Allocation of service provider resources based on a capacity to provide the service

• US 10,558,989 B2
• Filed: 11/16/2018
• Issued: 02/11/2020
• Est. Priority Date: 12/19/2017
• Status: Active Grant

• Current Assignee: Capital One Services LLC (Capital One Financial Corporation)
• Original Assignee: Capital One Services LLC (Capital One Financial Corporation)
• Inventors: Phillips, Jeremy, Liu, Zhe, Rephlo, Jeremy, Filler, Daniel
• Primary Examiner(s): Padot, Timothy
• Application Number:
  US16/193,500
• Publication Number:
  US 20190188743A1
• Time in Patent Office: 452 Days
• Field of Search: 705 731
• CPC Class Codes: B64U 2101/00 UAVs specially adapted for particular uses or applications
• CPC Class Codes: B64U 2101/20 for use as communications relays, e.g. high-altitude platforms
• CPC Class Codes: B64U 2101/60 for transporting passengers; for transporting goods other than weapons
• CPC Class Codes: G05D 1/0291 Fleet control monitoring fleets in traffic control systems for road vehicles G08G1/127, G08G1/127
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration

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.

Claims

1. A method, comprising:

determining, by a device, a current demand associated with a service that is provided within a region;

identifying, by the device, a model that is associated with the service,

the model being 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 the service providers providing the service in the region,

the model being trained using machine learning including at least one of;

data cleansing,

unsupervised training, or

classification, and

the model being created using at least one of;

a logistic regression,

a Naï

ve Bayesian classifier, or

a support vector machine (SVM) classifier;

determining, by the device using the model that is associated with the service and based on the current demand associated with the service, a predicted future demand for the service during a time period;

determining, by the device, 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;

determining, by the device, 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

causing, by the device, 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 exceeds the current capacity to provide the service.

2. The method of claim 1, further comprising:

obtaining real-time usage data associated with the service providers that are providing the service in the region;

obtaining environment data associated with the region,

the environment data indicating one or more environmental factors that affect the service in the region; and

determining the current demand based on the real-time usage data and the environment data.

3. The method of claim 1, wherein the one or more machines include at least one of:

an autonomous vehicle,

an unmanned aerial vehicle,

a portable kiosk, or

a transaction terminal.

4. The method of claim 1, further comprising:

receiving historical information associated with the service provided in a region larger than the region associated with the service, and

determining the predicted future demand based upon the historical information.

5. The method of claim 1, further comprising:

determining that the predicted future demand for the service exceeds the current capacity to provide the service;

identifying one or more additional service providers that are available to provide the service in the region; and

transmitting a notification to the one or more additional service providers to be available to provide the service in the region.

6. The method of claim 5, wherein the historical information associated with providing the service is associated with a region that is larger than the region associated with the service.

7. The method of claim 1, further comprising:

using a machine learning model to determine the current demand,

the machine learning model including a natural language processing model to identify key terms, words, or phrases associated with the service.

8. A device, comprising:

one or more memories; and

one or more processors, communicatively coupled to the one or more memories, to;

determine a current demand associated with a service that is provided within a region;

identify a model that is associated with the service,

the model being 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 the service providers providing the service in the region,

the model being trained using machine learning including at least one of;

data cleansing,

unsupervised training, or

classification, and

the model being created using at least one of;

a logistic regression,

a Naï

ve Bayesian classifier, or

a support vector machine (SVM) classifier;

determine, using the model that is associated with the service and based on the current demand associated with the service, a predicted future demand for the service during a time period;

obtain real-time usage data associated with service providers that are providing the service in the region to determine a current capacity;

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

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 exceeds the current capacity to provide the service.

9. The device of claim 8, wherein the one or more processors, when predicting the predicted future demand, are to:

predict the predicted future demand based on a prediction model,

the prediction model including at least one of;

a linear regression model,

a logistic regression model,

a decision tree model,

a random forest model,

a gradient boosting model, or

a neural network model.

10. The device of claim 8, wherein the one or more machines include at least one of:

an autonomous vehicle,

an unmanned aerial vehicle,

a portable kiosk, or

a transaction terminal.

11. The device of claim 8, wherein the one or more processors, when causing the one or more machines to relocate to the particular location of the region, are to:

cause the one or more machines to relocate to the particular location of the region using at least one of;

an address, or

navigational instructions.

12. The device of claim 8, wherein the one or more processors are further to:

determine that the predicted future demand for the service exceeds the current capacity to provide the service;

identify one or more additional service providers that are within a particular range of the region and are unqualified to provide the service; and

transmit qualification information to the one or more additional service providers to enable the one or more additional service providers to be qualified and available to provide the service during the time period.

13. The device of claim 8, wherein the one or more processors are further to:

identify one or more potential additional service providers that may provide the service; and

provide qualification information to the one or more potential additional service providers.

14. The device of claim 13, wherein the one or more processors are further to:

obtain the qualification information from a website or a certification database, the qualification information including at least one of;

information identifying a needed certification, or

information associated with a class to become certified.

15. 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;

determine a current demand associated with a service that is provided within a region, the current demand being based on real-time usage data associated with service providers that are currently providing the service in the region;

identify a model that is associated with the service,

the model being 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 the service providers providing the service in the region,

the model being trained using machine learning including at least one of;

data cleansing,

unsupervised training, or

classification, and

the model being created using at least one of;

a logistic regression,

a Naï

ve Bayesian classifier, or

a support vector machine (SVM) classifier;

determine, using the model that is associated with the service 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

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 exceeds the current capacity to provide the service.

16. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

obtain environment data associated with the region, the environment data indicating one or more environmental factors that affect the service in the region; and

determine the current demand based on the real-time usage data and the environment data.

17. The non-transitory computer-readable medium of claim 15, wherein the one or more machines include at least one of:

an autonomous vehicle,

an unmanned aerial vehicle,

a portable kiosk, or

a transaction terminal.

18. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

train the model to identify trends that indicate past demand for the service using consumer profile data, service provider data, and historical information; and

wherein, when predicting the predicted future demand, the one or more processors are to;

predict the predicted future demand based on the trends that indicate the past demand for the service.

19. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

obtain real-time usage data associated with service providers that are providing the service in the region;

obtain environment data associated with the region,

the environment data indicating one or more environmental factors that affect the service in the region; and

determine the current demand based on the real-time usage data and the environment data.

20. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

identify one or more additional service providers that are available to provide the service in the region; and

send a notification to the one or more additional service providers to be available to provide the service in the region.