HID Activity Recognition for Adaptive Computing Resource Control
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Solution Overview
Problem
Modern computer operating systems are overly feature-rich, making it difficult for users to discover features that enhance operational efficiency, leading to wasted computing resources like processor cycles, memory, and power due to inefficient usage.
Innovation Solution
Utilizing machine learning to adjust computing system operational characteristics based on Human Interface Device (HID) activity by training a model with HID data to identify user activities and recommend or implement features that improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If operating systems include many features to improve functionality, then system capability is enhanced, but user ability to discover and utilize efficiency-improving features deteriorates
Solution Approach 1:
The system automatically monitors HID activity patterns, trains machine learning models to identify user tasks, and autonomously adjusts operational characteristics without user intervention. This self-service approach resolves the contradiction by eliminating the need for users to discover features while the system independently optimizes performance based on observed behavior.
Solution Approach 2:
The system continuously collects HID activity data as feedback, processes it through machine learning models to understand user intent, and adjusts system operations accordingly. This closed-loop feedback mechanism enables the system to adapt to user needs automatically, maintaining high functionality while eliminating the discovery burden.
2Loss of information
If operating systems provide extensive video tutorials to describe features, then feature awareness is improved, but user time and attention are consumed
Solution Approach 1:
Instead of requiring users to watch tutorials, the system autonomously analyzes HID activity patterns to infer user tasks and automatically configures optimal operational characteristics. This eliminates the time-consuming tutorial requirement while still achieving the goal of enabling users to benefit from efficiency-improving features.
Solution Approach 2:
The system performs preliminary analysis of HID activity patterns and pre-configures optimal system settings before users need them. By anticipating user needs through pattern recognition, the system eliminates the need for reactive tutorial viewing and directly applies optimizations.
3Ease of operation
If computing systems operate with default settings to simplify operation, then ease of use is improved, but resource efficiency deteriorates due to wasted processor cycles, memory, and power
Solution Approach 1:
The system dynamically adjusts operational characteristics based on real-time HID activity analysis. Instead of static default settings, the system continuously adapts processor cycles, memory allocation, and power consumption to match actual user tasks, maintaining simplicity while eliminating resource waste.
Solution Approach 2:
The system changes operational parameters such as processor frequency, memory allocation, and power states based on inferred user tasks from HID patterns. This dynamic parameter adjustment maintains ease of operation while optimizing resource efficiency for each specific usage scenario.
Data Source
AI summary
Technologies are described for utilizing machine learning (“ML”) to adjust operational characteristics of a computing system based upon detected HID activity. Labeled training data is collected with user consent that includes data describing HID activity and data that identifies user activity taking place on a computing device when the data HID activity took place. A ML model is trained using the labeled training data that can receive data describing current HID activity and identify user activity currently taking place on another computing device based upon the current HID activity. The ML model can then select features of the other computing device that are beneficial to the identified user activity. The ML model can then cause one or more operational characteristics of the other computing device to be adjusted based upon the identified user activity, thereby saving valuable computing resources. A UI can also be presented that describes the identified features.