Personalized Computing Environment Provisioning for On-Demand Resource Use
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Solution Overview
Problem
Existing computing environments consume excessive power and network resources due to automated or scheduled powering on of hardware devices and launching of software applications that are not needed by users, as these configurations are often static and not tailored to individual user needs.
Innovation Solution
A system that dynamically provisions a computing environment by determining a session-specific profile for a user based on user-generated data, metadata, and time/location data, using machine learning models to personalize hardware and software configurations, and monitors user activities to apply productivity customizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated or scheduled powering on of hardware devices and launching of software applications is implemented, then user convenience and productivity are improved, but power and network resources are excessively consumed
Solution Approach 1:
The patent implements dynamic provisioning that automatically adjusts hardware and software configurations based on real-time user needs, session type, and historical behavior patterns. The system transitions from static pre-configured environments to dynamic on-demand provisioning, where computing resources are allocated only when and as needed by users, thereby maintaining productivity while reducing unnecessary power and network consumption.
Solution Approach 2:
The system changes key parameters including provisioning timing (from pre-scheduled to on-demand), configuration personalization (from generic to user-specific), and resource allocation (from fixed to flexible). These parameter changes enable the system to optimize the balance between user productivity and resource consumption by adapting to actual usage patterns rather than following rigid schedules.
2Ease of operation
If static hardware and software configurations are used for all users, then system complexity is reduced and ease of operation is improved, but adaptability to individual user needs deteriorates
Solution Approach 1:
The patent implements self-service provisioning where the system automatically determines user needs, selects appropriate configurations, and provisions resources without manual intervention. The system uses machine learning models to analyze user behavior patterns and autonomously configure hardware and software environments tailored to each user's preferences and requirements, thereby achieving both ease of operation and high adaptability.
Solution Approach 2:
The system performs preliminary analysis of user profiles, historical behavior, and session requirements before actual provisioning occurs. By pre-processing user data and predicting resource needs in advance, the system can quickly deploy personalized configurations when users log in, maintaining ease of operation while ensuring high adaptability to individual needs.
Data Source
AI summary
In some implementations, a device may determine a session-specific profile for a user that is to begin a session for a computing environment. The device may determine a computing configuration, for the computing environment, that is personalized for the user based on the session-specific profile for the user and based on historical behavior of the user. The computing configuration may include at least one of a hardware configuration or a software configuration for the computing environment. The device may cause configuration of the computing environment according to the computing configuration.