Dynamic Application Prioritization via User Persona Detection
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Solution Overview
Problem
Conventional application optimization methods fail to dynamically adjust to a user's changing persona, leading to suboptimal performance as static whitelists become obsolete and do not account for individual differences within the same persona group.
Innovation Solution
An Information Handling System (IHS) that uses a Machine Learning engine to identify and prioritize applications based on a user's current persona, dynamically optimizing resource allocation across multiple wireless links to ensure optimal bandwidth allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a static whitelist of applications is used for optimization, then the system structure is simple and easy to implement, but the optimization performance deteriorates over time as user personas change
Solution Approach 1:
The patent transforms the static whitelist into a dynamic system that automatically adapts to changing user personas. The ML engine continuously monitors user behavior patterns and updates the application whitelist in real-time, ensuring optimization performance remains high as user needs evolve throughout the day.
Solution Approach 2:
The system implements feedback loops where the ML engine analyzes user interaction data, identifies persona changes, and adjusts the optimized application list accordingly. This closed-loop approach allows the system to learn from user behavior and maintain optimal performance without manual intervention.
2Device complexity
If a static whitelist approach is used, then the device complexity is low, but the system cannot account for individual differences within the same persona group
Solution Approach 1:
The patent applies local quality by customizing optimization settings for each individual user within their persona group. Instead of applying uniform optimization rules to all users in the same persona, the system analyzes individual behavior patterns and adjusts optimization parameters specifically for each user's preferences and habits.
Solution Approach 2:
The system dynamically changes optimization parameters based on detected persona transitions and individual user characteristics. The ML engine adjusts multiple parameters including application priority weights, resource allocation settings, and optimization thresholds to match both the current persona context and individual user preferences.
3Productivity
If dynamic persona-based optimization is implemented, then application performance is improved, but the computational resources and system complexity increase
Solution Approach 1:
The patent applies partial action by selectively optimizing only the applications currently in the user's active persona whitelist, rather than continuously analyzing and optimizing all installed applications. This focused approach reduces computational overhead while maintaining high performance for the most relevant applications.
Solution Approach 2:
The system performs preliminary analysis during off-peak times to pre-process user behavior patterns and prepare optimization profiles. By anticipating persona transitions and pre-computing optimization parameters, the system reduces real-time computational requirements when actual optimization decisions are needed.
Data Source
AI summary
A system and method for Embodiments of systems and methods for managing performance optimization of applications executed by an Information Handling System (IHS) are described. In an illustrative, non-limiting embodiment, an IHS may include computer-executable instructions to identify a current persona of a user of the IHS, identify an application that is associated with the current persona, and prioritize the application associated with the current persona. The current persona being one of multiple modes of operating the IHS by the user.