Walmart Apollo, LLC

Muzhou Liang

Priyanka Bhatt

Cole Warren Dutcher

Anshika Singh

Saurabh Kumar

Shankara Bhargava

Shankar Bhargava

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 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.

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San Francisco, CA, United States of America

Muzhou Liang