A server can receive a plurality of records at a databases such that each record is associated with a phone call and includes at least one request generated based on a transcript of the phone call. The server can generate a training dataset based on the plurality of records. The server can further train a binary classification model using the training dataset. Next, the server can receive a live transcript of a phone call in progress. The server can generate at least one live request based on the live transcript using a natural language processing module of the server. The server can provide the at least one live request to the binary classification model as input to generate a prediction. Lastly, the server can transmit the prediction to an entity receiving the phone call in progress. The prediction can cause a transfer of the call to a chatbot.

A system and method for providing telephone event data are described. The system comprises a communications module; a processor coupled to the communications module; and a memory coupled to the processor, the memory storing processor-executable instructions which, when executed by the processor, configure the processor to receive, via the communications module and from a telco server, telephone event data; translate at least a portion of the telephone event data into a format compliant with a particular application executing on a computing device; and send, via the communications module and to the computing device executing the particular application, the translated portion of the telephone event data.

A method of testing an emergency network, includes: simulating, by test logic, a public emergency network entity and a plurality of interfaces between the public emergency network entity and a test device; monitoring, by the test logic, the plurality of interfaces to determine that data flowed correctly on each interface in response to an emergency alert sent from the test device such that the emergency alert is not actually sent to the public emergency network entity; and determining, by the test logic, a test status for each interface indicating a success or failure of a test, for each interface, the test status based on data correctness such that the test confirms that the emergency alert and associated emergency data would be sent to the public emergency network entity correctly.

A server can receive a plurality of records at a databases such that each record is associated with a phone call and includes at least one request generated based on a transcript of the phone call. The server can generate a training dataset based on the plurality of records. The server can further train a binary classification model using the training dataset. Next, the server can receive a live transcript of a phone call in progress. The server can generate at least one live request based on the live transcript using a natural language processing module of the server. The server can provide the at least one live request to the binary classification model as input to generate a prediction. Lastly, the server can transmit the prediction to an entity receiving the phone call in progress. The prediction can cause a transfer of the call to a chatbot.

According to one embodiment, a method, computer system, and computer program product for detecting and repairing gaps within a call is provided. The present invention may include requesting a personal discourse gap recovery model (PDGRM) for participants in a call, where the PDGRM may be a machine learning model that models a user's speech patterns based on word collocation, dictionary, and speaking rate. The present invention may further comprise detecting one or more gaps in the call based on network connectivity, inserting an audio delay into the call, constructing repaired dialogue to fill at least one of the one or more gaps in the call based on the PDGRM, and inserting the repaired dialogue into the call.

A server can receive a plurality of records at a databases such that each record is associated with a phone call and includes at least one request generated based on a transcript of the phone call. The server can generate a training dataset based on the plurality of records. The server can further train a binary classification model using the training dataset. Next, the server can receive a live transcript of a phone call in progress. The server can generate at least one live request based on the live transcript using a natural language processing module of the server. The server can provide the at least one live request to the binary classification model as input to generate a prediction. Lastly, the server can transmit the prediction to an entity receiving the phone call in progress. The prediction can cause a transfer of the call to a chatbot.

A system and method for automated customer response system mapping and duplication to provide for a common Interactive Voice Response (IVR) system import language or structure, comprising a contact center crawler which connects with an existing automated contact center system, may navigate and map that system by recording and selecting each available option or by interfacing with the code that makes up the IVR itself to generate a model, records the response to each selected option, and feeds the options and responses to a tree creator; a CX model generator which organizes the options and responses into a data map such as a tree or more graph-like or map-like structure, where logical conditions or operations such as loops and recursion may be present; and a coding engine that generates programmatic code representing the data model that is imported into another IVR platform for execution.

A system and method for automated customer response system mapping and duplication to provide for a common Interactive Voice Response (IVR) system import language or structure, comprising a contact center crawler which connects with an existing automated contact center system, may navigate and map that system by recording and selecting each available option or by interfacing with the code that makes up the IVR itself to generate a model, records the response to each selected option, and feeds the options and responses to a tree creator; a CX model generator which organizes the options and responses into a data map such as a tree or more graph-like or map-like structure, where logical conditions or operations such as loops and recursion may be present; and a coding engine that generates programmatic code representing the data model that is imported into another IVR platform for execution.

The present invention relates to a service system supporting a voice call using a digital assistant device, a method thereof, and a non-transitory computer readable medium having a computer program recorded thereon, and more particularly, to a service system which performs a call between a digital assistant device and a terminal corresponding to a call opponent through a voice command of a user and supports call conversion between the digital assistant device and a user terminal through the voice command of the user, a method thereof, and a non-transitory computer readable medium having a computer program recorded thereon.

Unexpected call disconnection during normal time is prevented. When a gateway device 1 installed on a POI border receives RTP packets even though reception of RTCP packets is stopped, the gateway device 1 generates RTCP packets and sends out the RTCP packets to the gateway device 1's own network side or the gateway device 1 generates a call control signal showing that media transfer is continued and sends out the call control signal to the gateway device 1's own network side. Thereby, even in the case of performing interruption monitoring of RTCP packets within the gateway device 1's own network, it is possible to prevent unexpected call disconnection during normal time accompanying change in RTCP packet sending-out conditions.