Abstract: An apparatus includes a memory and a processor. The memory stores first and second machine learning algorithms. The processor receives, from a user, voice signals associated with an information request and converts them into text. The processor uses the first machine learning algorithm to determine, based on the text, to automatically generate a reply to the request, rather than transmitting the request to an agent. The processor uses the second machine learning algorithm to generate, based on the set of text, the reply, which it transmits to the user. The processor receives feedback associated with the reply, indicating that the reply does or does not include the requested information. The processor uses the feedback to update either or both machine learning algorithms.

Abstract: An apparatus includes a memory and a processor. The memory stores first and second machine learning algorithms. The processor receives, from a user, voice signals associated with an information request and converts them into text. The processor uses the first machine learning algorithm to determine, based on the text, to automatically generate a reply to the request, rather than transmitting the request to an agent. This determination indicates that the text is associated with a probability that the automatically generated reply includes the requested information that is greater than a threshold. The processor uses the second machine learning algorithm to generate, based on the set of text, the reply, which it transmits to the user. The processor receives feedback associated with the reply, indicating that the reply does or does not include the requested information. The processor uses the feedback to update either or both machine learning algorithms.

Abstract: An apparatus includes a memory and processor. The memory stores previous requests and corresponding previous responses. The processor determines that a user device transmitted a new voice request, converts the voice request into a first set of text, and transmits the text to an agent device. The processor applies the machine learning algorithm to the first set of text to generate suggested responses, by identifying patterns shared by the first set of text and a subset of the previous requests that are associated with the suggested responses. The processor transmits the suggested responses to the agent device. The processor then determines that the agent device transmitted voice signals responding to the new request. The processor converts these voice signals into a second set of text. The processor stores the first set of text as a previous request, and the second set of text as a corresponding previous response.

Abstract: Apparatus and methods for enhancing security of an authentication process of a caller that initiated a call in an Interactive Voice Response (“IVR”) system are provided. The methods may include routing the call through the IVR system to a headset. After the routing, the methods may include retrieving data associated with the caller. In response to the retrieved data meeting one or more predetermined conditions, a graphical user interface of an agent computer paired to the headset may display a plurality of validation information for validating the caller. Selection by the agent of validation information may trigger methods for transmitting a request to the caller, via a caller mobile phone, to input the validation information selected by the agent. The methods may additionally include authenticating the caller based at least in part on the information input into the mobile phone.

Abstract: An apparatus includes a memory and a processor. The memory stores a machine learning algorithm configured to select between forwarding a request to an agent device and transmitting an automatically generated reply to the request. The processor receives feedback for a decision made by the algorithm, indicating whether the automatically generated reply includes the information sought by the request. If the algorithm decided to forward the request to the agent device, a reward is assigned to feedback that indicates that the reply does not include the information, while a punishment is assigned to feedback that indicates that the reply includes the information. If the algorithm decided to transmit the reply, a reward is assigned to feedback that indicates that the reply includes the information, and a punishment is assigned to feedback that indicates that the reply does not include the information. The processor updates the algorithm using the reward/punishment.

Abstract: Apparatus and methods for enhancing security of an authentication process of a caller that initiated a call in an Interactive Voice Response (“IVR”) system are provided. The methods may include routing the call through the IVR system to a headset. After the routing, the methods may include retrieving data associated with the caller. In response to the retrieved data meeting one or more predetermined conditions, a graphical user interface of an agent computer paired to the headset may display a plurality of validation information for validating the caller. Selection by the agent of validation information may trigger methods for transmitting a request to the caller, via a caller mobile phone, to input the validation information selected by the agent. The methods may additionally include authenticating the caller based at least in part on the information input into the mobile phone.

Abstract: An apparatus includes a memory and processor. The memory stores previous requests and corresponding previous responses. The processor determines that a user device transmitted a new voice request, converts the voice request into a first set of text, and transmits the text to an agent device. The processor applies the machine learning algorithm to the first set of text to generate suggested responses, by identifying patterns shared by the first set of text and a subset of the previous requests that are associated with the suggested responses. The processor transmits the suggested responses to the agent device. The processor then determines that the agent device transmitted voice signals responding to the new request. The processor converts these voice signals into a second set of text. The processor stores the first set of text as a previous request, and the second set of text as a corresponding previous response.

Abstract: An apparatus includes a memory and a processor. The memory stores first and second machine learning algorithms. The processor receives, from a user, voice signals associated with an information request and converts them into text. The processor uses the first machine learning algorithm to determine, based on the text, to automatically generate a reply to the request, rather than transmitting the request to an agent. This determination indicates that the text is associated with a probability that the automatically generated reply includes the requested information that is greater than a threshold. The processor uses the second machine learning algorithm to generate, based on the set of text, the reply, which it transmits to the user. The processor receives feedback associated with the reply, indicating that the reply does or does not include the requested information. The processor uses the feedback to update either or both machine learning algorithms.

Abstract: An apparatus includes a memory and a processor. The memory stores a machine learning algorithm configured to select between forwarding a request to an agent device and transmitting an automatically generated reply to the request. The processor receives feedback for a decision made by the algorithm, indicating whether the automatically generated reply includes the information sought by the request. If the algorithm decided to forward the request to the agent device, a reward is assigned to feedback that indicates that the reply does not include the information, while a punishment is assigned to feedback that indicates that the reply includes the information. If the algorithm decided to transmit the reply, a reward is assigned to feedback that indicates that the reply includes the information, and a punishment is assigned to feedback that indicates that the reply does not include the information. The processor updates the algorithm using the reward/punishment.

Abstract: Embodiments of the present invention provide a system for automatically identifying and mapping interaction attributes to generate custom assessment functionality. The system is configured for identifying one or more interaction channels associated with an entity, gathering flow sets associated with the one or more interaction channels, identifying one or more segments associated with the one or more interaction channels based on the flow sets, mapping one or more attributes with the one or more segments, and generating an assessment functionality based on mapping the one or more attributes with the one or more segments.

Abstract: When a caller initiates an interaction with an interactive voice response (“IVR”) system, the caller may be transferred to a live agent. Apparatus and methods are provided for integrating automated tools into the interaction after the caller been transferred to the agent. The agent may determine which AI responses are appropriate for the caller. AI may be leveraged to suggest responses for both caller and agent while they are interacting with each other. Such human-computer interaction may shorten response time of human agents and improve efficiency of IVR systems.

Abstract: Aspects of the disclosure relate to computing systems for receiving user requests through channels of a webpage and performing computational functions corresponding to the user requests. In one embodiment, a computing platform may detect a change corresponding to input parameters for performing a user request through a first representation of a webpage. The computing platform may update, based on the detected change corresponding to the input parameters, a natural language processing application associated with a second representation of the webpage. The computing platform may generate, based on the updated natural language processing application associated with the second representation of the webpage, a logical table including one or more questions to ask a user through the second representation of the webpage in response to one or more user inputs.

Abstract: Aspects of the disclosure relate to computing systems for receiving user requests through channels of a webpage and performing computational functions corresponding to the user requests. In one embodiment, a computing platform may detect a change corresponding to input parameters for performing a user request through a first representation of a webpage. The computing platform may update, based on the detected change corresponding to the input parameters, a natural language processing application associated with a second representation of the webpage. The computing platform may generate, based on the updated natural language processing application associated with the second representation of the webpage, a logical table including one or more questions to ask a user through the second representation of the webpage in response to one or more user inputs.