Alert Prioritization System Using ML Similarity Matching
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Solution Overview
Problem
Existing systems struggle to accurately and efficiently label alerts generated from software programs, leading to delays in flagging critical alerts and potential business impacts.
Innovation Solution
A system utilizing one or more processors and memory to store instructions for prioritizing alerts through a method involving machine learning models. This system receives alerts, transmits rating prompts to user devices, trains models based on user ratings, and associates similar alerts with existing ratings, thereby prioritizing and labeling alerts automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual alert labeling is used, then accuracy of alert prioritization can be maintained through user judgment, but the time required to label each alert increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing alerts and preparing candidate prioritization labels before user review. The machine learning model pre-generates predictions and confidence scores, so that when users do review alerts, the groundwork is already done, reducing the actual labeling time while maintaining accuracy through user verification of pre-prepared options.
Solution Approach 2:
The system implements feedback loops where user corrections of automated prioritization labels are fed back into the machine learning model for retraining. This continuous feedback improves model accuracy over time, allowing the system to progressively reduce manual intervention while maintaining or improving labeling precision, thus resolving the time-accuracy tradeoff.
2Productivity
If machine learning models are used to automatically label alerts, then the speed of alert processing increases, but the accuracy may decrease without sufficient training data
Solution Approach 1:
The system dynamically adapts the machine learning model through continuous retraining as new labeled data becomes available. The model evolves from static to dynamic, improving its accuracy over time while maintaining high processing speed. This dynamic adaptation allows the system to progressively close the accuracy gap with manual labeling while sustaining automated processing benefits.
Solution Approach 2:
The system applies partial automation by using the machine learning model to generate candidate labels and confidence scores, then selectively applying human review based on confidence thresholds. High-confidence predictions are accepted automatically for speed, while low-confidence cases receive human review for accuracy, achieving a balance between processing speed and labeling precision through partial application of automated action.
3Measurement precision
If all alerts are reviewed by users, then the most critical alerts can be identified accurately, but the overall efficiency of alert management decreases
Solution Approach 1:
The system applies local quality by directing full human review resources selectively to only those alerts that are both critical and low-confidence in automated predictions. Non-critical alerts or high-confidence cases receive automated processing only. This localized application of human expertise maintains accurate critical alert identification while preserving overall management efficiency through differentiated handling.
Solution Approach 2:
The system segments the alert stream into different categories based on criticality and confidence levels, then applies different processing strategies to each segment. Critical high-confidence alerts are automatically labeled for efficiency, critical low-confidence alerts receive human review for accuracy, and non-critical alerts are handled with minimal resources. This segmentation resolves the contradiction by applying appropriate resources to appropriate alert types.
Data Source
AI summary
The system may include one or more processors and memory that may store instructions that are configured to cause the system to perform a method. For example, the system may receive a first alert from a first application and determine, using a machine learning model, whether the first alert is similar to a previous alert. The determination may be based on the second alert satisfying a predetermined similarity threshold. When the first alert is similar to the previous alert, the system may associate a previous rating label with the first alert, the previous rating label being asssociated with the previous alert and transmit the first alert with the previous rating label to one or more user devices for display.