IP Library Granted Patent US 11,587,002
Granted Patent B1
US 11,587,002 · App. 17/243,183 · Granted Feb 21, 2023

Systems and methods for automated carrier routing including destination and carrier selection

Inventors: Shellie Crandall (Palm Beach Gardens, FL); Brian McCabe (Jupiter, FL)
Assignee: G4S Retail Solutions (USA) Inc.
G06Q10/06312G06N20/00G06Q10/06315G06Q10/0835G06Q10/0838G06Q30/0205
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Quick Facts
Patent No.
US 11,587,002
App. No.
17/243,183
Granted
Feb 21, 2023
Kind
B1
Abstract

In response to a request for currency transportation services for a retail location (e.g., providing currency to the retail location or picking-up currency from the retail location), one or more currency transportation requirements are identified and provided as input to a currency transportation model to identify and/or assign a carrier and/or a financial institution location to serve as a final destination or an origin for the currency transportation services. The output of the currency transportation model is utilized to initiate the currency transportation services by transmitting a notification to a carrier-operated computing entity to schedule the requested currency transportation services.

Claims (57)

1. A computer-implemented method comprising:

receiving, via one or more processors, an automatically generated request for currency transportation services associated with a cash handling device, wherein the request for currency transportation services comprises a retail location identifier;

retrieving, from one or more memory storage areas, a location-specific retail profile comprising currency transportation requirement data associated with the cash handling device;

retrieving, based at least in part on the location-specific retail profile, scheduling data for previously scheduled currency transportation services for the cash handling device;

accessing, via the one or more processors, a selected machine-learning based currency transportation model based at least in part on the location-specific retail profile, wherein the selected machine-learning based currency transportation model is one of a plurality of accessible machine-learning based currency transportation models each applicable for different retail location characteristics and wherein each of the plurality of accessible machine-learning based currency transportation models are trained using supervised learning based at least in part on historical data from completed currency transportation services;

providing, via the one or more processors: (a) at least a portion of the request for currency transportation services, (b) at least a portion of the currency transportation requirement data, and (c) at least a portion of the scheduling data, as input to the selected machine-learning based currency transportation model for automatically initiating a currency transportation service to address the request for currency transportation services;

executing, via the one or more processors, the selected machine-learning based currency transportation model to automatically assign a carrier and a final destination for transporting currency from the cash handling device to the final destination to satisfy the request for currency transportation services, wherein the automatically assigned carrier and final destination comply with the at least a portion of the currency transportation requirement data and wherein the carrier is selected from a plurality of whitelisted carriers each having a respective carrier-operated computing entity;

transmitting, via the one or more processors, a notification for provision to the respective carrier-operated computing entity of the automatically assigned carrier to initiate the currency transportation service by a representative of the carrier; and

updating, via the one or more processors, the scheduling data based at least in part on the currency transportation service.

2. The computer-implemented method of claim 1 , further comprising:

retrieving, based at least in part on the location-specific retail profile, a master retail profile comprising additional currency transportation requirement data; and

wherein the automatically assigned carrier and final destination comply with the additional currency transportation requirement data.

3. The computer-implemented method of claim 2 , wherein the additional currency transportation requirement data comprises aggregate currency transportation requirement data establishing currency transportation requirements for currency transportation services provided for a plurality of retail locations; and

wherein retrieving scheduling data for previously scheduled currency transportation services for the cash handling device additionally comprises retrieving scheduling data for previously scheduled currency transportation services for each of the plurality of retail locations.

4. The computer-implemented method of claim 3 , wherein the additional currency transportation requirement data identifies a maximum percentage of currency transported from the plurality of retail locations by a single carrier.

5. The computer-implemented method of claim 1 , wherein the currency transportation requirement data identifies a maximum percentage of currency transported from the cash handling device to a single final destination.

6. The computer-implemented method of claim 1 , wherein the currency transportation requirement data identifies a maximum percentage of currency transported from the cash handling device to final destinations located within a defined geographical area.

7. The computer-implemented method of claim 1 , wherein the scheduling data comprises historical data identifying previously completed currency transportation services and pending data identifying uncompleted currency transportation services.

8. The computer-implemented method of claim 1 , wherein the selected machine-learning based currency transportation model is configured to predict future currency transportation service requests and to minimize transportation costs across a plurality of currency transportation

services from the cash handling device based at least in part on the predicted future currency transportation service requests and the currency transportation service request.

