AI Responder Scheduling With Source-of-Truth Alert Integration
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Solution Overview
Problem
Existing computing systems fail to efficiently deploy information technology personnel and computing solutions to minimize downtime and maximize resolution of disruptions and incidents, often leading to longer downtimes, incorrect actions, and worker fatigue due to outdated and inaccurate data from third-party vendors.
Innovation Solution
Integrate a source of truth system with a computing system to provide automatic streamline alerting, utilizing an artificial intelligence model to predict and schedule responders based on up-to-date data for effective disruption resolution, and dynamically adjust schedules as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing computing systems use third-party vendor data for personnel scheduling, then the system can operate with basic functionality, but the data becomes outdated and inaccurate leading to longer downtimes and incorrect actions
Solution Approach 1:
The system proactively integrates with source of truth systems before disruptions occur to obtain current personnel schedule data, mappings, and organizational structure. This preliminary data acquisition ensures that when disruptions happen, the AI model can immediately generate accurate responder recommendations without relying on outdated third-party vendor data, thereby reducing downtime and improving data reliability simultaneously
2Productivity
If manual deployment of information technology personnel is used, then personnel can be deployed based on experience, but the process is inefficient and does not maximize resolution effectiveness
Solution Approach 1:
An AI model acts as an intermediary between the disruption detection system and the personnel deployment process. The AI model receives disruption details, queries the integrated source of truth systems for relevant personnel data and schedules, analyzes compatibility using natural language processing, and generates optimized responder recommendations. This intermediary layer automates the deployment process, improving resolution efficiency while managing system complexity through modular architecture
Solution Approach 2:
The patent replaces manual, experience-based personnel deployment with an automated AI-driven system. The AI model uses natural language processing to analyze disruption descriptions, automatically queries source of truth systems for personnel data, and generates deployment recommendations without human intervention. This substitution of mechanical/AI processes for manual processes significantly improves resolution efficiency while the modular design keeps system complexity manageable
3Speed
If automated actions are executed without accurate responder scheduling, then response time may be faster, but the actions may be incorrect due to outdated data
Solution Approach 1:
The system implements feedback loops where the AI model continuously monitors disruption status and updates responder recommendations based on real-time data from source of truth systems. When personnel schedules change or new information becomes available, the system re-evaluates and adjusts its recommendations, ensuring both rapid response and high accuracy by validating actions against current data before execution
Data Source
AI summary
In certain embodiments, a system and accompanying method configured to provide automatic streamline alerting by integrating with source of truths and methods of use thereof is provided. In particular, the system detects a triggering condition, such as an anomaly affecting a computing system. An automated action is executed to attempt to resolve the triggering condition. An integrated source of truth system obtains data including mappings between responders and the computing system. Based on the data and by utilizing an artificial intelligence model, a prediction is generated for a schedule for at least one responder to resolve the triggering condition affecting the computing system. A notification is transmitted to the at least one transmitter in accordance with the schedule to resolve the triggering condition. The artificial intelligence model is then trained based on the prediction, the data, resolution of the triggering condition, other training data, or a combination thereof.