Discovery Engine for Video Content Relevance
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Solution Overview
Problem
Current systems fail to enhance the user experience by integrating video content with application content, such as web browsing, leading to a disjointed viewing experience despite the increased availability of media sources and devices.
Innovation Solution
A system and method that displays video content alongside application content, using a discovery engine to determine relevant video content based on the application content and user input, allowing seamless integration and selection of related media within the same display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video content and application content are displayed simultaneously in separate regions, then the user can access both media and application sources, but the contents are not related and the viewing experience is disjointed
Solution Approach 1:
The system analyzes application content (such as web pages or documents) and provides feedback by suggesting related video content. The discovery engine extracts entities, topics, or keywords from the application content and uses them to query video databases, creating a feedback loop that connects the two content types based on their semantic relationship.
Solution Approach 2:
The discovery engine acts as an intermediary between application content and video content. It bridges the two separate content domains by analyzing the application content and translating it into video content recommendations, thereby connecting previously unrelated content sources through a mediating intelligence layer.
2Reliability
If the system determines relevant video content based on application content analysis, then the viewing experience is enhanced, but the system complexity increases
Solution Approach 1:
The discovery engine is designed as a universal component that can handle multiple types of application content (web pages, documents, emails, etc.) and provide video recommendations across different contexts. This multi-functional design consolidates what would otherwise require multiple separate systems into a single versatile engine.
Solution Approach 2:
The system performs self-service by automatically analyzing application content and generating video recommendations without requiring manual user input or configuration. The discovery engine autonomously extracts relevant information from the application content and queries the video database, eliminating the need for complex user-side setup or manual content pairing.
3Loss of information
If the discovery engine analyzes application content to suggest video content, then relevant media suggestions are provided, but the processing time increases
Solution Approach 1:
The system performs preliminary action by pre-processing and indexing video content in advance, organizing it by entities, topics, and keywords. When application content is analyzed, the discovery engine can quickly query pre-organized video databases rather than performing full-content analysis in real-time, significantly reducing processing time while maintaining recommendation quality.
Data Source
AI summary
Methods, systems, and devices for viewing video content are provided. Video content is displayed in a video region of a display, while application content is displayed in an application region of the display. Based on the application content, candidate video content relevant to the application content is determined and, in response to user input, selected video content is displayed in the video region of the display.