A method for creating a textual summary of a call includes transcribing speech to text in real time using a speech-to-text generating unit configured for execution upon one or more data processors, automatically matching, in real-time, text to predetermined intents and extracted entities using an intent recognizing unit for execution upon the one or more data processors, automatically mapping the predetermined intents and extracted entities into a call summary using one or more mapping functions, and displaying the call summary using an agent user interface for execution upon the one or more data processors. A contact center call summarization system may include a contact center communication device, a speech-to-text generating unit, an intent recognizing unit, and an agent user interface.

Artificial agents utilized for voice interactions continue to improve in their capacity to conduct more sophisticated interactions. Rather than just presenting a limited set of options, artificial agents are continuing to narrow the gap between generated speech and natural human speech. A requirement is often in place that spoken interactions be recorded, however, storing speech, even with data compression, is a resource-demanding task. Generated speech may be provided from content, such as text, and speech data. By recording an identifier of the content and associated speech data, storage processing and space requirements can be greatly reduced. Playback may be provided from a waveform of audio provided by the human participant and by selecting the content associated with the content identifier and generating speech of the content utilizing settings provided by the speech data.

A mobile application for a wireless handheld computing device, such as a smartphone, is disclosed in combination with a communication link establishment and management system. Systems and methods are disclosed for calling desired contacts using a smartphone that can take advantage of the power and efficiency of agent-assisted dialing provided by the communication link establishment and management system. The systems and methods automatically integrate with a customer relationship management (CRM) system connected to the communication link establishment and management system.

A mechanism is provided for implementing an emergency response cognitive computing system. The emergency response cognitive computing system identifies a first party about which a communication is being performed between a caller and the emergency response system. The emergency response cognitive computing system generates, in real-time, a transcript of the communication as the communication is being conducted. The emergency response cognitive computing system performs natural language processing on the transcript of the communication to identify portions of content corresponding to medical concepts. The emergency response cognitive computing system processes patient information to identify elements of the patient information referencing concepts corresponding to medical concepts. The emergency response cognitive computing system outputs the elements of the patient information to a second party involved in the communication.

A device may receive audio data of a first call between a first user and a second user. The device may generate, based on the audio data, time series data associated with an audio signal of the first call and may process, using a first machine learning model, the time series data to generate first call insight information regarding one or more first insights associated with the first call. The device may process the audio data to generate image data associated with the audio signal and may process, using a second machine learning model, the image data to generate second call insight information regarding one or more second insights associated with the first call. The device may combine the first call insight information and the second call insight information to generate combined call insight information and cause an action to be performed based on the combined call insight information.

Methods, systems, and computer programs are presented for searching and labeling the content of voice conversations. An Engagement Intelligence Platform (EIP) analyzes conversation transcripts to find states and information for each of the states (e.g., interest rate quoted and value of the interest rate). An annotator User Interface (IU) is provided for performing queries, such as, “Find calls were the agent asked the customer for their name and the customer did not answer;” “Find calls where the customer objected after the interest rate for the loan was quoted, “Find calls where the agent asked for consent for recording the call, but no customer confirmation was received.” The EIP analyzes the conversation and labels (e.g., “tags”) the text where the conversation associated with the label took place, such as, “An interest rate was provided.” The labels are customizable, so each client can define its own labels based on business needs.

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.

Electronic conferences are a common method of conducting a meeting when the participants are not at the same location. When a conference drops a portion of the audio, such carrying speech from a speaker, participants may be excluded from the full content of the conference. By having a device associated with a speaking participant convey both audio and generated text from the speech provided by the speaking participant, a server may be able to determine that speech is missing from the audio portion and automatically insert text content. As a result, poor audio quality may be mitigated with text and omitting unwanted text when the audio quality is sufficient.

A device may receive audio data of a first call between a first user and a second user. The device may generate, based on the audio data, time series data associated with an audio signal of the first call and may process, using a first machine learning model, the time series data to generate first call insight information regarding one or more first insights associated with the first call. The device may process the audio data to generate image data associated with the audio signal and may process, using a second machine learning model, the image data to generate second call insight information regarding one or more second insights associated with the first call. The device may combine the first call insight information and the second call insight information to generate combined call insight information and cause an action to be performed based on the combined call insight information.

A system, method, and computer-readable medium are disclosed for intelligent User workload orchestration for virtual meetings. A virtual meeting for multiple users is initiated. Voice data of multiple users is received and converted to text data. Machine learning processes the text data to action items to be performed by specific users (i.e., meeting attendees) in real-time. Minutes of the meeting and action items (in the form of real-time and passive recommendations) are generated and provided. In order to perform the action items, determination is made as to which third party applications support the action items and intelligent user workload orchestration is employed.