Anomalous Group Identification System
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Solution Overview
Problem
Human-driven analysis of large datasets is inefficient and inaccurate, particularly in identifying anomalous groups within organizations, due to the inability to consider all data attributes and the time-intensive nature of finding control groups for meaningful comparisons.
Innovation Solution
An end-to-end system automatically finds control groups and identifies anomalous groups by using a computing device, service system, storage system, and processing system with components like metric, feature, matching, and anomaly components to analyze user data, calculate Mahalanobis distances, and provide user interfaces for visualizing outlier detection and intervention effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-driven analysis is used to identify anomalous groups in large datasets, then the analysis can be performed with human judgment and context understanding, but the analysis becomes time-intensive and inaccurate when considering all data attributes
Solution Approach 1:
The patent replaces manual human analysis with an automated computer-based system that performs statistical calculations, Mahalanobis distance computations, and anomaly detection algorithms. This substitution enables the system to process all data attributes simultaneously and identify anomalous groups accurately without the time constraints and cognitive limitations of human analysis.
Solution Approach 2:
The system transforms the analysis approach by changing from qualitative human judgment to quantitative statistical measures. It calculates Mahalanobis distances, determines p-values, and uses statistical thresholds to objectively identify anomalies, replacing subjective human assessment with precise mathematical parameters that can be computed efficiently at scale.
2Measurement precision
If all data attributes are considered in the analysis, then the identification of anomalous groups becomes more accurate, but the complexity of the analysis increases beyond human capability
Solution Approach 1:
The patent replaces the human cognitive system with a computational system capable of handling high-dimensional data. The computer automatically performs matrix operations, calculates Mahalanobis distances across multiple attributes, and processes statistical tests without being overwhelmed by complexity, enabling accurate anomaly detection even when considering dozens or hundreds of data attributes simultaneously.
Solution Approach 2:
The system segments the complex analysis task into distinct computational components: data preprocessing, Mahalanobis distance calculation, statistical significance testing, and anomaly classification. This segmentation allows each component to be optimized independently and processed efficiently, managing the overall complexity through modular computation rather than holistic human analysis.
3Reliability
If control groups are manually identified for comparison, then the comparison can be based on expert knowledge, but the process becomes time-intensive and may miss meaningful comparisons
Solution Approach 1:
The patent replaces manual control group selection with automated statistical matching. The system computationally identifies control groups that are statistically similar to treatment groups based on multiple attributes, using algorithms to match characteristics and ensure valid comparisons. This automated process is both faster and more reliable than manual selection, as it consistently applies statistical criteria without human bias or oversight errors.
Solution Approach 2:
The system performs self-service by automatically identifying and matching control groups without requiring human intervention. It uses the available data to autonomously determine appropriate control groups, calculate comparison metrics, and identify anomalies, enabling the entire control group selection and comparison process to serve itself through algorithmic computation rather than human analysis.
Data Source
AI summary
A method may include retrieving metric data on a plurality of groups of users, the metric data including: a value of a performance metric for each of the plurality of groups; and an indication that a first group of the plurality of groups is anomalous with respect to a value of the performance metric of a control group of the plurality of groups; and presenting a user interface, the user interface including: a first portion including a visualization of a comparison of the value of the performance metric for the first group and values of the performance metric of other groups in the plurality of groups; and a second portion including a visualization of trend data of the performance metric for the first group over a period of time.