The present disclosure provides a call processing method, apparatus, electronic device and storage medium and relates to the field of cloud computing. The method may comprise: obtaining a calling subscriber's status information in real time while an intelligent dialogue robot is used to make a call with the calling subscriber; when it is determined that a call form of the intelligent dialogue robot needs to be adjusted, correspondingly adjusting the call form of the intelligent dialogue robot according to current status information of the calling subscriber. The solution of the present disclosure may be employed to improve the call performance of the intelligent dialogue robot.

The invention concerns a method for establishing a telecommunication connection between a calling subscriber (1) and a target subscriber (5), in which a calling terminal (R) of the calling subscriber (1) and a target terminal (Z) of the target subscriber (5) are assigned to a switching unit (100) and the calling subscriber (1) enters a callback request (CCBS-REQ, CCNR-REQ) in the switching unit (100) if the target terminal (Z) is busy or cannot be reached when a first call is made from the calling terminal (R) to the target terminal (Z), in which configuration data regarding the accessibility of the target subscriber (5) for a callback are entered into the switching unit (100). Then the time is set for the switching unit (100) to make the pending callback (RC) to the calling subscriber (1) in order to generate a second call to the target subscriber (5) by processing the configuration data concerning the accessibility of the target subscriber (5) for a callback, at a time when the target subscriber (5) is reachable for a callback. Next, the switching unit (100) makes the pending callback (RC) to the calling subscriber (1) at the set time.

The invention concerns a method for establishing a telecommunication connection between a calling subscriber (1) and a target subscriber (5), in which a calling terminal (R) of the calling subscriber (1) and a target terminal (Z) of the target subscriber (5) are assigned to a switching unit (100) and the calling subscriber (1) enters a callback request (CCBS-REQ, CCNR-REQ) in the switching unit (100) if the target terminal (Z) is busy or cannot be reached when a first call is made from the calling terminal (R) to the target terminal (Z), in which configuration data regarding the accessibility of the target subscriber (5) for a callback are entered into the switching unit (100). Then the time is set for the switching unit (100) to make the pending callback (RC) to the calling subscriber (1) in order to generate a second call to the target subscriber (5) by processing the configuration data concerning the accessibility of the target subscriber (5) for a callback, at a time when the target subscriber (5) is reachable for a callback. Next, the switching unit (100) makes the pending callback (RC) to the calling subscriber (1) at the set time.

Disclosed in some examples, are methods, systems, and machine-readable mediums which build and utilize an artificial intelligence model to predict debtor payment timing. The past debtor payment history and other debtor information may be for a plurality of accounts over a past time period. Once the model is created, it may be used when a debtor misses a payment to determine a prediction of when the debtor will pay. The model uses characteristics of past debtors and their payment dates to predict, based upon the characteristics of the late debtor, when the late debtor will make a payment. The predicted timing may include a predicted probability for whether the payment will be made within the predicted timing. The predicted timing may be a specific date, or a window (e.g., a three-day window).

Disclosed in some examples, are methods, systems, and machine-readable mediums which build and utilize an artificial intelligence model to predict debtor payment timing. The past debtor payment history and other debtor information may be for a plurality of accounts over a past time period. Once the model is created, it may be used when a debtor misses a payment to determine a prediction of when the debtor will pay. The model uses characteristics of past debtors and their payment dates to predict, based upon the characteristics of the late debtor, when the late debtor will make a payment. The predicted timing may include a predicted probability for whether the payment will be made within the predicted timing. The predicted timing may be a specific date, or a window (e.g., a three-day window).

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.

According to one embodiment, facilitating communications through a communication device can comprise monitoring, by the communication device, one or more environmental sensors of the communication device including at least one sensor collecting biometric information. A presence of one or more people can be detected by the communication device based on the monitoring or the one or more environmental sensors and the detected one or more people can be identified by the communication device based at least in part on input from the one or more environmental sensors. One or more possible actions related to communications through the communication device can be determined by the communication device based on the identified one or more people. A user interface including a prompt for at least one of the determined one or more possible actions related to communications through the communication device can then be provided.

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.

Implementations are directed to using an assistant to initiate automated telephone calls with entities. Some implementations identify an item of interest, identify a group of entities associated with the item, and initiate the calls with the entities. During a given call with a given entity, the assistant can request a status update regarding the item, and determine a temporal delay before initiating another call with the given entity to request a further status update regarding the item based on information received responsive to the request. Other implementations receive a request to perform an action on behalf of a user, identify a group of entities that can perform the action, and initiate a given call with a given entity. During the given call, the assistant can initiate an additional call with an additional entity, and generate notification(s), for the user, based on result(s) of the given call and/or the additional call.

A computer-implemented method includes identifying a mobile phone. The method includes identifying one or more input devices. The one or more input devices are associated with the mobile phone. The method includes collecting behavior information from the input devices. The method includes applying machine learning to the behavior information to yield a schedule.