A topic guidance method, system, and computer program product for suggesting, via a processor on a computer, a conversation topic for the agent to engage the customer based on a learned conversation topic model, the conversation model being a static model.

An application determines an emotional state of a user based on evaluating facial recognition data of the user captured from the user interacting with the application. The application receives a request from the user to initiate a support call. The request identifies the emotional state of the user. The application predicts, from a set of outcomes of support calls processed by support agents interacting with users having different emotional states, an emotional state that increases a likelihood of achieving a specified outcome for the support call based on the emotional state of the user. The application identifies an available support agent having the predicted emotional state, and assigns the user to interact with the identified support agent for the support call.

One or more embodiments related to a method that includes querying a data store for current interaction data between call center personnel and customers. The call center personnel are grouped into call center groups. The method further includes determining, for at least some call center groups, a current interaction metric specific to the call center group. The current interaction method is provided for each of the at least some call center groups.

A method for matching a first entity with at least one second entity selected from a plurality of second entities, comprising defining a plurality of multivalued scalar data representing inferential targeting parameters for the first entity and a plurality of multivalued scalar data of each of the plurality of second entities, representing respective characteristic parameters for each respective second entity; and performing an automated optimization with respect to an economic surplus of a respective match of the first entity with the at least one of the plurality of second entities, and an opportunity cost of the unavailability of the at least one of the plurality of second entities for matching with an alternate first entity.

Techniques are described for generating metric(s) that predict survey score(s) for a service session. Model(s) may be trained, through supervised or unsupervised machine learning, using training data such as communications from previous service sessions between service representative(s) and individual(s), and survey scores provided by the serviced individual to rate the session on one or more criteria (e.g., survey questions). The model(s) may be trained to output, based on an input session record, metric(s) that each correspond to a survey score that would have been provided by the individual had they completed the survey. The model may be a concatenated model that combines a language model output from a language classifier recurrent neural network, and an acoustic model output from an acoustic feature layer convolutional neural network. Individuals can be clustered according to the metric(s) and/or other factors, and the cluster(s) can be employed for routing incoming service requests.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a coordinating callers with customer service representatives is described. One of the methods includes identifying a number of callers. The method also includes dynamically adjusting a number of customer service representatives based on the number of callers.

Methods and systems are provided for the management, routing and reporting of toll-free telecommunications calls and data. Methods and systems are provided for pre-populating a call routing template based on natural language inputs and populating telecommunications routing codes at nodes of a call routing decision tree to generate a call routing template that may be identified and presented to a user interface based at least in part on a natural language input.

A computer-implemented method for providing an objective evaluation to a customer service representative regarding his performance during an interaction with a customer may include receiving a digitized data stream corresponding to a spoken conversation between a customer and a representative; converting the data stream to a text stream; generating a representative transcript that includes the words from the text stream that are spoken by the representative; comparing the representative transcript with a plurality of positive words and a plurality of negative words; and generating a score that varies according to the occurrence of each word spoken by the representative that matches one of the positive words, and/or the occurrence of each word spoken by the representative that matches one of the negative words. Tone of voice, as well as response time, during the interaction may also be monitored and analyzed to adjust the score, or generate a separate score.

Disclosed are systems, methods, and non-transitory computer-readable storage media for utilizing a virtual assistant to assist a user with a communication session between the user and a third party service. A user can use a communication application to enter a message directed to the virtual assistant and request assistance to communicate with a 3.sup.rd party service. In response, the virtual assistant can access a set of communication instructions associated with the 3.sup.rd party service. The set of communication instructions can include a set of commands for communicating with the 3.sup.rd party service, services provided by the 3.sup.rd party service and data needed by the 3.sup.rd party service to facilitate communication. The virtual assistant can use the communication instructions to gather data needed by the 3.sup.rd party service, communicate with the 3.sup.rd party service and present the user with data received from the 3.sup.rd party service.

A call assistant device is used to command a call management system to perform a specified task in association with a specified call. The call assistant device can be an Internet of Things (IoT) based device, which can include one or more buttons and connect to a communication network wirelessly. When a user activates the call assistant device, e.g., presses a button, the call assistant device sends a message to the call management system to perform a specified task. Upon receiving the message, the call management system executes the specified task in association with a specified call of the user. The task to be performed can be any task that can be performed in association with a call, e.g., generating a summary of the call, bookmarking a specified moment in the call, sending a panic alert to a particular user, or generating an action item.