Event Viewership Control Using AI Content Quality Scoring
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Solution Overview
Problem
Existing video-sharing platforms face challenges with low-quality and duplicate content, user data privacy concerns, and ineffective content curation, leading to a poor user experience and reduced platform utility.
Innovation Solution
A system and method for controlling event viewership by assigning quality scores to multimedia content using a trained comparison-based model, enabling real-time regulation of content visibility based on quality, event type, and participant privacy protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all user-uploaded multimedia content is made visible on the platform, then the quantity of content increases, but the quality and user experience deteriorate due to low-quality and duplicate content
Solution Approach 1:
A trained comparison-based model serves as an intermediary between user-uploaded content and the platform's content library. The model automatically compares uploaded multimedia files against existing content, detects duplicates, and assigns quality scores without human intervention, thereby maintaining content quality while allowing high-volume user submissions.
Solution Approach 2:
The system implements self-service content curation through automated duplicate detection and quality assessment. The comparison-based model independently evaluates uploaded content, identifies duplicates, and determines visibility without requiring manual review, enabling the platform to handle large volumes of user-generated content efficiently.
2Manufacturing precision
If manual content review and curation is implemented, then content quality improves, but the time and resources required for content moderation increase
Solution Approach 1:
The patent replaces the mechanical system of manual content review with an automated computational model. The comparison-based model uses machine learning algorithms to perform duplicate detection and quality assessment, substituting human moderators with an automated system that processes content rapidly and consistently without fatigue or variability.
Solution Approach 2:
The system performs self-service content moderation through the automated comparison model, which independently evaluates uploaded content against established quality criteria and existing library content, eliminating the need for external human review and significantly reducing moderation time.
3Adaptability or versatility
If user data is collected and stored for platform operations, then the platform functionality improves, but user privacy concerns increase
Solution Approach 1:
The patent extracts and separates personally identifiable information from the content moderation process. The comparison-based model performs duplicate detection and quality assessment without requiring access to or storage of user personal data, extracting only the necessary content metadata for evaluation while leaving sensitive user information isolated and protected.
Solution Approach 2:
The system introduces an intermediary layer between user data and platform operations. The comparison model acts as a mediator that processes content evaluations without directly accessing or storing sensitive user information, thereby enabling platform functionality while maintaining a protective barrier around user privacy data.
Data Source
AI summary
Disclosed is a system for managing and transmitting multi-media content to a remote computing device over a network. The system includes a memory and a processor. The memory stores computer-executable instructions. The processor is configured to execute the computer-executable instructions to receive event entry data pertaining to entry of one or more event participants into an event. The processor is configured to receive one or more multi-media content files pertaining to the event from a plurality of user devices associated with one or more event participants. The processor is configured to assign, using a trained comparison-based model, a quality score to each of one or more multi-media content files. The comparison-based model is trained on a plurality of historical event recordings. The processor is configured to control a viewership of the event in real-time based on the quality score of each of the multi-media content files.