| CPC G06Q 20/401 (2013.01) [G06N 20/00 (2019.01); G06Q 2220/00 (2013.01)] | 20 Claims |
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1. A computing platform comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
train, based on historical nonfungible tokens (NFTs) and test cases for the historical NFTs, a machine learning model, wherein training the machine learning model configures the machine learning model to output remediation actions to address identified errors in execution of the test cases on a given NFT;
receive an event processing request comprising client information;
generate, based on the client information, an NFT corresponding to the client information;
establish a connection with a first user device;
receive, from the first user device, one or more test cases for servicing event processing requests with the client information corresponding to the NFT;
generate, based on the one or more test cases and using a distributed evaluation function, a plurality of soft tokens corresponding to the NFT, wherein each soft token comprises a virtual clone token comprising a copy of a subset of the client information corresponding to the NFT, and wherein a unique soft token is generated for each of the one or more test cases;
identify that at least one soft token of the plurality of soft tokens failed a corresponding test case;
input, into the machine learning model and based on identifying that the at least one soft token failed the corresponding test case, information of the test case failure, to produce a remediation action configured to validate the client information for servicing the event processing request;
generate, using the machine learning model and based on the at least one soft token and the remediation action, a remedial token, wherein the remedial token comprises a copy of the at least one soft token updated based on the remediation action;
identify whether the remedial token passed the corresponding test case;
establish a connection with a second user device;
cause, at the second user device, display of a token validation interface and an indication of whether the remedial token passed the corresponding test case;
receive, from the second user device and based on causing the display of the token validation interface, an implementation input, and in response:
overwrite, based on identifying that the remedial token passed the corresponding test case and based on the implementation input, the NFT with the remedial token; or
override, based on the implementation input, implementation of the remediation action;
send, to an event processing system, the event processing request and the remedial token; and
refine, based on the remediation action, the machine learning model, wherein the refining configures the machine learning model to automatically implement the remediation action if the machine learning model identifies that a soft token fails the corresponding test case.
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