Adaptive Notification System for Telecom Event Correlation
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Solution Overview
Problem
Conventional ticketing and service systems in the telecommunications industry face challenges such as generating multiple tickets for a single event, requiring significant human effort, producing false alarms, and lacking effective reporting, which can lead to customers discovering problems before they are recognized in the system.
Innovation Solution
An adaptive notification and ticketing system that uses machine learning and complex event processing to analyze network event data, generate models, and predict service impact events, thereby adjusting notifications and ticketing to focus on significant events, reducing unnecessary tickets and improving response efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional ticketing systems generate a ticket for each network event, then complete event tracking is achieved, but ticket quantity increases significantly and human effort increases
Solution Approach 1:
The patent merges multiple related network events into a single consolidated ticket by identifying causal relationships between events. When events are determined to be causally related (one event causing another), they are combined into one ticket rather than creating separate tickets for each event, thereby reducing overall ticket volume while maintaining complete event tracking.
Solution Approach 2:
The patent introduces an event correlation engine as an intermediary component that analyzes network events, determines causal relationships, and decides whether to consolidate or separate tickets. This intermediary processing layer automatically filters and correlates events before ticket creation, reducing the burden on human operators without losing track of any events.
2Measurement precision
If conventional systems process all network events individually, then detailed monitoring is achieved, but false alarms increase and human effort increases
Solution Approach 1:
The patent implements feedback mechanisms where the system learns from historical event data and correlation patterns to improve its ability to distinguish between significant events and noise. By analyzing past events and their relationships, the system refines its correlation algorithms to reduce false alarms while maintaining detailed monitoring of actual problems.
Solution Approach 2:
The patent performs preliminary correlation analysis and event relationship determination before tickets are created. By pre-processing events to identify causal relationships in advance, the system can filter out events that are likely to be false alarms or redundant, reducing the number of false tickets generated while maintaining precise monitoring.
3Loss of information
If conventional systems generate tickets for all events, then comprehensive reporting is achieved, but reporting effectiveness decreases due to information overload
Solution Approach 1:
The patent segments ticket information into hierarchical levels, organizing events by their causal relationships and significance. Instead of presenting all events flatly, the system structures reports to show only the root cause events and their impacts, segmenting detailed information into expandable sections that users can access as needed, thereby improving reporting effectiveness without losing information completeness.
4Speed
If conventional systems respond to each event separately, then rapid individual response is achieved, but overall response efficiency decreases
Solution Approach 1:
The patent merges the response process for causally related events into a single unified response action. When multiple events are determined to be part of the same causal chain, the system consolidates them into one ticket that can be addressed in a single response action, maintaining rapid response capability while significantly improving overall response efficiency by eliminating redundant responses.
Data Source
AI summary
Aspects of the present disclosure include an adaptive notification and ticketing system for a telecommunications network. The system includes a computing device and a plurality of network devices associated with the telecommunications network. Data is generated about a plurality of past network events associated with the plurality of network devices. The computing device is utilized to generate a model from the data. The model may be used to interpret new network events and generate an output indicative of a service impact event. The new network events are applied to the model using the computing device to generate the output indicative of a service impact event. The computing device generates a responsive action from the output indicative of a service impact event. The service impact event is a network event that disrupts a network service associated with the telecommunications network.