DBL and ALS Brightness Control via Semi-Supervised Machine Learning
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Solution Overview
Problem
Current information handling systems face issues with dynamic backlight (DBL) and ambient light sensor (ALS) brightness control systems often working cumulatively or against each other, leading to deleterious effects on the user's front-of-screen experience, such as washout or dimming, due to simultaneous operation without proper coordination.
Innovation Solution
A DBL and ALS brightness control management system that employs semi-supervised machine learning to learn user behavior and preferences, adjusting brightness levels gradually or stepping them to minimize disruption, and weights the operation of both systems based on location and ambient light levels to optimize the viewing experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If both DBL and ALS control systems operate simultaneously to adjust brightness, then power consumption is reduced and viewing experience is optimized, but conflicts arise causing washout or dimming effects that degrade the front-of-screen experience
Solution Approach 1:
A machine learning model is introduced as an intermediary between the DBL and ALS control systems. The model predicts whether ALS should be engaged based on current context (location, ambient light, user behavior) and outputs a confidence score that determines the degree of ALS engagement. This intermediary prevents direct conflict between the two control systems by intelligently mediating their interaction.
Solution Approach 2:
The system dynamically changes the engagement parameter of ALS based on predicted confidence scores. When confidence is high, ALS is fully engaged; when confidence is low, ALS engagement is reduced or disabled. This parameter adjustment resolves the contradiction by adapting the ALS contribution to current conditions, preventing washout or dimming while maintaining power savings.
2Ease of operation
If ALS control system operates to adjust brightness based on ambient light levels, then viewing experience is improved, but conflicts with DBL control system cause deleterious effects on front-of-screen experience
Solution Approach 1:
The system incorporates feedback loops where the machine learning model continuously learns from user interactions and system performance. User behavior data, location information, and ambient light conditions are fed back into the model to refine predictions about when ALS should be engaged. This feedback mechanism ensures ALS operates effectively without causing conflicts with DBL.
Solution Approach 2:
The ALS engagement level is made dynamic rather than static. The system adjusts the degree of ALS engagement in real-time based on predicted confidence scores, location context, and ambient light conditions. This dynamic approach allows ALS to provide viewing experience improvements while adapting to prevent conflicts with DBL control.
3Loss of energy
If DBL control system adjusts brightness dynamically, then power is conserved, but simultaneous operation with ALS control system without coordination creates conflicts
Solution Approach 1:
The machine learning model performs preliminary action by predicting whether ALS should be engaged before the actual brightness adjustment occurs. By analyzing location, ambient light, and user behavior in advance, the model determines the optimal engagement level for ALS, preventing conflicts before they arise and ensuring reliable coordinated operation between DBL and ALS.
Data Source
AI summary
A method of operating or an information handling system operating a dynamic backlight and ambient light sensor (DBL and ALS) brightness control management system comprising a digital display having a selectable brightness level, a processor operatively connected to the digital display for executing code instructions of a dynamic backlight (DBL) control system for modifying brightness levels of some or all portions of the display screen in response to inputs relating to display content type and associated optimal contrast levels for the display content and the processor executing code instructions of an ambient light sensor (ALS) control system to modify brightness levels of some or all portions of the display screen in response to detected ambient light levels of the information handling system where the processor executing code instructions of the DBL and ALS brightness control management system adjusts operation of either the DBL control system or the ALS control system based on location or detected ambient light levels and wherein the adjustment to the DBL control system or the ALS control system prevents interfering impact by both systems.