Aspects of the disclosure include methods, apparatuses, and non-transitory computer-readable storage mediums for processing media streams. An apparatus includes processing circuitry that sends a message to a media aware network element that is configured to process a plurality of audio streams of a conference call. The message indicates that the plurality of audio streams is to be down mixed by the media aware network element. The processing circuitry receives the down mixed plurality of audio streams from the media aware network element and decodes the down mixed plurality of audio streams to receive the conference call.

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.

Provided are a call deflection and response system and method, wherein a voice call from a caller device is received, a skill group is determined to resolve an issue associated with the call, and a callback or a text response to the issue is provided to the caller device, providing a context-based personalized response. A caller leaves a detailed voicemail explaining an issue needing resolution, which is electronically transcribed and then run through a classifier to determine concepts and intents associated with the call. Based on the concepts and intents, responsibility for the call and associated files are transferred to a particular skill group on a response system for resolution. A response entity from the appropriate skill group determines and provides an issue response via callback or text message to the caller device, e.g., to the caller's mobile phone.

Dynamically routing and re-evaluating a work item based on actions taken on the work item (e.g., adding context information). The augmented routing system categories a work item into one or more dynamic work categories and identifies active knowledge workers and/or knowledge articles based on the work categories. The work item is displayed in a dynamic knowledge worker view, which allows the knowledge worker to take action on the work item. The actions a knowledge worker may take are based on permissions of the knowledge worker, one of the actions that a knowledge worker may take on a work item is to add context information to the work item. After an action is taken on a work item, the system re-evaluates the work item, which may result in the work item being added/removed from one or more work categories; and added/removed from one or more dynamic customized knowledge worker views.

In a method for testing an audio communication system of an aircraft, it is detected whether a jack plug of a headset is plugged into a jack of the audio communication system. Subject to the condition that no jack plug is detected, an electrical connection is made between an audio output of the jack and a microphone input of the jack by means of a test bridge circuit provided for at the jack. Furthermore, a test input signal is applied to an audio input connected to the audio output of the jack, and a test output signal is tapped off at a microphone output connected to the microphone input of the jack. The test output signal is used to ascertain a functional state of the audio communication system. Furthermore, an aircraft having an audio communication system is described.

In a method for testing an audio communication system of an aircraft, it is detected whether a jack plug of a headset is plugged into a jack of the audio communication system. Subject to the condition that no jack plug is detected, an electrical connection is made between an audio output of the jack and a microphone input of the jack by means of a test bridge circuit provided for at the jack. Furthermore, a test input signal is applied to an audio input connected to the audio output of the jack, and a test output signal is tapped off at a microphone output connected to the microphone input of the jack. The test output signal is used to ascertain a functional state of the audio communication system. Furthermore, an aircraft having an audio communication system is described.

A communications handler receiving incoming communications determines an appropriate contact center agent to receive the communication and modifies a graphical user interface (GUI) to notify the agent of the incoming communication. A plurality of communication session indicators provide status information for various communication sessions, and allow the agent to select one of several simultaneous communication sessions, which in turn alters the GUI to present information about that selected communication session. By selecting the corresponding communication session identifier, the agent can replace information for one communication session with another. The communication session indicators are updated to reflect the corresponding status of the communication session. The communication sessions include voice and non-voice channel types, wherein the non-voice channel types may include chat sessions, text sessions, and email sessions. Agent session data accessed by the communications handler allows selection of agents authorized and available to handle the incoming communication session.

A computer system routes contact center interactions. Interactions between contact center agents and contact center queries that are received at a contact center are monitored. A ranking model is trained according to the categories of the contact center queries and the interaction scores of each handled query using machine learning. The ranking model is tested according to various metrics to ensure that the ranking model ranks the agents according to one or more selected business outcomes. A net score may be determined for each contact center agent for each query category based on a predicted interaction score and one or more non-interaction features. Incoming queries may then be routed to an appropriate contact center agent based on the category of the incoming query. Embodiments may further include a method and program product for routing contact center interactions in substantially the same manner described above.

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 corelates 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.

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.