Walmart Apollo, LLC

Salesforce Com, Inc.

Wal-mart Stores, Inc.

Shankara Bhargava Subramanya

Snehasish Mukherjee

Madhusudan Mathihalli

Haoxuan Chen

Guoxun Yuan

Phani Ram Sayapaneni

Girish Natarajan

Deepa Mohan

Vivek Kaul

Jagannath Putrevu

Walter Macklem

Jun He

Luke A Ball

Feng Guo

Susan Marie Kimberlin

Ghodratollah Aalipour Hafshejani

Jia Chen

Kanishka Maheshwari

David Nathanson

Junhao Jiang

Chris Bolte

Christopher P Bender

Arpit Sharma

Jingsi Zhang

Tanmay Peter Kuruvilla

Komal Arvind Dhuri

Kushal Bhatt

Luyang Chen

Ron Tal

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed configured to run on the one or more processors, cause the one or more processors to and perform operations: receiving a command from a user; transforming, using a trained machined learning model, the command by detecting an intent to add one or more recipe ingredients; determining a recipe from a set of recipes; determining one or more items and one or more quantities or sizes of the one or more items; selecting a respective quantity or size from quantities or sizes of available items of the one or more items, wherein the respective quantity or size, as selected, is overruled when a different quantity or size preference is indicated by the user; and automatically adding to the shopping cart the one or more items. Other embodiments are described.

Voice-enabled recipe selection

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 voice command from a user; transforming the voice command; transforming the voice command can include using a natural language understanding and rules execution engine into (a) an intent of the user to add recipe ingredients to a cart and (b) a recipe descriptor; determining a matching recipe from a set of ingested recipes based on the recipe descriptor; determining items and quantities associated with the items that correspond to a set of ingredients included in the matching recipe using a quantity inference algorithm; and automatically adding all of the items and the quantities associated with the items to the cart. Other embodiments are disclosed.

A method for automatically rectifying in real-time anomalies in natural language processing systems. The method can include determining an output corresponding to a user request from a user device for a user based on a new request template or machine learning. The method further can include retrieving one or more entity rules corresponding to entity data of the user request. The method also can include overwriting entity information of the entity data corresponding to the one or more entity rules. Additionally, the method can include outputting the output. Furthermore, the method can include transmitting, to the user device, a response to the user. Other embodiments are disclosed.

Automatically rectifying in real-time anomalies in natural language processing systems

Systems and methods for e-commerce systems using natural language understanding are described. A computing device is configured receive a user utterance including at least one identified semantic component and at least one missing semantic component and generate a context stack including a set of context entries. Each of the context entries includes a root intent element, an entity list element, and a dialogue stack and each context entry in the set of context entries is associated with one of a user utterance or a system utterance. The computing device is further configured to review at least one context entry in the set of context entries to locate the at least one missing semantic element within the dialogue stack and generate an intent flow execution request including the at least one semantic element from the first speech data and the missing semantic element.

System and method for handling multi-turn conversations and context management for voice enabled ecommerce transactions

Systems and methods for e-commerce systems using natural language understanding are described. A computing device is configured receive a user utterance and identify at least one semantic element within the user utterance. An intent associated with the at least one semantic element is identified and an intent flow associated with the identified intent is executed. The intent flow includes a set of tasks executed in a predetermined order. A system utterance is generated by instantiating a response template selected from a plurality of response templates associated with the executed intent.

Systems and methods for e-commerce API orchestration using natural language interfaces

This application relates to systems and methods for proactively predicting user intents on personal agents. In some examples, a user intent prediction system can include a computing device configured to obtain user intent data identifying a desired action by a user on a network-enabled tool. The computing device is further configured to obtain contextual data characterizing a user's interaction with the network-enabled tool. The computing device can then determine at least one predicted future intent of the user on the network-enabled tool based on the user intent data and the contextual data and present the at least one predicted future intent to the user.

