Automated Incident Resolution System with ML Feedback
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Solution Overview
Problem
In complex enterprise technology systems, resolving incidents is often hindered by the difficulty in identifying and distributing critical information effectively, leading to inefficient use of resources and potential duplication of efforts, as numerous personnel respond to issues without clear, coordinated action plans.
Innovation Solution
An automated system utilizing a machine learning model that receives incident information, applies historical data to recommend actions, and updates based on responses, facilitating more efficient decision-making and resource allocation during incident resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple personnel are deployed to resolve incidents in complex enterprise systems, then the urgency and importance of incident resolution is addressed, but resource duplication and inefficiency increase
Solution Approach 1:
The system implements feedback loops where incident data, resolution actions, and outcomes are continuously collected and fed back into the machine learning model. This enables the system to learn from past incidents and improve future recommendations, ensuring that resource deployment becomes increasingly optimized while maintaining high resolution effectiveness.
Solution Approach 2:
The incident resolution system enables self-service capabilities by automatically analyzing incident data, generating resolution recommendations, and updating its own model based on outcomes. This reduces the need for manual analysis and coordination overhead, allowing the system to serve itself while improving efficiency.
2Ease of operation
If comprehensive monitoring and management of disparate operatives is implemented, then incident resolution coordination improves, but system complexity and difficulty of management increase
Solution Approach 1:
The machine learning model acts as an intermediary between incident data and resolution recommendations. It processes complex monitoring data and translates it into actionable, easy-to-understand recommendations, simplifying the coordination process while maintaining comprehensive oversight of disparate operatives and operations.
Solution Approach 2:
The system replaces manual monitoring and coordination mechanisms with automated machine learning-based recommendations. This substitution reduces the complexity of human coordination by providing algorithmic guidance based on historical patterns, while still enabling comprehensive management of multiple operatives.
3Productivity
If historical incident data is utilized to improve resolution efficiency, then resource allocation optimization improves, but information processing requirements and system complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing historical incident data in structured formats before incidents occur. This preparation enables rapid analysis and recommendation generation during actual incidents, improving resolution efficiency while distributing the processing complexity over time rather than concentrating it during critical resolution moments.
Data Source
AI summary
An automated system is provided for facilitating resolution of an incident on a digital processing system. The automated system has a data storage unit with information on previous incidents, a user interface and a resolution facilitation server. The server receives incident information from a monitoring system, including status information for at least one operating parameter of the digital processing system. The server is configured to apply a machine learning model to determine a first recommended action using the incident information and the previous incident information, present the first recommended action to the user, and receive a recommendation response. The server is also configured to establish a recommendation score based at least in part on the recommendation response and to update the machine learning model using the incident information, the first action recommendation, and/or the first recommendation score.