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 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 system and method for automated chatbot testing to provide training and quality assurance of conversational artificial intelligence systems, comprising a chatbot testing administrator interface which allows chatbot makers to define what a chatbot is supposed to do, create test scripts to test the performance of the chatbot, and review the results of the chatbot tests, a chatbot testing server which provides and interface between chatbot testing agents and the administrator interface, instantiates chatbot agents and distributes them across available hardware and runs testing programs which activate, configure, and deactivate chatbot testing agents as needed. A plurality of chatbot agents may be running in parallel to provide automated testing based upon test script configuration.

A system and method for automated chatbot testing to provide training and quality assurance of conversational artificial intelligence systems, comprising a chatbot testing administrator interface which allows chatbot makers to define what a chatbot is supposed to do, create test scripts to test the performance of the chatbot, and review the results of the chatbot tests, a chatbot testing server which provides and interface between chatbot testing agents and the administrator interface, instantiates chatbot agents and distributes them across available hardware and runs testing programs which activate, configure, and deactivate chatbot testing agents as needed. A plurality of chatbot agents may be running in parallel to provide automated testing based upon test script configuration.

A system for synthesizing real IVR scenarios, automatically reproducing test scenarios, and providing accurate reports on those scenarios. An IVR tester uses a telephony device (website, mobile app, VOIP, etc.) and an automated IVR test case generator to place test calls to an IVR system. The automated IVR test case generator intercepts and collects the phone call's audio stream, dual-tone multi-frequency signals, and available metadata (e.g., response times, post-speech silence timeout, etc.). Once the call is complete, the automated IVR test case generator pre-populates a webpage or other user-interface technology and a database with the audio stream, dual-tone multi-frequency signals, and available metadata from the test scenario phone call. The IVR tester may then save that test scenario and continue to the next scenario. The saved scenario may now be used automatically by the IVR test case generator for regression testing or duplicated and edited for use in other systems.

A system for synthesizing real IVR scenarios, automatically reproducing test scenarios, and providing accurate reports on those scenarios. An IVR tester uses a telephony device (website, mobile app, VOIP, etc.) and an automated IVR test case generator to place test calls to an IVR system. The automated IVR test case generator intercepts and collects the phone call's audio stream, dual-tone multi-frequency signals, and available metadata (e.g., response times, post-speech silence timeout, etc.). Once the call is complete, the automated IVR test case generator pre-populates a webpage or other user-interface technology and a database with the audio stream, dual-tone multi-frequency signals, and available metadata from the test scenario phone call. The IVR tester may then save that test scenario and continue to the next scenario. The saved scenario may now be used automatically by the IVR test case generator for regression testing or duplicated and edited for use in other systems.

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.

An abnormality monitoring system based on a roaming system, including a monitoring host, a mobile device terminal, and a roaming device group, where the roaming device group includes a cluster service host and several roaming hosts; and any one of the roaming hosts is configured to: receive real-time data uploaded by the mobile device terminal; determine, through analysis, whether the real-time data is normal or abnormal; and when determining that the real-time data is abnormal, send a solving policy to the mobile device terminal; or if a solving policy corresponding to the abnormal real-time data cannot be found, report the abnormal real-time data to the monitoring host, and after receiving a solving policy fed back by the monitoring host, send, to the mobile device terminal corresponding to the abnormal real-time data, the solving policy fed back by the monitoring host.

An abnormality monitoring system based on a roaming system, including a monitoring host, a mobile device terminal, and a roaming device group, where the roaming device group includes a cluster service host and several roaming hosts; and any one of the roaming hosts is configured to: receive real-time data uploaded by the mobile device terminal; determine, through analysis, whether the real-time data is normal or abnormal; and when determining that the real-time data is abnormal, send a solving policy to the mobile device terminal; or if a solving policy corresponding to the abnormal real-time data cannot be found, report the abnormal real-time data to the monitoring host, and after receiving a solving policy fed back by the monitoring host, send, to the mobile device terminal corresponding to the abnormal real-time data, the solving policy fed back by the monitoring host.