A hard phone, a method for implementing a traffic operation, a call center system, and a non-transitory computer-readable storage medium. The hard phone may include: a processing module (11), an input module (12), a sending module (13), a display device (14), and a receiving module (15). The display device (14) displays different types of traffic operation instructions such that a user carries out selection. The input module (12) receives the traffic operation instruction inputted by the user and an identity identification of a target attendant, and transmit to the processing module (11). The processing module (11) processes the traffic operation instruction and the identity identification according to a preset protocol, and then transmit same to the sending module (13), and send to a server via the sending module (13). The receiving module (15) receives and transmit the execution result to the processing module (11).

A system and method for integrating audio data collected, such as audio data and analytical data, to perform behavioral analysis on the audio data, using an application of acoustic signal processing and machine learning algorithms, by converting the audio data to text data and performing behavioral analysis on the text data. The behavioral analysis data from the audio application of acoustic signal processing is combined with machine learning algorithms and speech to text data to provide a call agent with feedback to assist in the next best action or insight into customer behaviors.

An event log computing system receives event data associated with one or more events that may impact operations of a contact center. The event log system may analyze event data and one or more contact center performance metrics to determine an actual impact level of the event on each of the one or more contact center performance metrics. The event log system may also analyze anomalies in one or more contact center performance metrics and event data in the event log to identify events in the event log that may be associated with the anomalies.

Methods and systems are disclosed herein for improving the quality of audio for use in a biometric. A biometric system may use machine learning to determine whether audio or a portion of the audio should be used as a biometric for a user. A sample of the user's voice may be used to generate a voice signature of the user. Portions of the audio that do not meet a similarity threshold when compared with the voice signature may be removed from the audio. Additionally or alternatively, interfering noises may be detected and removed from the audio to improve the quality of a voice biometric generated from the audio.

Embodiments provide a method for providing a call center supplemental workflow tool, including: receiving from a database a workflow decisional matrix, a plurality of questions, and a plurality of scripts; displaying a web-based graphical user interface on a display providing at least one question or one script; receiving user input; determining, at least another question or at least another script, to be displayed, wherein determining is based upon the user input and the decisional matrix and advances the workflow; based upon the decisional matrix, determining that the workflow has been completed; providing an output of the questions displayed and the user input received; and the output being provided to a primary software application. Other aspects are described and claimed.

A system and method for testing of automated contact center customer response systems using a customer response testing system and a real time conversation engine, wherein the customer response testing system generates simulated human queries using persona profiles, sends test cases containing those queries to a contact center under test, and receives and analyzes the responses to determine whether the contact center's automated response systems understand the queries and respond appropriately.

A system and method may identify a fraud ring based on call or interaction data by analyzing by a computer processor interaction data including audio recordings to identify clusters of interactions which are suspected of involving fraud each cluster including the same speaker; analyzing by the computer processor the clusters, in combination with metadata associated with the interaction data, to identify fraud rings, each fraud ring describing a plurality of different speakers, each fraud ring defined by a set of speakers and a set of metadata corresponding to interactions including that speaker; and for each fraud ring, creating a relevance value defining the relative relevance of the fraud ring.

A device may receive audio data of a first call between a first user and a second user. The device may generate, based on the audio data, time series data associated with an audio signal of the first call and may process, using a first machine learning model, the time series data to generate first call insight information regarding one or more first insights associated with the first call. The device may process the audio data to generate image data associated with the audio signal and may process, using a second machine learning model, the image data to generate second call insight information regarding one or more second insights associated with the first call. The device may combine the first call insight information and the second call insight information to generate combined call insight information and cause an action to be performed based on the combined call insight information.

Apparatus for the transfer of sensitive information between two parties facilitated by an intermediary, the apparatus adapted to: receive from the first party voice signals and data signals; determine a parameter relating to an estimate of the time required for sensitive information to be determined from the data signals received from the first party; transmit to the intermediary the received voice signals and the parameter; determine sensitive information from the received data signals; and transmit the sensitive information to the second party. A corresponding method is also provided.

The present invention is a system and method of routing incoming communications to a CSR and providing guidance to the CSR based on the incoming communication using feedback information such as sentiment feedback, survey feedback, and feedback from actions taken by CSRs based on skill level. A CEC system receives an incoming communication, analyzes the communication and creates metadata based off of the analysis. The metadata is used by the RAE routing module to route the communication to an appropriate party. The metadata is also used by the GAE guidance module to determine the guidance to provide to the CSR. The CSR then performs an action based on the guidance. The CEC system continues to monitor the interaction until the interaction is completed. The communication metadata, the communication, the guidance, and the CSRs action are all provided to a RAS rules analysis module wherein the RAS analyzes the data and automatedly updates the rules (RAR and GAR) according to the analysis.