Favorite Scene Tagging for Media Personalization

The patent segments the video program into discrete scenes that can be independently tagged and voted on by users. This segmentation allows the service to collect granular user feedback at the scene level, enabling personalized recommendations without requiring complex analysis of entire programs. Users can tag specific scenes they enjoyed, and the system uses these segmented data points to generate recommendations.

The patent adds a new dimension to user interaction by introducing scene-level tagging and voting capabilities beyond traditional program-level searching and browsing. This dimensional expansion from program-level to scene-level interaction enables more nuanced user engagement and provides richer data for personalization without fundamentally complicating the core service architecture.

The patent implements preliminary action by having users tag scenes during or after watching video programs, collecting preference data in real-time as users consume content. This preliminary collection of tagged scene data eliminates the need for complex post-processing analysis, as the system already has structured user preference information ready when generating recommendations.

The patent establishes a feedback loop where user tags on scenes are aggregated and used to generate personalized recommendations, which users then consume and tag further. This continuous feedback mechanism refines the understanding of user preferences over time, improving recommendation accuracy without requiring increasingly complex data processing, as the system learns from each interaction cycle.

The patent implements self-service by enabling users to automatically tag scenes they enjoy through simple voting mechanisms during video playback. Users can easily indicate their preferences without manual input, and the system automatically collects and aggregates these tags. This self-service approach enhances user experience while keeping the data collection process simple and automated.

The patent uses copying by creating a simplified digital representation of user preferences through scene tags, which are then aggregated and used to generate recommendations. Instead of analyzing complex user behavior patterns, the system copies and processes these simple tag data points, reducing the complexity of data analysis while maintaining effective personalization capabilities.