Procedural Digital Object Generation via Few-Shot Model and One-Way Function
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Solution Overview
Problem
The existing procedural generation of digital objects lacks uniqueness and creativity, often resulting in repetitive outputs due to reliance on preset components, which fails to impress human viewers and creates an unfavorable ecosystem for creators as digital objects are frequently traded, leading to high turnover.
Innovation Solution
A system that uses user-specific parameters and one-way functions to generate unique digital objects, incorporating elements like cryptographic tokens and few-shot models, and introduces a time-based leaderboard to incentivize users to hold onto digital objects, while employing emoji sequence IDs for wallet address identification and blockchain-based tracing for digital objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If preset components are used for procedural generation, then ease of manufacture is improved, but uniqueness and creativity deteriorate
Solution Approach 1:
The patent changes the fundamental parameters of procedural generation by replacing preset component combinations with continuous function-based generation. Instead of selecting from discrete presets, the system uses mathematical functions with adjustable parameters (seeds, transformation parameters) to generate unique digital objects, thereby maintaining ease of generation while significantly improving uniqueness
Solution Approach 2:
The patent substitutes the mechanical system of preset component selection with a mathematical function-based system. Rather than mechanically assembling predefined parts, the system uses computational mathematics (hash functions, transformation functions) to generate digital objects, replacing the discrete mechanical selection process with continuous mathematical transformation
2Manufacturing precision
If random elements are used for procedural generation, then uniqueness is improved, but machine creativity deteriorates
Solution Approach 1:
The patent transitions from random parameter selection to systematic parameter transformation. Instead of relying on randomness, the system uses deterministic mathematical functions with controllable parameters (seeds, transformation rules) that can be systematically varied to produce creative variations, thereby maintaining uniqueness while enabling machine creativity through parameter control
Solution Approach 2:
The system enables self-service creativity by using the input data itself (through hashing and transformation) to determine the output characteristics. The digital object generation process serves itself by deriving unique properties from the input parameters through mathematical transformation, eliminating the need for external random elements while maintaining creativity
3Productivity
If digital objects are frequently traded, then liquidity is improved, but creator ecosystem deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where trading activity and holding patterns are tracked and used to adjust generation parameters and reward distributions. This feedback loop allows the system to maintain liquidity while protecting the creator ecosystem by adjusting generation difficulty and rewarding long-term holders, thereby balancing trading activity with creator sustainability
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.