Granted Patent B1

US 12,547,970 · App. 18/519,424 · Granted Feb 10, 2026

Inventor: Vince Scalabrino (Palatine, IL)

Assignee: WALGREEN CO.

G06Q10/08345G06N20/00G06Q10/0838G06Q30/0283

Abstract

Claims (134)

1 . A computer-implemented method for adjusting order delivery fees, the method comprising:

training, by one or more processors, a plurality of machine learning (ML) models for each of a plurality of delivery hubs by performing one or more statistical analyses on one or more sets of historical order data and one or more sets of historical delivery data associated with the respective delivery hub, each of the plurality of ML models being associated with one or more environmental conditions and trained to determine combinations of optimal order delivery fees and optimal delivery times for the respective delivery hub;

receiving, at the one or more processors, a set of order data associated with a particular order by a customer, the order data indicating order details associated with the particular order and customer details associated with the customer;

receiving, at the one or more processors, a set of delivery data associated with the particular order, wherein the delivery data includes data on fleet availability of a fleet of delivery vehicles and expected demand for delivery services at a time of the particular order;

determining, by the one or more processors, a delivery hub from the plurality of delivery hubs to fulfil the particular order based upon the order details and the customer details, wherein the delivery hub is associated with a plurality of delivery vehicles of the fleet of delivery vehicles;

determining, by the one or more processors, one or more current or predicted future conditions associated with the delivery hub, wherein the one or more current or predicted future condition comprise at least one of weather conditions or traffic conditions;

selecting, by the one or more processors, an ML model from the plurality of ML models for the delivery hub based upon a correspondence between the one or more current or predicted future conditions associated with the delivery hub and the one or more environmental conditions associated with the ML model;

loading, by the one or more processors, the set of order data and the set of delivery data associated with the particular order into the ML model;

generating, by the ML model, one or more optimal combinations of variable order delivery fees and delivery times for the particular order by the customer to provide order fulfilment based on the set of order data and the set of delivery data associated with the particular order, wherein the variable order delivery fees are determined based upon the order details of the particular order, availability of the fleet of delivery vehicles at the time of the particular order, and the expected demand for delivery services at the time of the particular order;

communicating, via a user interface, to the customer, one or more options associated with the optimal combinations of variable order delivery fees and delivery times;

receiving, at the one or more processors, a customer selection associated with the one or more options, the customer selection indicating one or more of the delivery times associated with the one or more optimal combinations;

generating, at the one or more processors, a distribution plan for one or more autonomous delivery vehicles of the plurality of delivery vehicles to fulfill the particular order based at least in part upon the set of delivery data and the one or more of the delivery times based on the customer selection and the one or more current or predicted future conditions associated with the delivery hub, the distribution plan including a delivery plan for the one or more of the delivery vehicles to deliver the particular order to a delivery location associated with the customer; and

dispatching and controlling, by the one or more processors via a fleet communication interface, the one or more autonomous delivery vehicles to fulfil the particular order according to the distribution plan by the one or more of the delivery times based on the customer selection by:

establishing a communication connection with the one or more autonomous delivery vehicles,

controlling operation of the one or more autonomous vehicles along a route according to the distribution plan by transmitting commands to cause the one or more autonomous delivery vehicles to operate autonomously to travel along the route according to the distribution plan,

monitoring operation of the one or more autonomous delivery vehicles along the route, and

determining fulfilment of the particular order based upon monitoring the operation of the one or more autonomous delivery vehicles.

2 . The computer-implemented method of claim 1 , wherein all of the plurality of delivery vehicles comprise autonomous delivery vehicles.

3 . The computer-implemented method of claim 1 , wherein the order data includes data indicative of at least one of:

date and time of the particular order;

product inventory status for a product associated with the particular order;

product weight of a product associated with the particular order;

customer subscription status of the customer;

order priority type of the particular order;

customer order history of the customer; and

a delivery address for the particular order.

4 . The computer-implemented method of claim 1 , wherein the data on fleet availability includes data indicative of at least one of:

current locations of the plurality of the delivery vehicles;

current operating statuses of the plurality of delivery vehicles;

current routes being traversed by the plurality of delivery vehicles;

cargo capacity of the plurality of delivery vehicles;

expected future locations of the plurality of delivery vehicles;

expected future operating status of the plurality of delivery vehicles; and

expected future cargo capacity of the plurality of delivery vehicles.

5 . The computer-implemented method of claim 1 , wherein the delivery data includes delivery demand data indicative of at least one of:

total number of queued deliveries;

number of queued priority deliveries;

order information of the queued deliveries;

locations of the queued deliveries; and

weather and traffic conditions associated with the queued deliveries.

6 . The computer-implemented method of claim 1 , wherein generating the one or more optimal combinations of variable order delivery fees and delivery times includes analyzing, via the ML model, a plurality of current orders to predict optimal delivery fees for each order of the current orders, wherein each order is delivered within a predefined time period.

7 . The computer-implemented method of claim 6 , wherein analyzing the plurality of current orders includes predicting future orders placed during the predefined time period.

