Dynamic Device Clustering for Software Deployment
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Solution Overview
Problem
Current computing systems face inefficiencies in managing and deploying applications and security components across diverse user devices within an organization, as administrative users manually group devices based on static criteria, leading to potential compatibility issues and uneven deployment performance.
Innovation Solution
A dynamic device clustering system that identifies inventory and usage information to automatically group devices into clusters for simultaneous deployment, using machine learning to optimize clustering criteria based on usage patterns and collaboration data, ensuring synchronized deployment across devices used by collaborating users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If administrative users manually group devices based on static criteria, then deployment management is simplified, but deployment compatibility and performance are reduced
Solution Approach 1:
The patent transitions from static device grouping criteria to dynamic clustering that automatically adapts based on real-time usage patterns, collaboration data, and device characteristics. The system continuously updates cluster assignments as usage behaviors change, ensuring devices are always grouped with compatible counterparts without requiring manual intervention.
Solution Approach 2:
The system enables devices to be automatically clustered based on their own usage patterns and collaboration relationships without administrative intervention. The clustering mechanism autonomously analyzes usage data, identifies compatibility patterns, and assigns devices to appropriate clusters, reducing manual management while improving deployment reliability.
2Device complexity
If devices are divided into static collections, then deployment process is simplified, but deployment efficiency and synchronization are reduced
Solution Approach 1:
The system performs preliminary analysis of usage patterns and collaboration relationships to pre-establish optimal device clusters before deployment. By proactively identifying compatibility patterns and grouping devices in advance based on predicted usage scenarios, the system prepares deployment targets ahead of time, reducing actual deployment complexity while improving execution efficiency.
Solution Approach 2:
The system continuously monitors deployment performance and usage patterns, using this feedback to dynamically adjust cluster assignments. Real-time feedback loops allow the system to learn from deployment outcomes and optimize future clustering decisions, improving deployment efficiency without increasing process complexity.
3Device complexity
If manual device grouping is used, then system complexity is reduced, but deployment accuracy and user experience are reduced
Solution Approach 1:
The patent replaces manual administrative grouping operations with automated machine learning-based clustering algorithms. The system uses computational models to analyze usage patterns, collaboration data, and device characteristics, substituting human judgment with algorithmic precision to determine optimal device groupings, thereby improving deployment accuracy without significantly increasing system complexity.
Solution Approach 2:
The system dynamically adjusts clustering parameters based on changing usage patterns and device states. By monitoring multiple parameters including application usage frequency, collaboration relationships, and device specifications, the system continuously optimizes cluster assignments to maintain high deployment accuracy while managing complexity through parameter-based automation.
Data Source
AI summary
A set of devices is inventoried to identify components on the device. Usage information is also identified, indicating a level of usage of the different components. The set of devices is dynamically divided into different collections and deployment control signals are generated to control deployment of an item, onto the devices, based upon the identified collections.