Adaptive Control Reconfiguration for Computing Use Types
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Solution Overview
Problem
Existing operating systems lack the ability to dynamically adapt to different use types and prioritize computing functions based on changing user needs during a single session, limiting their efficiency and performance.
Innovation Solution
A customization component within the control system that assesses the current use type and dynamically reconfigures resource allocation by using a machine-trained model or discrete program to prioritize computing functions, adjusting resource access and process management based on state information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the operating system is designed to provide adequate service to a broad class of general-purpose users, then the system has wide applicability and ease of operation, but it cannot dynamically adapt to different use types or prioritize computing functions based on changing user needs
Solution Approach 1:
The operating system implements dynamic reconfiguration by repeatedly reassessing the use type that most accurately characterizes the current use of the computing system during a single session. The system transitions from a static, general-purpose configuration to a dynamic state where resource allocation and control parameters are continuously adjusted based on detected use patterns, enabling adaptation without requiring multiple fixed configurations
Solution Approach 2:
The system performs self-diagnosis and self-adjustment by automatically detecting use types and reconfiguring control settings without user intervention. A machine-trained model or discrete computer program autonomously monitors system usage patterns, identifies the current use type, and modifies resource allocation accordingly, allowing the system to serve itself rather than requiring manual reconfiguration by users
2Ease of operation
If the control system manually changes behavior through a control panel, then users have some ability to customize, but the opportunities for customization are limited and require user knowledge and time
Solution Approach 1:
The system eliminates the need for manual user intervention by implementing automatic use type detection and control reconfiguration. The machine-trained model continuously monitors system state and autonomously adjusts control parameters based on detected usage patterns, completely removing the time users would otherwise spend manually reconfiguring the system while maintaining full customization capability
Solution Approach 2:
The system implements continuous feedback loops where usage data is collected, analyzed by the machine-trained model, and used to automatically adjust control settings. This closed-loop system constantly monitors user behavior patterns and dynamically modifies system behavior in response, providing adaptive customization without requiring users to spend time on manual configuration
3Productivity
If the system prioritizes computing functions by increasing their access to resources, then performance of prioritized functions improves, but resource allocation becomes less balanced across all functions
Solution Approach 1:
The system applies differentiated resource allocation strategies to different computing functions based on their priority and the detected use type. Instead of uniform resource distribution, the control system dynamically adjusts resource access rights, CPU time slices, and memory allocation for specific functions according to their current importance to the detected use pattern, optimizing performance where needed while maintaining adequate support for other functions
Solution Approach 2:
The system dynamically modifies resource allocation parameters such as CPU priority levels, memory allocation sizes, and I/O access rights based on the detected use type. The machine-trained model adjusts these parameters in real-time to match the current usage scenario, enabling the system to optimize performance for prioritized functions while maintaining flexible adaptation to different use types through parameter modulation rather than fixed allocation
Data Source
AI summary
A computing system adapts operation of a control system based on how the system is being used. The computing system obtains state information that describes a current state of operation of the computing system, including information indicative of interaction by a user with an application executed by the computing system. A machine-trained model processes the state information to determine use type information that identifies a use type associated with the user, the use type corresponding to a predefined category of computing behavior that characterizes a manner of using the computing system. The computing system modifies operation of the control system based on the use type information by automatically adjusting an operating parameter of the control system that affects allocation of computing resources to the application, thereby dynamically optimizing scheduling, throttling, bandwidth allocation, or other operating parameters for the identified use type.