Modular Rich-Media Document Generation for Faster Video Composition
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Solution Overview
Problem
Converting text into rich-media video documents is complex and inefficient due to tasks like video material collection, voiceover writing, and editing, with limited auxiliary means for improving efficiency.
Innovation Solution
A rich-media document auxiliary generation apparatus utilizing modules for material extraction, theme sorting, semantic retrieval, structured data generation, illustration recommendation, and video composition, assisted by intelligent analysis engines and AI models to streamline the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for video material collection, voiceover writing, and video editing, then the quality of rich-media documents can be maintained, but the generation process is highly complex and time-consuming
Solution Approach 1:
The system segments the complex rich-media generation process into distinct functional modules: material extraction module, theme sorting module, semantic retrieval module, structured data text generation module, illustration recommendation module, and video composition module. Each module handles specific tasks independently, reducing overall process complexity while maintaining productivity.
Solution Approach 2:
The system introduces an intelligent analysis engine as an intermediary between raw materials and final rich-media output. This engine automatically performs material extraction, theme identification, and content generation, mediating the complex transformation process and reducing manual intervention requirements.
2Measurement precision
If comprehensive manual work is performed for rich-media document generation, then high-quality output can be achieved, but a significant amount of work and time is required
Solution Approach 1:
The system performs preliminary actions by pre-extracting materials from raw inputs, pre-sorting themes, and pre-generating structured data before actual rich-media composition. This preparation work is automated in advance, reducing both time and manual effort required for final content creation while maintaining quality standards.
Solution Approach 2:
The intelligent analysis engine enables self-service automation where the system automatically extracts key materials, identifies themes, generates text content, and composes videos without requiring continuous manual intervention. This self-service capability significantly reduces generation time while preserving content quality through algorithmic precision.
3Loss of information
If traditional text-based research outcomes are used, then the format is simple, but the information transmission is less rich and less memorable
Solution Approach 1:
The system creates composite rich-media documents that integrate multiple information types: extracted text materials, generated voiceovers, selected illustrations, and compiled video content. This composite structure enriches information transmission and memorability while the automated conversion process maintains high productivity through modular processing.
Data Source
AI summary
Disclosed in the present disclosure is a rich-media document auxiliary generation apparatus. The apparatus comprises a material extraction module, a theme sorting module, a semantic retrieval module, a structured data text generation module, an illustration recommendation module and a video composition module. The present disclosure uses intelligent means to assist a user to efficiently generate a high-quality rich-media composite document, thereby quickly and accurately describing a theme event in an all-round way.