AI-Guided Data Anomaly Detection With Heat Map Binning
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Solution Overview
Problem
Existing systems fail to detect subtle anomalies in real-time, leading to incorrect reporting and operational failures, and generate excessive false positives, overwhelming support teams and systems.
Innovation Solution
A system that bins data into smaller collections for simultaneous evaluation, uses AI models to predict anomaly causes and solutions, and displays anomalies through a heat map on a GUI, dynamically adjusting thresholds based on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing anomaly detection systems process large volume data, then they can detect anomalies, but they generate excessive false positives overwhelming support teams
Solution Approach 1:
The patent segments data into multiple bins based on configuration settings, allowing parallel processing and more precise anomaly detection in each segment. This segmentation approach reduces false positives by contextualizing anomalies within specific data bins rather than treating all data uniformly.
Solution Approach 2:
The system applies different visual appearances to display elements based on local anomaly characteristics. Anomalous bins are highlighted with distinct visual cues (e.g., red color), while normal bins have different appearances. This local differentiation improves detection precision without generating false alarms.
2Measurement precision
If existing systems process data to detect anomalies, then they can identify issues, but they fail to detect subtle anomalies in real-time
Solution Approach 1:
By dividing data into bins, the system can process multiple segments simultaneously, maintaining real-time detection speed while improving subtle anomaly detection through focused analysis of each bin's characteristics.
Solution Approach 2:
The system uses color changes in the heat map to indicate anomalies. Display elements change visual appearance (e.g., to red) when anomalies are detected, providing immediate real-time visual feedback that enhances detection precision without sacrificing speed.
3Measurement precision
If the system divides data into multiple bins for analysis, then it improves anomaly detection precision, but it increases system complexity
Solution Approach 1:
The patent implements segmentation into bins with configurable settings, managed through a software application with a graphical user interface. This structured approach to segmentation improves detection precision while keeping system complexity manageable through configuration-based control.
Solution Approach 2:
The heat map display serves multiple functions: it visualizes data bins, indicates anomalies through color changes, and provides drill-down capability for detailed analysis. This multi-functionality improves detection precision without proportionally increasing system complexity.
4Measurement precision
If the system provides detailed anomaly information through heat map, then it improves detection accuracy, but it increases information processing load
Solution Approach 1:
The system performs preliminary anomaly detection and visualizes results in the heat map before detailed analysis is needed. This preliminary action reduces identification time by pre-processing and highlighting potential anomalies, allowing users to quickly locate and investigate only relevant areas.
Solution Approach 2:
Color changes in the heat map provide immediate visual cues about anomaly locations and severity. This visual encoding improves identification accuracy by making anomalies instantly recognizable, reducing the time and cognitive load required to process detailed information.
Data Source
AI summary
An example operation may include one or more of ingesting data from a plurality of systems, dividing the data into a plurality of bins based on binning configuration settings, identifying a bin of data among the plurality of bins which contains an anomaly at a point in time based on thresholds for the plurality of bins, identifying a different bin of data among the plurality of bins which does not contain the anomaly at the point in time based on the thresholds for the plurality of bins, generating a heat map comprising a plurality of display elements corresponding to the plurality of bins including a display element corresponding to the bin of data with the anomaly with a different visual appearance than a display element corresponding to the different bin which does not contain the anomaly, and rendering the heat map via a GUI.