Walmart Apollo, LLC

Rahul Ramkumar

Kannan Achan

Vidya Sagar Kalidindi

Sushant Kumar

Vidyanand Krishnan

Stephen Dean Guo

Sinduja Subramaniam

Rahul Iyer

Soumya Wadhwa

Praveenkumar Kanumala

Gaurav Agarwal

Sree Vasthav Shatdarshanam Venkata

Kamiya Motwani

Devadas Pattathil

Basant Choudhary

Surya Prasanna Kumar

Ryan Wayne Travis

Lucinda Frink Newcomb

Thomas Russel Ward

Tejal Kumar Patted

Derrick Lagomarsino

Evren Korpeoglu

Hyun Duk Cho

Kaushiki Nag

Shirpaa Manoharan

Rahul Sridhar

Luyi Ma

Nimesh Sinha

Mark Ibbotson

Apoorv Reddy Arrabothu

Atul Kochhar

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: outputting, by a machine-learning model, a probability that a user will re-order two or more items at a present time; determining the two or more items to recommend to the user based on the probability exceeding a predetermined threshold that the user will re-order the two or more items at the present time; sending instructions to display the two or more items to the user, wherein the user interface comprises a single-click option to add to an electronic cart the two or more items; and after receiving the single-click option from the user interface, adding the two or more items to the electronic cart. Other embodiments are disclosed.

Single-select predictive platform model

In some examples, a system may be configured to, implement a first set of operations that generate a first set of data characterizing an importance of the most recently added anchor item to the user. Further, the system may be configured to, implement a second set of operations that generate a second set of data characterizing a likelihood of an occurrence of a substitution rejection event associated with the user. That way, based on the first set of data and the second set of data, the system may be configured to generate output data, and implement a set of notification operations based on the output data.

Methods and apparatus for recommending substitutions

An optimized substitution system can include a computing device that is configured to receive a notification indicating an order and a first item identifier and, based on the first item identifier, identify a set of substitute item identifiers. Each substitute item identifier of the set of substitute item identifiers includes a probability value using an optimization computation based on a similarity and a value of the corresponding set of substitute item identifiers and the first item identifier. The similarity and the value are stored in a database. The compute device is also configured to, based on the probability values corresponding to the set of substitute item identifiers, select a first substitute item identifier of the set of substitute item identifiers and update the order to include the first substitute item identifier and exclude the first item identifier.

Systems and methods for bi-level optimization of stochastic recommendations

The disclosed subject matter relates to a system and method for personalizing customer experience at a retailer's physical location in order to increase sales and customer satisfaction. The personalization is based upon classification of customer's online interaction with the retailer. Upon detecting the customer's presence at the retailer's physical location, data of the customer's online interactions is retrieved and classified based on the type of online interactions and temporal characteristics. Push content is transmitted to the customer, the push content being based upon at least the classification and data associated with retailer's physical location.

Methods and apparatus for providing context aware personalized in-store customer experience

A system comprising 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: determining a list of possible substitutes for an item when the item is out of stock; training a machine learning algorithm, using labeled training data as input and a list of possible substitutes for the item as output; determining, using the machine learning algorithm, as trained, a respective similarity score for each substitute of the list of possible substitutes; determining a respective historical substitution score for each possible substitute; determining a respective final score for each possible substitute comprises using at least one or more rectifiers having ReLU non-linearity; ranking each possible substitute; storing a selection of the highest ranked possible substitute as additional training data with the labeled training data; and re-training the machine learning algorithm. Other embodiments are disclosed herein.

Systems and methods for determining substitutions

In some examples, a system may be configured to generate one or more query attributes for a search query received from a computing device of a user. Additionally, the system may be configured to, based at least in part on historical data of the user including data characterizing one or more items associated with the user, generate relevant item data. In various examples, the relevant item data characterizing a set of relevant items. Moreover, the system may be configured to, based on the relevant item data, the historical data of the user and the one or more query attributes, implement a set of operations that generate a set of personalized search results associated with the search query.

Personalized ranking using deep attribute extraction and attentive user interest embeddings

A system comprising 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: determining a set of items to recommend to a user based on a probability exceeding a predetermined threshold that the user will re-order each item of the set of items at a present time, wherein the probability is determined based at least in part on previous transactions of the user and other users within a first period of time, and wherein the set of items includes at least a predetermined number of items; sending instructions to display the set of items to the user on a user interface, wherein at least a portion of the set of items is shown as selected on the user interface, and the user interface further comprises a single-click option to add to a cart all selected items of the set of items; receiving a selection of the single-click option to add to the cart the all selected items of the set of items; and after receiving the single-click option, adding the all selected items of the set of items to the cart. Other embodiments are disclosed.

Systems and methods for generating a set of personalized complementary recommendations is disclosed. A user identifier and an anchor item identifier are received. A set of personalized-weighted items and a set of complimentary-weighted items are each generated based on the user identifier and the anchor item identifier. The personalized-weighted items are generated by a trained supervised model. The complementary-weighted items are generated by a trained unsupervised model. A set of personalized complementary recommendations including a subset of the personalized-weighted items and a subset of the complementary-weighted items is generated.

System, non-transitory computer readable medium, and method for personalized complementary recommendations

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: determining a list of substitutes for an item; determining qualities for each substitute; determining a similarity score for each substitute; determining a historical substitution score for each substitute; determining a final score for each substitute using the similarity score for each substitute and the historical substitution score for each substitute; ranking each substitute based upon the final score for each substitute; facilitating a display, on a user interface of a user device, of a highest ranked substitute; receiving, from the user interface of the user device, a selection of the highest ranked substitute; and after receiving the selection of the highest ranked substitute, substituting the highest ranked substitute for the item of the list of items. Other embodiments are disclosed herein.

A system and method of generating user personalized search results is disclosed. A search query including one or more words is received and a set of relevance-based search results is generated in response to the search query. One or more query attributes are generated for the search query. Historic data for a user associated with the search query is received and a set of personalized search results is generated from the set of relevance-based search results based on the query attributes and the historic data for the user. The historic data includes one or more items associated with the user.

In some embodiments, a method can comprise retrieving an inventory layout of a facility and determining multiple pick lists at the facility. A first pick list for a first user and a second pick list of a second user each comprise a first common item. Receiving a first indication from the first mobile device that the first common item is not available at the facility. Analyzing a first set of rules of a substitute system to locate a first substitute item that is within a first predetermined percentage of a size of the first common item. After analyzing the first set of rules, automatically sending instructions to transfer the first route and the first pick list to the first mobile device.

Systems and methods for order filling

In some embodiments, a method can comprise receiving a request for one or more orders, and analyzing the one or more orders. Each of the one or more orders can comprise one or more items and at least a portion of the one or more items are available at a warehouse. The method can further comprise determining one or more pick lists based at least in part on the analyzing the one or more orders and also based at least in part on an inventory layout of the warehouse, determining a first route for a first pick list of the one or more pick lists, and determining a second route for a second pick list of the one or more pick lists, wherein the first pick list and the second pick list each comprise a first common item. Other embodiments of related methods and systems are also provided.

Growing community of inventors

Santa Clara, CA, United States of America

Rahul Ramkumar