Personalized Content Assembly Using Viewer Reaction Analysis
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Solution Overview
Problem
Communication service providers fail to offer personalized content that caters to individual viewer interests and time constraints, as their bundled services often lack flexibility and relevance.
Innovation Solution
A communication system that generates personalized content by analyzing viewer reactions to pre-recorded segments from friends and family, simulating conversations, and adjusting content based on trait information and context, using a network of cameras and sensors to create a virtual conference-like experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If service providers offer bundled voice, data and video services with fixed channel line-ups, then service delivery is simplified and standardized, but the content lacks flexibility and relevance to individual viewer interests
Solution Approach 1:
The system performs preliminary actions by capturing and storing video segments from multiple cameras during live events before they are needed. These segments are pre-processed and stored in a database with metadata, allowing them to be quickly retrieved and assembled later based on viewer preferences without requiring complex real-time processing
Solution Approach 2:
The system introduces an intermediary content assembly layer between the fixed channel line-up delivery and the viewer. This intermediary component analyzes viewer profiles, selects relevant pre-captured segments, and assembles personalized content sequences, thereby decoupling the standardized service delivery from the personalized content presentation
2Adaptability or versatility
If video-on-demand services provide variety of content selections, then viewer choice is increased, but time constraints and scheduling flexibility are not adequately addressed
Solution Approach 1:
The system performs preliminary action by pre-capturing and organizing video segments during live events, creating a ready-to-use library of content. This eliminates the need for viewers to wait for scheduled broadcasts or manually search through extensive video-on-demand catalogs, as personalized content sequences can be rapidly assembled and delivered on-demand
Solution Approach 2:
The system implements dynamics by enabling real-time adjustment of content assembly based on viewer interactions and reactions. As viewers consume content, the system dynamically modifies the sequence and selection of segments to maintain engagement, adapting the personalized delivery in real-time rather than using static pre-programmed schedules
3Measurement precision
If the system captures and analyzes viewer reactions in real-time, then content personalization accuracy is improved, but processing complexity and computational requirements increase
Solution Approach 1:
The system applies segmentation by dividing the complex task of content personalization into distinct modules: video capture from multiple cameras, reaction detection through image/audio sensors, segment selection based on viewer profiles, and content assembly. Each module handles a specific aspect of the process, reducing overall system complexity while maintaining high measurement precision through specialized processing at each stage
Solution Approach 2:
The system uses copying by creating simplified representations or profiles of viewer reactions and preferences based on detailed sensor data. Instead of processing all raw sensor inputs in real-time, the system generates condensed reaction profiles that capture essential viewer responses, reducing computational complexity while preserving the accuracy needed for effective content personalization
Data Source
AI summary
A system that incorporates teachings of the present disclosure may include, for example, receiving reaction information from one or more sensors located at a second location that is representative of a reaction by a second user at the second location to the presentation of the personalized content, analyzing the reaction information to determine a response based at least in part on trait information associated with different first users, selecting a second portion of the recorded segments based on a comparison of the determined response with a context identified for each of received recorded segments, and adjusting personalized content to include the selected second portion of the recorded segments. Other embodiments are disclosed.