Contextual Ticket Knowledge Graph for Automated Integration Issue Resolution
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Solution Overview
Problem
Existing integration systems face challenges in efficiently resolving issues, such as failures in data exchange between applications, which can lead to prolonged problem resolution times and require manual intervention.
Innovation Solution
A system that guides users in creating contextual tickets with relevant data, generates a ticket knowledge graph, and uses a machine-learning trained action determination engine to automatically determine and initiate actions for resolving issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used to resolve integration issues, then flexibility and judgment can be applied, but resolution time increases and productivity decreases
Solution Approach 1:
The system enables self-service by allowing the integration monitoring system to automatically detect issues, generate contextual tickets with relevant data, and initiate resolution actions without requiring continuous manual intervention. The machine-learning trained action determination engine autonomously determines appropriate actions based on the contextual ticket data.
Solution Approach 2:
The patent replaces manual mechanical processes with automated electronic systems. The machine-learning trained action determination engine substitutes human judgment and manual ticket creation with automated algorithms that process contextual data and determine resolution actions electronically, thereby increasing speed and reducing manual labor.
2Measurement precision
If contextual data is collected and processed to create knowledge graphs, then action determination accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of issue resolution into distinct modular components: issue detection, contextual data collection, knowledge graph generation, machine-learning based action determination, and action execution. Each component handles a specific aspect independently, making the overall complex system manageable and maintainable.
Solution Approach 2:
The ticket knowledge graph acts as an intermediary structure that bridges the gap between raw contextual data and the machine-learning trained action determination engine. It transforms and organizes the complex contextual information into a structured format that the ML engine can process to determine actions, simplifying the interaction between system components.
3Loss of time
If automated actions are initiated based on machine-learning analysis, then resolution time decreases, but the system requires more sophisticated infrastructure
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing contextual data related to integrations, applications, and infrastructure components. The machine-learning model is pre-trained on historical data to recognize patterns and determine appropriate actions. When an issue occurs, these pre-prepared resources enable rapid automated response without requiring complex real-time analysis infrastructure.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for resolving a contextual ticket. The methods, systems, and apparatus include actions of receiving a request from a user to generate a contextual ticket that indicates an issue with an integration, obtaining baseline information for the issue, generating, based on the baseline information, a ticket knowledge graph, providing the ticket knowledge graph to a machine-learning trained action determination engine, receiving, from the machine-learning trained action determination engine, an indication of an action for resolving the issue, and initiating, based on the indication of the action for resolving the issue, the action for resolving the issue.