Unique Digital Object Generation via Biometric Few-Shot Models
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Solution Overview
Problem
Existing digital object generation techniques, such as procedural generation, often produce repetitive or near-repetitive outputs due to reliance on preset components, failing to create unique and personalized digital content that human viewers appreciate as unique.
Innovation Solution
A system that utilizes user-specific parameters and one-way functions to generate unique digital objects, incorporating features like few-shot models and cryptographic protocols, allowing for personalized and varied outputs by processing user inputs through a combination of cryptographic tokens and AI models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If preset components are used for procedural generation, then generation speed is improved, but uniqueness of output deteriorates
Solution Approach 1:
The patent changes the fundamental parameters of procedural generation by replacing preset components with user-specific biometric data (fingerprint, facial recognition, iris patterns). This transformation maintains fast generation speeds while ensuring absolute uniqueness, as biometric parameters are inherently variable and cannot be replicated by preset libraries.
Solution Approach 2:
The patent extracts the randomness element from preset component libraries and replaces it with extracted biometric features from user data. By taking out the conventional randomness mechanism and substituting it with biometric-derived parameters, the system achieves both speed (through efficient biometric processing) and uniqueness (through irreproducible biometric patterns).
2Manufacturing precision
If randomness is used for procedural generation, then uniqueness is improved, but perceived creativity deteriorates
Solution Approach 1:
The patent introduces an intermediary layer between raw biometric data and generated content. This intermediary processing layer interprets biometric patterns and translates them into meaningful creative expressions, making the output appear creatively derived rather than randomly generated. The intermediary maintains uniqueness while adding perceived creativity through intelligent transformation.
Solution Approach 2:
The patent replaces the mechanical randomness system with a biometric-based deterministic system. Instead of using random number generators that produce unpredictable but meaningless output, the system uses biometric features as deterministic inputs that produce unique yet interpretable creative content, enhancing perceived creativity while maintaining uniqueness.
3Adaptability or versatility
If user-specific parameters are incorporated, then personalization is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal biometric processing framework that handles multiple types of biometric data (fingerprint, facial recognition, iris patterns) through a single unified system. This multi-functional approach achieves high personalization across different biometric modalities while reducing overall system complexity through standardized processing pipelines and shared infrastructure.
Data Source
AI summary
Disclosed herein is digital object generator that makes uses a one-way function to generate unique digital objects based on the user specific input. Features of the input are first extracted via a few-shot convolutional neural network model, then evaluated weight and integrated fit. The resulting digital object includes a user decipherable output such as a visual representation, an audio representation, or a multimedia representation that includes recognizable elements from the user specific input.