Walmart Apollo, LLC

Priyanka Bhatt

Shankara Bhargava

Saurabh Kumar

Anshika Singh

Muzhou Liang

Neeraj Agrawal

Cole Warren Dutcher

Shankar Bhargava

Akshay Kumar

Tanishka Agarwal

A method can include receiving a signal from a user device of a user. The method can further include processing, via a machine learning model, user intent labels, wherein: the machine learning model is pre-trained based on historical input data and historical output data associated with multiple users comprising the user, the historical input data comprise historical feature embedding vectors associated with the multiple users, and the historical output data comprise historical intent labels based at least in part on uttered intents of the multiple users. The method can also include processing one or more user intent candidates of the user intent labels. The method can further include processing one or more user interface components for the one or more user intent candidates. Additionally, the method can include transmitting the one or more user interface components to be presented on a user interface executed on the user device of the user. Other embodiments are described.

Personalizing user interface displays in real-time

A system can receive a first set of data. The first set of data can include information indicating a first set of user sessions and for each of the first set of user sessions having an associated summary and a corresponding agent indicated intent. The system can also, based on the first set of data, determine a set of utterances and for each of the set of utterances a corresponding set of intents. Additionally, the system can receive a second set of data. The second set of data including information indicating a second set of user sessions and for each of the second set of user sessions having an associated determined utterance and corresponding interaction of a user. Moreover, the system can validate a corresponding intent of one or more utterances of the set of utterances, based on the second set of data.

Systems and methods for utilizing feedback data

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 cause the one or more processors to perform receiving one or more user utterances; re-training a pre-trained natural language processing (NLP) algorithm on the one or more user utterances; using the pre-trained NLP algorithm, as re-trained, as one or more layers in a neural network; combining at least one first output of at least one first output layer of the neural network with at least one second output of at least one second output layer of the neural network to create a final output of the neural network, wherein: the at least one first output layer of the neural network is different than the at least one second output layer of the neural network; and wherein the pre-trained NLP algorithm, as re-trained, is used to determine the final output of the neural network; and coordinating displaying a customized graphical user interface (GUI) using the final output of the neural network. Other embodiments are disclosed herein.

Systems and methods for generating a customized graphical user interface

A method implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include receiving, via a computer network, a user interaction signal from a user device for a user. The method further can include after receiving the user interaction signal, determining, in real-time via a machine learning model, a plurality of user intent labels based at least in part on transaction data, interaction data, and incident data for the user. The machine learning model can include pre-trained based on historical input data and historical output data associated with multiple users comprising the user. The historical input data can comprise historical feature embedding vectors for historical transaction data, historical interaction data, and historical incident data associated with the multiple users. The historical output data can include historical intent labels determined based at least in part on user-uttered intents by the multiple users in historical conversation data, identified by natural language understanding. In some embodiments, the method also can include, after determining the plurality of user intent labels, determining, in real-time, one or more user intent candidates of the plurality of user intent labels based on a confidence threshold. In a few embodiments, the method further can include, after determining the one or more user intent candidates, determining, in real-time, one or more user interface components for the one or more user intent candidates. After determining the one or more user interface components, the method further can include transmitting, via the computer network, the one or more user interface components to be presented on a user interface executed on the user device for the user. Other embodiments are described.

Growing community of inventors

Faridabad, India

Priyanka Bhatt