A system and method are disclosed for generating a teleconference space for two or more communication devices using a computer coupled with a database and comprising a processor and memory. The computer generates a teleconference space and transmits requests to join the teleconference space to the two or more communication devices. The computer stores in memory identification information, and audiovisual data associated with one or more users, for each of the two or more communication devices. The computer stores audio transcription data, transmitted to the computer by each of the two or more communication devices and associated with one or more communication device users, in the computer memory. The computer merges the audio transcription data from each of the two or more communication devices into a master audio transcript, and transmits the master audio transcript to each of the two or more communication devices.

Methods to reduce agent effort and improve customer experience quality through artificial intelligence. The Agent Assist tool provides contact centers with an innovative tool designed to reduce agent effort, improve quality and reduce costs by minimizing search and data entry tasks The Agent Assist tool is natively built and fully unified within the agent interface while keeping all data internally protected from third-party sharing.

A transcript of a conference (e.g., a video conference, an audio conference, or a telephone call with two or more participants) is processed to extract a conference summary. Scores are determined for strings of the transcript that are used to select strings for inclusion in the conference summary. Determining the scores includes determining respective sentence vectors for strings. A sentence vector has elements corresponding to words in the transcript that are proportional to occurrences of the word in the string and inversely proportional to occurrences of the word in the transcript. A short video conference summary or a short audio conference summary is then generated using timestamps from the transcript associated with strings (e.g., sentences) that have been selected for inclusion in the conference summary. The short video or audio summary may be presented to users to enable efficient storage and transmission of conference information within a unified communications system.

Agent conversation item selection is automated by a server that automatically detects speech in a call and converts that speech to text. Software running on the server retrieves one or more items from a data store based on a determination that the text includes one or more keywords or a change in the subject of the call. The keywords can include phrases. The retrieved items include one or more of scripts, articles, manuals, daily bulletins regarding a system state, or any resource that can be used to assist with a customer call or interaction. The software running on the server generates a user interface (UI) output based on the retrieve items, and transmits the UI output to an agent device. Software running on the agent device receives the UI output and displays the retrieved items on a display of the agent device.

A method for identifying entities may include, during a voice communication with a caller via a caller device, sending to the caller device a first voice prompt that asks the caller to identify a particular entity, receiving from the caller device caller input data indicative of a voice response of the caller, and analyzing the caller input data to determine a set of words spoken by the caller. The method may also include, for each segment of two or more segments of the set of words, determining a level of string matching between the segment and a corresponding segment in a record stored in a database, determining, and based upon the levels of string matching, a level of match certainty for the particular entity from among at least three possible levels of match certainty, and/or selecting, based upon the level of match certainty, a pathway of the algorithmic dialog.

Techniques are described relating to automatically classifying telephone calls into a particular category using machine learning and artificial intelligence technology. As one example, calls to a customer service phone number can be classified as related to prohibited activity, or as legitimate. In particular, a number of different telephony variables as well as additional variables can be used to make such a classification, after training an appropriate machine learning model. The training process may use an externally provided call classification score that is provide by an outside entity as an input, and can be calibrated so that the output score of the trained classifier provides a score that corresponds to a real-world percentage chance of an unclassified call falling into a particular category. Thus, a classifier score of “95” can indicate that a call is in fact believed to be 95% likely to correspond to prohibited activity, for example.

A system and method for intent-based active callback management using enhanced callback objects, utilizing a cloud callback system comprising at least a profile manager, callback manager, interaction manager, media server, and environment analyzer, allowing users to call businesses, agents in contact centers, or other users who are connected to a cloud callback system, and, failing to connect to the individual they called, allow for an automatic callback object to be created, whereby the two users may be automatically called and bridged together at a time when both users are available.

A processor may receive a call transcript including text and form a text string including at least a portion of the text. The processor may generate a situation description of the call transcript, which may comprise processing the text string using a transformer-based machine learning model. The processor may generate a trouble description of the call transcript, which may comprise creating a sentence embedding of the situation description, creating sentence embeddings for a plurality of utterances within the portion of the text, determining respective similarities between the sentence embedding of the situation description and each of the sentence embeddings for each respective one of the plurality of utterances, and selecting at least one of the plurality of utterances having at least one highest determined respective similarity as the trouble description. The processor may store a call summary comprising the situation description and the trouble description in a non-transitory memory.

Disclosed herein are systems and methods for handling unwanted telephone calls through a branching node. In one aspect, an exemplary method comprises, intercepting a call request from a terminal device of a calling party to a terminal device of a called party, establishing a connection through the branching node via two different communication channels, a first communication channel being with the terminal device of the called party and a second communication channel being with a call recorder; duplicating media data between the terminal devices such that one data stream is directed towards a receiving device of the media data and a second data stream is directed towards the call recorder; recording and sending the recorded call to an automatic speech recognizer for converting the media file to digital information suitable for analysis; and when the call is unwanted, handling the call based on classification of the call.

Machine learning technology can analyze in real-time the data from a call between a person and a customer service representative. Based on this analysis, a server can determine a sentiment score that describes a sentiment expressed by the person or the customer service representative. If the server determines that the sentiment score is less than or equal to a pre-determined value, the server can inform the customer service representative's manager so that the manager can take further action to help the person and/or the customer service representative.