Video Content Generation Using Entity and Relation Extraction
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Solution Overview
Problem
Existing natural language processing systems lack the capability to generate comprehensive and coherent visual content in response to user inputs, limiting the richness and interactivity of human-computer interactions.
Innovation Solution
A system that performs automatic speech recognition, natural language understanding, coreference resolution, entity extraction, attribute and relation extraction, and composite image generation to create synchronized audio-visual outputs based on user inputs, using trained machine learning components to generate narratives and visual content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If natural language processing systems are used to process user inputs, then speech recognition and understanding capabilities are improved, but the ability to generate comprehensive visual content is insufficient
Solution Approach 1:
The patent merges speech recognition, natural language understanding, and visual content generation capabilities into a single integrated system. The processing system receives speech input, processes it through NLU to extract entities and attributes, then generates both audio responses and synchronized visual content (images or video clips) that correspond to the extracted information, creating a multi-modal response system.
Solution Approach 2:
The system is designed to handle multiple types of user inputs (speech, text) and generate multiple types of outputs (audio responses, visual content, text transcripts). The same processing pipeline can accommodate different domains (weather forecasts, narratives, information delivery) and response modalities, making the system universally applicable across various interaction scenarios.
2Reliability
If existing NLP systems process user inputs, then language understanding is improved, but the richness and interactivity of human-computer interactions are limited
Solution Approach 1:
The system transitions from unimodal text-based interaction to multimodal interaction by adding visual and audio dimensions. Instead of only processing text input and generating text output, the system processes speech input and generates synchronized audio-visual responses, creating a more immersive and engaging interaction experience that mirrors natural human communication.
Solution Approach 2:
The system incorporates feedback mechanisms where the generated visual content and audio responses are synchronized with the user's speech input in real-time. The system can adjust its responses based on the extracted entities and attributes from the user's input, creating an adaptive and interactive conversation flow that enhances engagement.
3Reliability
If comprehensive visual content generation is implemented, then response coherence is improved, but system complexity increases
Solution Approach 1:
The system is divided into distinct processing modules: speech recognition module, natural language understanding module (with entity extraction and attribute extraction sub-components), and visual content generation module. Each module handles a specific aspect of processing independently, making the overall complex system more manageable and easier to implement through modular architecture.
Solution Approach 2:
The natural language understanding component acts as an intermediary between the speech recognition and visual content generation modules. It processes the raw speech input, extracts meaningful entities and attributes, and prepares structured data that guides the visual content generation, thereby coordinating the complex interactions between different system components.
4Ease of operation
If audio-visual content is generated from user inputs, then user engagement is improved, but processing time increases
Solution Approach 1:
The system generates visual content selectively based on the extracted entities and attributes from the user input. Instead of always generating comprehensive audio-visual content, it determines the appropriate level of detail and type of visual content needed for each specific interaction, balancing engagement quality with processing time efficiency.
Data Source
AI summary
Techniques for generating content associated with a user input/system generated response are described. Natural language data associated with a user input may be generated. For each portion of the natural language data, ambiguous references to entities in the portion may be replaced with the corresponding entity. Entities included in the portion may be extracted, and image data representing the entity may be determined. Background image data associated with the entities and the portion may be determined, and attributes which modify the entities in the natural language sentence may be extracted. Spatial relationships between two or more of the entities may further be extracted. Image data representing the natural language data may be generated based on the background image data, the entities, the attributes, and the spatial relationships. Video data may be generated based on the image data, where the video data includes animations of the entities moving.