Walmart Apollo, LLC

Wal-mart Stores, Inc.

Vijay Raghavendra

Ashish Kumar Gupta

Abilash Amarthaluri

Venkatesh Kandaswamy

Zuzar Fakhruddin Nafar

Rohit Deep

Omer Ovenc

Behzad Ahmadi

Anthony Soohoo

Karthik Deivasigamani

Harman Kochar

Sreyash D Kenkre

Arvind Senthil Kumaran

Charles Myslinsky

Suleman Ashfaq

Yitao Yao

Mark Paul Palaima

Jayashree S Kolhatkar

Alok Bhanot

A system can include one or more processors and non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform a method for determining one or more substitutes for a product requested in an inquiry from a user. The method comprises one or more of the steps including receiving an inquiry from a user, which comprises a requested product; extracting one or more attributes of the requested product based on the inquiry; determining a context of the inquiry; and determining one or more substitutes for the requested product from an inventory database based on a distance between the requested product and each substitute of the one or more substitutes, as projected in a product graph relating to a domain of the requested product and the context of the inquiry. In many embodiments, the product graph relating to the domain is generated by a machine learning module configured to dynamically learn information about the domain from various sources, including one or more expert opinions, the inventory database, and/or public records; and dynamically build the product graph according to the information about the domain. An exemplary machine learning module can be configured to dynamically build the product graph by: creating one or more product categories in the domain; extracting one or more entity nodes from the one or more product categories based on the information about the domain; and build one or more connections, including an entity-entity connection between each pair of related entity nodes, a product-entity connection between each product node and each of one or more entity nodes associated with the each product node, and a product-product connection between each pair of related product nodes. Other embodiments are disclosed.

System and method for determining substitutes for a requested product

A system can include one or more processors and non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform a method for determining one or more substitutes for a product requested in an inquiry from a user and the order of the one or more substitutes to be transmitted to the user. The method can comprise: receiving an inquiry from a user, wherein the inquiry comprises an image and a text describing a requested product; extracting one or more visual attributes of the requested product based on the image; extracting one or more textual attributes of the requested product based on the text; determining a first projected requested product, in a product graph relating to a domain of the requested product, based on the one or more visual attributes of the requested product; determining a second projected requested product in the product graph based on the one or more textual attributes of the requested product; and determining one or more substitutes for the requested product. In this embodiment, the one or more substitutes for the requested product can be determined by: determining one or more first candidates, each first candidate of the one or more first candidates existing in both the product graph and an inventory database and comprising a visual distance between the first projected requested product and the each first candidate in the product graph; determining one or more second candidates, each second candidate of the one or more second candidates existing in both the product graph and the inventory database and comprising a textual distance between the second projected requested product and the each second candidate in the product graph, the one or more substitutes comprising the one or more first candidates and the one or more second candidates; determining a score of a candidate substitute of the one or more substitutes based on a visual distance and a textual distance of the candidate substitute of the one or more substitutes; and sorting the one or more substitutes based on the score of the candidate substitute of the one or more substitutes. In this embodiment, after the one or more substitutes are determined and sorted, this method can further comprise: in response to receiving the inquiry from the user, automatically transmitting to the user higher ranked ones of the one or more substitutes. Additionally, in this embodiment, the product graph can be dynamically generated by a machine learning module based on information about the domain of the requested product; and the product graph can comprise: (a) one or more product nodes, the one or more product nodes comprising the one or more substitutes; (b) one or more entity nodes, the one or more entity nodes comprising the one or more visual attributes and the one or more textual attributes of the requested product; (c) one or more entity-entity connections between each pair of related entity nodes of the one or more entity nodes; (d) one or more product-entity connections between a product node of the one or more product nodes and an entity node of the one or more entity nodes, wherein the entity node is related to the product node; and (e) one or more product-product connection between a first product node of the one or more product nodes and a second product node of the one or more product nodes, wherein the first product node and the second product node are determined to be substitutable by or complimentary of each other. Other embodiments are disclosed.

System and method for determining substitutes for a requested product and the order to provide the substitutes

A real-time predictive intelligence platform comprises: receiving from a user through a meta API definitions for predictive intelligence (PI) artifacts that describe a domain of an online transaction system for least one business entity, each of the PI artifacts including types, component modules and behavior bundles; exposing an entity API based on the PI artifacts for receiving entity events from the online transaction system comprising records of interactions and transactions between customers and the online transaction system; responsive to receiving an entity event through the entity API, executing the component modules and behavior bundles to analyze relationships found between past entity events and metrics associated with the past entity events, and computing a probabilistic prediction and/or a score, which is then returned to the online transaction system in real-time; and processing entity event replicas using modified versions of the PI artifacts for experimentation.

Real-time predictive intelligence platform

Growing community of inventors

San Jose, CA, United States of America

Vijay Raghavendra