US-20260127625-A1 - EQUIPMENT SERVICE, SALES, AND CONSUMER ANALYTICS PORTAL

US20260127625A1US 20260127625 A1US20260127625 A1US 20260127625A1US-20260127625-A1

Abstract

Disclosed herein are system, method, and computer program product embodiments for an equipment service, sales, and consumer analytics portal, comprising: receiving data via a network, where the data includes sensor data from a beverage system located at an edge of the network, consumable product data from the beverage system, inventory system data, and internet data; generating, by a machine learning model, a prediction using the received data including one of a repair or preventative maintenance action for the beverage system, or an action regarding the consumable product; generating, by the machine learning model a sequence of one or more actions based on the generated prediction and the received data; and initiating the sequence of one or more actions by performing at least one of sending a message to a client device associated with the beverage system or sending a command to the beverage system.

Inventors

Cheuk Chi LAU
Xuejun Li
Jacob LIETZ
Caroline ECO

Assignees

PEPSICO, INC.

Dates

Publication Date: 20260507
Application Date: 20241104

Claims (20)

1 . A computer-implemented method, comprising: receiving, at a computing device, data via a network, wherein the data includes at least one of sensor data from a beverage system located at an edge of the network, consumable product data from the beverage system located at the edge of the network, inventory system data, and internet data; generating, by a machine learning model at the computing device, a prediction using the received data, wherein the prediction includes one of a repair action for a component at the beverage system, a preventative maintenance action for the component at the beverage system, or an action regarding the consumable product; generating, by the machine learning model at the computing device, a sequence of one or more actions based on the generated prediction and the received data; and initiating, by the computing device, the sequence of one or more actions by performing at least one of sending a message to a client device associated with the beverage system or sending a command to the beverage system.
2 . The computer-implemented method of claim 1 , wherein the sensor data includes: (i) foot traffic data surrounding the beverage system, (ii) eye tracking data indicating a consumable product and a duration a user looked at the consumable product, and (iii) a geolocation of the beverage system.
3 . The computer-implemented method of claim 2 , wherein the prediction comprises a recommended planogram for the beverage system, the recommended planogram based off of the sensor data, an amount the consumable product is purchased at the beverage system, and data of a customer that purchased the consumable product at the beverage system.
4 . The computer-implemented method of claim 3 , further comprising moving the consumable product within the beverage system to match the recommended planogram.
5 . The computer-implemented method of claim 1 , wherein the prediction comprises an alert that a quantity of the consumable product is below a predefined threshold.
6 . The computer-implemented method of claim 5 , wherein the prediction comprises a restocking order, the restocking order comprising: (i) a location of the beverage system; (ii) the consumable product to restock; (iii) a restock quantity; and (iv) a recommended restock time.
7 . The computer-implemented method of claim 6 , wherein the recommended restock time is based off (i) a time the consumable product is purchased, (ii) a geolocation of the beverage system, and (iii) traffic near the beverage system.
8 . A system, comprising: a memory; and at least one processor coupled to the memory and configured to: receive data via a network, wherein the data includes at least one of sensor data from a beverage system located at an edge of the network, consumable product data from the beverage system located at the edge of the network, inventory system data, and internet data; generate, by a machine learning model at the computing device, a prediction using the sensor data and the consumable product data, wherein the prediction includes one of a repair action for a component at the beverage system, a preventative maintenance action for the component at the beverage system, or an action regarding the consumable product; and generate, by the machine learning model at the computing device, a sequence of one or more actions based on the generated prediction and the received data; and initiate, by the computing device, the sequence of one or more actions by performing at least one of sending a message to a client device associated with the beverage system or sending a command to the beverage system.
9 . The system of claim 8 , wherein the sensor data includes: (i) foot traffic data surrounding the beverage system, (ii) eye tracking data indicating a consumable product and a duration a user looked at the consumable product, and (iii) a geolocation of the beverage system.
10 . The system of claim 9 , wherein the prediction comprises a recommended planogram for the beverage system, the recommended planogram based off of the sensor data, an amount the consumable product is purchased at the beverage system, and data of a customer that purchased the consumable product at the beverage system.
11 . The system of claim 10 , wherein the at least one processor is further configured to move the consumable product within the beverage system to match the recommended planogram.
12 . The system of claim 8 , wherein the prediction comprises an alert that a quantity of the consumable product is below a predefined threshold.
13 . The system of claim 12 , wherein the prediction comprises a restocking order, the restocking order comprising: (i) a location of the beverage system; (ii) the consumable product to restock; (iii) a restock quantity; and (iv) a recommended restock time.
14 . The system of claim 13 , wherein the recommended restock time is based off (i) a time the consumable product is purchased, (ii) a geolocation of the beverage system, and (iii) traffic near the beverage system.
15 . A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising: receiving, at a computing device, data via a network, wherein the data includes at least one of sensor data from a beverage system located at an edge of the network, consumable product data from the beverage system located at the edge of the network, inventory system data, and internet data; generating, by a machine learning model at the computing device, a prediction using the received data, wherein the prediction includes one of a repair action for a component at the beverage system, a preventative maintenance action for the component at the beverage system, or an action regarding the consumable product; generating, by the machine learning model at the computing device, a sequence of one or more actions based on the generated prediction and the received data; and initiating, by the computing device, the sequence of one or more actions by performing at least one of sending a message to a client device associated with the beverage system or sending a command to the beverage system.
16 . The non-transitory computer-readable device of claim 15 , wherein the sensor data includes: (i) foot traffic data surrounding the beverage system, (ii) eye tracking data indicating a consumable product and a duration a user looked at the consumable product, and (iii) a geolocation of the beverage system.
17 . The non-transitory computer-readable device of claim 16 , wherein the prediction comprises a recommended planogram for the beverage system, the recommended planogram based off of the sensor data, an amount the consumable product is purchased at the beverage system, and data of a customer that purchased the consumable product at the beverage system.
18 . The non-transitory computer-readable device of claim 17 , the operations further comprising moving the consumable product within the beverage system to match the recommended planogram.
19 . The non-transitory computer-readable device of claim 15 , wherein the prediction comprises: (1) an alert that a quantity of the consumable product is below a predefined threshold, and (2) a restocking order, the restocking order comprising: (i) a location of the beverage system; (ii) the consumable product to restock; (iii) a restock quantity; and (iv) a recommended restock time.
20 . The non-transitory computer-readable device of claim 19 , wherein the recommended restock time is based off (i) a time the consumable product is purchased, (ii) a geolocation of the beverage system, and (iii) traffic near the beverage system.

