AI Resource Threshold Control for Dynamic Utilization Monitoring
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Solution Overview
Problem
Existing resource utilization management systems are inefficient and time-consuming, often leading to over-utilization errors due to static monitoring metrics that fail to accommodate dynamic user needs and resource availability changes.
Innovation Solution
A system utilizing network, user, and terminal computing devices with integrated software applications for authenticating users and applying customizable resource utilization thresholds, including encryption and AI techniques to enhance accuracy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional static monitoring metrics are used, then device complexity is reduced, but resource utilization accuracy deteriorates and over-utilization occurs
Solution Approach 1:
The patent implements dynamic monitoring metrics that automatically adjust resource utilization thresholds based on historical data, current system state, and predicted future needs. This allows the system to adapt to changing conditions without requiring complex manual configuration, resolving the contradiction between measurement precision and device complexity by making the metrics flexible rather than static.
Solution Approach 2:
The system employs self-adjusting algorithms that automatically optimize monitoring parameters based on observed resource usage patterns. The monitoring system serves itself by learning from historical data and autonomously tuning thresholds, eliminating the need for external intervention while maintaining high accuracy without excessive complexity.
2Measurement precision
If frequent monitoring is implemented, then resource utilization accuracy improves, but loss of time and computational resources increases
Solution Approach 1:
The patent implements periodic monitoring with dynamically adjusted intervals based on resource criticality and system state. Critical resources are monitored more frequently while less critical ones use longer intervals, optimizing the balance between monitoring accuracy and time consumption by matching monitoring frequency to actual need rather than applying uniform frequent monitoring.
Solution Approach 2:
The system changes monitoring parameters such as threshold values and sampling intervals dynamically based on current conditions. When resources are stable, monitoring frequency is reduced; when anomalies are detected, frequency increases automatically, thereby maintaining high detection accuracy while minimizing overall time and computational resource expenditure.
3Adaptability or versatility
If customizable utilization demand metrics are established, then adaptability to individual user needs improves, but device complexity increases
Solution Approach 1:
The patent enables users to customize their own utilization metrics through intuitive interfaces that guide them in setting parameters based on their specific needs. The system provides templates and recommendations that simplify the customization process, allowing users to achieve high adaptability without requiring complex system configuration or technical expertise.
Solution Approach 2:
The system pre-configures standardized metric templates for common user scenarios, allowing users to quickly deploy customized monitoring solutions without building complex metrics from scratch. These pre-prepared templates can be easily modified to meet individual needs, reducing the complexity of metric establishment while maintaining high adaptability.
4Measurement precision
If AI techniques are used for metric creation, then measurement precision and customization improve, but use of energy and computational resources increases
Solution Approach 1:
The patent applies AI techniques selectively rather than universally - using machine learning for complex metric creation where high precision is needed, while relying on simpler rule-based approaches for routine monitoring. This partial application of AI maintains measurement precision for critical functions while avoiding the excessive energy consumption that would result from applying AI to all monitoring operations.
Solution Approach 2:
The system performs AI-based metric optimization in advance during off-peak periods when computational resources are more abundant, pre-calculating optimal thresholds and parameters. This preliminary AI processing reduces the need for intensive real-time computational operations, thereby improving metric accuracy while managing energy consumption by shifting heavy computational loads to appropriate time windows.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient and secure management of resource utilization by ensuring compliance with individual user-defined thresholds, reducing over-utilization errors through real-time monitoring and categorization.
Implementation Method 1
The terminal computing device verifies the user computing device by receiving a certificate of authority from the user computing device that is encrypted using an encryption key stored by the user computing device. The terminal computing device decodes, or decrypts, the certificate authority using a public key
Data Source
AI summary
Disclosed are systems and methods for automatically applying resource utilization thresholds. The systems and methods allow resource utilization to be managed effectively, efficiently, and in a secure fashion. The system can utilization artificial intelligence technology to enhance the accuracy and customization of resource utilization thresholds by properly classifying resource utilization demands and applying threshold limitations directed to particular classifications of resource utilizations.