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Data model generation using generative adversarial networks

  • US 10,460,235 B1
  • Filed: 10/04/2018
  • Issued: 10/29/2019
  • Est. Priority Date: 07/06/2018
  • Status: Active Grant
First Claim
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1. A method for generating data models, comprising:

  • receiving, by a model optimizer from an interface, a data model generation request;

    provisioning, by the model optimizer, computing resources with a data model;

    generating, by a dataset generator, a synthetic dataset for training the data model using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric;

    generating, by the model optimizer, at least one of a statistical correlation score between the synthetic dataset and the reference dataset, a data similarity score between the synthetic dataset and the reference dataset, and a data quality score for the synthetic dataset;

    training, by the computing resources, the data model using the synthetic dataset, wherein training the data model using the synthetic dataset comprises determining that the synthetic dataset satisfies a criterion concerning the at least one of the statistical correlation score between the synthetic dataset and the reference dataset, the data similarity score between the synthetic dataset and the reference dataset, and the data quality score for the synthetic dataset;

    evaluating, by the model optimizer, performance criteria of the data model;

    storing, by the model optimizer in a model storage, the data model and metadata of the data model based on the evaluation of the performance criteria of the data model; and

    processing production data using the data model.

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