to identify which specific revision is causing issues, as each model's findings are directly attributable to its target firmware version.
Data Source
AI summary
This disclosure describes systems, methods, and devices related to anomaly detection of CPE firmware revisions. A method may include collecting metrics data for a plurality of customer-provided equipment (CPE) models over a window of time; training a first autoencoder for a first CPE model of the plurality of CPE models using at least a portion of the metrics data to detect anomalies within a plurality of firmware versions of the first CPE model; identifying, using the first autoencoder, that a first firmware version of the plurality of firmware versions is anomalous across a first time series; and storing data indicating that the first firmware version of the plurality of firmware versions is anomalous across the first time series. Metrics data may include one or more of interactive voice response (IVR) session data; calls handled data; and truck schedule data.