Sub-asset Content Delivery via Contextual Segmentation
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Solution Overview
Problem
Current audio and video delivery systems provide a generic experience to all viewers, with limited personalization, as they offer the same content in the same order to all subscribers, and only allow selection of complete assets like TV programs or songs, without the ability to choose specific portions.
Innovation Solution
A method and apparatus that deliver a customized list of supplemental content based on the context of the media being consumed and user preferences, allowing selection and presentation of content at the sub-asset level, such as individual scenes from a program or portions of a song, using contextual information and user data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If complete assets (TV programs, songs) are offered for selection, then content availability is improved, but content granularity is worsened (cannot select specific portions)
Solution Approach 1:
The patent segments complete media assets into smaller sub-assets or portions that can be individually selected and presented. Instead of offering only entire TV programs or songs, the system divides content into manageable units that users can choose based on their specific preferences and context, thereby improving content granularity while maintaining availability.
Solution Approach 2:
The patent extracts specific portions or segments from complete media assets to create standalone selectable content units. By taking out relevant portions from entire programs or songs based on contextual information and user preferences, the system enables selection of specific content portions without requiring users to select the complete asset.
2Device complexity
If generic content delivery is used, then system complexity is reduced, but personalization is worsened (same content for all viewers)
Solution Approach 1:
The patent performs preliminary actions by collecting user preference information and analyzing contextual data in advance. The system pre-processes user profiles, viewing history, and contextual information to prepare personalized content recommendations before the user actually requests content, thereby achieving personalization without significantly increasing operational complexity during content delivery.
Solution Approach 2:
The patent implements feedback mechanisms that continuously gather user interaction data, viewing preferences, and contextual information. This feedback is used to dynamically adjust and personalize content recommendations, allowing the system to adapt to individual users while maintaining a relatively simple delivery infrastructure.
3Measurement precision
If contextual search refinement is implemented, then content relevance is improved, but personalization is worsened (same search results for all subscribers)
Solution Approach 1:
The patent applies local quality by tailoring search results and content recommendations to each individual user based on their specific preferences, viewing history, and contextual information. Instead of providing uniform search results to all subscribers, the system customizes the relevance and presentation of results according to each user's local characteristics and needs.
Solution Approach 2:
The patent changes key parameters of content delivery by incorporating user-specific preference information into the search and recommendation process. By adjusting parameters such as content selection criteria, ordering preferences, and presentation formats based on individual user profiles, the system achieves both contextual relevance and personalization simultaneously.
Data Source
AI summary
A method and apparatus for delivering an ordered list of items of supplemental content to a consumer comprising determining a context of a portion of media selected for consumption, determining consumer preference information corresponding to the consumer, generating the ordered list of items of supplemental content as a function of the context and of the consumer preference information.