Anomaly Detection Pipeline with Generative AI Explanation
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Solution Overview
Problem
Conventional anomaly detection systems in time series data lack sufficient detail in explaining anomalies, making it difficult for human administrators to understand the origin of anomalies and derive precise actions to mitigate them.
Innovation Solution
The implementation of an EPAG module that includes a Vector to Image Processor, an Explainability Manager, and a Policy Manager, which generates human-readable explanations and automated action recommendations by transforming input vectors into visual representations, using fine-tuned VLM models for explanation and policy classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional anomaly detection algorithms are used, then anomaly detection capability is provided, but the explanation detail is insufficient for human understanding
Solution Approach 1:
The patent introduces an Explainability Manager as an intermediary component between the anomaly detection algorithm and human administrators. This manager generates natural language explanations that bridge the gap between complex algorithmic outputs and human-understandable reasoning, thereby reducing information loss without requiring administrators to understand the complex underlying algorithms.
Solution Approach 2:
The system is segmented into distinct functional modules: anomaly detection module, explanation generation module (Explainability Manager), and policy automation module. This segmentation allows each module to specialize in its function, with the explanation module specifically tasked with generating detailed human-readable explanations, thereby improving explanation detail while keeping overall system complexity manageable through modular design.
2Extent of automation
If simple anomaly detection alerts are provided, then the system remains simple to operate, but automatic actions cannot be systematically defined
Solution Approach 1:
The Policy Manager performs preliminary classification of anomalies into predefined categories with associated automated actions before actual anomaly resolution is needed. By pre-defining policies and actions for different anomaly types, the system enables systematic automatic actions without requiring complex real-time decision-making, thereby increasing automation extent while maintaining manageable complexity.
Solution Approach 2:
The system implements feedback loops where anomaly explanations and policy classifications inform subsequent automated actions and system adjustments. This feedback mechanism allows the system to learn from past anomalies and improve its policy automation capability over time, enabling more sophisticated automatic actions without proportionally increasing system complexity.
3Loss of information
If detailed reasoning is provided for anomalies, then human understanding is improved, but the system complexity increases
Solution Approach 1:
The Explainability Manager serves as an intermediary that translates complex algorithmic reasoning into natural language explanations. It receives detailed internal reasoning from the anomaly detection algorithm and transforms it into human-readable format, thereby preserving information quality while shielding users from system complexity. The manager acts as a buffer that manages the translation layer between complex system internals and simple user interface.
Data Source
AI summary
One example method includes receiving an input vector that includes time series data indicative of an anomaly, generating, based on the input vector, a visual image that corresponds to the time series data, using a first vision-language model (VLM) to transform the visual image into output text that explains the anomaly, building a prompt that comprises the visual image and the explanation text, using a second VLM to generate a recommendation based on the prompt, and resolving a cause of the anomaly by implementing the recommendation.