Systems and methods for training and employing a machine learning system in providing service level upgrade offers
First Claim
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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Accused Products
Abstract
A front-end system collects user attribute value data and organizes the data into one or more training data sets and one or more test data sets. The front-end system provides at least some of the test data sets to an input layer of a machine learning system. Within the machine learning system, one or more predictive models are constructed. At an output layer, the predictive models provide output data that includes at least a value indicative of whether a user will upgrade service levels based at least in part on the attribute values logically associated with the respective user. A back-end system generates upgrade offers for subsequent communication to those users identified as being likely to upgrade.
184 Citations
53 Claims
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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:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A method of training 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 method comprising:
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forming by at least one front-end system processor an initial data set from 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 with a number of known non-upgraded users; determining 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; and terminating 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; providing the machine learning system with at least a portion of the training data subset as a training example; 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. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38)
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39. An upgrade offer presentation system that identifies users likely to upgrade from a lower service level to a higher service level offered by a service provider and provides upgrade offers to candidate users identified as likely to upgrade, comprising:
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at least one back-end, non-transitory, processor-readable, storage medium that stores processor-executable instructions; and at least one back-end system processor communicably coupled to an output layer of a machine learning system and to the at least one back-end, non-transitory, processor-readable, storage medium, the at least one back-end system processor to execute the processor-executable instructions and in response; receive output data from the output layer of the machine learning system, the output data including, for each of at least some users included in a user population, at least a value indicative of a likelihood that the respective user will upgrade from a lower service level to a higher service level offered by a service provider; 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; assess the value indicative of the likelihood that the respective user will upgrade against at least one defined threshold value or at least one defined threshold value range; 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. - View Dependent Claims (40, 41, 42, 43, 44, 45, 46)
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47. A method of operating upgrade offer presentation system that identifies users likely to from a lower service level to a higher service level offered by a service provider and provides upgrade offers to candidate users identified as likely to upgrade, the method comprising:
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receiving, by at least one back-end system processor, output data from an output layer of a machine learning system communicably coupled to the at least one back-end system processor, the output data including, for each of at least some users included in a user population, at least a value indicative of a likelihood that the respective user will upgrade from a lower service level to a higher service level offered by a service provider; determining 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; and terminating 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; assessing, by the at least one back-end system processor, the value indicative of the likelihood that the respective user will upgrade against at least one defined threshold value or at least one defined threshold value range; 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. - View Dependent Claims (48, 49, 50, 51, 52, 53)
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Specification