Content Channel Generation Using Seed References
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Solution Overview
Problem
Current media systems, such as broadcast television and online platforms, fail to consistently and predictably provide channels dedicated to specific types of content, leading to user inefficiency in finding desired content amidst vast and fragmented content libraries.
Innovation Solution
A method and apparatus for generating and utilizing content channels that use seed references to select and present content assets based on user-provided criteria, such as keywords, meta-data, and user feedback, allowing for personalized and adaptive content delivery across multiple services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually search for content in vast and fragmented content libraries, then users can find desired content, but users spend excessive time searching between content playback
Solution Approach 1:
The system automatically generates content channels by selecting and organizing content assets without user intervention. The server autonomously processes seed information, applies selection criteria, and delivers curated content streams, allowing the system to serve itself rather than requiring manual user searching.
Solution Approach 2:
The system pre-generates content channels by selecting and organizing content assets in advance based on seed information and predefined criteria. This preliminary organization of content into thematic channels occurs before user request, so when users access the system, content is already prepared and ready for immediate playback without searching.
2Reliability
If content is organized into dedicated channels like broadcast television, then content delivery is consistent and predictable, but the system lacks flexibility to adapt to user preferences and fragmented content sources
Solution Approach 1:
The system dynamically generates content channels by applying selection criteria to seed information and available content assets. Rather than using fixed static channel assignments, the system adapts channel content based on user preferences, content availability, and selection rules, allowing both consistency in delivery and adaptability to changing conditions.
Solution Approach 2:
The system changes parameters such as seed information, selection criteria, and content asset attributes to generate different content channels. By varying these parameters based on user preferences and content sources, the system maintains reliable structured delivery while adapting to diverse user needs and fragmented content libraries.
3Extent of automation
If the system automatically selects content assets based on seed references, then content delivery becomes passive and efficient, but the system complexity increases due to automated selection algorithms
Solution Approach 1:
The server acts as an intermediary between seed information and content assets, implementing the automated selection logic. This intermediary layer processes seed references, applies selection criteria, and delivers content without requiring complex client-side algorithms, centralizing complexity in the server while keeping user-facing systems simple.
Solution Approach 2:
The automated content selection system is segmented into distinct functional components: seed processing, content asset evaluation, selection criteria application, and content delivery. This segmentation allows the complex automation to be managed through modular, independent processes that can be developed and maintained separately.
Data Source
AI summary
An apparatus and method for creating channels dedicated to a particular type of content. The method includes acquiring seed content and using the seed content in the creation or updating of a content list. Additional content for the channel is acquired based on the common features of the content list.