Walmart Apollo, LLC

Shengyang Zhang

Mingang Fu

Madhavan Kandhadai Vasantham

Vidyanand Krishnan

Arun Prasad Nagarathinam

Surnaik Prakash Srivastava

Pawan Kumar

Ankit Jasuja

Apeksha Mehta

Jennifer Chen

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform certain operations. The operations can include determining an estimated arrival time of a user at a physical store. The operations also can include generating an estimated wait time using a machine learning model and based on input data comprising the estimated arrival time and dynamic wait time data for the physical store. The operations additionally can include sending the estimated wait time to at least one of the physical store or a mobile device of the user. Other embodiments are disclosed.

Automatic determination of pickup wait times

In some examples, a system may include a first computing device communicatively coupled to a second computing device. Additionally, the first computing device is configured to obtain, from the second computing device, check-in data indicating an arrival of the user of the second computing device at a first location, and in response to obtaining the check-in data, determine current wait times. Moreover, the first computing device is configured to determine a first number of customers waiting for service, determine a first number of associates available to assist the first number of customers, and determine an expected wait time for the user operating the second computing device based at least on the current wait times. In some examples, the first number of customers waiting for service, and the first number of associates available. Further, the first computing device is configured to transmit the expected wait time to the second computing device.

Methods and apparatus for determining wait times

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform certain acts. The acts can include determining an estimated arrival time of a user at a physical store. The acts also can include generating an estimated wait time using a machine learning model and based on input data comprising the estimated arrival time and dynamic wait time data for the physical store. The acts additionally can include sending the estimated wait time to at least one of the physical store or a mobile device of the user. Other embodiments are disclosed.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one more processors and perform training a machine learning model based on historical input data for pickups by users that occurred during a historical time period and output data comprising actual wait times that occurred for the pickups by the users; receiving an order from a user for a pickup from a physical store during a selected time slot of a selected date; adding the order to a queue of assembled checked-in orders; determining an estimated arrival time based on geo-tracking; generating an estimated wait time using the machine learning model, as trained, and can be based on input data; and sending the estimated wait time to at least one of the physical store or the mobile device of the user. Other embodiments are disclosed.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one more processors and perform: obtaining historical demand data for pickup time slots at a physical store for a first time period; training a machine learning model to create a trained model based on the historical demand data for the pickup time slots over the first time period; generating, using the trained model, a projected demand for future pickup time slots at the physical store for a second time period; generating a time-slot capacity for each of the future pickup time slots at the physical store for the second time period based at least in part on the projected demand; and sending the time-slot capacities for the future pickup time slots to the physical store. Other embodiments are described.

Automatic generation of dynamic time-slot capacity

A system is provided and generally includes a server, an associate computing device, and a customer computing device. The server may receive data from the customer computing device indicating that a customer is picking up items from a predetermined location. The server may compute an estimated wait time for the customer based on one or more machine learning processes. In some examples, a number of unexpected customers that may arrive is determined. The machine learning process may compute the estimated wait time based on the number of unexpected customers. The machine learning process may be trained with historical data. The estimated wait time is transmitted to the customer computing device, and is displayed to the customer. In some examples, the server sends a list of customers waiting to be serviced to the associate computing device. The list may be prioritized based on estimated wait times for those customers.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: providing, via an electronic platform, access to one or more order placement user interfaces; collecting order placement information associated with the one or more order placement user interfaces; analyzing, by a conversion determination network of a machine learning architecture, the order placement information; generating actual conversion information for client sessions based on the actual availability of the order placement options during the client sessions; generating predicted conversion information for the client sessions based on a full availability of all of the order placement options during the client sessions; and generating lost demand information based, at least in part, on the actual conversion information and the predicted conversion information. Other embodiments are disclosed herein.

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Santa Clara, CA, United States of America

Shengyang Zhang