Allocation of service provider resources based on a capacity to provide the service
First Claim
1. One or more devices, comprising:
- one or more memories; and
one or more processors, communicatively coupled with at least one of the one or more memories, to;
identify a service that is provided within a region;
identify a model that is associated with the service,the model having been trained based on consumer profile data relating to consumers that have received the service in the region, service provider data associated with service providers that have provided the service in the region, and historical information associated with providing the service,wherein, identifying the model associated with the service, the one or more processors are to;
train the model using machine learning including at least one of;
data cleansing,unsupervised training, orclassification,the model being created using at least one of;
a logistic regression,a Naï
ve Bayesian classifier, ora support vector machine (SVM) classifier;
determine a current demand associated with the service in the region;
predict, using the model and based on the current demand associated with the service, a predicted future demand for the service during a time period;
determine a current capacity to provide the service based on real-time service provider information associated with service providers that are providing the service in the region;
determine whether the predicted future demand for the service exceeds the current capacity to provide the service or whether the current capacity to provide the service exceeds the predicted future demand for the service; and
perform an action associated with the service based on whether the predicted future demand for the service exceeds the current capacity to provide the service or whether the current capacity to provide the service exceeds the predicted future demand for the service,where the one or more processors, when performing the action, are to;
cause one or more machines to relocate to a particular location of the region to facilitate providing the service during the time period when the predicted future demand for the service is determined to exceed the current capacity to provide the service,
the one or more machines including at least one of;
an autonomous vehicle,
an unmanned aerial vehicle,
a portable kiosk, or
a transaction terminal.
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Accused Products
Abstract
An example includes one or more devices may include one or more memories and one or more processors, communicatively coupled with at least one of the one or more memories, to identify a service that is provided within a region; identify a model that is associated with the service, the model having been trained based on consumer profile data, service provider data, and historical information; determine a current demand associated with the service in the region; predict, using the model and based on the current demand associated with the service, a future demand for the service during a time period; determine a current capacity to provide the service based on real-time service provider information associated with service providers that are providing the service in the region; and perform an action associated with the service based on the future demand for the service and the current capacity to provide the service.
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Citations
20 Claims
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1. One or more devices, comprising:
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one or more memories; and one or more processors, communicatively coupled with at least one of the one or more memories, to; identify a service that is provided within a region; identify a model that is associated with the service, the model having been trained based on consumer profile data relating to consumers that have received the service in the region, service provider data associated with service providers that have provided the service in the region, and historical information associated with providing the service, wherein, identifying the model associated with the service, the one or more processors are to; train the model using machine learning including at least one of; data cleansing, unsupervised training, or classification, the model being created using at least one of; a logistic regression, a Naï
ve Bayesian classifier, ora support vector machine (SVM) classifier; determine a current demand associated with the service in the region; predict, using the model and based on the current demand associated with the service, a predicted future demand for the service during a time period; determine a current capacity to provide the service based on real-time service provider information associated with service providers that are providing the service in the region; determine whether the predicted future demand for the service exceeds the current capacity to provide the service or whether the current capacity to provide the service exceeds the predicted future demand for the service; and perform an action associated with the service based on whether the predicted future demand for the service exceeds the current capacity to provide the service or whether the current capacity to provide the service exceeds the predicted future demand for the service, where the one or more processors, when performing the action, are to; cause one or more machines to relocate to a particular location of the region to facilitate providing the service during the time period when the predicted future demand for the service is determined to exceed the current capacity to provide the service,
the one or more machines including at least one of;
an autonomous vehicle,
an unmanned aerial vehicle,
a portable kiosk, or
a transaction terminal. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A non-transitory computer-readable medium storing instructions, the instructions comprising:
one or more instructions that, when executed by one or more processors, cause the one or more processors to; identify a service that is provided within a region; train a model that is associated with the service based on; consumer trends in consumer profile data relating to consumers that have received the service in the region, service provider trends in service provider data associated with service providers that have provided the service in the region, and historical trends in historical information associated with providing the service, wherein, the one or more instructions, that cause the one or more processors to train the model associated with the service, cause the one or more processors to; train the model using machine learning including at least one of; data cleansing, unsupervised training, or classification, the model being created using at least one of; a logistic regression, a Naï
ve Bayesian classifier, ora support vector machine (SVM) classifier; determine a current demand for the service in the region; predict, using the model and based on the current demand for the service, a predicted future demand for the service during a time period; determine a current capacity to provide the service during the time period based on real-time service provider information associated with service providers that are providing the service in the region; determine whether the predicted future demand for the service exceeds the current capacity to provide the service or whether the current capacity to provide the service exceeds the predicted future demand for the service; and perform an action associated with the service based on whether the predicted future demand for the service exceeds the current capacity to provide the service during the time period or whether the current capacity to provide the service exceeds the predicted future demand for the service, where the one or more instructions, that cause the one or more processors to perform the action, cause the one or more processors to; cause one or more machines to relocate to a particular location of the region to facilitate providing the service during the time period when the predicted future demand for the service is determined to exceed the current capacity to provide the service, the one or more machines including at least one of;
an autonomous vehicle,
an unmanned aerial vehicle,
a portable kiosk, or
a transaction terminal.- View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A method, comprising:
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identifying, by one or more devices, a service that is provided within a region; obtaining, by one or more devices, a model that is associated with the service, the model having been trained based on consumer profile data relating to consumers that have received the service in the region, service provider data associated with service providers that have provided the service in the region, and historical information associated with providing the service, wherein training the model includes; training the model using machine learning including at least one of; data cleansing, unsupervised training, or classification, the model being created using at least one of; a logistic regression, a Naï
ve Bayesian classifier, ora support vector machine (SVM) classifier; determining, by the one or more devices, a current demand associated with the service in the region; predicting, by the one or more devices, using the model, and based on the current demand associated with the service, a predicted future demand for the service during a time period; determining, by the one or more devices, a current capacity to provide the service based on real-time service provider information for devices associated with particular service providers that are providing the service in the region; comparing, via the one or more devices, the current capacity to provide the service and the predicted future demand for the service to determine whether the current capacity is capable of meeting the predicted future demand for the service; and performing, via the one or more devices, an action associated with the service based whether the current capacity is capable of meeting the predicted future demand for the service or whether the current capacity is not capable of meeting the predicted future demand for the service, where performing the action includes; causing one or more machines to relocate to a particular location of the region to facilitate providing the service during the time period when the current capacity is not capable of meeting the predicted future demand for the service, the one or more machines including at least one of; an autonomous vehicle, an unmanned aerial vehicle, a portable kiosk, or a transaction terminal. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification