Media Rendering Device Control via Trained Neural Network
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Solution Overview
Problem
Conventional media rendering devices often render content based on predefined rules, which may not align with user preferences, leading to user disengagement due to the need for manual adjustments and limited time for operation.
Innovation Solution
A media rendering device equipped with a trained neural network model that captures user images to determine user types and profiles, automatically adjusting settings and recommending content to enhance user experience and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If media rendering devices use predefined rules to control content rendering, then device complexity is reduced and ease of operation is improved, but user engagement deteriorates due to inability to adapt to individual user preferences
Solution Approach 1:
The system performs self-service by automatically capturing user images, determining user types and emotional states, and adjusting rendering parameters without requiring manual user input. The trained network model enables the device to autonomously adapt to user preferences and optimize content rendering based on real-time user state analysis.
Solution Approach 2:
The patent replaces manual mechanical adjustment operations with an automated image processing and neural network-based control system. The trained network model processes user images to determine user characteristics and automatically controls rendering parameters, substituting the need for manual device operation with intelligent automated control.
2Adaptability or versatility
If media rendering devices manually adjust settings to match user preferences, then adaptability is improved, but loss of time increases due to manual search and modification requirements
Solution Approach 1:
The system performs preliminary action by pre-training the network model with user profile information and preferences before actual content rendering. The device captures user images and determines user types in advance, preparing the rendering parameters proactively so that when content is to be rendered, the appropriate settings are already determined, eliminating the need for time-consuming manual adjustments during content consumption.
Solution Approach 2:
The system implements feedback by continuously capturing user images, analyzing emotional states and user types through the trained network model, and using this information to dynamically adjust rendering parameters. This closed-loop feedback mechanism enables the device to learn from user responses and automatically optimize content delivery, eliminating the need for manual search and adjustment while improving adaptability to individual preferences.
3Adaptability or versatility
If media rendering devices implement automated user recognition and content recommendation systems, then adaptability and user engagement are improved, but device complexity increases due to neural network model integration
Solution Approach 1:
The trained network model serves multiple functions within the media rendering device: it determines user types from images, analyzes emotional states, identifies user preferences, and controls rendering parameters. This multi-functional approach consolidates what could be separate complex subsystems into a single versatile neural network model, reducing overall system complexity while maintaining high adaptability to user needs.
Data Source
AI summary
A media rendering device controlled based on a trained neural network is provided. The media rendering device captures an image of a user, and determines a user-type of the user and user-profile information of the user or the user-type based on the captured image. The user-type corresponds to an age group, a gender, an emotional state, and/or a geo-location, associated with the user. The user-profile information corresponds to interests or preferences of the user or the determined user-type. The media rendering device further determines device-assistive information based on application of the trained neural network model on the determined user-type. The media rendering device is further controlled based on the determined device-assistive information, to change at least one configuration setting of the media rendering device or to output media content.