Abstract: In certain embodiments, intent prediction and dialogue generation may be facilitated. In some embodiments, a chat initiation request may be obtained from a user. The latest activity information associated with the user may be provided to a prediction model to obtain a first set of predicted intents of the user. For each intent of the first set of predicted intents, a candidate question may be selected from a question set based on the candidate question matching the intent. In some embodiments, the candidate questions may be simultaneously presented on the chat interface.

Abstract: In certain embodiments, pre-chat intent prediction and dialogue generation may be facilitated. In some embodiments, a chat initiation request may be obtained from a user. The latest activity information associated with the user may be provided to a prediction model to obtain a first set of predicted intents of the user. For each intent of the first set of predicted intents, a candidate question may be selected from a question set based on the candidate question matching the intent. Within ten seconds of the chat initiation request, the candidate questions may be simultaneously presented on the chat interface. Based on negative feedback obtained via the chat interface with respect to the candidate questions, additional candidate questions matching the second set of predicted intents may be presented on the chat interface. In some embodiments, the negative feedback may be provided to the prediction model to update configurations of the prediction model.

Abstract: Embodiments disclosed herein generally relate to a system and method for initiating an interactive chat via HTTP request. A web server of an organization computing system receives the HTTP request from a web client executing on a remote client. The HTTP request is triggered by a selection of a dialogue request embedded in an electronic mail message. The web server transmits an API call to a back-end computing system of the organization computing system based on information included in the HTTP request. The back-end computing system parses the API call to identify a user identifier corresponding to a user of the remote client device and a request identifier corresponding to the selected dialogue request embedded in the electronic mail message. The back-end computing system initiates the interactive chat via a text-based communication channel. The back-end computing system generates and transmits an electronic message comprising a response to the dialogue request.

Abstract: A system for transitioning a telephony or in-person servicing to an artificial intelligence (AI) chat session is disclosed. The system may receive a phone call from a user device associated with a user, and transmit a voice request for personally identifiable information associated with the user. The system may also receive and authenticate the requested personally identifiable information and, in response, generate an authentication token. The system may further receive a servicing intent from the user device, and generate a corresponding servicing intent token. Also, the system may generate an API call to an AI chatbot model, transmit the authentication token and the servicing intent token to the AI chatbot model, and map the servicing intent token to a stored servicing intent. Finally, the system may transmit a message to the user device via the AI chat session.

Abstract: A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of providing automated natural dialogue with a customer. The system may generate one or more events and commands temporarily stored in queues to be processed by one or more of a dialogue management device, an API server, and an NLP device. The dialogue management device may create adaptive responses to customer communications using a customer context, a rules-based platform, and a trained machine learning model.

Abstract: The disclosed technology includes systems and methods for controlling enrollment and secure persistent SMS texting account servicing communications. A method is provided that includes receiving, at an enrollment web portal, enrollment data including: enrollment credentials identifying a user for authentication, a phone number of a mobile device associated with the user, and consent by the user to persistently interact with an account servicing system via SMS texting. The method includes: processing the received enrollment data, authenticating the user responsive to processing the received enrollment data, storing the phone number of the mobile device associated with the user in a phone number data storage, and generating, responsive to the authenticating, a revocable token for persistent access to a natural dialogue module via a SMS texting gateway for the mobile device identified by the phone number.

Abstract: A system for transitioning a telephony or in-person servicing to an artificial intelligence (AI) chat session is disclosed. The system may receive a phone call from a user device associated with a user, and transmit a voice request for personally identifiable information associated with the user. The system may also receive and authenticate the requested personally identifiable information and, in response, generate an authentication token. The system may further receive a servicing intent from the user device, and generate a corresponding servicing intent token. Also, the system may generate an API call to an AI chatbot model, transmit the authentication token and the servicing intent token to the AI chatbot model, and map the servicing intent token to a stored servicing intent. Finally, the system may transmit a message to the user device via the AI chat session.

Abstract: Embodiments disclosed herein generally relate to a system and method for initiating an interactive chat via HTTP request. A web server of an organization computing system receives the HTTP request from a web client executing on a remote client. The HTTP request is triggered by a selection of a dialogue request embedded in an electronic mail message. The web server transmits an API call to a back-end computing system of the organization computing system based on information included in the HTTP request. The back-end computing system parses the API call to identify a user identifier corresponding to a user of the remote client device and a request identifier corresponding to the selected dialogue request embedded in the electronic mail message. The back-end computing system initiates the interactive chat via a text-based communication channel. The back-end computing system generates and transmits an electronic message comprising a response to the dialogue request.

Abstract: In certain embodiments, pre-chat intent prediction and dialogue generation may be facilitated. In some embodiments, a chat initiation request may be obtained from a user. The latest activity information associated with the user may be provided to a prediction model to obtain a first set of predicted intents of the user. For each intent of the first set of predicted intents, a candidate question may be selected from a question set based on the candidate question matching the intent. Within ten seconds of the chat initiation request, the candidate questions may be simultaneously presented on the chat interface. Based on negative feedback obtained via the chat interface with respect to the candidate questions, additional candidate questions matching the second set of predicted intents may be presented on the chat interface. In some embodiments, the negative feedback may be provided to the prediction model to update configurations of the prediction model.

Abstract: A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of providing automated natural dialogue with a customer. The system may generate one or more events and commands temporarily stored in queues to be processed by one or more of a dialogue management device, an API server, and an NLP device. The dialogue management device may create adaptive responses to customer communications using a customer context, a rules-based platform, and a trained machine learning model.

