| CPC G06F 16/3344 (2019.01) [G06F 16/31 (2019.01); G06F 16/3347 (2019.01); G06F 16/355 (2019.01); G06F 40/186 (2020.01); G06F 40/279 (2020.01); G06F 40/295 (2020.01); G06N 20/20 (2019.01); G06Q 50/18 (2013.01)] | 30 Claims |
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1. A method of deploying program code based on a natural language document, the method comprising:
obtaining, by a natural language processing (NLP) computer system, an unstructured natural language document comprising text sections;
extracting, by the NLP computer system from the unstructured natural language document, document feature information, the document feature information comprising sets of features and feature values extracted from text sections of the unstructured natural language document;
determining, by the NLP computer system from the text sections of the unstructured natural language document, a set of one or more text sections as having not yet been encoded into self-executing program code;
selecting, by the NLP computer system in response to determining the set of one or more text sections as having not yet been encoded into self-executing program code, a text section of the set of one or more text sections as a candidate text section of the unstructured natural language document;
determining, by the NLP computer system, a set of candidate feature information associated with the candidate section, the set of candidate feature information comprising features and feature values of the document feature information extracted from the candidate section;
determining, by the NLP computer system based on the set of candidate feature information, a program code template, the program code template comprising a set of code template fields, the set of code template fields comprising:
a first parameter value field; and
a second parameter value field;
determining, by the NLP computer system using a deep learning model, code parameters corresponding to the set of code template fields, the code parameters comprising:
a first feature value corresponding to the first parameter value field; and
a second feature value corresponding to the second parameter value field;
generating, by the NLP computer system, self-executing program code, the generating of the self-executing program code comprising populating the set of code template fields with corresponding values of the set of candidate feature information, the populating of the set of code template fields with corresponding values of the set of candidate feature information comprising:
populating the first parameter value field with the first feature value; and
populating the second parameter value field with the second feature value,
wherein the self-executing program code is configured to execute an operation relating to the second feature value responsive to occurrence of a condition related to the populated value of the first parameter value field;
deploying, by the NLP computer system, the program code on a distributed ledger database of a peer-to-peer network;
determining that the condition related to the populated value of the first parameter value field occurs; and
executing, in response to determining that the condition related to the populated value of the first parameter value field occurs, the self-executing program code deployed on the distributed ledger database to cause the operation relating to the second feature value in response to the occurrence of the condition related to the populated value of the first parameter value field.
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