Personalized Content Generation Through Media Key-Moment Segmentation
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Solution Overview
Problem
Conventional systems and methods used by content platforms fail to efficiently determine the most important portions of content that highly interest viewers, leading to inadequate engagement and interaction with recorded or streamed content.
Innovation Solution
A presentation platform with an analytics subsystem that analyzes engagement data to identify segments of media assets satisfying specific thresholds, generates user profiles based on content features and interactions, and personalizes content delivery using machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional systems provide content without segmentation and personalization, then the content delivery is simple and fast, but user engagement and interest in important portions of content are insufficient
Solution Approach 1:
The media asset is divided into multiple segments based on content features (e.g., topics, speakers, key moments) and user engagement metrics. This segmentation allows the system to identify and deliver specific important portions of content to individual users rather than presenting the entire media asset, thereby improving user engagement while maintaining efficient content delivery through targeted presentation.
2Measurement precision
If the system analyzes all engagement data to determine important content portions, then content personalization accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The system performs preliminary analysis of engagement data during and after content delivery to identify important portions. By pre-processing engagement data and pre-identifying key segments before personalized delivery, the system reduces the complexity of real-time analysis while maintaining high accuracy in determining content importance for each user.
Solution Approach 2:
The analytics subsystem acts as an intermediary between the content delivery system and user devices. It processes engagement data, identifies important content portions, and generates personalized segment recommendations, thereby simplifying the overall system architecture by centralizing the complex analysis functions in a dedicated intermediary component.
3Productivity
If the system generates personalized content compilations for each user, then user interest and engagement are enhanced, but data processing requirements and system resources increase
Solution Approach 1:
Instead of processing all content data for all users uniformly, the system applies local quality by tailoring content segmentation and personalization to each individual user's preferences, engagement patterns, and demographics. This approach enhances user engagement by delivering relevant content while reducing overall data processing requirements by focusing analysis only on portions of content likely to be important for each specific user.
Data Source
AI summary
Methods, systems, and apparatuses for identifying key moments of a media asset are described herein. Key moments may be identified, for example, based on analyzing data associated with the media asset and user engagement data associated with the media asset. The data associated with the media asset may be provided to a machine learning model configured to determine one or more first segments of the media asset that satisfy a first threshold. The user engagement data may be analyzed to determine one or more second segments of the media asset that satisfy a second threshold. A compilation of segments of the media asset, representing the key moments thereof, may be generated based on the one or more first segments and the one or more second segments.