Walmart Apollo, LLC

Arun Prasad Nagarathinam

Madhavan Kandhadai Vasantham

Sahana Vijaykumar

Mingang Fu

Vidyanand Krishnan

Shengyang Zhang

Surnaik Prakash Srivastava

Apeksha Mehta

Jennifer Chen

Pawan Kumar

Ankit Jasuja

Rohit Jain

Deepak R Deshpande

Jagtej Bewli

Syed Aman

Arash Asadi-Shahmirzadi

Sneha Narahalli Balasubramanya

David Win

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 includes a processor coupled with a data store and configured to transmit an availability message to a user device and receive a pickup message from the user device. The processor is also configured to calculate an arrival time based on the initiation time, the initiation location, and a distribution center location for the distribution center and update the arrival time at continuous intervals based on real-time information. The processor is also configured to transmit a preparation message to a distribution center device, receive an arrival message from a distribution center image sensor, and transmit a delivery message to the distribution center device. The processor is also configured to transmit an order receipt message to the distribution center image sensor and receive a departure message from the distribution center image sensor including an image identifying the vehicle associated with the item order based on the corresponding vehicle identifier.

Automated curbside fulfillment

A method and system of handling an alternative pick-up using vehicle information is disclosed. Imaging data is received from at least one imaging device. The at least one imaging device is configured to provide a field-of-view of a predetermined area associated with a retail location. A vehicle identifier for each vehicle within the image data is extracted and compared to each user profile associated with an order in a set of orders. Each user profile includes a primary vehicle identifier and at least one secondary vehicle identifier and the extracted vehicle identifier is compared to the at least one secondary vehicle identifier in each user profile. The extracted vehicle identifier is associated with a first order in the set of orders when the extracted vehicle identifier matches the secondary vehicle identifier of the user profile of the first order.

Systems and methods for handling alternate pickup using vehicle recognition

A system is provided and generally includes a server and an associate computing device. The server may receive location video data comprising at least one image from a camera directed to a location, such as a designated area of a parking lot. The server may detect one or more objects in the image, and determine a bounding box based on the detected objects. The server may also determine a confidence value corresponding to the bounding box. The confidence value may be determined based on an area of the image defined by the bounding box and a predefined class. The server may then generate an arrival message based on the determined confidence value, and transmit the arrival message to the associate computing device.

Methods and apparatus for vehicle arrival notification based on object detection

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 or more processors and perform: receiving transaction data comprising items previously purchased; comparing the first item to the second item; identifying the first item to be related to the second item; aggregating the first product type and the second product type into a first category bundle having a first theme defined by the first and second product types; assigning a first affinity score to the first and second items based on the transaction data; detecting the first item as being a first item of interest; and when the first affinity score of the first item exceeds a threshold, displaying, to the user while browsing the one or more websites, the first category bundle comprising the first and second items. Other embodiments are described.

Automated lists

A system is provided and generally includes a server (e.g., application server), an associate computing device, and a client computing device. The server can receive a user arrival notification that indicates a user has arrived to pick up purchased goods. The server can transmit a scan initiation request to a remote terminal that causes the remote terminal to scan at least one network. The server can receive scan data for the scanned network from the remote terminal indicating, for example, a signal strength of that network. The server can determine a current wait time based on the received scan data for the scanned network, and can transmit the current wait time to the remote terminal.

Method and apparatus for predicting wait times

A historical-data module may retrieve historical prices and/or sales numbers for products at a presently-relevant, historical reference time and at other times in an interval encompassing the reference time. A comparison module may generate relative price values and/or sales-number values by comparing prices and/or sales numbers at the reference time to those at other times in the interval for various products. A selection module may select a subset of products with a favorable weighted combination of relative price values and/or sales-number values for a product report presented over a communication network, potentially with a graph of historical prices for the products. Information about a viewer of the product report may further tailor the report. Viewer information may include signals of product interest identified in social-networking posts by natural language processing, viewer search and/or purchase data, the viewer's relations in a social graph of a social networking platform, and/or geo-targeting.

Engaging retail customers over communication networks

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.

Categories of items are dynamically generated by analyzing purchasing habits across multiple customers. Product categories showing affinity one with another are grouped together and offered to customers as a bundle. Particular items within bundled categories are selected by analyzing purchasing habits with respect to various customer demographic profile habits.

A system and method includes a computer network system configured to provide output to a plurality of distribution centers for a retailer, an inputting system configured to allow ordering of a quantity of an item from a vendor having a minimum order quantity (MOQ) and vendor pack size for the item, an allocation system configured to allocate the order quantity based on an allocation policy including specific distribution centers and allocation percentages associated with the specific distribution centers, and an inventory allocation tool (IAT) configured to receive the allocation policy, order quantity, MOQ, and vendor pack size as inputs, optimizes distribution of the order quantity among the specific distribution centers so that MOQ and vendor pack size according to the allocation policy are respected, and outputs an executable purchase order over the computer network system for each specific distribution center.

Growing community of inventors

Santa Clara, CA, United States of America

Arun Prasad Nagarathinam