ML Error Prediction Engine for Automated QoS Resolution
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Solution Overview
Problem
Business organizations face challenges in promptly and cost-effectively addressing customer complaints related to Quality of Service (QoS) issues due to the complexity of distributed computing systems, lack of direct control over leased resources, and inadequate technical support resources, leading to increased costs and customer dissatisfaction.
Innovation Solution
A machine learning-based computing system that analyzes historic data records and error messages to identify error conditions, predicts the probability of success for potential solutions, and initiates the most effective solution, updating the knowledge base for adaptive improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional technical support methods (call centers, chat lines, self-help websites) are used to address customer QoS complaints, then customer service coverage is provided, but the time and cost to resolve issues increases due to limited technical support personnel and system complexity
Solution Approach 1:
The patent implements an automated self-service system where the computing system automatically diagnoses QoS issues by analyzing performance data, identifies root causes, and applies resolutions without human intervention. The system monitors itself and autonomously resolves problems such as network connectivity issues, server performance degradation, and application errors, eliminating the need for manual technical support while reducing resolution time.
Solution Approach 2:
The patent replaces the mechanical system of human technical support personnel with an automated computing-based diagnostic and resolution system. Instead of relying on technicians to manually analyze logs, identify problems, and apply fixes, the system uses automated algorithms to perform these functions, substituting human labor with computational processes that operate faster and without fatigue.
2Productivity
If more technical support personnel are hired to reduce issue resolution time, then customer satisfaction improves, but manpower costs and operational expenses increase
Solution Approach 1:
The system performs self-diagnosis and self-resolution of QoS issues, eliminating the need for additional technical support personnel. The automated system continuously monitors performance metrics, identifies problems, and applies fixes autonomously, maintaining high resolution speed without requiring increased human resources.
Solution Approach 2:
The patent changes the operational parameters of the support system by transitioning from human-based resolution to automated computational resolution. This parameter change enables the system to process and resolve multiple issues simultaneously at high speed without the linear cost increases associated with hiring additional staff.
3Measurement precision
If manual analysis of QoS issues is performed by technical support personnel, then accurate diagnosis can be achieved, but the complexity of distributed computing systems and lack of direct control over leased resources makes thorough diagnosis difficult and time-consuming
Solution Approach 1:
The patent implements a universal monitoring and diagnostic system that can analyze QoS issues across diverse computing environments including on-premises infrastructure, cloud services, and leased resources. The system aggregates performance data from multiple sources and applies unified diagnostic algorithms, enabling accurate diagnosis regardless of the underlying system complexity or ownership structure.
Solution Approach 2:
The system acts as an intermediary between the complex distributed computing infrastructure and the diagnostic process. It collects, normalizes, and analyzes performance data from various sources, translating complex system states into actionable diagnostic information without requiring direct human intervention or deep understanding of each specific component.
4Reliability
If extensive technical analysis and multiple diagnostic steps are performed to ensure thorough problem resolution, then solution accuracy improves, but the time required to resolve issues and manpower costs increase
Solution Approach 1:
The system continuously collects and pre-analyzes performance data before issues manifest, maintaining an up-to-date understanding of system health. When QoS problems occur, the diagnostic process leverages this pre-collected information to rapidly identify root causes without requiring extensive real-time analysis, ensuring both accuracy and speed.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors the effectiveness of applied resolutions and learns from outcomes. This feedback loop enables the system to refine its diagnostic algorithms and resolution strategies over time, improving solution reliability while maintaining efficient resolution times through automated iteration and optimization.
Data Source
AI summary
A machine learning computing system for predicting a probability of success of an identified computing device error condition may include at least a first data repository storing a plurality of historic data records corresponding to one or more computing device error conditions and a second data repository storing a plurality of solutions to each of the computing device error conditions stored in the first data repository. A server is configured to receive a computing device error message from at least one computing center device and analyze the computing device error message to identify an associated error condition category. The server identifies at least two solutions to an associated error condition and predict a probability of success for each of the at least two solutions. The server then initiates at least one solution that has a greatest probability of success and updates the second data repository.