Abstract: A text-based real-time communication interface, such as a chatbot, is presented to a user for the exchange of customer support information. A user's freeform text input is analyzed using machine learning algorithms to derive the meaning of the input text as well as to determine the user sentiment expressed therein. These determinations may be further supported by signals extracted from session-based activity, which signals can be used to infer the intended workflow of the user and whether or not that workflow was achieved. The expressed user sentiment is considered along with other historical or session-based user data to generate tailored questions and responses to be delivered in real-time to the user. The responses are displayed to the user along with information that routes the user to a workflow resolution.

Abstract: A text-based real-time communication interface, such as a chatbot, is presented to a user for the exchange of customer support information. A user's freeform text input is analyzed using machine learning algorithms to derive the meaning of the input text as well as to determine the user sentiment expressed therein. These determinations may be further supported by signals extracted from session-based activity, which signals can be used to infer the intended workflow of the user and whether or not that workflow was achieved. The expressed user sentiment is considered along with other historical or session-based user data to generate tailored questions and responses to be delivered in real-time to the user. The responses are displayed to the user along with information that routes the user to a workflow resolution.