Real-Time Video Stream Customization for User Engagement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video stream content delivery systems fail to efficiently utilize network resources due to diminished user engagement caused by undesirable content elements, leading to sub-optimal resource utilization.
Innovation Solution
A machine learning/generative artificial intelligence-based content customization engine modifies user-selected elements of streaming video content in real-time by using user customization data to generate a customized version without altering the master content, employing a generative AI model to replace, redact, or alter specific features based on user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If video stream content delivery systems deliver standard content without customization, then network resource utilization is efficient, but user engagement diminishes due to undesirable content elements
Solution Approach 1:
The system performs preliminary analysis of user preferences and content characteristics before content delivery, using machine learning models to predict which content elements will be desirable to each user. This allows the system to pre-identify customization opportunities without altering the master content, thereby maintaining network efficiency while preparing personalized content variations for delivery.
Solution Approach 2:
The system applies local quality by customizing only specific elements of the video content (such as scenes, objects, or characteristics) that are relevant to user preferences, while leaving the rest of the content unchanged. This selective customization approach maintains overall content integrity and minimizes network resource overhead while significantly improving user engagement through personalized content elements.
2Reliability
If the master video content is modified to create customized versions, then user engagement is enhanced, but the complexity of content management increases
Solution Approach 1:
The system segments the video content into distinct, independently modifiable elements or scenes. Each segment can be selectively customized based on user preferences without affecting other segments. This segmentation allows the master content to remain intact while enabling flexible customization of specific portions, thereby reducing content management complexity compared to modifying the entire content stream.
Solution Approach 2:
The system creates lightweight copies or representations of the master content with embedded customization parameters, rather than maintaining multiple full versions of customized content. These copies serve as delivery vehicles that can be generated on-demand from the master content, reducing storage and management complexity while still enabling personalized content delivery.
3Adaptability or versatility
If real-time customization of video content is implemented, then personalized viewing experience is provided, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of video content to identify and tag customizable elements, and pre-trains machine learning models with user preference data before actual content delivery. This preliminary preparation enables rapid real-time customization by simply applying pre-computed transformations rather than performing full analysis during delivery, thereby minimizing processing time while maintaining personalization capability.
Solution Approach 2:
The system applies partial customization by focusing computational resources only on the specific content elements that are most relevant to user preferences, rather than processing the entire video stream. This selective approach reduces processing time and computational overhead while still providing meaningful personalization for the most impactful content elements.
Data Source
AI summary
In various embodiments, machine learning-based customization for video stream content delivery systems and applications are provided. In some embodiments, a machine learning model-based content customization engine may modify in real-time how user-selected elements of content are presented at the user's equipment (UE). A request for video content from a UE may include user content selection data and user customization data. The user content selection data is used to initiate streaming of a selected title of video content from a content server, and the user customization data is used as the basis to modify selected elements of streaming video content prior to display by the UE. Video content data from the content server and user customization data may be input to a generative artificial intelligence (GAI) model that outputs customized video content data where one or more elements of content are modified based on the user customization data.