Notification Traffic Anomaly Detection and Shaping

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Solution Overview

Problem

Notification platforms face challenges due to excessive communication, leading to network overload and negative customer experiences, particularly from sudden spikes in notification volumes.

Innovation Solution

A notification traffic anomaly detector is introduced, utilizing a processor and memory to analyze notification traffic data with machine learning models, identifying anomalies in notification request volumes, bias metric values, and non-preference metric values. In response to detected anomalies, the system generates anomaly notifications to modify notification processing, such as throttling or merging notifications.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If notification volume is increased to improve customer engagement, then customer communication effectiveness is improved, but network overload and system efficiency deteriorate

Engineering Contradiction:

Improvecustomer communication effectivenessVSAvoidnetwork efficiency

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary actions by detecting anomalies in notification traffic patterns before they cause network overload. The anomaly detection module continuously monitors notification volumes and identifies deviations from normal patterns, enabling preventive throttling or merging operations before the network becomes overloaded, thus maintaining both communication effectiveness and network efficiency

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms where the anomaly detection module continuously monitors notification traffic and provides real-time information to the notification merging module. This closed-loop feedback enables dynamic adjustment of notification processing - when anomalies are detected, the system responds by merging or throttling notifications, and continues monitoring to ensure the adjustment achieves the desired effect without over-correcting

Inventive Principle:

Principle #23Feedback

2Productivity

If notification volume is increased to improve customer engagement, then communication reach is improved, but customer experience deteriorates due to excessive communication

Engineering Contradiction:

Improvecommunication reachVSAvoidcustomer experience degradation

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The notification merging module combines multiple redundant or similar notifications into a single consolidated notification. When the anomaly detection module identifies patterns indicating excessive communication (such as multiple notifications to the same customer within a short period), the merging module consolidates these into one notification, maintaining customer awareness while eliminating the harmful effect of notification fatigue

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system dynamically changes notification parameters such as throttling rates and merging thresholds based on detected traffic patterns. When anomaly detection identifies excessive notification volumes, the system adjusts parameters to reduce the frequency and volume of notifications sent to customers, thereby maintaining communication reach while preventing experience degradation from over-communication

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If real-time anomaly detection is implemented, then notification traffic control is improved, but system complexity increases

Engineering Contradiction:

Improvenotification traffic controlVSAvoidsystem architecture complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The anomaly detection system operates autonomously without requiring manual configuration or intervention. The detection module automatically learns normal traffic patterns, identifies anomalies, and triggers appropriate responses (throttling or merging) based on predefined rules. This self-service capability simplifies operation while the modular architecture manages complexity by separating detection, analysis, and execution functions into independent components

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250202761A1Notification Traffic Anomaly Detection and Traffic Shaping

Publication Date: 2025.06.19 AT&T INTELLECTUAL PROPERTY I L P

AI summary

Concepts and technologies disclosed herein are directed to a notification traffic anomaly detector. The notification traffic anomaly detector can receive, from a notification system, notification traffic data associated with at least one of notification requests or notifications processed by the notification system. The notification traffic anomaly detector can determine, using at least one machine learning model, whether the notification traffic data indicates an anomaly associated with the notification requests and/or the notifications processed by the notification system. In response to determining that the notification traffic data indicates an anomaly associated with the notification requests and/or the notifications processed by the notification system, the notification traffic anomaly detector can generate an anomaly notification and provide the anomaly notification to the notification system while at least one of notification requests or the notifications is being processed by the notification system.