AI Incident Team Matching for Faster Technical Response
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Solution Overview
Problem
Traditional IT incident response methods lack precision, speed, and scalability, leading to inefficiencies, resource misallocation, and increased response times due to manual dispatching, lack of real-time data utilization, skill mismatches, and high dependency on human judgment.
Innovation Solution
An AI-driven system that converts unstructured incident descriptions into vectors, compares them with historical data to predict the best response teams, and automatically connects them for immediate incident resolution, incorporating continuous learning and feedback loops for improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual dispatching methods are used to engage incident response teams, then human judgment and experience can be applied to match incidents with appropriate teams, but response time delays and inconsistencies occur due to availability and familiarity constraints
Solution Approach 1:
The system pre-processes and stores incident description data in vector format during non-incident periods, preparing the data structure for rapid comparison when incidents occur. This preliminary preparation eliminates the need for real-time manual analysis while maintaining accurate matching capabilities.
Solution Approach 2:
The patent replaces the manual mechanical process of dispatcher judgment with an automated computational system that uses vector similarity algorithms. This substitution eliminates human availability constraints while maintaining or improving matching accuracy through consistent algorithmic application.
2Reliability
If traditional incident response methods are used, then existing processes and human expertise are leveraged, but scalability issues arise as organizations grow and incident volume increases
Solution Approach 1:
The vector-based incident description system serves multiple functions: it enables rapid similarity searching, supports automated team matching, and provides a standardized data format that scales with organization size. This universal approach replaces multiple specialized manual processes with a single scalable system.
Solution Approach 2:
The system transforms incident descriptions from unstructured text to structured vector parameters, enabling computational processing. This parameter transformation allows the system to handle increasing incident volumes and organizational complexity while maintaining consistent response quality.
3Productivity
If automated systems are implemented to reduce response time, then speed and consistency improve, but the system complexity and implementation difficulty increase
Solution Approach 1:
The system performs the complex vectorization and data preparation work in advance, during non-incident periods. This preliminary action reduces the computational burden during actual incidents, maintaining high response speed while managing system complexity through staged processing.
4Ease of operation
If manual team selection processes are used, then flexibility in decision-making is maintained, but resource utilization becomes inefficient with overburdened and underutilized teams
Solution Approach 1:
The system incorporates feedback loops that continuously learn from incident outcomes and team performance data. This feedback mechanism enables the system to adapt to organizational changes, maintain flexible matching decisions, and optimize resource utilization by identifying patterns in team effectiveness and workload distribution.
Data Source
AI summary
Methods and apparatuses for automated engagement of technical incident response teams using artificial intelligence include a server that receives an incident response request including unstructured computer text comprising a description of an active technical incident and a requested incident response team. The server converts the unstructured computer text into a first vector and compares the first vector to historical vectors generated from incident descriptions contained in historical incident tickets, each historical incident ticket having an assigned incident response team. The server generates a similarity score for each historical incident ticket based upon the comparison between the corresponding historical vector and the first vector and identifies proposed incident response teams using the assigned teams from the historical incident tickets that have a similarity score above a threshold. The server connects to computing devices of team members on the proposed teams to establish a communication channel for the active technical incident.