Anomaly Detection Service for Generative Model Validation
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Solution Overview
Problem
Current anomaly detection in network performance management is hindered by the high dimensionality of network data, making human validation of generative model outputs infeasible and subjective, and existing statistical techniques ineffective due to dimensionality issues and sparsity.
Innovation Solution
A generative model validation and anomaly detection service that uses unsupervised learning to compare high-dimensional latent space representations with original input data, quantitatively assessing the latent space and selecting optimal dimensions for anomaly identification in network performance indicators, thereby improving network performance management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human validation is used to assess generative model outputs, then subjective assessment can be performed, but the process becomes infeasible due to high dimensionality of network data
Solution Approach 1:
The patent replaces human subjective validation with an automated computational system that uses unsupervised learning algorithms to assess generative model outputs. The system automatically compares high-dimensional latent space representations with original input data, eliminating the need for human operators to manually evaluate complex network data while maintaining or improving assessment accuracy.
Solution Approach 2:
The patent introduces an intermediary automated validation service that acts as a bridge between the generative model and human operators. This service includes components that quantitatively assess latent space representations, compare generated data with original data, and provide structured feedback, making the high-dimensional data assessment process feasible and objective.
2Reliability
If high-dimensional latent space representations are used for anomaly detection, then comprehensive network data analysis is achieved, but existing statistical techniques become ineffective due to dimensionality issues and sparsity
Solution Approach 1:
The patent changes the parameter space by transforming high-dimensional network data into latent space representations through unsupervised learning. This transformation modifies the data parameters from raw high-dimensional features to compressed latent variables that capture essential patterns while reducing dimensionality, making the data suitable for effective anomaly detection.
Solution Approach 2:
The patent applies dimensionality change by projecting high-dimensional network data into a lower-dimensional latent space while preserving essential patterns and relationships. This dimensional transformation allows statistical techniques to remain effective by working with condensed representations that maintain the critical information needed for anomaly detection.
3Productivity
If automated generative model configuration is implemented, then resource utilization is reduced, but model validation and anomaly detection require sophisticated unsupervised learning techniques
Solution Approach 1:
The patent implements self-service through automated generative model configuration where the system automatically validates models and detects anomalies without requiring extensive human intervention or complex manual processes. The unsupervised learning framework enables the system to self-assess and self-optimize, reducing resource utilization while handling the sophistication of validation internally.
Data Source
AI summary
A method, a device, and a non-transitory storage medium provide a validation and anomaly detection service. The service includes quantitatively assessing latent space data representative of network performance data, which may be generated by a generative model, based on quantitative values pertaining to quantitative criteria. The quantitative criteria may include Hausdorff distances, divergence, joint entropy, and/or total correlation. The service further includes generating geogrid data for services areas of deployed network devices and service areas for prospective and new deployments based on selected latent space data and corresponding network performance data.