A method comprising: receiving an interactive voice response (IVR) tree configured to implement one or more tasks, each associated with one or more IVR node paths comprising a plurality of IVR nodes arranged in a hierarchical relationship; analyzing the IVR tree to identify one or more intent IVR nodes, each associated with one of the tasks; with respect to each of the intent IVR nodes, identifying a plurality of corresponding entity IVR nodes included within the IVR node path associated with the intent IVR node; assembling one or more task-specific chatbot skills, each comprising (i) one of the intent IVR nodes, and (ii) at least some of the plurality of corresponding entity IVR nodes, wherein each of the task-specific chatbot skills is configured to perform one of the tasks by conducting a dialog with a user; and generating a chatbot comprising at least one of the task-specific chatbot skills.

A virtual assistant system for communicating with customers uses human intelligence to correct any errors in the system AI, while collecting data for machine learning and future improvements for more automation. The system may use a modular design, with separate components for carrying out different system functions and sub-functions, and with frameworks for selecting the component best able to respond to a given customer conversation.

Among other things, a developer of an interaction application for an enterprise can create items of content to be provided to an assistant platform for use in responses to requests of end-users. The developer can deploy the interaction application using defined items of content and an available general interaction model including intents and sample utterances having slots. The developer can deploy the interaction application without requiring the developer to formulate any of the intents, sample utterances, or slots of the general interaction model.

Systems and methods are configured for predictive prompt generation for an interaction between a party and an automated prompt system. In various embodiments, metadata is received on the interaction and provided as input to a multi-label predictive model to generate interaction probabilities for a plurality of prompt information data objects. Each probability generated by the predictive model provides a likelihood a particular information data object needs to be provided to the first party during the interaction. Accordingly, one or more of the prompt information data objects are identified based on the probability of each piece found in the one or more prompt information data objects that satisfy a set threshold and one or more notifications are provided so that the automated prompt system provides at least one of the prompt information data objects to the first party during the interaction.

A device may receive, from an interactive voice response (IVR) device, a first request for a code. The first request may be based on the IVR device having received a call from a user device. The device may determine user device information, associated with the user device, based on the first request. The device may determine the code based on at least one of the first request or the user device information. The device may send, to the IVR device, the code to enable the IVR device to configure a call tree based on the code. The device may receive, from the IVR device, a second request for an experience. The experience may be based on the code. The device may send, to the user device, a message to cause the user device to provide the experience.

A system and method for handling calls at a call facility wherein each call is handled by a distinct call container instance, the system comprising a platform server programmed to perform the steps of maintaining an idle container inventory for handling new incoming calls, upon receiving a new call, assigning the new call to one of the idle containers in the inventory, upon completion of a call within one of the containers, causing the container that completed the call to be killed, monitoring the idle container inventory and stage instantiation of additional idle containers to replenish the idle container inventory as containers complete calls and are killed off.

Systems and processes are disclosed for handling a multi-part voice command for a virtual assistant. Speech input can be received from a user that includes multiple actionable commands within a single utterance. A text string can be generated from the speech input using a speech transcription process. The text string can be parsed into multiple candidate substrings based on domain keywords, imperative verbs, predetermined substring lengths, or the like. For each candidate substring, a probability can be determined indicating whether the candidate substring corresponds to an actionable command. Such probabilities can be determined based on semantic coherence, similarity to user request templates, querying services to determine manageability, or the like. If the probabilities exceed a threshold, the user intent of each substring can be determined, processes associated with the user intents can be executed, and an acknowledgment can be provided to the user.

A system of handling callers, uses a computer, that receives a call, assigns an operator to handle the call, and automatically recognizes at least one aspect of a voice from a caller, and automatically forms a response to be given to the caller. The caller is prevented from knowing they are speaking with a computer by receiving responses from multiple different similar sounding operators. The computer providing sound to the caller which interferes with the caller being able to determine that the caller has been placed on hold from the operator, e.g., an average of multiple people talking in the background. The computer also maintains a ledger of the call, where the ledger includes information about recognized voice from the caller, and responses which were given to the caller, for each of a plurality of exchanges which occur during the call and distributes that ledger.

Systems and methods are configured for predictive prompt generation for an interaction between a party and an automated prompt system. In various embodiments, metadata is received on the interaction and provided as input to a multi-label predictive model to generate interaction probabilities for a plurality of prompt information data objects. Each probability generated by the predictive model provides a likelihood a particular information data object needs to be provided to the first party during the interaction. Accordingly, one or more of the prompt information data objects are identified based on the probability of each piece found in the one or more prompt information data objects that satisfy a set threshold and one or more notifications are provided so that the automated prompt system provides at least one of the prompt information data objects to the first party during the interaction.

An example operation may include one or more of receiving a data file comprising a list of interactive voice response (IVR) prompts, identifying an IVR prompt with incorrect content that will cause an error during one or more of reading and playing of the IVR prompt, modifying the incorrect content via addition or removal of an element from text content within the IVR prompt, and storing the modified IVR prompt in memory.