Content Channel Generation via Seed Feedback
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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 for generating content channels seeded by content references, where user feedback is utilized to update and weight these seeds, allowing for personalized selection of content assets from multiple services, enabling automatic content playback without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually search through vast and fragmented content libraries, then they can find specific content, but it consumes excessive time and effort
Solution Approach 1:
The system performs self-service by automatically generating content channels and selecting content assets without requiring manual user intervention. The server autonomously processes user feedback, updates seeds, and curates content channels, eliminating the time users would otherwise spend manually searching through fragmented content libraries.
Solution Approach 2:
The system implements feedback mechanisms where user interactions (views, ratings, preferences) are continuously collected and processed. This feedback loop enables the server to dynamically update seeds and refine content channel generation, improving content relevance over time and reducing user search effort.
2Adaptability or versatility
If content channels are generated without user feedback, then they can be created quickly, but they lack personalization and relevance to user preferences
Solution Approach 1:
The system performs preliminary actions by pre-processing user feedback and pre-updating seeds in advance of content channel generation. This allows the system to have personalization ready before users actually access content channels, making the personalization appear instantaneous while the complex seed updating occurs beforehand in the background.
Solution Approach 2:
The server acts as an intermediary between user feedback and content channel generation. It mediates the complex seed updating process by receiving feedback, processing it through seed update logic, and then using the updated seeds to generate personalized content channels, thereby managing complexity internally while presenting a simple user interface.
3Reliability
If the system processes detailed user feedback to update seeds, then content relevance improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by selectively processing only the most relevant feedback signals and updating only the necessary portions of seeds. Rather than processing all possible feedback data equally, it focuses computational resources on the most impactful updates, maintaining content relevance while reducing processing time.
4Adaptability or versatility
If content channels aggregate from multiple services and providers, then content variety increases, but system complexity increases
Solution Approach 1:
The server implements multi-functionality by serving as a universal platform that integrates multiple content services and providers through a single content channel generation system. It handles diverse content sources, user feedback, seed updating, and channel curation through unified processes, thereby managing integration complexity internally while presenting a versatile content aggregation capability to users.
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.