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Systems and methods for training and employing a machine learning system in providing service level upgrade offers

  • US 10,387,795 B1
  • Filed: 03/30/2015
  • Issued: 08/20/2019
  • Est. Priority Date: 04/02/2014
  • Status: Active Grant
First Claim
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1. A training system to train at least a machine learning system that identifies users likely to upgrade from a lower service level to a higher service level offered by a service provider, the system comprising:

  • at least one front-end, non-transitory, processor-readable, storage medium that stores at least one of processor-executable instructions or data; and

    at least one front-end system processor communicably coupled to an input layer of a machine learning system and to the at least one front-end, non-transitory, processor-readable, storage medium, the at least one front-end system processor that in use, executes the processor-executable instructions and in response;

    form an initial data set from data representative of a user population, the initial data set including at least;

    a training data subset that includes digital data representative of attribute value information logically associated with a number of known upgraded users and digital data representative of attribute value information logically associated with a number of known non-upgraded users;

    receive output data from an output layer of the machine learning system;

    determine whether one or more training parameters indicative of one or more performance aspects of the machine learning system has plateaued and whether a defined number of machine learning system training epochs has been reached;

    terminate the provision of the training data subset to the machine learning system responsive to determining that the defined number of machine learning system training epochs has been reached and determining that the one or more training parameters has plateaued;

    provide at least a portion of the training data subset as training examples to the input layer of the machine learning system;

    from the training parameters of the machine learning system, identify a defined service level to which the respective user is predicted to most likely upgrade;

    from the training parameters of the machine learning system, identify a time or temporal range as most likely for the respective user to upgrade service levels; and

    in response to identifying the defined service level to which the respective user is predicted to most likely upgrade and identifying the time or temporal range most likely for the respective user to upgrade service levels, generate and communicate an upgrade offer for the respective user indicative at the time or temporal range identified as most likely for the respective user to upgrade service levels and for the defined service level to which the respective user is predicted to most likely upgrade.

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