Context-Aware Video Personalization via Content Filtering Engine
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Solution Overview
Problem
Conventional media content distribution systems fail to provide personalized video content streams based on user preferences and contextual information, limiting user experience and increasing bandwidth usage by transmitting identical content streams to all users.
Innovation Solution
Implementing a content filtering engine that processes user preferences and contextual information to dynamically control parameters such as video, audio, VR, or AR content, camera angle, language, and video quality, generating a personalized content stream unique to each user, either on a user device or a server, reducing the need for multiple content streams and enhancing user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single content stream is delivered to all users, then bandwidth usage is reduced and system complexity is minimized, but user experience and content personalization deteriorate
Solution Approach 1:
The patent segments the media content stream into multiple segments with different parameters (video quality, audio tracks, language, camera angles). Each segment is tagged with metadata describing its characteristics. This allows the system to transmit a comprehensive stream once and then selectively filter segments for individual users based on their preferences, achieving personalization without multiplying the total bandwidth requirement.
Solution Approach 2:
The patent performs preliminary tagging and organization of content segments with detailed metadata before distribution. User profiles with preferences are pre-configured. When a user requests content, the system quickly filters and assembles the personalized stream by matching pre-tagged segments with pre-stored user preferences, eliminating the need for real-time complex processing during content delivery.
2Adaptability or versatility
If a single content stream is delivered to all users, then device complexity is reduced, but user experience and content relevance deteriorate
Solution Approach 1:
The patent introduces a content filtering engine as an intermediary component that sits between the content delivery system and the user device. This engine handles the complex task of analyzing user preferences, filtering content segments, and assembling personalized streams. By centralizing this functionality in a dedicated component rather than distributing complexity across all devices or manual user configuration, the system achieves personalization while keeping individual user devices simple.
3Adaptability or versatility
If content parameters are manually configured for each user, then content personalization is achieved, but ease of operation and user convenience deteriorate
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically discovers user preferences through multiple channels: analyzing explicit user selections, inferring preferences from viewing history and behavior patterns, and utilizing contextual information from user profiles and device sensors. The content filtering engine autonomously assembles personalized streams without requiring users to manually configure each parameter, making the personalization process transparent and convenient while maintaining high adaptability.
Data Source
AI summary
Processing logic receives an indication of at least one of content preferences or contextual information associated with a request to view a media content stream and controls one or more parameters of the media content stream according to the at least one of the content preferences or contextual information to create a personalized media content stream. The processing logic further provides the personalized media content stream to a user device.