Systems and methods for proactively predicting user intents in personal agents

An e-commerce voice representation system is provided and generally includes a server, a database, and a customer computing device. The server may generate a hierarchical ontology to semantically represent user queries generated by the customer computing device. The server may receive from the customer computing device data representing an e-commerce user query. The server may parse the e-commerce user query based on syntactic dependencies between the words in the e-commerce user query and translating those dependencies into semantic relations between the words. The received e-commerce user query may be tagged with actions, entities, and properties of the hierarchical ontology. Identified products may be linked to conceptual classes of the hierarchical ontology. Syntactic dependencies between words of the e-commerce user query may then be determined, and a semantic representation of the e-commerce user query may be generated based on the syntactic dependencies and mapping rules.

Method and apparatus for parsing and representation of digital inquiry related natural language

A method for automatically rectifying in real-time anomalies in natural language processing systems. The method can include receiving command data from a user device of a user. The command data can correspond to a user request. The method further can include retrieving, from a new template database, a new request template corresponding to the user request. Additionally, the method can include retrieving, from the new template database, an output instruction corresponding to the new request template, when the new request template is found. The method further can include determining, by a machine learning system, the output instruction corresponding to the user request, when the new request template is not found. The method also can include transmitting the output instruction to a request processing system. The request processing system can be configured to perform the output instruction and transmit, to the user device, a response to the user request. Other embodiments are disclosed.

A system for targeting advertising comprises a processor configured to compute a similarity score between a plurality of customers in a seed set and each of a pool of in-store purchasers having no history of online purchases from the online store of an entity. Each user in the seed set has a history of online purchases from the online store of the entity and a history of in-store purchases from stores of the entity. The processor is configured for selecting a subset of the pool of in-store purchasers having similarity scores above a predetermined threshold. The processor is configured for initiating rendering of advertisements for the online store to the subset of the pool of in-store purchasers.

System and method for rendering advertisements using lookalike model

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 computer processors and perform configuring one or more advertisements for a sponsored product customized to fit a page type associated with a page format of a website; analyzing one or more inputs to create merged data; generating a respective rank of the advertisements of the sponsored product using a conversion probability; adjusting the respective rank the one or more advertisements of the sponsored product as a function of the merged data; automatically designating one of the one or more advertisements of the sponsored product that is ranked higher than other advertisements to be positioned closest to a predefined portion of a webpage; and sending instructions to display the one of the one or more advertisements closest to the predefined portion of the webpage of the website. Other embodiments are disclosed.

System for page type based advertisement matching for sponsored product listings on e-commerce websites and method of using same

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 online query from an electronic device of a user, the online query comprising natural language of the user, determining an intent of the online query as at least one of a product-related intent or a non-product-related intent, predicting an entity from the online query if the intent of the online query is determined to be a product-related intent, mapping the entity predicted from the online query to product metadata associated with one or more products, and coordinating a display of product information of the one more products on the electronic device. The entity can include a product name, a product attribute, a product price range, an average customer review, or a product brand.

Systems and methods for mapping a predicted entity to a product based on an online query

Systems and methods for e-commerce systems using natural language understanding are described. A computing device is configured receive at least one application-specific element for a natural language conversation application, at least one shared element, and a user utterance, The system identifies at least one intent based on the at least one application-specific element and the at least one shared element and generates a system utterance based on the at least one application-specific element.

Systems and methods for server-less voice applications

An e-commerce voice response determination system is provided and generally includes a server and a customer computing device. The server may determine, based on a user query, a plurality of product descriptions. The server may determine a similarity value between the user query and each of the product descriptions based on the similarity between each word of the user query and each word of the product descriptions. The server may determine the product description with the most similarity to the user query, and provide a response to the customer computing device based on that product description. In some embodiments, the server may determine whether the user query matches a predefined query. If the user query matches the predefined query, the server provides a response to the customer computing device based on a predefined response to the predefined query.

Growing community of inventors

Santa Clara, CA, United States of America

Shankara Bhargava Subramanya