LLM Media Editing Architecture for Natural-Language Requests
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Solution Overview
Problem
Existing media content editing software is complex and difficult for typical users to navigate, leading to underutilization of its powerful editing capabilities due to lack of knowledge and intuitive user interfaces.
Innovation Solution
A media content editing architecture utilizing machine learning techniques, including a large language model (LLM) and prompt manager, interprets user input to predict and perform editing actions, with a system evolving process that refines the architecture based on user feedback and viewer engagement indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional media content editing software is used, then powerful editing capabilities are provided, but the software becomes complex and difficult for typical users to navigate
Solution Approach 1:
The patent introduces a natural language processing intermediary that mediates between the user and the complex editing software. Users provide simple text descriptions of desired edits, and the NLP system translates these into appropriate editing commands, eliminating the need for users to navigate complex interfaces while maintaining access to powerful editing capabilities
Solution Approach 2:
The patent replaces the traditional mechanical interaction model (clicking buttons, navigating menus) with a linguistic-based system. Instead of manually operating the software through its interface, users describe their intentions in natural language, and the system automatically interprets and executes the appropriate editing actions
2Adaptability or versatility
If traditional media content editing software is used, then editing tools are provided, but users underutilize capabilities due to lack of knowledge and intuitive interfaces
Solution Approach 1:
The system provides self-service by automatically understanding user intent and selecting appropriate editing operations without requiring users to search through documentation or experiment with different tools. The natural language interface allows users to directly express what they want to achieve, and the system handles the complexity of selecting and applying the correct editing capabilities
Solution Approach 2:
The NLP intermediary bridges the gap between user intent and system capabilities, translating simple language descriptions into sophisticated editing operations. This mediator enables users to access advanced features without needing specialized knowledge, thereby increasing utilization of available capabilities and improving overall productivity
Data Source
AI summary
Examples are provided relating to media content editing architectures utilizing machine learning techniques. One aspect includes a method for media content editing, the method comprising: receiving a media content from a user; receiving an editing request for the media content from the user; and editing the media content based on the editing request to generate edited media content by: retrieving a prompt from a prompt pool, wherein the retrieved prompt is selected based on the editing request; parsing the retrieved prompt and the editing request using a large language model to generate one or more editing actions to be performed on the media content; and performing the one or more editing actions on the media content to generate the edited media content.