US20250371325
2025-12-04
Physics
G06N3/0475
An advanced AI-powered content generation system integrates multiple specialized AI components to produce consistent, coherent, and engaging multi-modal content. It processes user input to identify key elements, ensuring continuity across the content generation process. The system features a feedback loop that learns and adapts based on user preferences, allowing for personalized content experiences. Its modular architecture supports seamless integration of AI components for text, images, audio, and interactive elements, maintaining consistency across modalities over time. Additionally, it employs blockchain technology to manage rights, licenses, and royalties, transforming content creation, consumption, and management.
Traditional content creation methods are often manual and time-consuming, relying heavily on human expertise. Existing AI-based systems have advanced content generation using techniques like NLP and computer vision but often focus on a single modality, lacking comprehensive multi-modal experiences. These systems struggle with maintaining consistency and continuity, especially in complex content involving characters and narratives. Furthermore, they do not effectively incorporate user feedback to dynamically adapt to user preferences, limiting the creation of believable and consistent content.
The proposed system includes a Characteristic Tracker and Central AI Coordinator to analyze user input and maintain content continuity. The Adaptive Content Generator comprises various Generative AI modules for different modalities, ensuring overall coherence and continuity. A feedback loop through a User Interface and Generative AI Training System enables continuous learning and adaptation, aligning content with user expectations. This system revolutionizes content creation across domains like entertainment and education while ensuring rights management via blockchain.
The system's computing architecture involves hardware processors that receive user input, segment it into elements like plot and characters, and flag key elements for consistency. Generative AI subsystems process these elements to create a cohesive user experience, displayed on user devices. User feedback is incorporated to update and enhance the content, making it more personalized and high-quality. The system supports creating experiences such as novel chapters or movie scenes, ensuring consistency across time and modalities.
The system's generative AI subsystems can process and generate various content forms, including text, images, videos, sounds, and environments. Outputs are checked for consistency of key elements across modalities. A generative AI training system enhances performance by training subsystems on user feedback and inputs. This allows the subsystems to generate specific content portions, such as musical elements or motion sequences, further enhancing the system's versatility and adaptability.