Walmart Apollo, LLC

Abilash Amarthaluri

Zuzar Fakhruddin Nafar

Venkatesh Kandaswamy

Chandni Jain

Behzad Ahmadi

Omer Ovenc

Abon Chaudhuri

Rohit Deep

Krishna Chaitanya Yalamanchili

Ashish Kumar Gupta

George Roumeliotis

Alessandro Magnani

Shreyansh Prakash Gandhi

Theban Stanley

Anthony Soohoo

Omar Saeed Siddiqui

Qian Li

Karthik Deivasigamani

Vijay Raghavendra

Nikita Maple Sudan

Arvind Senthil Kumaran

Harman Kochar

Suleman Ashfaq

Sreyash D Kenkre

Vishal Parmar

Cecilia Seto

Charles Myslinsky

Abhinandan Krishnan

Bo Ling

Benjamin Matthew Breuner

An advertising system is provided and generally includes a computing device that can receive attribute data from a plurality of information sources for an item. The computing device can determine a first number of sources for which the same attribute value for an attribute of the item is received, and can determine a second number of sources for which any attribute value for the same attribute of the item is received. The computing device can generate an attribute confidence value for the attribute based on the first number of sources and the second number of sources, indicating how likely the attribute value for the attribute is correct. The computing device can determine an item confidence value for the item based on multiple attribute confidence values for the item. Based on the item confidence value, the computing device may provide an attribute error signal indicating an attribute value error.

Method and apparatus for advertisement information error detection and correction

Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of receiving attribute data comprising a set of unstructured attribute data and a set of structured attribute data, analyzing the set of unstructured attribute data by processing through a first set of one or more Long Short Term Memory (LSTM) layers, to obtain an unstructured semantic signature, analyzing the set of the structured attribute data by processing through a first set of one or more Convolutional Neural Network (CNN) layers, to obtain a structured semantic signature, analyzing the unstructured semantic signature and the structured semantic signature, and classifying the item in one or more item categories. Other embodiments are disclosed herein.

Systems and methods for classification using structured and unstructured attributes

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

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 receiving a digital image comprising multiple items; determining an embedding for the digital image using a machine learning algorithm trained on one or more combined digital images, the combined digital image comprising one or more annotated digital images; identifying an item of the multiple items in the digital image; and facilitating an alteration of a GUI on an electronic device in response to identifying the item in the digital image.

Systems and methods for identifying items in a digital image

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 a title of an item associated with an online catalog; interpreting, using a natural language model, one or more attributes of the predetermined set of attributes; determining a first title quality score for the title based on a first rule; determining a second title quality score for the title based on a second rule; determining an aggregated title quality score for the title based on at least the first title quality score and the second title quality score; generating a content quality list for the title; and sending instructions to display, on a user interface of an electronic device, a content quality dashboard comprising the content quality list for the title of the item. Other embodiments are disclosed.

Systems and methods for a title quality scoring framework

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 receiving one or more digital images from a repository of digital images; annotating the one or more digital images from the repository of digital images; digitally altering the one or more digital images, as annotated, from the repository of digital images; digitally combining the one or more digital images, as annotated and digitally altered, with at least one or more portions of one or more other digital images of the repository of digital images to create one or more combined digital images; training a machine learning algorithm on the one or more combined digital images; and storing the machine learning algorithm, as trained, in the one or more non-transitory computer readable storage devices.

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: for each respective item of one or more items: identifying one or more respective item attributes in an existing title for the respective item; and determining at least one respective item attribute of the one or more respective item attributes to include in an alternative title for the respective item based on an attribute importance score for each respective item attribute of one or more item attributes of the respective item; generating at least one first modified title for at least one first item of the one or more items, the at least one first modified title comprising at least one first item attribute of the at least one first item; and coordinating displaying of the at least one first modified title for the at least one first item on a website. Other embodiments are disclosed herein.

Systems and methods for automatically modifying titles for products

Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of receiving an external catalog comprising external attributes for each product of a plurality of products, mapping the external attributes from the external catalog to internal attributes for each product of the plurality of products in an internal catalog for an online retailer using an ensemble learning technique comprising a plurality of algorithms, incorporating the external attributes of the external catalog into the internal attributes in the internal catalog as mapped, and coordinating displaying of the external attributes and the internal attributes on a website of the online retailer.

Systems and methods for matching data from an external catalog with data in an internal catalog

Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of identifying one or more product attributes in an existing title for a product, determining at least one product attribute to include in an alternative title for the product, determining that the at least one product attribute in the alternative title for at least a the product does not correspond to the one or more product attributes in the existing title for the product, automatically inserting the at least one product attribute in the alternative title for the product into a modified title for the product, and coordinating displaying of the modified title for the product on a website associated with the online retailer.

Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of receiving search queries from users, coordinating displaying of search results for the search queries, receiving an indication that the users selected products in the search results, associating each search query with a product type based on products selected by the users, identifying product attributes in each search query, determining an attribute weight for each product attribute for the product type, determining which product attributes are recorded in product information for each product, determining a product attribute score for each product, receiving an additional search query, and selecting a first portion of products for display in additional search results for the additional search query.

Systems and methods for determining product search results based on product attribute scores

Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of displaying a plurality of facets of a product type on a website of an online retailer in a plurality of facet orders, determining a first individual number of times a facet was selected when in a first position, determining a second individual number of times the facet was selected when in a second position, estimating a first estimated number of times the facet would have been selected if the facet had been in the first position rather than the second position, determining a ranking of the plurality of facets, and coordinating displaying at least a portion of the plurality of facets on the website of the online retailer in an order of the ranking.

Growing community of inventors

San Jose, CA, United States of America

Abilash Amarthaluri