Service Outage Alert Suppression Using Entity Activity Data
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Solution Overview
Problem
Current service outage detection systems using machine learning models often produce false positives due to unpredictable events and lack of accurate baseline usage data, leading to inefficient resource allocation and unnecessary equipment maintenance.
Innovation Solution
A system that utilizes internal entity data, such as non-recurring meeting counts, and external data like weather and holiday information to predict service usage levels, reducing false positives and improving resource allocation by using a combination of internal entity data and external data to determine baseline and future usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used to detect service outages, then service monitoring capability is improved, but false positive rate increases
Solution Approach 1:
The patent segments the service monitoring system into multiple independent components: a service monitoring component that detects potential outages, a prediction component that analyzes entity-specific data (meetings, events), and a suppression component that filters false positives. This segmentation allows each component to specialize in one function, improving overall detection accuracy while reducing false alarms through collaborative verification.
Solution Approach 2:
The patent introduces an intermediary prediction component that acts as a mediator between the service monitoring component and the alerting system. This intermediary analyzes entity-specific contextual data (such as scheduled meetings and events) to verify whether detected service degradation is genuine or caused by expected low-usage periods, thereby filtering false positives before they reach the alerting stage.
2Reliability
If service monitoring is continuously performed, then service performance is improved, but resource allocation efficiency deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation based on predicted entity activity levels. The system adjusts monitoring intensity and resource allocation in real-time according to contextual factors such as scheduled meetings and events. During high-activity periods, monitoring is intensified; during low-activity periods (such as scheduled breaks or meetings), monitoring is reduced, optimizing resource utilization while maintaining service reliability.
3Measurement precision
If baseline usage data is collected historically, then service understanding is improved, but adaptability to unpredictable events deteriorates
Solution Approach 1:
The patent performs preliminary actions by collecting and analyzing entity-specific data (scheduled meetings, events, and other contextual information) before service outages occur. This proactive approach allows the system to predict expected usage patterns and distinguish them from actual service problems, enabling adaptive response to unpredictable events while maintaining accurate baseline comparisons.
Data Source
AI summary
A system may include a processing unit; a storage device comprising instructions, which when executed by the processing unit, configure the processing unit to perform operations comprising: receiving a service outage alert for a service used by an entity; retrieving a current count of non-recurring meetings for the entity; determining that the service outage alert is a false positive based on a current service load for the service and the current count; and based on the determining, suppressing the service outage alert.