Station Library Generation With Editorial Feedback and Relevance Control
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Solution Overview
Problem
Existing media service systems struggle to efficiently generate personalized and dynamic media playlists that incorporate human editorial input while maintaining relevance and coherence, particularly in cloud-based environments.
Innovation Solution
A system and method that utilizes a media server to generate a station library by combining machine-generated and human-edited metadata, allowing for dynamic adjustment of media playlists based on human input, seasonal considerations, and user preferences, while ensuring relevance and coherence through correlation values and editorial weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine-generated metadata is used to create station libraries, then productivity and automation are improved, but measurement precision and relevance of editorial content deteriorate
Solution Approach 1:
The patent combines machine-generated metadata with human editorial input by allowing editors to modify, add to, or remove from automatically generated station libraries. The system merges automated processing with human expertise, where editors can adjust correlations between media items and stations, ensuring both efficiency and editorial accuracy are maintained.
Solution Approach 2:
The system incorporates feedback mechanisms where editors review and modify machine-generated station libraries. Editors can adjust correlations, add new relationships, or remove inappropriate connections, and these changes are fed back into the system to improve future automated generation accuracy.
2Measurement precision
If human editors manually create station libraries, then measurement precision and editorial quality are improved, but productivity and time consumption worsen
Solution Approach 1:
The system performs preliminary actions by automatically generating station libraries before human editors review them. This pre-generation handles the bulk of the work, and editors only need to make targeted adjustments, significantly reducing their time investment while maintaining high editorial quality.
Solution Approach 2:
The automated system serves itself by generating station libraries independently of human input. The machine learning models autonomously analyze media metadata, determine correlations, and create station libraries without requiring continuous human direction, freeing editors for higher-value tasks.
3Adaptability or versatility
If dynamic adjustments are made to station libraries based on user preferences, then adaptability is improved, but device complexity and processing requirements worsen
Solution Approach 1:
The system segments the station library into multiple components that can be independently adjusted. Correlations between media items and stations are stored as separate data structures, allowing the system to dynamically adjust specific relationships without redesigning the entire library. This modular approach enables customization while managing processing complexity.
Solution Approach 2:
The station library is designed as a dynamic structure that can adapt to changing user preferences and media content. Correlations are not fixed but can be updated in real-time based on user interactions, allowing the system to respond flexibly to individual needs without requiring complete system reprocessing.
Data Source
AI summary
A machine may form all or part of a network-based system configured to provide media service to one or more user devices. The machine may be configured to define a station library within a larger collection of media files. In particular, the machine may access metadata that describes a seed that forms the basis on which the station library is to be defined. The machine may determine a genre composition for the station library based on the metadata. The machine may generate a list of media files from the metadata based on a relevance of each media file to the station library. The machine may determine the relevance of each media file based on a similarity of the media file to the genre composition of the station library as well as a comparison of metadata describing the media file to the accessed metadata that describes the seed.