Aspects of call routing based on technical skills of users are discussed. Responses to a set of questions posed to a user are received to assess a technical skill level of the user. The user may be categorized in a category from among a plurality of categories based on the technical skill level and a decision may be provided to a route a call from the user to one of a human agent and a virtual agent based on the categorization.

A method includes connecting a call from a client device to a destination having an interactive voice response service; transcribing audio from the destination during the call to identify menu options of the interactive voice response service; generating visualizations representing the menu options; and outputting the visualizations to a display associated with the client device. A system includes a telephony system, an automatic speech recognition processing tool, and a visualization output generation tool. The telephony system connects a call from a client device to a destination having an interactive voice response service. The automatic speech recognition processing tool transcribes audio from the destination during the call to identify menu options of the interactive voice response service. The visualization output generation tool generates visualizations representing the menu options. The telephony system outputs the visualizations to a display associated with the client device.

A system for utilizing conversational artificial intelligence (AI) to train contact center agents according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored therein that, in response to execution by the at least one processor, causes the system to place a virtual call from an automated training system to an agent device of an agent, connect the virtual call to a chatbot in response to establishing a communication connection with the agent device, transmit one or more statements from the chatbot, receive, from the agent device, one or more agent responses of the agent corresponding to the one or more statements, and analyze the one or more agent responses to determine one or more training characteristics associated with AI-based contact center training of the agent.

Disclosed embodiments may include a queue management system . The system may receive one or more utterances comprising a customer intent from a user device, determine a first queue from a plurality of queues in which to place the user based on the user intent, and receive first urgency data comprising battery indication data from the user device. The system may then determine, using a machine learning model, a first dynamic priority score for the user based on the user intent and the first urgency data including battery indication data associated with the user device. Based on the first dynamic priority score for the user, the system may assign an initial user-specific position within the first queue to the user that differs from a default initial position in the first queue. Based on updated urgency data, the system may dynamically update the user’s position to a second user-specific position.

A method and a system for interacting with one or more computer resource assets at a location. The system includes a processor, a storage, and an interface suite including a first interface configured to communicate with a user device and a second interface configured to interact with at least one computing resource asset at the location. The process is configured to receive a call, via the first interface, from the user device, the call being directed to a destination phone number, provide a conversational avatar by a machine learning platform based on the destination phone number, operate the conversational avatar by the machine learning platform to communicate with the user device and interact with a user of the user device using conversational language, generate by the machine learning platform a command to perform an operation or a function, and send, based on one or more words or sounds spoken during the interaction with the user, the command to a computing resource asset at the location to perform the operation or the function.

A computerized-method for improving queries operation in a multichannel contact center is provided herein. The computerized-method includes: (i) operating a stream processing application for each new query of a customer to store query-related data and to identify one or more queries of the customer in a cloud-contact data store. The cloud-contact data store may have one or more interactions-queue types, when one or more queries have been identified: (a) operating a repetition module on the identified one or more queries of the customer to filter out two or more queries having a common query-topic; (b) operating a Natural Language Understanding (NLU) module on the filtered two or more queries having a common topic to identify two or more identical queries. Each query of the identified two or more identical queries have a unique query identification number; and (c) handling the two or more identical.

Various embodiments relate generally to data science and data analysis, computer software and systems, and control systems to provide a platform to implement automated responses to data representing electronic messages, among other things, and, more specifically, to a computing and data platform that implements logic to facilitate implementation of an automated predictive response computing system independent of electronic communication channel or payload of an electronic message payload, the automated predictive response computing system being configured to implement, for example, an automated voice-text response engine configured to build and adaptively implement conversational data flows based on, for example, classification of an electronic message and a predictive response. In some examples, a method may include detecting an electronic message includes inbound voice data, analyzing inbound voice data, invoking an automated response application, and selecting a response, among other things.

Methods and systems described in this disclosure allow customers to personalize their phone experience when calling into an organization. In some embodiments, customers who may benefit from this service are identified based on the content of the customer's previous or current phone calls to the organization. The identified customers may be invited to enroll and to provide preferences for a customized Interactive Voice Response experience. In some embodiments, the customer can elect to hear the balances of one or more of his accounts without going through a phone menu or asking a representative to look up the relevant amounts. Once enrolled, when the customer dials into the organization and upon successful authentication, the organization proactively states the customer's account balances with no further customer request.

An example method is described. The method includes receiving, by a computing system, data indicative of a call into an interactive voice response (IVR) system from a user device and determining, by the computing system and based on the data, a set of actions performed by the user device within the IVR system and a corresponding set of results performed by the IVR system during the call. Additionally, the method includes converting, by the computing system, the set of actions and the corresponding set of results into a sequence of code pairs using a dictionary established based on training data, determining, by the computing system, an activity pattern during the call based on the sequence of code pairs; and calculating, by the computing system, a probability that the call is fraudulent based on the activity pattern during the call.

Technology is described for routing communications between contacts and agents. Associations may be generated between agents and contacts who intend to communicate with the agents. Weightings for the associations between the agents and the contacts may be determined using a machine learning model to produce weighted associations. The weightings may represent a predicted interaction metric between the agents and the contacts. Selected pairs of agents and contacts may be determined by applying matching rules to the weightings. Communications may be routed between the contacts and the agents in accordance with the selected pairs of agents and contacts.