CUSTOMIZING VIDEO CONTENT USING MACHINE LEARNING MODELS

Smart overview of the Invention

The patent application introduces a system for customizing video content using machine learning models. It involves identifying objects within an input video and generating a natural language description tailored to a user's preferences. This description is then used by a machine learning model to create customized video content, retaining certain objects from the original video while replacing other portions with new content.

Background

Traditional video content remains static and may become outdated or irrelevant over time due to changing contexts or user preferences. Creating multiple versions of a video for different audiences is labor-intensive. While generative AI can create new content from text descriptions, it struggles to incorporate existing video elements. This invention leverages AI to dynamically adapt video content, enhancing its relevance without entirely recreating it.

System Components

The system comprises a computing environment where a video customization module operates. This environment includes a computer with a processor, memory, and storage, capable of executing the methods described. It can connect to networks, clouds, and other devices, enabling the customization process to be distributed across various platforms and locations.

Technical Details

The computing environment features a processor set for executing instructions, volatile and persistent storage for data retention, and a communication fabric for component interaction. It supports various peripheral devices and network modules, facilitating data exchange and remote processing. The system is designed to be adaptable, supporting different hardware and software configurations.

Implementation

Instructions for customizing video content are stored in the video customization module, enabling the computer to perform the necessary operations. The system utilizes network modules to communicate with external devices and download additional instructions as needed. This flexible architecture supports the creation of personalized video experiences tailored to individual user preferences.