Virtual Assisting System for Real-Time Anomaly Resolution
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Solution Overview
Problem
Existing virtual environment technologies face challenges in real-time anomaly detection and resolution, leading to poor user experience and increased service tickets due to limited capabilities of traditional virtual assistance tools in measuring and addressing user experience issues.
Innovation Solution
A virtual assisting system that continuously monitors predefined trigger parameters, computes a User Experience Index (UXI) to identify anomaly events, and implements self-healing actions or user interventions to resolve issues dynamically, with the ability to generate new resolution policies based on self-learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional virtual assistance tools are used to monitor and resolve anomaly events, then the system structure remains simple, but the measurement capability of user experience is limited and response time is delayed
Solution Approach 1:
The system segments the complex virtual environment monitoring into multiple independent data collection agents deployed across different virtual desktops. Each agent collects specific metrics (CPU usage, memory usage, network latency, etc.) and sends them to a central server for processing. This segmentation allows comprehensive user experience measurement without requiring a monolithic complex system, as each component remains relatively simple while the collective system achieves high measurement precision.
2Measurement precision
If comprehensive monitoring of trigger parameters is implemented to detect anomaly events, then measurement precision improves, but processing time and computational resources increase
Solution Approach 1:
The system pre-defines multiple trigger parameters (CPU usage thresholds, memory usage thresholds, network latency thresholds, etc.) and establishes anomaly detection rules in advance. When monitoring data is collected, the system simply compares current values against these pre-set thresholds rather than performing complex real-time analysis. This preliminary action approach enables fast anomaly detection with high precision, as the comparison operation is computationally inexpensive and can be executed in real-time without significant processing delays.
3Extent of automation
If manual intervention is required for resolving anomaly events, then automation level remains low, but system complexity and operational overhead increase
Solution Approach 1:
The system implements self-service automation through predefined resolution policies that automatically execute when anomalies are detected. Each trigger parameter is associated with specific resolution actions (e.g., restarting services, reallocating resources, notifying users). When an anomaly event occurs, the system automatically retrieves and executes the corresponding resolution policy without human intervention. This self-service capability achieves high automation levels while keeping system complexity manageable through rule-based automation rather than complex AI-driven decision-making.
4Reliability
If repetitive remedial measures are implemented for multiple users, then service quality improves, but time and effort of helpdesk team increases substantially
Solution Approach 1:
The system implements universal resolution policies that can be applied across multiple virtual desktops and users simultaneously. A single anomaly detection and resolution workflow serves all users experiencing the same issue, rather than requiring separate manual intervention for each user. The centralized server manages and distributes these universal policies, enabling one team to efficiently handle numerous concurrent anomaly events across the entire virtual environment, thereby maintaining consistent service quality while minimizing helpdesk time consumption.
Data Source
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AI summary
Disclosed subject matter relates to virtual environment including a method of resolving anomaly events occurring in virtual environment in real-time. A virtual assisting system identifies trigger points in relation to the anomaly events affecting user experience and retrieves resolution policies associated with each trigger point from policy database. Further, virtual assisting system computes a cumulative weightage score for each trigger point based on metrics and weights associated with each predefined trigger parameter recorded in resolution policies and thereafter computes a User Experience Index (UXI) value by correlating the cumulative weightage scores. Finally, one or more actions corresponding to the one or more trigger points are implemented based on the UXI value to resolve the anomaly events occurring in the virtual environment. The present disclosure dynamically interprets repetitive anomaly events that affect user experience and performs self-healing of anomaly events dynamically without the need for the users to raise service tickets.