9. The computer-implemented method of claim 1 , further comprising providing one or more carrier profiles as input to the selected machine-learning based currency transportation model, wherein the one or more carrier profiles comprise routing data indicative of one or more existing carrier routes.

10. The computer-implemented method of claim 1 , further comprising transmitting a notification for provision to the cash handling device confirming the currency transportation service.

11. A system comprising:

one or more memory storage areas; and

one or more processors collectively configured to:

receive an automatically generated request for currency transportation services associated with a cash handling device, wherein the request for currency transportation services comprises a retail location identifier;

retrieve, from the one or more memory storage areas, a location-specific retail profile comprising currency transportation requirement data associated with the cash handling device;

retrieve, based at least in part on the location-specific retail profile, scheduling data for previously scheduled currency transportation services for the cash handling device;

access, a selected machine-learning based currency transportation model based at least in part on the location-specific retail profile, wherein the selected machine-learning based currency transportation model is one of a plurality of accessible machine-learning based currency transportation models each applicable for different retail location characteristics and wherein each of the plurality of accessible machine-learning based currency transportation models are trained using supervised learning based at least in part on historical data from completed currency transportation services;

provide (a) at least a portion of the request for currency transportation services, (b) at least a portion of the currency transportation requirement data, and (c) at least a portion of the scheduling data, as input to the selected machine-learning based currency transportation model for automatically initiating a currency transportation service to address the request for currency transportation services;

execute the selected machine-learning based currency transportation model to automatically assign a carrier and a final destination for transporting currency from the cash handling device to the final destination to satisfy the request for currency transportation services, wherein the automatically assigned carrier and final destination comply with the at least a portion of the currency transportation requirement data and wherein the carrier is selected from a plurality of whitelisted carriers each having a respective carrier-operated computing entity;

transmit a notification for provision to the respective carrier-operated computing entity of the automatically assigned carrier to initiate the currency transportation service by a representative of the carrier; and

update the scheduling data based at least in part on the currency transportation service.

12. The system of claim 11 , wherein the one or more processors are further configured to:

retrieve, based at least in part on the location-specific retail profile, a master retail profile comprising additional currency transportation requirement data; and

wherein the automatically assigned carrier and final destination comply with the additional currency transportation requirement data.

13. The system of claim 12 , wherein the additional currency transportation requirement data comprises aggregate currency transportation requirement data establishing currency transportation requirements for currency transportation services provided for a plurality of retail locations; and

wherein retrieving scheduling data for previously scheduled currency transportation services for the cash handling device additionally comprises retrieving scheduling data for previously scheduled currency transportation services for each of the plurality of retail locations.

14. The system of claim 13 , wherein the additional currency transportation requirement data identifies a maximum percentage of currency transported from the plurality of retail locations by a single carrier.

15. The system of claim 11 , wherein the currency transportation requirement data identifies a maximum percentage of currency transported from the cash handling device to a single final destination.

16. The system of claim 11 , wherein the currency transportation requirement data identifies a maximum percentage of currency transported from the cash handling device to final destinations located within a defined geographical area.

17. The system of claim 11 , wherein the scheduling data comprises historical data identifying previously completed currency transportation services and pending data identifying uncompleted currency transportation services.

18. The system of claim 11 , wherein the selected machine-learning based currency transportation model is configured to predict future currency transportation service requests and to minimize transportation costs across a plurality of currency transportation services from the cash handling device based at least in part on the predicted future currency transportation service requests and the currency transportation service request.

19. A computer program product comprising a non-transitory computer readable medium having computer program instructions stored therein, the computer program instructions when executed by a processor, cause the processor to:

receive an automatically generated request for currency transportation services associated with a cash handling device, wherein the request for currency transportation services comprises a retail location identifier;

retrieve, from one or more memory storage areas, a location-specific retail profile comprising currency transportation requirement data associated with the cash handling device;

retrieve, based at least in part on the location-specific retail profile, scheduling data for previously scheduled currency transportation services for the cash handling device;

access a selected machine-learning based currency transportation model based at least in part on the location-specific retail profile, wherein the selected machine-learning based currency transportation model is one of a plurality of accessible machine-learning based currency transportation models each applicable for different retail location characteristics and wherein each of the plurality of accessible machine-learning based currency transportation models are trained using supervised learning based at least in part on historical data from completed currency transportation services;

