AI NFT Generation Using Few-Shot Feature Extraction

The patent replaces the mechanical system of preset components and rule-based assembly with an AI/ML-based generative model. Instead of programmatically assembling predefined digital assets according to fixed rules, the system uses trained neural networks to learn from existing NFTs and generate novel outputs, substituting deterministic algorithms with probabilistic machine learning models that produce unique results while maintaining generation efficiency

The system changes the parameters of the generation process by using learned models with adjustable parameters (weights, biases, attention mechanisms) that can be fine-tuned to control the balance between uniqueness and visual coherence. By modifying model parameters, training data composition, and generation conditions, the system can produce diverse NFTs without requiring complete redesign of the generation pipeline

The patent applies preliminary action by pre-training the machine learning models on large datasets of existing NFTs before actual generation. The models learn patterns, styles, and compositional rules in advance, so that during actual NFT generation, they can produce creative outputs efficiently without requiring complex real-time computations. The heavy computational work is performed beforehand during the training phase

The system uses an intermediary approach by employing a hybrid architecture that combines elements of rule-based systems with machine learning models. The ML model acts as an intermediary that processes input parameters and generates intermediate representations, which are then refined or assembled into final NFTs. This intermediary layer reduces the computational burden compared to using pure end-to-end generative models