Dynamic Media Stream Personalization via Real-Time Metadata
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Solution Overview
Problem
Current streaming media systems insert advertisements randomly, lacking contextual relevance to the media content, resulting in poor consumer engagement.
Innovation Solution
A system and method that dynamically selects media items, such as advertisements, based on meta data generated in real-time, using a meta data server and media server that consider user information, community interactions, and media stream context, allowing for unique and relevant content presentation to each consumer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If advertisements are inserted at specified points in the stream following television model, then the system structure is simple and easy to implement, but the advertisements bear no contextual relationship with the underlying media content and therefore rarely engage the consumer's attention
Solution Approach 1:
The system dynamically selects and inserts media items based on real-time analysis of media content metadata and consumer profile data. The advertisement selection is not fixed but adapts dynamically to the specific media stream being played and the consumer's characteristics, resolving the contradiction between simple insertion and contextual relevance.
Solution Approach 2:
The system changes the parameters of advertisement selection by using metadata tags from the media content and consumer profile attributes to determine which advertisements to insert. This transforms the static television model into a dynamic system where advertisement selection parameters are continuously adjusted based on content and consumer matching.
2Adaptability or versatility
If media items are dynamically selected based on dynamically-generated meta data, then each consumer is provided with a unique experience with contextually relevant media items, but the system complexity increases with multiple servers and real-time processing requirements
Solution Approach 1:
The system is segmented into distinct functional components: a metadata server that generates and stores media content descriptions, a media server that selects appropriate media items based on metadata and consumer profiles, and client devices that deliver personalized streams. This segmentation manages complexity by distributing functions across separate modules.
Solution Approach 2:
The metadata server pre-generates and stores metadata descriptions of media content before the actual media streaming occurs. This preliminary action allows the media server to quickly match content with consumer profiles during streaming without performing complex analysis in real-time, thus managing system complexity while maintaining dynamic personalization.
3Loss of energy
If the same media stream is requested by different media consumers, then resource efficiency is improved by using the same stream, but individualized content delivery is reduced
Solution Approach 1:
The system maintains the same base media stream for all consumers (preserving resource efficiency) but applies local quality variations by inserting different dynamically-selected media items based on individual consumer profiles. This allows the core content to remain identical while the personalized overlays provide individualized experiences.
Solution Approach 2:
The system merges the common media stream (shared by all consumers for efficiency) with individually-selected dynamic media items (personalized for each consumer). This combination achieves both resource efficiency through stream reuse and individualization through personalized media item insertion.
Data Source
AI summary
Systems and methods are described allowing dynamic selection of media items for presentation within a media stream based on dynamically-generated information that describes the content of the media stream or the stream's consumer. Systems may include meta data servers and media servers that work together to dynamically select media items and dynamically build a media stream containing the selected media items to the consumer. The media items are selected based on dynamically-generated meta data. Such meta data may be generated by previous consumers of the media stream and provide an accurate and dynamic description of the contents of the media stream. Because the media items are dynamically selected based on dynamically-generated meta data, even though the same media stream may be requested by different media consumers, each media stream is individually generated and may be a unique stream that reflects the impressions of previous consumers of the stream.


