Context-Adaptive Video Streaming Using Generative AI Profiles
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Solution Overview
Problem
Existing video stream content delivery systems fail to adapt video content to contemporary user preferences, leading to diminished engagement due to outdated or offensive elements, resulting in sub-optimal network resource utilization.
Innovation Solution
A machine learning/generative artificial intelligence-based video context adaption engine adjusts video content elements in real-time to match user-selected context profiles, allowing for customization without modifying the original content, using a generative AI model to generate updated video content based on user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video content is delivered in its original form without modification, then network resource utilization is efficient, but user engagement diminishes due to outdated or offensive elements
Solution Approach 1:
The system creates a copy of the original video content and applies modifications to the copy rather than the original. A generative AI model generates customized video content based on the original content and user context profiles, allowing the original to remain unchanged for efficient storage while delivering adapted versions to users.
Solution Approach 2:
The system dynamically adapts video content based on real-time user context profiles. The generative AI model adjusts content elements such as dialogue, scenery, and cultural references according to selected contexts (e.g., time period, cultural norms), making the content delivery adaptive rather than static.
2Adaptability or versatility
If video content is customized for each user context, then user engagement is enhanced, but system complexity increases due to real-time generation requirements
Solution Approach 1:
The system introduces a generative AI model as an intermediary between the original video content and the user. This intermediary processes the original content along with user context profiles to generate customized versions, shielding users from system complexity while enabling sophisticated content adaptation.
Solution Approach 2:
The system modifies specific parameters of the video content (dialogue, scenery, cultural references) based on user context profiles without fundamentally changing the overall content structure. This allows customization while maintaining manageable system complexity by focusing modifications on discrete controllable parameters.
3Ease of operation
If video content elements are modified in real-time, then user experience is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing by pre-identifying modifiable content elements and their corresponding context profile parameters before actual customization. This preparation work enables faster real-time generation by having the framework ready for rapid content adaptation when users request customized video.
Data Source
AI summary
Systems and methods for machine learning-based contextual customization of on-demand video streaming content are provided. A video context adaption engine may adjust one or more elements of video content data based on selected context profiles. A context profile may represent a set of characteristics that defines circumstances and/or features that form the setting for events, scenes, dialogue, actions, and other plot devices appearing in a work of video content. The video content data may be input as a prompt to a generative artificial intelligence (AI) machine learning model that outputs customized video content data that comprises an update or modification to one or more elements of content within the video content data based on the selected context profile(s). The customized video content data may then be served to a client application for presentation on user equipment.