Description

BACKGROUND In a food service environment, machines may be used for a variety of tasks such as food preparation, food storage, beverage storage, and sales. These machines often include numerous parts that may fail as a result of manufacturing defects, user error, or environmental exposure. Repairing the machines is often a costly and laborious task for a variety of reasons. First, a failure needs to be identified. This often does not occur until a third party is able to physically inspect the machine. Second, the repair may be delayed because the third part may not have the means to fix the error upon arriving for an inspection. In addition to errors, machines often require preventative maintenance to extend their lifetimes. Similar to error detection, preventative maintenance also requires third party inspection. Thus, there is a need to detect and diagnose errors or likely errors in foods machines. Additionally, these machines often include a wide range of consumable products such as different types of food and beverages. In addition to tracking the inventory levels of the consumable product, there is a need to track demand for the consumable products. Demand for certain products may vary by location and time. For example, one municipality may have a higher demand for a first product whereas a neighboring municipality may have a higher demand for a second product. Demand may vary at a more granular level. For example, a beverage system may sell more products at one corner of an intersection as compared to a different corner. Furthermore, certain products may be sold at higher rates based on their location (e.g., at eye level) within a beverage machine. Demand may also vary by time. For example, demand for caffeinated beverages may peak between 7 am-10 am. Demand may also fluctuate based on regional events such as professional sporting events or concerts. For example, there may be high demand for a basketball player's favorite snack or drink when that player's team travels to a city for a game. Given the wide variety of circumstances that impact demand for consumable products, there is a need to not only recognize, but also predict when those circumstances are likely to occur to ensure those products are properly stocked at the relevant machines. As discussed above, these systems may include numerous components, consumable products, and in addition, interface with hundreds or thousands of users each day. Each component, product, and interaction may include data points useful for analyzing the system and products therein. Given the concerns above, there is a need to: (1) collect real time data from a network edge; (2) perform predictive analysis on the real-time data; and (3) execute actions in response to the predictive analysis. BRIEF SUMMARY Disclosed herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for an equipment service, sales and consumer analytics portal. Some embodiments relate to a method receiving, at a computing device, data via a network, where the data includes at least one of sensor data from a beverage system located at an edge of the network, consumable product data from the beverage system located at the edge of the network, inventory system data, and internet data. The method further includes generating, by a machine learning model at the computing device, a prediction using the received data. The prediction may include a repair action for a component at the beverage system, a preventative maintenance action for the component at the beverage system, or an action regarding the consumable product. Additionally, the method includes generating, by the machine learning model at the computing device, a sequence of one or more actions based on the generated prediction and the received data. The method further includes initiating, by the computing device, the sequence of one or more actions by performing at least one of sending a message to a client device associated with the beverage system or sending a command to the beverage system. Some embodiments relate to a system with a memory and at least one processor coupled to the memory. The at least one processor is configured to receive data via a network, where the data includes at least one of sensor data from a beverage system located at an edge of the network, consumable product data from the beverage system located at the edge of the network, inventory system data, and internet data. The at least one processor is further configured to generate, by a machine learning model, a prediction using the received data. The prediction may include a repair action for a component at the beverage system, a preventative maintenance action for the component at the beverage system, or an action regarding the consumable product. Additionally, at least one processor is further configured to generate, by the machine learning model, a sequence of one or more actions based o