Neural Network Default Filter Settings for Device Control
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Solution Overview
Problem
Parents face challenges in effectively controlling and customizing content access for their children on electronic devices, as existing device control applications lack personalized and efficient filtering solutions.
Innovation Solution
A content filter setting method utilizing an artificial neural network that learns from user preferences and identifying information to generate and modify default filter settings, allowing users to choose and customize filter settings through a user interface, and restrict content access based on these settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration of filter settings is required for each user, then customization accuracy is improved, but setup time and complexity increase
Solution Approach 1:
The system performs preliminary actions by training the neural network model in advance using aggregated data from multiple users. This pre-training enables the system to generate accurate default filter settings automatically when a new user arrives, eliminating the need for manual configuration while maintaining high customization accuracy.
Solution Approach 2:
The system enables self-service by allowing the neural network model to automatically generate personalized filter settings based on user identifying information without requiring manual user input. The model learns from aggregated data and serves each user individually with customized defaults, reducing setup time while maintaining accuracy.
2Reliability
If comprehensive filter settings are provided for all users, then content control effectiveness is improved, but device complexity and user burden increase
Solution Approach 1:
The system applies local quality by providing different levels of filter settings to different users based on their identifying information. The neural network analyzes user characteristics and generates customized default settings tailored to each user's needs, rather than applying uniform comprehensive settings to all users. This reduces complexity for users who don't need full customization while maintaining effectiveness for those who do.
Solution Approach 2:
The system changes parameters dynamically by adjusting the granularity and scope of filter settings based on user identifying information processed by the neural network. The model determines which filter categories require detailed configuration and which can use simplified defaults, optimizing the balance between control effectiveness and setup complexity for each user.
3Productivity
If default settings are generated without user input, then setup efficiency is improved, but personalization accuracy deteriorates
Solution Approach 1:
The system implements feedback by continuously learning from user interactions and manual adjustments made to generated default settings. When users modify the automatically generated filter settings, the system incorporates this feedback into the neural network model, improving future predictions. This creates a closed-loop system that maintains high setup efficiency while progressively improving personalization accuracy through learned user preferences.
Solution Approach 2:
The system performs preliminary action by generating informed default settings using the pre-trained neural network model before the user completes setup. The model uses user identifying information to predict appropriate filter settings, providing a head start that maintains personalization accuracy while enabling users to quickly review and adjust settings if needed, rather than configuring everything from scratch.
Data Source
AI summary
A content filter setting method includes enabling a user to choose a setting of a filter for a particular application in a user interface of a user device. The setting of the filter is received from the user via the user interface, and a model is applied to determine a plurality of default settings of a plurality of filters of the particular application based on the setting of the filter and the identifying information of the user. The plurality of default settings is displayed in the user interface, and modified settings of the default settings are received via the user interface.