Disclosed implementations use automated transcription and intent detection and an AI model to evaluate interactions between an agent and a customer within a call center environment. The evaluation flow used for manual evaluations is leveraged so that the evaluators can correct the AI evaluations when appropriate. Based on such corrections, the AI model can be retrained to accommodate specifics of the business and center—resulting in more confidence in the AI model over time.

An electronic apparatus is provided. The electronic apparatus includes a voice receiver, a communication interface, and a processor configured to, based on a user voice being obtained through the voice receiver, identify a mobile apparatus having a user account corresponding to the user voice from among at least one mobile apparatus communicably connected to the electronic apparatus through the communication interface, and transmit a control signal corresponding to the user voice to the identified mobile apparatus through the communication interface.

Systems and methods for providing enhanced teleconferencing. An example method includes receiving audio streams from a plurality of client devices of participants of a teleconference; converting the audio streams for a first conversation within the teleconference into first text; converting the audio streams for a second conversation within the teleconference into a second text; analyzing the first text to identify one or more topics being discussed in the first conversation; analyzing the second text to identify one or more topics being discussed in the second conversation; and presenting, in a teleconference user interface, at least one of the one or more topics being discussed in the first conversation or the one or more topics being discussed in the second conversation.

The present invention is a system and method of continuous sentiment tracking and the determination of optimized agent actions through the training of sentiment models and applying the sentiment models to new incoming interactions. The system receives conversations comprising incoming interactions and agent actions and determines customer sentiment on a micro-interaction level for each incoming interaction. Based on interaction types, the system correlates the determined sentiment with the agent action received prior to the sentiment determination to create and train sentiment models. Sentiment models include agent action recommendations for a desired sentiment outcome. Once trained, the sentiment models can be applied to new incoming interactions to provide CSRs with actions that will yield a desired sentiment outcome.

Embodiments disclosed are directed to a computing system that performs steps to automatically generate fine-grained call reasons from customer service call transcripts. The computing system extracts, using a natural language processing (NLP) technique, a set of events from a set of text strings of speaker turns. The computing system then identifies a set of clusters of events based on the set of events and labels each cluster of events in the set of clusters of events to generate a set of labeled clusters of events. Subsequently, the computing system assigns each event in the set of events to a respective labeled cluster of events in the set of labeled clusters of events.

A method, computer system, and a computer program product for interpreting conference call interruptions is provided. The present invention may include analyzing a conference call including at least two participants. The present invention may also include identifying at least one interrupted segment in the analyzed conference call. The present invention may further include reconstructing the identified at least one interrupted segment of the analyzed conference call and integrating it with the uninterrupted (heard) portion of the conference call.

A system and method for integrating audio data collected, such as audio data and analytical data, to perform behavioral analysis on the audio data, using an application of acoustic signal processing and machine learning algorithms, by converting the audio data to text data and performing behavioral analysis on the text data. The behavioral analysis data from the audio application of acoustic signal processing is combined with machine learning algorithms and speech to text data to provide a call agent with feedback to assist in the next best action or insight into customer behaviors.

The communication system includes a communication unit that receives a conversation of a user, an accumulation unit that accumulates a conversation frame that describes a structure of a conversation generated on a basis of the conversation of the user collected via the communication unit, and a control unit that obtains a feeling parameter related to a feeling of the user who sends the conversation in units of the collected conversation. The control unit further extracts the conversation frame from the conversation on a basis of the feeling parameter, and accumulates the conversation frame in the accumulation unit.

A device that is configured to assign users to an issue cluster based on issue types for the users. The device is further configured to identify available agents and to assign each available agent to one or more knowledge area clusters based on knowledge scores. A knowledge score indicates an expertise level for an agent in a knowledge area. The device is further configured to identify an issue cluster that is associated with an issue type and to identify a user from the issue cluster. The device is further configured to identify a knowledge area cluster that is associated with the issue type and to identify an agent from the knowledge area cluster. The device is further configured to establish a network connection between a user device associated with the user and a user device associated with the agent.

An electronic device can implement a zero-latency digital assistant by capturing audio input from a microphone and using a first processor to write audio data representing the captured audio input to a memory buffer. In response to detecting a user input while capturing the audio input, the device can determine whether the user input meets a predetermined criteria. If the user input meets the criteria, the device can use a second processor to identify and execute a task based on at least a portion of the contents of the memory buffer.