Walmart Apollo, LLC

Wal-mart Stores, Inc.

Other

Madhavan Kandhadai Vasantham

Sreekanth Sreedhararaj

Mingang Fu

Mahesh Tyagarajan

Shengyang Zhang

Arun Prasad Nagarathinam

Vikrant Tare

Shiva K Potu

Sahana Vijaykumar

Manish P Gupta

Vidyanand Krishnan

Deepak R Deshpande

Amritayan Nayak

Srinivasan Venkatesan

Syed Aman

Surnaik Prakash Srivastava

Ankit Jasuja

Jennifer Chen

Apeksha Mehta

Dhruv Rajnikant Sojitra

Yashwanth Thumallapalle

Venkateswararao Chennamsetty

Pawan Kumar

Rohit Jain

Aditya Arcot Srinivasan

Pushkar Raj Pande

Sneha Narahalli Balasubramanya

Deyi Zhang

Bipin Mulik

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

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 operations including: authenticating at least one source of online data transmissions that matches a respective computing device of a user; batching the online data transmissions into one or more micro-batches; transmitting the one or more micro-batches to one or more respective backend services; receiving respective responses from the one or more respective backend services in response to each one of the one or more micro-batches; and performing each respective task of one or more tasks associated with the respective responses. Other embodiments are disclosed.

Integrated gateway platform for fulfillment services

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 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.

An integrated gateway system configured to perform: receiving online data transmissions from a user computing device of a user; authenticating that a source of the online data transmissions matches the user computing device; transmitting the online data transmissions to the internal gateway system; authenticating credentials of the user as a pre-authorized user; restricting a number of incoming calls using a rate-limiting throttle system; transmitting the online data transmissions to the communication management system; batching the online data transmissions into one or more micro-batches based on one or more rules; transmitting the one or more micro-batches to one or more respective backend services using an events stream system; receiving respective responses transmitted from the one or more respective backend services in response to each one of the one or more micro-batches; performing each respective task of one or more tasks based on the respective responses from the one or more respective backend services. Other embodiments are disclosed.

The embodiments described herein enable the efficient storage and searching of large amounts of data to retrieve desired data with high accuracy and fast response time. The embodiments include for example parsing an input document containing input data into multiple sub-documents based on the structure of the input document and generating for each sub-document, a unique ID, pathway, and metadata. The embodiments also include storage of the ID and pathway for each sub-document in an index database and storage of the input document itself in a separate database. When a query for information from the input document is received, the ID and pathway of the sub document containing the queried information are identified and used to locate the queried information from the input document.

Systems and methods for high efficiency data querying

Systems and methods for optimizing delivery vehicle resources (e.g. a plurality of vehicles) are described herein. Available time slots for the plurality of vehicles are determined and presented to a user. In response to receiving a delivery order indicating a selected time slot, the delivery order is assigned to a vehicle from the plurality of vehicles based on a time slot indicated in the delivery order and a set of delivery parameters. A delivery route is calculated for each vehicle having a delivery order based on the set of delivery parameters. An optimized delivery route is calculated for each vehicle having a delivery order based on the set of delivery parameters.

Systems and methods for determining delivery time and route assignments

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 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 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 orders from physical stores for fulfillment from a distribution center, each of the orders comprising a set of items and a requested delivery date; generating a stack building plan for each of the orders using simulated annealing; obtaining routes for delivering the orders in trailers from the distribution center to the physical stores based at least in part on the stack building plan; and generating a load design for each of the routes to deliver in a trailer of the trailers a load for one or more of the orders, such that floor spot assignments for stacks for each of the one or more of the orders in the load carried by the trailer satisfy sequence-of-delivery constraints and center-of-gravity constraints. Other embodiments are disclosed.

Automatic generation of load and route design

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 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.

Growing community of inventors

Foster City, CA, United States of America

Madhavan Kandhadai Vasantham