Agent Network Alerting System for Anomaly Detection
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Solution Overview
Problem
In agent networks, there is a lack of information available to entities about the performance of agents, making it difficult to detect and mitigate integrity issues and other problems, which can lead to poor user experiences, operational inefficiencies, and increased latency.
Innovation Solution
A graphical user interface and alerting system that receives usage data from agent computer systems, generates metric values using a common set of metrics, calculates risk scores, and compares them to a threshold to identify problematic agents, providing a graphical representation for further analysis and potential mitigation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a network of agents is used to interface with service providers, then the infrastructure can support multiple service providers and users, but there is a lack of information available to entities about agent performance
Solution Approach 1:
The system implements feedback by collecting usage data from agents and service providers, processing this data through normalization and metric generation, and providing feedback reports that show agent performance scores and risk levels. This closed-loop feedback mechanism enables entities to monitor and evaluate agent performance continuously.
Solution Approach 2:
The patent introduces an intermediary processing system that sits between agents and entities, collecting usage data from multiple sources, normalizing it to a common format, generating standardized metrics, and presenting processed information to entities. This intermediary layer transforms raw, heterogeneous data into actionable performance insights.
2Reliability
If usage data is collected from multiple agents with different data formats, then comprehensive performance monitoring is enabled, but data normalization and processing complexity increases
Solution Approach 1:
The system implements a universal data processing framework that can handle multiple data formats from different agents through a single normalization module. This multi-functional approach allows the same processing pipeline to accommodate heterogeneous data sources without requiring separate processing systems for each agent type.
Solution Approach 2:
The patent applies parameter changes by transforming usage data from various formats into a standardized set of metrics with consistent parameter structures. The normalization process adjusts data parameters to common scales and formats, enabling uniform analysis across diverse agent data sources while reducing processing complexity.
3Measurement precision
If metric values are generated using a common set of metrics for all agents, then anomaly detection capability is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-defining a common set of metrics and normalization rules before data collection begins. This preparation work is done in advance, so when usage data arrives from agents, the processing can proceed efficiently using predetermined criteria rather than requiring complex real-time analysis decisions.
Solution Approach 2:
The patent applies partial action by focusing on generating a core set of essential metrics that provide sufficient anomaly detection capability without computing every possible performance parameter. This selective approach achieves adequate detection precision while reducing computational overhead and processing time.
Data Source
AI summary
In some examples a system can receive sets of usage data from agent computer systems associated with agents. The agents can be associated with service providers that provide services to service users. The system can generate a corresponding set of metric values for a common set of metrics for each agent based on a corresponding set of usage data. The common set of metrics can be used for all of the agents to detect anomalies related to the agents. The system can generate a score for each agent based on the corresponding set of metric values, wherein the score indicates a risk level associated with the agent. The system can compare the scores for the agents to a predefined threshold to identify one or more agents that may be problematic. The system can then generate a graphical user interface indicating the one or more identified agents.