8 . The computer-implemented method of claim 1 , wherein determining the one or more optimal combinations of variable order delivery fees and delivery times includes predicting at least one combination of variable order delivery fees and delivery times that includes a monetary incentive for choosing a later delivery time.

9 . A computing system for adjusting order delivery fees, comprising:

one or more processors;

a communication interface communicatively connected to the one or more processors and configured to communicate with customer computing devices and a fleet of delivery vehicles via a network; and

one or more non-transitory memories communicatively connected to the one or more processors, storing computer-executable instructions that, when executed by the one or more processors, cause the computing system to:

train a plurality of machine learning (ML) models for each of a plurality of delivery hubs by performing one or more statistical analyses on one or more sets of historical order data and one or more sets of historical delivery data associated with the respective delivery hub, each of the plurality of ML models being associated with one or more environmental conditions and trained to determine combinations of optimal order delivery fees and optimal delivery times for the respective delivery hub;

receive a set of order data associated with a particular order by a customer, the order data indicating order details associated with the particular order and customer details associated with the customer;

receiving, at the one or more processors, a set of delivery data associated with the particular order, wherein the delivery data includes data on fleet availability of the fleet of delivery vehicles and expected demand for delivery services at a time of the particular order;

determine a delivery hub from the plurality of delivery hubs to fulfil the particular order based upon the order details and the customer details, wherein the delivery hub is associated with a plurality of delivery vehicles of the fleet of delivery vehicles;

determine one or more current or predicted future conditions associated with the delivery hub, wherein the one or more current or predicted future condition comprise at least one of weather conditions or traffic conditions;

select an ML model selected from the plurality of ML models for the delivery hub based upon a correspondence between the one or more current or predicted future conditions associated with the delivery hub and the one or more environmental conditions associated with the ML model;

load the set of order data and the set of delivery data associated with the particular order into the ML model;

generate one or more optimal combinations of variable order delivery fees and delivery times for the particular order by the customer to provide order fulfilment based on the set of order data and the set of delivery data associated with the particular order via the ML model, wherein the variable order delivery fees are determined based upon the order details of the particular order, availability of the fleet of delivery vehicles at the time of the particular order, and the expected demand for delivery services at the time of the particular order;

communicate one or more options associated with the optimal combinations of variable order delivery fees and delivery times to the customer via the communication interface;

receive a customer selection associated with the one or more options, the customer selection indicating one or more of the delivery times associated with the one or more optimal combinations;

generate a distribution plan for one or more autonomous delivery vehicles of the plurality of delivery vehicles to fulfill the particular order based at least in part upon the set of delivery data and the one or more of the delivery times based on the customer selection and the one or more current or predicted future conditions associated with the delivery hub, the distribution plan including a delivery plan for the one or more of the delivery vehicles to deliver the particular order to a delivery location associated with the customer; and

dispatch and control, via the communication interface, the one or more autonomous delivery vehicles to fulfil the particular order according to the distribution plan by the one or more of the delivery times based on the customer selection by:

establishing a communication connection with the one or more autonomous delivery vehicles,

controlling operation of the one or more autonomous vehicles along a route according to the distribution plan by transmitting commands to cause the one or more autonomous delivery vehicles to operate autonomously to travel along the route according to the distribution plan,

monitoring operation of the one or more autonomous delivery vehicles along the route, and

determining fulfilment of the particular order based upon monitoring the operation of the one or more autonomous delivery vehicles.

10 . The computing system of claim 9 , wherein the executable instructions further cause the computing system to:

generate the order data, the order data including data indicative of at least one of:

date and time of the particular order;

product inventory status for a product associated with the particular order;

product weight of a product associated with the particular order;

customer subscription status of the customer;

order priority type of the particular order;

customer order history of the customer; and

a delivery address for the particular order.

11 . The computing system of claim 9 , wherein the executable instructions further cause the computing system to:

generate the data on fleet availability, the data on fleet availability including data indicative of at least one of:

current locations of the plurality of the delivery vehicles;

current operating statuses of the plurality of delivery vehicles;

current routes being traversed by the plurality of delivery vehicles;

cargo capacity of the plurality of delivery vehicles;

expected future locations of the plurality of delivery vehicles;

expected future operating status of the plurality of delivery vehicles; and

expected future cargo capacity of the plurality of delivery vehicles.

12 . The computing system of claim 9 , wherein the executable instructions further cause the computing system to:

generate the delivery data, the delivery data including delivery demand data indicative of at least one of:

total number of queued deliveries;

number of queued priority deliveries;

order information of the queued deliveries;

locations of the queued deliveries; and

weather and traffic conditions associated with the queued deliveries.

13 . The computing system of claim 9 , wherein the executable instructions that cause the computing system to generate the one or more optimal combinations of variable order delivery fees and delivery times cause the computing system to analyze a plurality of current orders via the ML model to predict optimal delivery fees for each order of a plurality of current orders, wherein each order is delivered within a predefined time period.

