Media Classification by Physiological State Data
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Solution Overview
Problem
Current systems fail to effectively predict and prevent exposure to media that may trigger negative behaviors in individuals with susceptibility to temptations, due to the complexity of data involved in identifying user propensities for problematic behaviors.
Innovation Solution
A system and method that utilize a computing device to classify media based on user propensities by analyzing physiological state data and identifying potential triggers, blocking the transmission of media items with themes associated with problematic behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If systems analyze physiological state data to identify user propensities for problematic behaviors, then the ability to prevent exposure to triggering media is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex classification task into multiple components: physiological state data acquisition, propensity identification, media theme analysis, and transmission blocking decisions. This modular approach manages complexity by dividing the overall system into distinct functional modules that can be developed and maintained independently.
Solution Approach 2:
The system performs preliminary classification of media items and users before actual media transmission occurs. By pre-identifying user propensities and pre-classifying media themes, the system makes blocking decisions based on predetermined criteria, reducing real-time processing complexity while maintaining high prediction accuracy.
2Object-affected harmful factors
If the system blocks transmission of media items based on user propensities, then harmful exposure is reduced, but information loss occurs when potentially beneficial media is incorrectly blocked
Solution Approach 1:
The system incorporates feedback mechanisms where blocking decisions are continuously refined based on user responses and outcomes. This allows the system to learn from false positives and adjust classification thresholds, reducing information loss while maintaining protection against harmful content.
Solution Approach 2:
The system dynamically adjusts classification parameters and blocking thresholds based on individual user profiles and contextual factors. By modifying decision parameters rather than applying fixed rules, the system can distinguish between harmful and beneficial media more accurately, reducing false blocks.
Data Source
AI summary
A system and method for classifying media according to user negative propensities is illustrated. The system includes a computing device configured to obtain a physiological state data as a function of a user input, identify a user propensity for problematic behavior associated with a human subject as a function of the physiological state data, wherein the user propensity for problematic behavior identifies an problematic behavior from a predetermined plurality of problematic behaviors, receive a media item containing a principal theme to be transmitted to a device operated by the human subject, and block transmission of the media item to the device operated by the human subject as a function of the principal theme and the user propensity for problematic behavior.