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.

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 conversation augmentation system can automatically augment a conversation with content items based on natural language from the conversation. The conversation augmentation system can select content items to add to the conversation based on determined user “intents” generated using machine learning models. The conversation augmentation system can generate intents for natural language from various sources, such as video chats, audio conversations, textual conversations, virtual reality environments, etc. The conversation augmentation system can identify constraints for mapping the intents to content items or context signals for selecting appropriate content items. In various implementations, the conversation augmentation system can add selected content items to a storyline the conversation describes or can augment a platform in which an unstructured conversation is occurring.

A conversation augmentation system can automatically augment a conversation with content items based on natural language from the conversation. The conversation augmentation system can select content items to add to the conversation based on determined user “intents” generated using machine learning models. The conversation augmentation system can generate intents for natural language from various sources, such as video chats, audio conversations, textual conversations, virtual reality environments, etc. The conversation augmentation system can identify constraints for mapping the intents to content items or context signals for selecting appropriate content items. In various implementations, the conversation augmentation system can add selected content items to a storyline the conversation describes or can augment a platform in which an unstructured conversation is occurring.

A voice-controlled system for allowing a guest of a hospitality establishment to play desired media content on an in-room device includes one or more servers coupled to a network and a plurality of in-room devices. A user device captures audio from a microphone and converts the audio into a text transcript utilizing a voice-to-text conversion service. The user device passes the text transcript to the servers where a particular room of the hospitality establishment with which the user device is currently associated is determined. The servers check a program guide for the particular room in order to determine a channel on which a program name determined according to the text transcript is currently playing, and determine one or more target in-room devices of the particular room. The servers send commands causing the target in-room devices to play the channel on which the program name is currently playing.

A computer based system and method for identifying complaint interactions, including: detecting appearances of linguistic structures related to complaints in an interaction; calculating at least one sentiment metric of the interaction; and classifying the interaction as being or not being a complaint interaction based on the detected linguistic structures and the at least one sentiment metric, for example using a trained supervised learning model.

A computer based system and method for identifying complaint interactions, including: detecting appearances of linguistic structures related to complaints in an interaction; calculating at least one sentiment metric of the interaction; and classifying the interaction as being or not being a complaint interaction based on the detected linguistic structures and the at least one sentiment metric, for example using a trained supervised learning model.

A half-duplex wireless network device is structured to transmit and receive information over a unique pattern of one or more transmission slots and one or more reception slots within each of a sequence of communication frames to accomplish virtual full-duplex communication.

A voice-controlled system for allowing a guest of a hospitality establishment to play desired media content on an in-room device includes one or more servers coupled to a network and a plurality of in-room devices. A user device captures audio from a microphone and converts the audio into a text transcript utilizing a voice-to-text conversion service. The user device passes the text transcript to the servers where a particular room of the hospitality establishment with which the user device is currently associated is determined. The servers check a program guide for the particular room in order to determine a channel on which a program name determined according to the text transcript is currently playing, and determine one or more target in-room devices of the particular room. The servers send commands causing the target in-room devices to play the channel on which the program name is currently playing.

For modifying a device notification, an indication module detects a user awareness indication of a device notification for a device. A notification module modifies the device notification in response to detecting the user awareness indication and concurrently maintains a notification response option.