ML-Based Device Setting Prediction for CE Picture Quality
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Solution Overview
Problem
Conventional consumer electronic devices lack the ability to automatically adjust settings based on user context, leading to suboptimal picture quality due to limited user knowledge and experience.
Innovation Solution
A method and system that utilize machine learning models to predict and recommend device settings by analyzing user-initiated adjustments and contexts, clustering users into groups to determine preferred settings, and providing these settings for user selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional consumer electronic devices use default device settings, then the device complexity remains low and ease of operation is maintained, but the picture quality is suboptimal due to limited user knowledge and experience
Solution Approach 1:
The system enables automatic picture quality optimization by having the device self-adjust settings based on machine learning models trained on user behavior data. The device monitors user-initiated adjustments and contexts, then automatically applies optimal settings without requiring user expertise, thus improving picture quality while maintaining ease of operation.
Solution Approach 2:
The system implements a feedback loop where user adjustments and contextual information are continuously collected, analyzed by machine learning models, and used to generate optimized device settings. This feedback mechanism allows the system to learn from user behavior patterns and progressively improve picture quality recommendations.
2Manufacturing precision
If the system automatically adjusts device settings based on user context, then picture quality is optimized, but the extent of automation increases system complexity
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on aggregated user behavior data from multiple devices before deployment. This pre-processing of data and model training occurs in advance, allowing the automated picture quality optimization to function effectively without requiring complex real-time processing during actual device operation.
3Measurement precision
If the system collects and analyzes user behavioral data to predict device settings, then the accuracy of setting recommendations improves, but the quantity of data processing and storage requirements increases
Solution Approach 1:
The system merges data from multiple user devices and contexts into a unified training dataset for machine learning models. By aggregating and combining behavioral data across different users and devices, the system achieves higher prediction accuracy while efficiently utilizing the collective data rather than requiring extensive individual device storage.
Data Source
AI summary
One embodiment provides a method comprising receiving device setting behavioral data collected from one or more consumer electronic (CE) devices, and generating one or more machine learning (ML) models based on a portion of the device setting behavioral data. In one embodiment, the method comprises predicting, via the one or more ML models, a device setting suitable for a CE device based on a current user context, and providing a recommendation comprising the predicted device setting to the CE device. In another embodiment, the method comprises clustering, via the one or more ML models, at least one user associated with the one or more user-initiated adjustments into at least one user group, and determining one or more user-preferred device settings that the user group prefers most. The one or more user-preferred device settings are provided to a CE device as one or more new device settings available for user selection.