Diagnostic Headline System for Anomaly Resolution
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Solution Overview
Problem
Current anomaly detection systems alert clients to issues but fail to provide a clear diagnosis or actionable steps for resolution, leaving customers without the necessary knowledge to address system anomalies effectively.
Innovation Solution
The Headliner system, a multi-source analysis engine driven by structured domain models, analyzes historical and current performance data to identify anomalies, determine their causes, and recommend actionable steps for resolution, presenting findings in an intuitive and client-friendly format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If anomaly detection systems alert clients to issues, then clients are informed of system problems, but clients lack the knowledge to address the anomalies effectively
Solution Approach 1:
The patent introduces an intermediary diagnostic system that translates complex anomaly data into simple, actionable headlines. This intermediary layer processes raw performance data, identifies anomalies, and presents them in a client-friendly format with recommended actions, bridging the gap between technical systems and end users.
Solution Approach 2:
The system enables clients to independently diagnose and resolve issues by providing them with clear anomaly descriptions and recommended actions. Instead of requiring expert intervention, the system empowers clients to take self-service actions based on the presented diagnostic information.
2Loss of information
If detailed diagnostic information is provided to clients, then clients gain knowledge to resolve issues, but the information becomes complex and difficult to understand
Solution Approach 1:
The patent segments complex diagnostic information into distinct, manageable components: anomaly detection, cause identification, and recommended actions. Each segment is presented separately as a simple headline, making the information digestible while maintaining completeness.
Solution Approach 2:
The system uses visual differentiation (analogous to color changes) to highlight the importance and type of various diagnostic elements. Different visual styles are applied to anomalies, causes, and recommendations to help clients quickly understand the nature and priority of each piece of information.
Data Source
AI summary
A method for a diagnostic headline system using simple linguistic terms is described. The method comprises receiving historical and current performance data for a component of a system. An anomaly is determined in the current performance data by comparing the current performance data and the historical performance data for the component. The anomaly is determined whether or not it indicates an error in the system. The cause of the anomaly is determined. Steps for addressing the cause of the anomaly are recommended. A display is formatted of the current performance data, the error, and the recommended steps for addressing the cause of the anomaly.