Personalized Media Editing for Stress-Aware Content Filtering
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Solution Overview
Problem
Existing systems fail to effectively prevent inappropriate content from being viewed by users who are not suited for it, despite age ratings and content indications.
Innovation Solution
An intelligent dynamic personalized system that uses artificial intelligence and machine learning to analyze user personality traits, behavior, and preferences to edit media content in real-time, ensuring it aligns with the users' stress levels and appropriateness, and can intervene when irregular behavior is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If age ratings and content indications are provided, then content classification is improved, but inappropriate content is still viewed by unsuitable users
Solution Approach 1:
The system continuously monitors user behavior during content consumption and provides feedback to dynamically adjust content delivery. Behavioral indicators such as viewing patterns, physiological responses, and interaction data are fed back to the system, which then real-time modifies or blocks content based on detected stress levels and appropriateness criteria, creating a closed-loop control system that adapts to user state
Solution Approach 2:
The system introduces an intermediary layer between the user and the content delivery system. This intermediary actively monitors behavioral indicators and stress levels, and selectively filters or modifies content before it reaches the user, acting as a mediator that enforces appropriateness based on real-time user state assessment
2Reliability
If real-time content editing is implemented, then content appropriateness is improved, but system complexity increases
Solution Approach 1:
The system segments the content delivery process into distinct functional modules: behavioral indicator collection, stress level analysis, content appropriateness evaluation, and real-time content modification. Each module operates independently but coordinates with others, allowing the complex system to be managed through modular components that can be developed, tested, and maintained separately
Solution Approach 2:
The system performs preliminary analysis of user behavioral indicators and establishes baseline stress levels before content delivery begins. By pre-processing user data and setting up monitoring frameworks in advance, the system reduces the computational complexity required during real-time content editing, as much of the heavy lifting is done beforehand
3Measurement precision
If user behavior monitoring is enhanced, then stress level detection is improved, but user privacy concerns increase
Solution Approach 1:
The system applies different levels of monitoring intensity and data collection based on local context and user state. Rather than uniformly monitoring all users at all times, the system adjusts monitoring granularity based on detected stress levels, content type, and user preferences, collecting more detailed data only when necessary for safety while maintaining privacy during normal states
Data Source
AI summary
Aspects of the subject disclosure may include, for example: obtaining first data that is associated with a first user and that is indicative of personality trait(s) of the first user; obtaining second data that is associated with a second user and that is indicative of personality trait(s) of the second user; receiving first usage data that is indicative of viewing by the first user of media content; receiving second usage data that is indicative of viewing by the second user of the media content; determining change(s) to be made to the media content, wherein the change(s) are in accordance with the first and second personality traits, and wherein the change(s) are designed to minimize a stress level of the first user and/or the second user; editing the media content to reflect the change(s); and providing edited media content to the first and second users. Other embodiments are disclosed.