A computer device may include a memory storing instructions and processor configured to execute the instructions to obtain a text file generated based on a communication session; select topics for classifying communication sessions; and apply one or more trained machine learning models to the obtained text file to determine one or more most likely topics for the communication session. The processor may be further configured to apply regular expression binary classifiers, each associated with a different topic, to the obtained text file to determine a likelihood the communication session is associated with a particular topic; select a topic for the communication session based on the determined one or more most likely topics and the determined likelihood the communication session is associated with the particular topic; and classify the communication session based on the selected topic.

A method for operating a device for handling a phone call is disclosed. In one aspect the method comprises (a) receiving a phone call from a calling party, (b) determining an action to be performed by the device among a predetermined list of actions related to the handling of a phone call, as a function at least of identity information about the calling party, and (c) performing the determined action for handling the phone call. The device may be programmable and mobile, e.g., a smartphone.

An alert generator in a communication system for processing a call includes at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor configure to operate on the call. Heuristic logic is configured to transform the call classifiers into a plurality of weighted sub-metrics for the call, and aggregate normalized Gaussian logic is configured to transform the weighted sub-metrics into a metric control. A threshold analyzer is configured to generate an alert signal to the communication system based on the metric control meeting a condition.

The invention provides an interrogator for obtaining information associated with a caller identification, such as a telephone number, transmitted within an incoming telephone call including: a receiving device configured to receive the incoming telephone call and to extract the caller identification from the incoming telephone call; an interrogating device configured to receive the extracted caller identification from the receiving device and to interrogate information associated with the caller identification from an external data base, which is configured to operate independently from the user voice communication device for which the incoming telephone call is intended for; and a forwarding device configured to receive and to forward the interrogated information.

System and methods for providing calling party information are disclosed. In one implementation, a server for providing calling party information includes one or more processors configured to receive a query for calling party information from a carrier. The query includes an identifier associated with the calling party. The processors are further configured to determine a score based on one or more previously obtained queries, generate the calling party information based on the score, and transmit the calling party information to the carrier. The carrier transmits the calling party information to a device associated with the called party as a Caller ID Name (CNAM).

A method whereby an audio content is analyzed for speech semantic content and speech vocal content to determine audio content metrics. Additionally, the method may also incorporate the similarity of the audio content to other audio contents and weight the analyses to determine audio content metrics.

Systems and methods for mapping a customer journey in an interactive voice response (IVR) system to a category from a categories list, build a directed graph based on one or more sample IVR customer journeys; generate a black list based on the directed graph; for a given customer journey: filter one or more non-informative menus from the plurality of available menus based on the black list; concatenate the respective associated menu prompt of each menu that was not filtered, and one or more user responses to one or more menu prompts from the series of interactions in the given customer journey, into a concatenated word string; calculate a similarity score between the concatenated word string and a category name of each category from the categories list; and map the given customer journey to the category whose category name produces the highest similarity score.

Embodiments include method, systems and computer program products for enhancing a customer service process. Aspects include receiving customer data associated with a customer and creating a customer profile based on the customer data. Aspects also include obtaining customer service representative data comprising a customer service profile for each of a plurality of customer service representatives and determining a matching subset of the plurality of customer service representatives for the customer based upon the customer profile and the customer service representative data. Next, aspects include displaying, to the customer, the matching subset of the plurality of customer services representatives for the customer. Aspects also include selecting a first customer service representative from the matching subset of the plurality of customer service representatives for the customer based on a customer input.

An analytics system to analyze and report customer experience, comprising a customer interface device, an analysis server which analyzes data from a customer and a machine, and the database which is connected to the customer interface device and to the analysis server wherein the database receives data from the customer interface device, and/or the analysis server. The analytics system analyzes and reports customer experience.

Devices, computer-readable media and methods for delivering customer service content associated with a reason code are disclosed. Examples of the present disclosure may include a processor of a telecommunication network identifying a reason code associated with calls from customers to a customer call center and determining a set of shared traits among the customers. The shared traits may be based upon first network event data and first customer account data associated with the customers. The processor may further determine a customer with a propensity to call score that exceeds a threshold and with a customer profile that matches the set of shared traits. The customer profile may be based upon second network event data and second customer account data associated with the customer. The processor may further deliver a customer service content associated with the reason code to the customer via a communication modality that is determined for the customer.