Screen Content Update Sensing via GPU APIs for Low-Power Context Processing
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Solution Overview
Problem
Capturing and processing screen frames at high frequencies for user context analysis significantly impacts power consumption and battery life in computing devices, despite the use of specialized AI engines, due to excessive compute and bandwidth requirements.
Innovation Solution
Implementing an API-based mechanism in the GPU driver to asynchronously notify AI engines of screen updates, using HID events and graphical rendering information to determine active screens and changed content, thereby reducing unnecessary screen captures and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If screen frames are captured frequently for user context analysis, then the accuracy of user context analysis is improved, but power consumption increases significantly
Solution Approach 1:
The system transitions from continuous screen capture to periodic capture triggered by screen update events. The GPU driver notifies the AI engine only when screen content actually changes, creating an event-driven periodic capture mechanism that reduces unnecessary captures while maintaining analysis accuracy.
Solution Approach 2:
The system implements a feedback mechanism where the GPU driver monitors screen rendering events and provides notifications to the AI engine only when changes occur. This feedback loop enables intelligent triggering of screen captures based on actual content changes rather than fixed time intervals.
2Loss of information
If screen frames are captured frequently for user context analysis, then the completeness of contextual data is improved, but compute resources are excessively consumed
Solution Approach 1:
The GPU driver performs preliminary monitoring of screen rendering events before AI processing. By pre-detecting when screen content changes through graphics rendering event monitoring, the system prepares trigger conditions in advance, avoiding unnecessary AI computation on unchanged frames.
Solution Approach 2:
The system extracts only the essential triggering information (screen update events) from the graphics pipeline, separating the detection function from the AI processing function. This allows the AI engine to receive only relevant capture triggers without processing all intermediate rendering data.
3Speed
If screen captures are performed at high frequency, then real-time user context analysis is achieved, but battery life is significantly reduced
Solution Approach 1:
The system uses event-driven periodic capture instead of continuous capture. Screen captures are triggered periodically based on actual rendering events rather than fixed time intervals, maintaining real-time responsiveness when updates occur while extending battery life during static periods.
Solution Approach 2:
The capture frequency dynamically adapts to screen activity levels. The system transitions from static fixed-rate capture to dynamic event-driven capture, adjusting the capture rate based on actual screen rendering events to optimize between real-time analysis and battery conservation.
4Loss of information
If continuous screen capture process is operated, then comprehensive user context is captured, but unnecessary screen captures consume bandwidth
Solution Approach 1:
The GPU driver provides feedback about actual screen rendering events to the AI engine, enabling the system to distinguish between frames that contain new information and those that are duplicates. This feedback mechanism prevents bandwidth waste by triggering captures only when contextual information actually changes.
Solution Approach 2:
The graphics rendering pipeline itself provides the triggering information for captures through its event mechanism. The system uses the existing graphics infrastructure's event notifications to self-determine when captures are necessary, eliminating the need for separate continuous monitoring that would consume additional bandwidth.
Data Source
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AI summary
Various systems and methods for contextual capture and processing of screen capture data are disclosed. An example method for screen capture data processing in a computing device may include: determining an active screen of the computing device based on a user interaction event; identifying a graphics rendering event associated with a software application presented in the active screen; identifying screen capture data in a buffer (e.g., of graphics processing circuitry such as a GPU) that corresponds to the graphics rendering event; and communicating a contextual screen update event via an application programming interface (e.g., a an API operated by a GPU driver). The receipt of this contextual screen update event can be used by an AI engine to control whether to perform contextual processing on particular frames of the screen capture data.