Method and system for unique, procedurally generated digital objects via few-shot model
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.
1. A method of employing an artificial intelligence model to generate a unique set of image data comprising:
training a machine learning model with a set of training data configured to extract visual features associated with cryptographic tokens connected to a plurality of smart contracts;
receiving user specific parameters associated with a first user and including a plurality of cryptographic tokens;
extracting visual features from the user specific parameters via the machine learning model;
identifying, via the machine learning model, a set of elements from the extracted visual features that meet a threshold model confidence for amalgamation; and
generating a model-unique image that corresponds to the user specific parameters, wherein the model-unique image that incorporates the visual features of the user specific parameters.
2. The method of claim 1 , further comprising:
minting the model-unique image as a cryptographic token.
3. The method of claim 2 , further comprising:
encoding the model-unique image to a cryptographic token ID stored in a smart contract associated with the cryptographic token.
4. The method of claim 1 , wherein the machine learning model is further trained to extract cryptographic elements from the user specific parameters including a generation of token for each cryptographic token, and wherein said generating the model-unique image further corresponds to the cryptographic elements.
5. The method of claim 1 , wherein said generating further incorporates a salt element.
6. The method of claim 1 , further comprising:
training the machine learning model to identify contextual rarity in a cryptographic token as a function of adherence to predetermined categories as opposed to object uniqueness.
7. The method of claim 6 , wherein the model-unique image incorporates elements of the contextual rarity of the user specific parameters.
8. The method of claim 1 , wherein the machine learning model is any of:
a few-shot model;
a hidden Markov model;
a neural network; or
a heuristic model.
9. A system of employing an artificial intelligence to generate a unique set of image data comprising:
a processor; and
a memory including instructions that when executed cause the processor to:
train a machine learning model with a set of training data configured to extract visual features associated with cryptographic tokens connected to a plurality of smart contracts;
receive user specific parameters associated with a first user and including a plurality of cryptographic tokens;
extract visual features from the user specific parameters via the machine learning model;
identify, via the machine learning model, a set of elements from the extracted visual features that meet a threshold model confidence for amalgamation; and
generate a model-unique image that corresponds to the user specific parameters, wherein the model-unique image that incorporates the visual features of the user specific parameters.
10. The system of claim 9 , the instructions further comprising:
minting the model-unique image as a cryptographic token.
11. The system of claim 10 , the instructions further comprising:
encoding the model-unique image to a cryptographic token ID stored in a smart contract associated with the cryptographic token.
12. The system of claim 9 , wherein the machine learning model is further trained to extract cryptographic elements from the user specific parameters including a generation of token for each cryptographic token, and wherein said generating the model-unique image further corresponds to the cryptographic elements.
13. The system of claim 9 , wherein said generating further incorporates a salt element.
14. The system of claim 9 , the instructions further comprising:
training the machine learning model to identify contextual rarity in a cryptographic token as a function of adherence to predetermined categories as opposed to object uniqueness.
15. The system of claim 14 , wherein the model-unique image incorporates elements of the contextual rarity of the user specific parameters.
16. The system of claim 9 , wherein the machine learning model is any of:
a few-shot model;
a hidden Markov model;
a neural network; or
a heuristic model.
17. A non-transitory computer-readable medium contains a plurality of instructions that when executed by a processor cause the processor to employ an artificial intelligence model to generate a unique set of image data comprising:
train a machine learning model with a set of training data configured to extract visual features associated with cryptographic tokens connected to a plurality of smart contracts;
receive user specific parameters associated with a first user and including a plurality of cryptographic tokens;
extract visual features from the user specific parameters via the machine learning model;
identify, via the machine learning model, a set of elements from the extracted visual features that meet a threshold model confidence for amalgamation; and
generate a model-unique image that corresponds to the user specific parameters, wherein the model-unique image that incorporates the visual features of the user specific parameters.
18. The computer-readable medium of claim 17 , the instructions further comprising:
minting the model-unique image as a cryptographic token.
19. The computer-readable medium of claim 18 , the instructions further comprising:
encoding the model-unique image to a cryptographic token ID stored in a smart contract associated with the cryptographic token.
20. The computer-readable medium of claim 17 , wherein the machine learning model is further trained to extract cryptographic elements from the user specific parameters including a generation of token for each cryptographic token, and wherein said generating the model-unique image further corresponds to the cryptographic elements.
21. The computer-readable medium of claim 17 , wherein said generating further incorporates a salt element.
22. The computer-readable medium of claim 17 , the instructions further comprising:
training the machine learning model to identify contextual rarity in a cryptographic token as a function of adherence to predetermined categories as opposed to object uniqueness.
23. The computer-readable medium of claim 22 , wherein the model-unique image incorporates elements of the contextual rarity of the user specific parameters.
24. The computer-readable medium of claim 17 , wherein the machine learning model is any of:
a few-shot model;
a hidden Markov model;
a neural network; or
a heuristic model.