To provide a called one with information on the caller, a reference to a resource containing real-time or near-real-time information relating to the caller, or information deduced using the real-time or near-real-time information, or the real-time or near-real-time information, or the information deduced using the real-time or near-real-time information, is transmitted with a connection establishment request, and the information in the resource or the received information is displayed to the called one before the requested connection is established so that the called one can make a decision whether or not to answer the call.

A computer implemented method, in a transaction processing server, of initiating a transaction, is disclosed. The method comprises: receiving, from a server associated with a merchant, an indication of a pending transaction between the merchant and a consumer; receiving, from a missed call processing server, an indication of a missed call made by the consumer, the indication of the missed call made by the consumer comprising an indication of a telephone number associated with the consumer; identifying the missed call made by the consumer with the pending transaction between the merchant and the consumer; looking up, in a consumer account database, an account associated with the consumer using the telephone number associated with the consumer; and generating a transaction authorization request for a transaction between the account associated with the consumer and an account associated with the merchant.

A computer implemented method, in a transaction processing server, of initiating a transaction, is disclosed. The method comprises: receiving, from a server associated with a merchant, an indication of a pending transaction between the merchant and a consumer; receiving, from a missed call processing server, an indication of a missed call made by the consumer, the indication of the missed call made by the consumer comprising an indication of a telephone number associated with the consumer; identifying the missed call made by the consumer with the pending transaction between the merchant and the consumer; looking up, in a consumer account database, an account associated with the consumer using the telephone number associated with the consumer; and generating a transaction authorization request for a transaction between the account associated with the consumer and an account associated with the merchant.

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.

Unsolicited electronic communications such as robocalls and person-initiated solicitation calls are reduced by imposing tolls for completion of the connections to the called parties, and refunding the tolls to the entities indicated by the electronic communications as the calling parties. In this way, a dishonest originator of a spoofed call bears the cost of the toll, and the toll is not refunded to the dishonest originator. On the other hand, the toll collected from an honest originator of a non-spoofed call is refunded to the honest originator, making the toll transparent to the honest originator and avoiding annoyance of the honest caller caused by the toll. Unsolicited calls may be subjected to filtering, particularly filtering based on the indications of the origins of the calls.

One example method of operation may include identifying a call from a calling device destined for a mobile device, responsive to identifying a calling device number assigned to the calling device, accessing a call content application programming interface (API), operated by a content delivery device, configured to deliver to the mobile device a first caller identification name (CNAM) or a second CNAM, assigned to the calling device number, determining a context assigned to a mobile device number assigned to the mobile device, selecting one of the first CNAM and the second CNAM based on the context, and assigning the selected CNAM to the call.

One example method of operation may include identifying a call from a calling device destined for a mobile device, responsive to identifying a calling device number assigned to the calling device, accessing a call content application programming interface (API), operated by a content delivery device, configured to deliver to the mobile device a first caller identification name (CNAM) or a second CNAM, assigned to the calling device number, determining a context assigned to a mobile device number assigned to the mobile device, selecting one of the first CNAM and the second CNAM based on the context, and assigning the selected CNAM to the call.

Aspects of the subject disclosure may include, for example, identifying a first user associated with a first mobile device, and determining a first user type associated with the first user. Further embodiments can include identifying a first identity associated with the first user based on the first user type and identifying a second identity associated with the first user based on the first user type. Additional embodiments can include providing first alternate content to the first mobile device to mask the first identity and providing second alternate content to the first mobile device associated with the second identity. Other embodiments are disclosed.

An IP telephony system allows a user to register a telephony device that receives its native telephony service from a different telephony service provider as an extension telephone. The user can then place calls through the IP telephony system using the extension telephone. Such calls may or may not be established using the extension telephone's native telephony service provider.

Systems and methods for using secondary communication channel information, such as RTCP information to facilitate correlating business information to allow gateway recording. Using information in this secondary communication channel the recording system can correlate the business metadata either directly (e.g., using a phone extension) or through one or more indirect operations (e.g., a call ID-to-universal call id-to-phone extension). Additionally, information in the primary communication channel may be used to perform an indirect operation to correlate auxiliary business information (e.g., using the CALLID or UCID in SIP signaling to resolve the agent's phone extension and thereby access auxiliary business information.)