Abstract: A system for determining context and intent in a conversation using machine learning (ML) based artificial intelligence (AI) in omni channel data communications is disclosed. The system may comprise a data store to store and manage data within a network, a server to facilitate operations using information from the one or more data stores, and a ML-based AI subsystem to communicate with the server and the data store in the network. The ML-based AI subsystem may comprise a data access interface to receive data associated with a conversation with a user via a communication channel. The ML-based AI subsystem may comprise a processor to provide a proactive, adaptive, and intelligent conversation by applying hierarchical multi-intent data labeling framework, training at least one model with training data, and generating and deploying a production-ready model based on the trained and retained at least one model.

Abstract: A system for a digital content security and communications system using an artificial intelligence (AI) based machine learning and predictive analysis is disclosed. The system may comprise a data store to store and manage data within a network, a server to facilitate operations using information from the one or more data stores, and an analytics subsystem to communicate with the server and the data store in the network. The analytics subsystem may comprise a data access interface to receive communication data from a user via a first communication channel. The analytics subsystem may comprise a processor to provide a proactive, adaptive, and intelligent conversation with the user via a second communication channel.

Abstract: A system for determining context and intent in a conversation using machine learning (ML) based artificial intelligence (AI) in omni channel data communications is disclosed. The system may comprise a data store to store and manage data within a network, a server to facilitate operations using information from the one or more data stores, and a ML-based AI subsystem to communicate with the server and the data store in the network. The ML-based AI subsystem may comprise a data access interface to receive data associated with a conversation with a user via a communication channel. The ML-based AI subsystem may comprise a processor to provide a proactive, adaptive, and intelligent conversation by applying hierarchical multi-intent data labeling framework, training at least one model with training data, and generating and deploying a production-ready model based on the trained and retained at least one model.

Abstract: A system for determining context and intent in a conversation using machine learning (ML) based artificial intelligence (AI) in omnichannel data communications is disclosed. The system may comprise a data store to store and manage data within a network, a server to facilitate operations using information from the one or more data stores, and a ML-based AI subsystem to communicate with the server and the data store in the network. The ML-based AI subsystem may comprise a data access interface to receive data associated with a conversation with a user via a communication channel. The ML-based AI subsystem may comprise a processor to provide a proactive, adaptive, and intelligent conversation by applying hierarchical multi-intent data labeling framework, training at least one model with training data, and generating and deploying a production-ready model based on the trained and retained at least one model.

Abstract: A system for artificial intelligence (AI) based omnichannel data communications is disclosed. The system may comprise a data store to store and manage data within a network, a server to facilitate operations using information from the one or more data stores, and an analytics subsystem to communicate with the server and the data store in the network. The analytics subsystem may comprise a data access interface to receive communication data from a user via a first communication channel. The analytics subsystem may comprise a processor to provide a proactive, adaptive, and intelligent conversation with the user via a second communication channel. The system may also comprise an output interface to transmit the at least one appropriate response to the user at a user device using any number of AI-based and data processing techniques to better identify customer touchpoints and improve customer journey and experience during customer engagement scenarios.

Abstract: A system for artificial intelligence (AI) based omnichannel data communications is disclosed. The system may comprise a data store to store and manage data within a network, a server to facilitate operations using information from the one or more data stores, and an analytics subsystem to communicate with the server and the data store in the network. The analytics subsystem may comprise a data access interface to receive communication data from a user via a first communication channel. The analytics subsystem may comprise a processor to provide a proactive, adaptive, and intelligent to conversation with the user via a second communication channel. The system may also comprise an output interface to transmit the at least one appropriate response to the user at a user device using any number of AI-based and data processing techniques to better identify customer touchpoints and improve customer journey and experience during customer engagement scenarios.

Abstract: A system for a digital content security and communications system using n artificial intelligence (AI) based machine learning and predictive analysis is disclosed. The system may comprise a data store to store and manage data within a network, a server to facilitate operations using information from the one or more data stores, and an analytics subsystem to communicate with the server and the data store in the network. The analytics subsystem may comprise a data access interface to receive communication data from a user via a first communication channel. The analytics subsystem may comprise a processor to provide a proactive, adaptive, and intelligent conversation with the user via a second communication channel.

Abstract: A system for determining context and intent in a conversation using machine learning (ML) based artificial intelligence (AI) in omnichannel data communications is disclosed. The system may comprise a data store to store and manage data within a network, a server to facilitate operations using information from the one or more data stores, and a ML-based AI subsystem to communicate with the server and the data store in the network. The ML-based AI subsystem may comprise a data access interface to receive data associated with a conversation with a user via a communication channel. The ML-based AI subsystem may comprise a processor to provide a proactive, adaptive, and intelligent conversation by applying hierarchical multi-intent data labeling framework, training at least one model with training data, and generating and deploying a production-ready model based on the trained and retained at least one model.

Abstract: Disclosed are systems and methods for live broadcasting of one or more media streams, such as audio streams, via the internet such that it is accessible substantially by a number of listeners at a number of locations. Said system is operable such that the contents of each of said one or more media streams can originate from a call or other communication from a conventional telephony device which does not require internet connection.