Walmart Apollo, LLC

Haoxuan Chen

Snehasish Mukherjee

Shankara Bhargava Subramanya

Phani Ram Sayapaneni

Deepa Mohan

Ghodratollah Aalipour Hafshejani

Tanmay Peter Kuruvilla

John Brian Moss

Arpit Sharma

Jason Charles Benesch

Simral Chaudhary

Don Bambico

Lakshmi Manasa Velaga

Kotlasanthos Kumar

Sahana Radhakrishna

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

The disclosed subject matter relates to a system and method for providing an automated assistant that retrieves information from a knowledge base in response to a user's natural language communications. A refinement signal based on a subsequent user communication relevant to a previous search, establishes the type of search to be performed by the automated assistant in generating a response to the user communications. A reply communication to the user includes the selected results from search type selected. The types of searches are SPECIFIC, RELATIVE and NEGATION searches.

Methods and apparatus for refining a search

A system for generating a response to a customer query includes a computing device configured to obtain a first dataset, including a plurality of first phrase-intent pairs associated with a first domain. Each first phrase-intent pair includes a first phrase and a corresponding first intent. The computing device is configured to retrieve a set of configuration rules to configure a plurality of environments. The computing device is also configured to configure a first environment using the first dataset and the set of configuration rules to determine a result user intent based on a requested query associated with the first domain. The first environment embeds the plurality of first phrase-intent pairs in a vector space based on the set of configuration rules. The computing device is configured to perform operations based on the first environment.

Methods and apparatus for intent recognition

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.

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.

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Mountain View, CA, United States of America

Haoxuan Chen