LLM Media Editing Architecture for Easier Tool Navigation
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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 unexplored features and lack of knowledge about available tools.
Innovation Solution
Implementing a media content editing architecture that utilizes machine learning techniques, including a large language model (LLM) and a prompt manager, to interpret user input and perform edits through a dialog-assisted interface, with a system evolving process that refines the architecture based on user feedback and viewer engagement metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional media editing software with comprehensive editing tools is provided, then editing capabilities and functionality are improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent introduces a dialog-assisted interface with natural language processing as an intermediary layer between the user and the complex editing tools. This mediator translates simple user commands into appropriate editing operations, allowing users to access powerful editing capabilities without needing to understand or navigate the underlying software complexity. The system acts as a bridge that converts natural language requests into tool-specific parameters and actions.
Solution Approach 2:
The system employs machine learning models that automatically interpret user intent and select appropriate editing tools and parameters without requiring manual configuration. The AI-driven interface self-adapts to user preferences and editing patterns, providing personalized editing suggestions and automatically adjusting tool settings based on the media content being edited, thereby reducing the operational burden on users.
2Adaptability or versatility
If comprehensive editing tools are provided, then editing capabilities are improved, but ease of operation deteriorates due to difficulty in navigating and utilizing features
Solution Approach 1:
The dialog-assisted interface serves as an intermediary that translates natural language commands into editing operations. Users can describe desired edits in everyday language, and the system's natural language processing component interprets these commands, maps them to appropriate editing tools, and executes the desired operations, thereby making comprehensive editing capabilities accessible to users without technical expertise.
Solution Approach 2:
The patent replaces traditional mechanical interaction methods (menu navigation, tool selection, parameter adjustment) with an AI-driven natural language processing system. Instead of requiring users to manually navigate complex software interfaces and understand tool parameters, the system uses machine learning models to interpret user intent and automatically configure editing operations, substituting manual mechanical interaction with intelligent automated processing.
3Ease of operation
If machine learning techniques are used to simplify the interface, then ease of operation is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system performs preliminary processing by pre-training machine learning models on extensive datasets of editing operations and user preferences. These models are prepared in advance to quickly interpret user commands and suggest appropriate editing operations without requiring complex real-time processing. The preliminary training and model preparation work is done offline, allowing the runtime system to operate with simpler, faster inference processes.
Solution Approach 2:
The system implements dynamic adaptation where the machine learning models continuously learn from user interactions and editing outcomes, adjusting their behavior to better match user preferences and content characteristics. This dynamic learning capability allows the system to improve ease of operation over time while managing complexity through incremental updates rather than requiring the full complexity to be present from the start.
Data Source
AI summary
Examples are provided relating to system evolving architectures for refining media content editing systems. One aspect includes a method of refining a media content editing architecture, the method comprising: editing a media content using a large language model and a back-end tool service comprising a prompt pool and a plurality of application programming interfaces corresponding to a plurality of editing tools; publishing the edited media content; storing contextual information relating to the editing of the media content; and refining the media content editing architecture using the stored contextual information.