Contextual Video Adaptation Using Generative AI User Profiles
Find Innovative SolutionsGenerate Solutions
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delivery systems fail to adapt video content to contemporary user preferences, leading to diminished engagement due to outdated or offensive elements, resulting in sub-optimal network resource utilization.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, using a generative AI model to generate customized content without modifying the original video.
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form without modification, then network resource utilization is efficient, but user engagement diminishes due to outdated or offensive elements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 while keeping the original intact. A generative AI model generates customized video content based on the original content and user context profiles, allowing the original to be stored once 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 flexible and adaptive without requiring multiple static versions.
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 automatically generates customized content based on user context profiles, eliminating the need for manual content creation and reducing overall system complexity despite the customization capability.
Solution Approach 2:
The system changes parameters of the video content (such as dialogue text, visual elements, cultural references) based on user context profiles. The generative AI model adjusts these parameters dynamically to match selected contexts while maintaining the core narrative and structure of the original content.
3Adaptability or versatility
If generative AI models are used to generate customized content, then content relevance to user preferences improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing the original video content to extract key elements and structures that can be reused. The generative AI model is pre-trained on relevant data, enabling it to quickly generate customized content when user requests are received, reducing actual generation time.
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.