A call recommendation system based on artificial intelligence is provided. The call recommendation system includes a data collecting unit, a matching time predicting unit, a price determining unit, and a final ranking determining unit. When a service is requested from a service user, the data collecting unit collects first past data indicating a past location of the service user, first present data indicating a present location of the service user, second past data indicating a past location of a service provider, and second present data indicating a present location of the service provider. The matching time predicting unit inputs the first and second past data and the first and second present data to a recurrent neutral network (RNN) leaning model to predict a future location of the service user and a future location of the service provider and inputs first prediction data regarding the future location of the service user and second prediction data regarding the future location of the service provider to a prediction learning model to predict, when the service provider selects a service, a matching time required until the service provider is matched with a next service user after the service provider completes the service. The price determining unit determines a price for the service such that the price increases as the matching time increases. The final ranking determining unit determines a recommendation rating (or a recommendation priority) of a service among services required for the service provider based on preference data indicating preference of the service provider regarding a service and a price. The RNN learning model and the prediction learning model are based on a deep learning algorithm.

A method and apparatus for reducing the cost of long distance phone calls is disclosed. Accordingly, an originating phone network is operatively connected to a first intermediate point, and a receiving phone network is operatively connected to a second intermediate point. Each intermediate point is operatively connected to communicate via a data network. Users on the originating and destination phone networks may communicate based on virtual numbers. The virtual number is preferably within the local calling area of the originating phone network. The first and second intermediate points route calls to any geographic location, regardless of distance, over the data network based on the virtual number. This provides the advantage of allowing a user on the originating phone network to access a user at a distant location for a price that is substantially similar to the price of a local call.

A call recommendation system based on artificial intelligence is provided. The call recommendation system includes a data collecting unit, a matching time predicting unit, a price determining unit, and a final ranking determining unit. When a service is requested from a service user, the data collecting unit collects first past data indicating a past location of the service user, first present data indicating a present location of the service user, second past data indicating a past location of a service provider, and second present data indicating a present location of the service provider. The matching time predicting unit inputs the first and second past data and the first and second present data to a recurrent neutral network (RNN) leaning model to predict a future location of the service user and a future location of the service provider and inputs first prediction data regarding the future location of the service user and second prediction data regarding the future location of the service provider to a prediction learning model to predict, when the service provider selects a service, a matching time required until the service provider is matched with a next service user after the service provider completes the service. The price determining unit determines a price for the service such that the price increases as the matching time increases. The final ranking determining unit determines a recommendation rating (or a recommendation priority) of a service among services required for the service provider based on preference data indicating preference of the service provider regarding a service and a price. The RNN learning model and the prediction learning model are based on a deep learning algorithm.

The present invention relates to a method for routing communications in a telecommunications network comprising a first telephony user device and a server system. The method includes at the first telephony user device: receiving instructions to initiate communications with a contact associated with an address; initiating communications via a channel within the telecommunications network to a local address for the server system; and transmitting information associated with the contact address to the server system via the telecommunications network. A communications action is performed in relation to the contact at the server system. Other methods, devices, systems and networks for routing communications are disclosed.

The present invention relates to a method for routing communications in a telecommunications network comprising a first telephony user device and a server system. The method includes at the first telephony user device: receiving instructions to initiate communications with a contact associated with an address; initiating communications via a channel within the telecommunications network to a local address for the server system; and transmitting information associated with the contact address to the server system via the telecommunications network. A communications action is performed in relation to the contact at the server system. Other methods, devices, systems and networks for routing communications are disclosed.