CG-Based Synthetic Training Data Generation for AI Models

The patent uses computer-generated images to create copies of real-world training data scenarios. Instead of collecting actual photographs and manual paintings, the system generates synthetic images that replicate real-world conditions, including lighting, textures, and object appearances. This copying approach maintains the realism needed for training while eliminating the time-consuming data collection process.

The patent replaces the mechanical process of manual data collection (photographing, painting, annotating) with an automated computer-based system. The system uses algorithms to generate training data automatically, substituting human labor with computational processes that can produce large volumes of training data rapidly and consistently.

The patent implements a self-service annotation system where the computer-generated images automatically include embedded annotations and metadata. Instead of requiring human annotators to manually label objects and features, the generation system itself embeds the necessary annotation information during the image creation process, making the system self-sufficient and eliminating human annotation labor.

The patent replaces the mechanical process of manual annotation with automated computational annotation. Algorithms automatically generate and attach metadata, labels, and annotations to the synthetic training images, substituting human annotators with computational processes that eliminate human errors while maintaining annotation accuracy through consistent algorithmic application.

The patent uses parameter changes to efficiently create specialized training scenarios. By modifying parameters such as lighting conditions, time of day, weather conditions, and environmental settings in the computer-generated system, the same base generation process can produce diverse specialized scenarios (nighttime, rain, snow, different locations) without requiring separate expensive data collection campaigns for each condition.

The patent creates a universal data generation system that can produce training data for multiple specialized scenarios using a single platform. The same computer-generated system can create images for various conditions (different times, weather, locations, objects), making the system multi-functional and eliminating the need for separate specialized data collection processes for each scenario type.

The patent uses copying to create synthetic representations of rare and sensitive scenarios without capturing or exploiting real-world harmful events. Instead of collecting actual images of accidents or medical procedures from real patients, the system generates synthetic copies that replicate the visual and structural characteristics needed for training, thereby avoiding ethical violations while still providing training value for rare scenarios.

The patent introduces a computer-generated intermediary system that mediates between the need for rare scenario training data and ethical constraints. This intermediary generates synthetic representations that serve as a buffer, allowing the model to learn from rare events without directly using or exploiting sensitive real-world data, thus resolving the ethical conflict while maintaining training effectiveness.