Multi-Stream Content Ranking for Live Event Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in efficiently navigating and tracking desired video content across multiple channels, leading to missed important events and a negative viewing experience due to frequent channel switching, which existing solutions like Picture In Picture (PIP) and automated schedulers do not adequately address, especially for live events.
Innovation Solution
A multi-stream ranking system that uses user behavior analysis and content recognition engines to assign interest scores, allowing the system to automatically switch channels or notify users of important events without requiring manual intervention, using a decoder, content sensor array, sensor, comparator, and switch to evaluate and rank content streams based on user profiles and behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually navigate and switch between multiple video channels to track desired content, then users can identify and select desired programming, but users miss important events and experience negative viewing due to frequent switching
Solution Approach 1:
The system performs automatic channel monitoring and event detection without requiring user intervention. The content sensor array continuously analyzes multiple streams, and the system autonomously identifies important events and delivers them to the user, making the system self-serve the user's need to track desired content.
Solution Approach 2:
The system pre-ranks multiple content streams using the content sensor array and interest score algorithms before user selection is needed. By continuously analyzing and ranking streams in advance, the system prepares the information so that when users want to find desired programming, the results are already organized and ready.
2Adaptability or versatility
If existing solutions like Picture In Picture (PIP) or automated schedulers are used, then users can view multiple streams concurrently, but these solutions do not adequately address live events and require complicated programming
Solution Approach 1:
The patent replaces manual programming and mechanical scheduling approaches with an intelligent content analysis system. Instead of requiring users to program automated schedulers or manually configure PIP displays, the content sensor array with machine learning algorithms automatically analyzes stream content, detects events, and determines what to display, substituting mechanical complexity with intelligent automation.
Solution Approach 2:
The content sensor array acts as an intermediary between the multiple video streams and the user. Rather than directly presenting multiple streams to the user (as in PIP) or requiring user-programmed schedules, the sensor array intermediates by analyzing, ranking, and selecting content based on detected events and user profiles, simplifying the user's interaction.
3Reliability
If the system continuously monitors and ranks multiple content streams in real-time, then important events are detected and delivered to users, but extensive computational resources are required
Solution Approach 1:
The system applies partial monitoring by focusing computational resources on analyzing only the portions of streams that contain potential events rather than continuously analyzing every frame of every stream. The content sensor array uses event detection algorithms that trigger analysis only when relevant content is detected, performing excessive action only where needed rather than uniformly across all streams.
Solution Approach 2:
The system applies different levels of analysis quality to different content streams based on their relevance and detected content characteristics. High-priority streams or streams containing detected events receive more intensive analysis, while lower-priority streams receive lighter monitoring, optimizing the distribution of computational resources across the multi-stream environment.
Data Source
AI summary
System and techniques for multiple stream tuning are described herein. A plurality of content streams may be received. The plurality of content streams may be ranked. A user attention level may be scored as the user observes at least one content stream of the plurality of content streams. The user attention level and a rank for the at least one content stream may be compared to remaining ranks of the plurality of content streams to produce a difference. A stream action may be performed on a set of content streams from the plurality of content streams based on the difference.