AI Camera Settings Personalization Through User Feedback
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Solution Overview
Problem
Current imaging devices automatically apply camera settings that do not align with individual user preferences, leading to frustration as users may not achieve their desired image look or feel.
Innovation Solution
An artificial intelligence model is trained based on user input to identify preferences, automatically applying customized camera settings to enhance media content according to user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated camera settings are applied for a large population of users, then device complexity is reduced and ease of operation is improved, but individual user preferences cannot be satisfied leading to user frustration
Solution Approach 1:
The system automatically captures user feedback on image processing results and uses this feedback to self-adjust processing parameters. The device serves itself by learning from user reactions without requiring manual reconfiguration, thereby maintaining ease of operation while adapting to individual preferences over time.
Solution Approach 2:
The system implements a feedback loop where user reactions to processed images are captured and used to adjust processing parameters. This feedback mechanism enables the system to adapt to individual user preferences while maintaining automated operation, resolving the contradiction between ease of use and adaptability.
2Ease of operation
If automated camera settings are applied for a large population of users, then ease of operation is improved, but the settings do not align with individual user preferences leading to user frustration
Solution Approach 1:
The system automatically adjusts processing parameters based on captured user feedback, enabling it to serve itself by learning individual preferences. This maintains the automated ease of operation while improving reliability of results by adapting to each user's specific preferences over time.
Solution Approach 2:
By implementing a feedback loop that captures user reactions and adjusts parameters accordingly, the system maintains automated operation (ease of use) while improving the reliability of meeting user expectations through continuous adaptation to individual preferences.
3Reliability
If customized camera settings are applied for each user, then user preferences are satisfied improving user satisfaction, but device complexity increases
Solution Approach 1:
The system uses machine learning models that automatically learn user preferences from feedback without requiring complex manual configuration interfaces. This self-service approach enables customized settings for each user while keeping the device interface simple and complexity hidden from the user.
Solution Approach 2:
The patent replaces complex mechanical or manual configuration systems with software-based machine learning models that automatically adapt to user preferences. This substitution enables personalized settings without adding physical complexity or user-facing complexity to the device.
4Adaptability or versatility
If machine learning models are used to identify user preferences, then adaptability to individual preferences is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing images with multiple processing parameters before user viewing. This allows the machine learning model to work with pre-prepared options rather than processing in real-time when the user needs the image, reducing perceived processing time while maintaining adaptability.
Solution Approach 2:
The system applies partial processing by generating multiple versions of images with different processing parameters applied. Rather than waiting for complete real-time analysis, it provides users with pre-processed options that capture the essential adaptability needs while reducing computational burden and time.
Data Source
AI summary
Generally, an artificial intelligence model is trained based on input from a user to identify preferences of the user for media content, such as images. Camera settings are automatically applied to media content based on the identified preferences of the user so that the user is automatically presented with media content displayed in accordance with his or her preferences.