provide (a) at least a portion of the request for currency transportation services, (b) at least a portion of the currency transportation requirement data, and (c) at least a portion of the scheduling data, as input to the selected machine-learning based currency transportation model for automatically initiating a currency transportation service to address the request for currency transportation services;

execute the selected machine-learning based currency transportation model to automatically assign a carrier and a final destination for transporting currency from the cash handling device to the final destination to satisfy the request for currency transportation services, wherein the automatically assigned carrier and final destination comply with the at least a portion of the currency transportation requirement data and wherein the carrier is selected from a plurality of whitelisted carriers each having a respective carrier-operated computing entity;

transmit a notification for provision to the respective carrier-operated computing entity of the automatically assigned carrier to initiate the currency transportation service by a representative of the carrier; and

update the scheduling data based at least in part on the currency transportation service.

20. The computer program product of claim 19 , wherein the computer program instructions when executed by a processor, further cause the processor to:

retrieve, based at least in part on the location-specific retail profile, a master retail profile comprising additional currency transportation requirement data; and

wherein the automatically assigned carrier and final destination comply with the additional currency transportation requirement data.

21. The computer program product of claim 20 , wherein the additional currency transportation requirement data comprises aggregate currency transportation requirement data establishing currency transportation requirements for currency transportation services provided for a plurality of retail locations; and

wherein retrieving scheduling data for previously scheduled currency transportation services for the cash handling device additionally comprises retrieving scheduling data for previously scheduled currency transportation services for each of the plurality of retail locations.

Assignments (9)
NOTES PATENT SECURITY AGREEMENT Recorded Jul 11, 2025
From: G4S RETAIL SOLUTIONS (USA) INC.
To: WILMINGTON TRUST, NATIONAL ASSOCIATION, AS NOTES COLLATERAL AGENT
Reel/Frame 071888/0745 →
RELEASE OF SECURITY INTEREST Recorded Aug 9, 2024
From: WILMINGTON TRUST, NATIONAL ASSOCIATION, AS NOTES COLLATERAL AGENT
To: G4S RETAIL SOLUTIONS (USA) INC.
Reel/Frame 068242/0972 →
SUPPLEMENTAL ABL PATENT SECURITY AGREEMENT (2019) Recorded Feb 21, 2024
From: G4S RETAIL SOLUTIONS (USA) INC.
To: CITIBANK, N.A., AS COLLATERAL AGENT
Reel/Frame 066641/0639 →
SUPPLEMENTAL PATENT SECURITY AGREEMENT (2019) Recorded Feb 21, 2024
From: G4S RETAIL SOLUTIONS (USA) INC.
To: CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH, AS COLLATERAL AGENT
Reel/Frame 066641/0646 →
SUPPLEMENTAL PATENT SECURITY AGREEMENT (2021) Recorded Feb 21, 2024
From: G4S RETAIL SOLUTIONS (USA) INC.
To: CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH, AS COLLATERAL AGENT
Reel/Frame 066641/0660 →
SUPPLEMENTAL NOTES PATENT SECURITY AGREEMENT (2021) Recorded Feb 21, 2024
From: G4S RETAIL SOLUTIONS (USA) INC.
To: WILMINGTON TRUST, NATIONAL ASSOCIATION
Reel/Frame 066641/0667 →
SUPPLEMENTAL NOTES PATENT SECURITY AGREEMENT (2019) Recorded Feb 21, 2024
From: G4S RETAIL SOLUTIONS (USA) INC.
To: WILMINGTON TRUST, NATIONAL ASSOCIATION
Reel/Frame 066641/0653 →
NOTES PATENT SECURITY AGREEMENT Recorded Feb 21, 2024
From: G4S RETAIL SOLUTIONS (USA) INC.; U. S. SECURITY ASSOCIATES HOLDING CORP.
To: WILMINGTON TRUST, NATIONAL ASSOCIATION, AS COLLATERAL AGENT
Reel/Frame 066641/0632 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 28, 2021
From: CRANDALL, SHELLIE; MCCABE, BRIAN
To: G4S RETAIL SOLUTIONS (USA) INC.
Reel/Frame 056073/0777 →
Continuity (1)
Provisional Application 63018071 · Apr 30, 2020
Cited By (1)
US 12,229,646