Machine Learning Media Content Filtering System
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Solution Overview
Problem
Current methods for monitoring and controlling media content in households, especially for children, lack effective mechanisms to filter out inappropriate content in real-time across various devices and locations, especially when parental supervision is absent.
Innovation Solution
A system utilizing machine learning to create user profiles, generate control rules based on viewing history, and analyze media content in real-time to prevent the presentation of inappropriate content, providing notifications and alternative distraction content when rules are breached.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional parental control methods are used to limit media content, then some filtering capability is provided, but real-time monitoring and control across multiple devices and locations cannot be achieved
Solution Approach 1:
The system creates a universal parental control framework that operates across multiple devices (TVs, computers, tablets, smartphones) and locations (home, remote areas) through a centralized server that communicates with various media players via network connections, enabling consistent content filtering regardless of device type or location
2Reliability
If comprehensive content monitoring is implemented across all devices, then content control effectiveness improves, but system complexity and computational requirements increase
Solution Approach 1:
A centralized server acts as an intermediary between parents and multiple media devices, handling all content analysis, machine learning model management, and control decisions. This intermediary approach consolidates complexity in one location while keeping individual devices relatively simple, as they only need to communicate basic playback information to the server
3Measurement precision
If machine learning analysis is performed on all media content in real-time, then content appropriateness is accurately determined, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing media content metadata, training machine learning models in advance with historical data, and pre-establishing content classification categories. This allows the system to make rapid real-time decisions during actual playback by comparing against pre-computed models and pre-categorized content, rather than analyzing every piece of content from scratch
Data Source
AI summary
Aspects of the subject disclosure may include, for example, embodiments that comprise provisioning a target user profile and obtaining viewing history data. Further embodiments include generating a group of control rules according to the target user profile and training a machine learning application according to the viewing history data and the group of control rules. Additional embodiments include receiving a first indication that a first media content is to be presented to a target user. Also, embodiments include determining by the machine learning application, that the first media content does not conform to the group of control rules and providing a first notification that the first media content does not conform to the group of control rules. Other embodiments are disclosed.