US 2025/0053859 A1Published application

Machine-learning techniques for predicting unobservable outputs

AssigneeEquifax Inc.

InventorsStephen MILLER, Lewis JORDAN, Felipe Alfonso Avila ROSALES, Terry WOODFORD, Matthew TURNER

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Abstract

In some aspects, a computing system can generate and optimize a machine learning model to estimate an unobservable capacity of a target system or entity. The computing system can access training vectors which include training predictor variables, training performance indicators, and task quantities. A training performance indicator indicating performance outcome corresponding to the predictor variables and a task quantity associated with a task assigned to the target entity that leads to the training performance indicator. The machine learning model can be trained by performing adjustments of parameters of the machine learning model to minimize a loss function defined based on the training vectors. The trained machine learning model can be used to estimate the capacity of the target system or entity for handling tasks and be used in assigning tasks to the target entity according to the determined capacity.

Claims20

Dependent claims

A method that includes one or more processing devices performing operations comprising:

determining, using a machine learning model trained using a training process and from predictor variables associated with a target entity, a capacity of the target entity for handling tasks, wherein the training process includes operations comprising:
accessing training vectors having elements representing training predictor variables, training performance indicators, and task quantities, wherein a particular training vector comprises particular values for the training predictor variables, respectively, a particular training performance indicator indicating performance outcome corresponding to the particular values for the training predictor variables, and a task quantity associated with a task assigned to the target entity that leads to the particular training performance indicator;
and
performing adjustments of parameters of the machine learning model to minimize a loss function of the machine learning model, wherein the loss function is defined based on the training predictor variables, the training performance indicators, and the task quantities;
and
transmitting, to a remote computing device, a responsive message including at least the determined capacity for use in assigning tasks to the target entity with task quantities according to the determined capacity.

3 more elements

A system comprising:

a processing device;
and
a memory device in which instructions executable by the processing device are stored for causing the processing device to:
determine, using a machine learning model trained using a training process and from predictor variables associated with a target entity, a capacity of the target entity for handling tasks, wherein the training process includes operations comprising:
accessing training vectors having elements representing training predictor variables, training performance indicators, and task quantities, wherein a particular training vector comprises particular values for the training predictor variables, respectively, a particular training performance indicator indicating performance outcome corresponding to the particular values for the training predictor variables, and a task quantity associated with a task assigned to the target entity that leads to the particular training performance indicator;
and
performing adjustments of parameters of the machine learning model to minimize a loss function of the machine learning model, wherein the loss function is defined based on the training predictor variables, the training performance indicators, and the task quantities;
and
transmit, to a remote computing device, a responsive message including at least the determined capacity for use in assigning tasks to the target entity with task quantities according to the determined capacity.

6 more elements

A non-transitory computer-readable storage medium having program code that is executable by a processor device to cause a computing device to perform operations, the operations comprising:

determining, using a machine learning model trained using a training process and from predictor variables associated with a target entity, a capacity of the target entity for handling tasks, wherein the training process includes operations comprising:
accessing training vectors having elements representing training predictor variables, training performance indicators, and task quantities, wherein a particular training vector comprises particular values for the training predictor variables, respectively, a particular training performance indicator indicating performance outcome corresponding to the particular values for the training predictor variables, and a task quantity associated with a task assigned to the target entity that leads to the particular training performance indicator;
and
performing adjustments of parameters of the machine learning model to minimize a loss function of the machine learning model, wherein the loss function is defined based on the training predictor variables, the training performance indicators, and the task quantities;
and
transmitting, to a remote computing device, a responsive message including at least the determined capacity for use in assigning tasks to the target entity with task quantities according to the determined capacity.

3 more elements

Dependencies are inferred from the claim text.