Walmart Apollo, LLC

Shankar Bhargava

Phani Ram Sayapaneni

Navin Naidu

Priyanka Bhatt

Muzhou Liang

Saurabh Kumar

Anshika Singh

Cole Warren Dutcher

Venkata Naga Vamsi Krishna Nandanavanam

Ashish Kumar Vishwakarma

A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors perform: receiving, by a chat bot and from a user, an indication to talk to a live agent; calling, by the chat bot, an automatic call distribution server to initiate a live-agent session for the user; storing metadata for the live-agent session in a distributed table and a distributed queue; pulling, from an automatic call distribution connector, one or more messages from a conversation between the user and the chat bot based on the metadata; and connecting the live agent to the user in the live-agent session, wherein the user communicates with the live agent in the live-agent session through a same interface for the chat bot. Other embodiments are disclosed.

Chat bot asynchronous agent escalation

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.

Personalizing user interface displays in real-time

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Santa Clara, CA, United States of America

Shankar Bhargava