Aspects of human assisted virtual agent support are discussed. A conversation between a user and a virtual agent may be monitored. A probability of the user abandoning the conversation may be predicted and a notification may be provided to a human agent to provide assistance in the conversation based on the probability.

Provided are systems, methods, and apparatuses initiating outbound communications. The system may include at least one processor of a telecommunications device comprising a display and a communication application, the at least one processor programmed or configured to: receive, with the communication application, a communication request comprising a number, the communication request initiated by a user of the telecommunications device, determine whether to automatically initiate a communication to the number based on recipient data associated with the number, in response to determining to not automatically initiate the communication to the number, prompt the user, on the display of the telecommunications device, with a selectable option configured to initiate the communication to the number upon selection, and in response to determining to automatically initiate the communication to the number, automatically initiate the communication to the number upon selection

A sentiment-based score is determined for a contact center engagement between a first contact center service operator and a contact center user. The sentiment-based score is indicated within a graphical user interface displaying information associated with multiple contact center engagements at a device of a second contact center service operator. Based on a request to participate in the contact center engagement received from the device of the second contact center service operator via the graphical user interface, information associated with the contact center engagement is transmitted to the device of the second contact center service operator, and a contact center session involving a device of the contact center user and the device of the second contact center service operator is established.

A system and a method of data communication between a first computing device, associated with a first user, and at least one second computing device associated with a second user may include: receiving, by the first computing device, one or more data elements pertaining to details of a transaction request from the second computing device, via a voice channel; extracting said transaction request details by the first computing device; transmitting, by the first computing device, one or more authentication data elements of an electronic wallet module, comprised in the first computing device, to the second computing device, via the voice channel; and carrying out the requested transaction by the first computing device, based on the extracted transaction request details and the electronic wallet authentication data.

An example method of operation may include one or more of identifying an outbound call placed by a mobile device subscribed to a protected carrier network, determining the outbound call is destined for a destination telephone number that was stored in a call history of the mobile device, determining the destination telephone number is a scam call suspect telephone number based on one or more identified call filter parameters associated with the destination telephone number, and forwarding a scam call notification to the mobile device while the outbound call is dialing the destination telephone number.

A service interruption manager function, SIMF, receives information that indicates that lawful interception, LI, service interruption associated with an LI task has occurred. Based on the received information, a determination is made of a status regarding the LI service interruption associated with the LI task, for example a determination whether the LI service interruption associated with the LI task has a current status that is any of: terminated, ongoing or initiated. A message is then transmitted, to a delivery function, DF, via an HI2 interface, the message comprising at least the determined status regarding the LI service interruption.

A centralized and robust threat assessment tool is disclosed to perform comprehensive analysis of previously-stored and subsequent communication data, activity data, and other relevant information relating to inmates within a controlled environment facility. As part of the analysis, the system detects certain keywords and key interactions with the dataset in order to identify particular criminal proclivities of the inmate. Based on the identified proclivities, the system assigns threat scores to inmate that represents a relative likelihood that the inmate will carry out or be drawn to certain threats and/or criminal activities. This analysis provides a predictive tool for assessing an inmate's ability to rehabilitate. Based on the analysis, remedial measures can be taken in order to correct an inmate's trajectory within the controlled environment and increase the likelihood of successful rehabilitation, as well as to prevent potential criminal acts.

A processing system may maintain a relationship graph that includes nodes and edges representing phone numbers and device identifiers having associations with the phone numbers. The processing system may obtain an identification of a first phone number or a first device identifier for a fraud evaluation and extract features from the relationship graph associated with at least one of the first phone number or the first device identifier. The plurality of features may include one or more device identifiers associated with the first phone number, or one or more phone numbers associated with the first device identifier. The processing system may then apply the features to a prediction model that is implemented by the processing system and that is configured to output a fraud risk value of the first phone number or the first device identifier and implement at least one remedial action in response to the fraud risk value.

Novel tools and techniques are provided for implementing automated robocall detection. In various embodiments, a computing system may compare first abstracted raw data, obtained from a portion of call data from a first call from a first originating party, with each of a plurality of abstracted raw data, obtained from portions of call data from a plurality of calls from a corresponding plurality of originating parties. In some instances, the plurality of abstracted raw data and the first abstracted raw data may each include at least one of word count data, phoneme count data, inter-word timing data, voice pitch estimation data, and/or background noise data. The computing system may determine whether the first abstracted raw data is indicative of the first call being a suspected unsolicited or unwanted communication, based at least in part on the comparison. If so, the computing system may perform one or more tasks.

A system and methods are provided to analyze audio signals from an incoming voice call. The system includes a processor and a computer readable medium operably coupled thereto, to perform voice analysis operations which include receiving a first audio signal comprising a first audio waveform of a first speech between at least two users during the incoming voice call, accessing speech segment parameters for analyzing the audio signals, determining one or more talk-over segments in the first audio waveform using the speech segment parameters, extracting audio features from each of the one or more talk-over segments, determining, using a machine learning (ML) model trained for interruption analysis of the audio signals, whether each of the one or more talk-over segments are a negative interruption or a non-negative interruption based on the audio features, and determining whether to output a first notification for the negative interruption or the non-negative interruption.