Profile-Based Media Element Alteration for Real-Time Streaming
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Solution Overview
Problem
Existing AI-aided generation and editing of images, videos, and audio items are resource-intensive and iterative, making it impractical for real-time generation of customized content items, especially for streamed content.
Innovation Solution
A method that utilizes media items associated with a user profile to alter content items with a trained machine learning model in response to a structured request, by accessing media items, retrieving labels, generating structured requests, and altering content items based on these labels and requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning and general adversarial networks are used to replace and manipulate faces and speech in content items, then the ability to generate customized content items is improved, but the resource consumption and processing time increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing media items associated with user profiles and storing them in an optimized format. When a content item needs customization, the pre-processed media items are readily available for rapid insertion, eliminating the need for real-time deep learning processing during content delivery.
Solution Approach 2:
The invention extracts specific elements (faces, speech) from media items associated with user profiles and stores them as separate, reusable components. This extraction allows these elements to be independently inserted into content items without requiring full deep learning processing of entire videos or images during playback.
2Manufacturing precision
If an iterative process with additional prompts is used to generate and edit images, then the quality and accuracy of generated content is improved, but the processing time and computational resources are wasted
Solution Approach 1:
The system performs preliminary processing of media items to extract and optimize usable elements before they are needed for content customization. This pre-processing ensures that when elements are inserted into content items, they are already in the correct format and quality, eliminating the need for iterative refinement during real-time content generation.
3Speed
If deep learning networks are trained to generate customized content items in real-time, then the responsiveness and user experience are improved, but the resource requirements make it impractical for streamed content
Solution Approach 1:
The system performs all resource-intensive processing in advance by pre-processing media items associated with user profiles and storing the results. During content streaming, only lightweight operations are needed to insert pre-processed elements, achieving real-time responsiveness without the computational burden of real-time deep learning processing.
Solution Approach 2:
The invention creates copies of processed media elements (faces, speech) from user profile data and stores them for rapid deployment. These copies can be instantly inserted into content items during streaming without requiring the original heavy processing operations, enabling real-time customization with minimal resource consumption.
Data Source
AI summary
Systems and methods are provided for altering a content item via a trained machine learning model. One or more media items associated with a user profile are accessed at a computing device, and for each media item, one or more labels associated with an element in the media item are retrieved. A content item is accessed, and a structured description of one or more elements in the content item is received. A structured request to alter one or more elements in the content item is generated based on the description. The one or more elements to alter are identified in the content item and based on the request. The one or more elements in the content item are altered using a trained machine learning model and based on the one or more labels. An altered content item comprising the one or more altered content item elements is generated for output.