A three-way calling terminal for a mobile human-machine coordination calling robot. Technical solutions include: a first speech interface, configured to transfer call audio between a call object and a back-end processing module; a CODEC1 module, configured to encode and decode the call audio between the call object and the back-end processing module; a second speech interface, configured to transfer call audio between the human agent and the call object; a CODEC2 module, configured to encode and decode the call audio between the human agent and the call object; a call control module, configured to process a control signal, and automatically make, answer, and hang up a call; a data processing submodule, configured to process speech data and perform data transfer between the data processing submodule and the back-end processing module; and a networking submodule, configured to be connected to the back-end processing module.

A three-way calling terminal for a mobile human-machine coordination calling robot. Technical solutions include: a first speech interface, configured to transfer call audio between a call object and a back-end processing module; a CODEC1 module, configured to encode and decode the call audio between the call object and the back-end processing module; a second speech interface, configured to transfer call audio between the human agent and the call object; a CODEC2 module, configured to encode and decode the call audio between the human agent and the call object; a call control module, configured to process a control signal, and automatically make, answer, and hang up a call; a data processing submodule, configured to process speech data and perform data transfer between the data processing submodule and the back-end processing module; and a networking submodule, configured to be connected to the back-end processing module.

Systems and methods for artificial intelligence communications agents are disclosed. Implementations relate to capturing individual agent's behaviors and modelling them in artificial intelligence (AI) learning models so that the agent's behavior can be easily replicated. Some implementations further relate to systems and methods for capturing human-computer interactions (HCl) performed by agents and using robotic process automation (RPA) to automate tasks that would otherwise require human interaction. The combination of AI learning models and RPA are used to provide artificial intelligence communications agents capable of responding to a variety of topics of conversation over a variety of communication mediums.

A text representation is received from a virtual assistant application on a user device. The text representation may be generated via a speech-to-text engine of the virtual assistant application from audio speech spoken by a customer. Device diagnostic data of the user device is also received from the virtual assistant application. An identifier of the customer is placed in an assistance queue. At least information in the text representation and the device diagnostic data is analyzed to determine an issue associated with the user device and a solution for resolving the issue, so that the solution is applied. In response to the identifier of the customer reaching a front of the assistance queue, session state information that includes at least the text representation and a description of the issue is provided to a support application. A voice support session is initiated between the support application and the virtual assistant application.

The present disclosure relates generally to systems, methods, and computer-readable storage media for providing a concierge service to handle a wide variety of topics and user intents via a common interface. The concierge service can be part of a connection management system that can dynamically manage and facilitate conversations between a user making a request or providing an instruction and one or more endpoints for the purposes of fulfilling the request or instruction. Such dynamic management may include transferring a communication session to a social network member endpoint based on an intent identified within natural language communications, tracking a dynamic sentiment score, and automatically switching the communication session to another endpoint based on a change in the dynamic sentiment score.

Techniques and architectures for implementing a team of virtual assistants are described herein. The team may include multiple virtual assistants that are configured with different characteristics, such as different functionality, base language models, levels of training, visual appearances, personalities, and so on. The characteristics of the virtual assistants may be configured by trainers, end-users, and/or a virtual assistant service. The virtual assistants may be presented to end-users in conversation user interfaces to perform different tasks for the users in a conversational manner. The different virtual assistants may adapt to different contexts. The virtual assistants may additionally, or alternatively, interact with each other to carry out tasks for the users, which may be illustrated in conversation user interfaces.

The invention provides a method, system, and a software program product for assisting a user and/or managing tasks of the user, by a mobile secretary cloud application configured to operate in a mobile client device and cloud server network. The mobile secretary cloud application reads data from another software application and operates at least one of another application and a third application based on the read data. Further, Artificial intelligence is utilized by the mobile secretary cloud application for operating another application and the third application.

The invention provides a method, system, and a software program product for assisting a user and/or managing tasks of the user, by a mobile secretary cloud application configured to operate in a mobile client device and cloud server network. The mobile secretary cloud application reads data from another software application and operates at least one of another application and a third application based on the read data. Further, Artificial intelligence is utilized by the mobile secretary cloud application for operating another application and the third application.

The embodiments of the present disclosure provide methods for managing data of a call center of smart gas, executed by a processor in a smart gas management platform of an Internet of Things system for managing data of a call center of smart gas. The method may include: constructing call consultation data of a user to be troubleshooted based on consultation information of the user to be troubleshooted collected by a customer service from a user terminal through a network; generating a location result of a gas fault based on the call consultation data; and generating a fault handling plan based on the location result of the gas fault and sending the fault handling plan to the user terminal.

The embodiments of the present disclosure provide methods for managing data of a call center of smart gas, executed by a processor in a smart gas management platform of an Internet of Things system for managing data of a call center of smart gas. The method may include: constructing call consultation data of a user to be troubleshooted based on consultation information of the user to be troubleshooted collected by a customer service from a user terminal through a network; generating a location result of a gas fault based on the call consultation data; and generating a fault handling plan based on the location result of the gas fault and sending the fault handling plan to the user terminal.