Abstract: The disclosed technology includes systems and methods for controlling enrollment and secure persistent SMS texting account servicing communications. A method is provided that includes receiving, at an enrollment web portal, enrollment data including: enrollment credentials identifying a user for authentication, a phone number of a mobile device associated with the user, and consent by the user to persistently interact with an account servicing system via SMS texting. The method includes: processing the received enrollment data, authenticating the user responsive to processing the received enrollment data, storing the phone number of the mobile device associated with the user in a phone number data storage, and generating, responsive to the authenticating, a revocable token for persistent access to a natural dialogue module via a SMS texting gateway for the mobile device identified by the phone number.

Abstract: The disclosed technology includes systems and methods for controlling enrollment and secure persistent SMS texting account servicing communications. A method is provided that includes receiving, at an enrollment web portal, enrollment data including: enrollment credentials identifying a user for authentication, a phone number of a mobile device associated with the user, and consent by the user to persistently interact with an account servicing system via SMS texting. The method includes: processing the received enrollment data, authenticating the user responsive to processing the received enrollment data, storing the phone number of the mobile device associated with the user in a phone number data storage, and generating, responsive to the authenticating, a revocable token for persistent access to a natural dialogue module via a SMS texting gateway for the mobile device identified by the phone number.

Abstract: System and method of generating an executable action item in response to natural language dialogue are disclosed herein. A computing system receives a dialogue message from a remote client device of a customer associated with an organization, the dialogue message comprising an utterance indicative of an implied goal. A natural language processor of the computing system parses the dialogue message to identify one or more components contained in the utterance. The planning module of the computing system identifies the implied goal. The computing system generates a plan within a defined solution space. The computing system generates a verification message to the user to confirm the plan. The computing system transmits the verification message to the remote client device of the customer. The computing system updates an event queue with instructions to execute the action item according to the generated plan upon receiving a confirmation message from the remote client device.

Abstract: A system for transitioning a telephony or in-person servicing to an artificial intelligence (AI) chat session is disclosed. The system may receive a phone call from a user device associated with a user, and transmit a voice request for personally identifiable information associated with the user. The system may also receive and authenticate the requested personally identifiable information and, in response, generate an authentication token. The system may further receive a servicing intent from the user device, and generate a corresponding servicing intent token. Also, the system may generate an API call to an AI chatbot model, transmit the authentication token and the servicing intent token to the AI chatbot model, and map the servicing intent token to a stored servicing intent. Finally, the system may transmit a message to the user device via the AI chat session.

Abstract: In certain embodiments, pre-chat intent prediction and dialogue generation may be facilitated. In some embodiments, a chat initiation request may be obtained from a user. The latest activity information associated with the user may be provided to a prediction model to obtain predicted intents of the user. For each intent of the current intents, a candidate question may be selected from a question set based on the candidate question matching the intent. Within ten seconds of the chat initiation request, the candidate questions may be simultaneously presented on the chat interface. A user selection of a first question of the candidate questions may be obtained via the chat interface responsive to the presentation of the candidate questions. Based on the user selection and the first question matching a first intent of the predicted intents, the first intent may be provided as reference feedback for the prediction model.

Abstract: The disclosed technology includes systems and methods for controlling enrollment and secure persistent SMS texting account servicing communications. A method is provided that includes receiving, at an enrollment web portal, enrollment data including: enrollment credentials identifying a user for authentication, a phone number of a mobile device associated with the user, and consent by the user to persistently interact with an account servicing system via SMS texting. The method includes: processing the received enrollment data, authenticating the user responsive to processing the received enrollment data, storing the phone number of the mobile device associated with the user in a phone number data storage, and generating, responsive to the authenticating, a revocable token for persistent access to a natural dialogue module via a SMS texting gateway for the mobile device identified by the phone number.

Abstract: Embodiments disclosed herein generally relate to a system and method for proactively generating an intervening message for a remote client device in response to an anticipated user action. A computing system receives one or more streams of user activity. The one or more streams of user activity include interaction with a server of an organization via an application executing on the remote client device. The computing system inputs the one or more streams of user activity into a prediction model. The computing system identifies an anticipated user action based on a prediction output from the prediction model. The computing system determines, based on a solution model, a proposed solution to the anticipated user action. The computing system generates an anticipated message to be transmitted to the remote client device of the user. The computing system transmits the anticipated message to the remote client device of the user.

Abstract: A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of providing automated natural dialogue with a customer. The system may generate one or more events and commands temporarily stored in queues to be processed by one or more of a dialogue management device, an API server, and an NLP device. The dialogue management device may create adaptive responses to customer communications using a customer context, a rules-based platform, and a trained machine learning model.

Abstract: Embodiments disclosed herein generally relate to a system and method for proactively generating an intervening message for a remote client device in response to an anticipated user action. A computing system receives one or more streams of user activity. The one or more streams of user activity include interaction with a server of an organization via an application executing on the remote client device. The computing system inputs the one or more streams of user activity into a prediction model. The computing system identifies an anticipated user action based on a prediction output from the prediction model. The computing system determines, based on a solution model, a proposed solution to the anticipated user action. The computing system generates an anticipated message to be transmitted to the remote client device of the user. The computing system transmits the anticipated message to the remote client device of the user.

Abstract: System and method of generating an executable action item in response to natural language dialogue are disclosed herein. A computing system receives a dialogue message from a remote client device of a customer associated with an organization, the dialogue message comprising an utterance indicative of an implied goal. A natural language processor of the computing system parses the dialogue message to identify one or more components contained in the utterance. The planning module of the computing system identifies the implied goal. The computing system generates a plan within a defined solution space. The computing system generates a verification message to the user to confirm the plan. The computing system transmits the verification message to the remote client device of the customer. The computing system updates an event queue with instructions to execute the action item according to the generated plan upon receiving a confirmation message from the remote client device.