Automated Error Attribution for Access Management Services
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Solution Overview
Problem
Access management systems for third-party software tools face challenges in efficiently diagnosing, mitigating, and resolving errors, which can be caused by the system itself, third-party services, or user error, leading to inefficient over-purchasing and tedious manual management.
Innovation Solution
The implementation of a server system that uses machine learning models to automatically attribute errors by analyzing logs generated during operations, distinguishing between causes such as system errors, third-party system errors, and user errors, and providing a graphical user interface for user input to classify errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual error diagnosis and mitigation is performed, then flexibility and adaptability are maintained, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing logs and attributing errors without requiring manual intervention. The error attribution system autonomously processes log data, identifies error causes, and generates diagnostic results, enabling the system to serve itself in the error diagnosis process.
Solution Approach 2:
The patent replaces manual mechanical analysis of logs with an automated machine learning-based error attribution system. The system uses trained models to automatically process log data and identify error causes, substituting human operators with an automated computational system that processes errors more quickly and consistently.
2Measurement precision
If comprehensive log analysis is performed to accurately identify error causes, then measurement precision improves, but system complexity and computational resources increase
Solution Approach 1:
The error attribution system is divided into distinct modular components: log collection modules that gather logs from different sources, log processing modules that parse and normalize log data, machine learning model modules that analyze patterns, and result generation modules that output diagnostic conclusions. This segmentation allows each component to specialize in specific tasks, improving accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediate processing layers between raw log data and final error attribution. Log processing modules serve as intermediaries that normalize and structure raw logs into a standardized format suitable for machine learning analysis. This intermediary layer simplifies the complexity of directly analyzing diverse raw logs while maintaining high measurement precision in error cause identification.
3Productivity
If automated error attribution is implemented, then productivity and speed improve, but system complexity and initial setup requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing logs from multiple sources before errors occur. The log collection infrastructure is established in advance, and machine learning models are trained beforehand on historical error data. When errors occur, the pre-configured system can immediately begin automated analysis without requiring setup or configuration at the moment of error occurrence, thus improving productivity while managing complexity through advance preparation.
Data Source
AI summary
A system and method for providing access to third-party software tools as a service. A service access manager can communicate with one or more third parties to manage licenses associated with third-party software tools. A machine learning model can be trained using logs generated by the system and causes of detected errors to automatically determine the cause of errors occurring in the future. Vendor logs generated by software instances instantiated by third-party systems can be collected and used to improve error attribution.