14 . The computing system of claim 13 , wherein the executable instructions that cause the computing system to analyze the plurality of current orders via the ML model further cause the computing system to predict future orders placed during the predefined time period.

15 . A tangible, non-transitory computer-readable medium storing executable instructions for adjusting order delivery fees that, when executed by one or more processors of a computing system, cause the computing system to:

train a plurality of machine learning (ML) models for each of a plurality of delivery hubs by performing one or more statistical analyses on one or more sets of historical order data and one or more sets of historical delivery data associated with the respective delivery hub, each of the plurality of ML models being associated with one or more environmental conditions and trained to determine combinations of optimal order delivery fees and optimal delivery times for the respective delivery hub;

receive a set of order data associated with a particular order by a customer, the order data indicating order details associated with the particular order and customer details associated with the customer;

receiving, at the one or more processors, a set of delivery data associated with the particular order, wherein the delivery data includes data on fleet availability of a fleet of delivery vehicles and expected demand for delivery services at a time of the particular order;

determine a delivery hub from the plurality of delivery hubs to fulfil the particular order based upon the order details and the customer details, wherein the delivery hub is associated with a plurality of delivery vehicles of the fleet of delivery vehicles;

determine one or more current or predicted future conditions associated with the delivery hub, wherein the one or more current or predicted future condition comprise at least one of weather conditions or traffic conditions;

select an ML model selected from the plurality of ML models for the delivery hub based upon a correspondence between the one or more current or predicted future conditions associated with the delivery hub and the one or more environmental conditions associated with the ML model;

load the set of order data and the set of delivery data associated with the particular order into the ML model;

generate one or more optimal combinations of variable order delivery fees and delivery times for the particular order by the customer to provide order fulfilment based on the set of order data and the set of delivery data associated with the particular order via the ML model, wherein the variable order delivery fees are determined based upon the order details of the particular order, availability of the fleet of delivery vehicles at the time of the particular order, and the expected demand for delivery services at the time of the particular order;

communicate one or more options associated with the optimal combinations of variable order delivery fees and delivery times to the customer via a communication interface;

receive a customer selection associated with the one or more options, the customer selection indicating one or more of the delivery times associated with the one or more optimal combinations;

generate a distribution plan for one or more autonomous delivery vehicles of the plurality of delivery vehicles to fulfill the particular order based at least in part upon the set of delivery data and the one or more of the delivery times based on the customer selection and the one or more current or predicted future conditions associated with the delivery hub, the distribution plan including a delivery plan for the one or more of the delivery vehicles to deliver the particular order to a delivery location associated with the customer; and

dispatch and control, via the communication interface, the one or more autonomous delivery vehicles to fulfil the particular order according to the distribution plan by the one or more of the delivery times based on the customer selection by:

establishing a communication connection with the one or more autonomous delivery vehicles,

controlling operation of the one or more autonomous vehicles along a route according to the distribution plan by transmitting commands to cause the one or more autonomous delivery vehicles to operate autonomously to travel along the route according to the distribution plan,

monitoring operation of the one or more autonomous delivery vehicles along the route, and

determining fulfilment of the particular order based upon monitoring the operation of the one or more autonomous delivery vehicles.

16 . The tangible, non-transitory computer-readable medium of claim 15 , wherein the executable instructions further cause the computer system to:

generate the order data, the order data including data indicative of at least one of:

date and time of the particular order;

product inventory status for a product associated with the particular order;

product weight of a product associated with the particular order;

customer subscription status of the customer;

order priority type of the particular order;

customer order history of the customer; and

a delivery address for the particular order.

17 . The tangible, non-transitory computer-readable medium of claim 15 , wherein the executable instructions further cause the computing system to:

generate the data on fleet availability, the data on fleet availability including data indicative of at least one of:

current locations of the plurality of the delivery vehicles;

current operating statuses of the plurality of delivery vehicles;

current routes being traversed by the plurality of delivery vehicles;

cargo capacity of the plurality of delivery vehicles;

expected future locations of the plurality of delivery vehicles;

expected future operating status of the plurality of delivery vehicles; and

expected future cargo capacity of the plurality of delivery vehicles.

18 . The tangible, non-transitory computer-readable medium of claim 15 , wherein the executable instructions further cause the computing system to:

generate the delivery data, the delivery data including delivery demand data indicative of at least one of:

total number of queued deliveries;

number of queued priority deliveries;

order information of the queued deliveries;

locations of the queued deliveries; and

weather and traffic conditions associated with the queued deliveries.

19 . The tangible, non-transitory computer-readable medium of claim 15 , wherein the executable instructions that cause the computing system to generate the one or more optimal combinations of variable order delivery fees and delivery times cause the computing system to analyze a plurality of current orders via the ML model to predict optimal delivery fees for each order of a plurality of current orders, wherein each order is delivered within a predefined time period.

20 . The tangible, non-transitory computer-readable medium of claim 19 , wherein the executable instructions that cause the computing system to analyze the plurality of current orders via the ML model further cause the computing system to predict future orders placed during the predefined time period.

Assignments (3)

Continuity (2)

References Cited (29)