Aspects of the disclosure relate to automated text-based customer query systems. A computing device associated with a text-based query system may receive, from a user communication device, a text input at the user communication device. The computing device may determine, based on the text input, a voice extensible markup language (VXML) application from a plurality of VXML applications. The computing device may determine, based on the VXML application, a text output, and send the text output for display at the user device.

A computer based system and method for identifying complaint interactions, including: detecting appearances of linguistic structures related to complaints in an interaction; calculating at least one sentiment metric of the interaction; and classifying the interaction as being or not being a complaint interaction based on the detected linguistic structures and the at least one sentiment metric, for example using a trained supervised learning model.

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.

An apparatus, method, and non-transitory computer readable medium are provided for accessing data on a phone that is inaccessible. The apparatus includes a memory configured to store a plurality of contact profiles in a contact list corresponding to an inaccessible device, and a processor coupled to the memory. The apparatus receives, from an unknown device over a voice connection, an access identifier corresponding to the inaccessible device and a contact identifier associated with a contact profile of the plurality of contact profiles; identifies the contact list based on matching the access identifier with the inaccessible device; determines a contact device of the contact profile in the contact list corresponding to the contact identifier; and connects, over the voice connection, the unknown device to the contact device corresponding to the contact profile.

An embodiment for simulating an interactive voice response system (IVRS) call with a digital twin agent of a user is provided. The embodiment may include receiving data relating to characteristics of the user, activities of the user, and historical data relating to one or more prior IVRS dialogs. The embodiment may also include creating a digital twin agent of the user. The embodiment may further include identifying one or more preferences of the user from the historical data. The embodiment may also include in response to determining the user is occupied, identifying a context of an issue requiring an IVRS call. The embodiment may further include initiating the IVRS call with the digital twin agent representing the user. The embodiment may also include transcribing an IVRS script and interacting with the IVRS in accordance with the IVRS script and the one or more preferences of the user.

A response handling arrangement comprises a response prioritizing device (26) with processing circuitry configured to obtain event data (ED) and/or status data (SD) in the communication network concerning a user, where the obtaining is triggered by the user initiating a connection to a response device (24) of the communication network, apply the event data (ED) and/or status data (SD) in a response selecting model (78) of a trained machine learning module (74), and obtain an indication (I) of a type of response to the user from the response selecting model of the trained machine learning module (74) based on the applied event data (ED) and/or status data (SD) for allowing a real-time response of the response type to be made to the user by the response device (24).

A method of using voice commands from a mobile device to remotely access and control a computer. The method includes receiving audio data from the mobile device at the computer. The audio data is decoded into a command. A software program that the command was provided for is determined. At least one process is executed at the computer in response to the command. Output data is generated at the computer in response to executing at least one process at the computer. The output data is transmitted to the mobile device.

A computer based system and method for identifying complaint interactions, including: detecting appearances of linguistic structures related to complaints in an interaction; calculating at least one sentiment metric of the interaction; and classifying the interaction as being or not being a complaint interaction based on the detected linguistic structures and the at least one sentiment metric, for example using a trained supervised learning model.

An example electronic apparatus for providing voice recognition control includes a display; and a processor, wherein the processor may be configured to obtain a content including at least one object; distinguish the at least one object within the content; display an instruction text in correspondence with a non-text object among the at least one object; and select the non-text object corresponding to the instruction text if a voice command corresponding to the instruction text is inputted.

In some embodiments, a system may process a user interface to identify textual or graphical items in the interface, and may prepare a plurality of audio files containing spoken representations of the items. As the user navigates through the interface, different ones of the audio files may be selected and played, to announce text associated with items selected by the user. A computing device may periodically determine whether a cache offering the interface to users stores audio files for all of the interface's textual items, and if the cache is missing any audio files for any of the textual items, the computing device may take steps to have a corresponding audio file created.