When a customer service representative (CSR) calls a customer, the customer may be able to authenticate himself or herself by providing the CSR with personal identifying information. However, the CSR may be unable to provide information to authenticate himself or herself to the customer. Thus, this patent document describes authentication techniques that can allow the CSR to authenticate himself or herself to the customer. For example, before or during a call that the second person (e.g., CSR) initiates to call a first person (e.g., customer), a notification message may be sent to the first person's user device. The content of notification message displayed on the user device may provide information to the first person which can allow the first person to determine whether the second person is trustworthy.

A computer may train a single-class machine learning using normal speech recordings. The machine learning model or any other model may estimate the normal range of parameters of a physical speech production model based on the normal speech recordings. For example, the computer may use a source-filter model of speech production, where voiced speech is represented by a pulse train and unvoiced speech by a random noise and a combination of the pulse train and the random noise is passed through an auto-regressive filter that emulates the human vocal tract. The computer leverages the fact that intentional modification of human voice introduces errors to source-filter model or any other physical model of speech production. The computer may identify anomalies in the physical model to generate a voice modification score for an audio signal. The voice modification score may indicate a degree of abnormality of human voice in the audio signal.

An anti-communications fraud apparatus 1 includes: an analysis unit 13 that analyzes a voiceprint of a communication voice of a calling party; a determination unit 14 that acquires, from a database 17 in which the voiceprint and a degree of fraud risk are stored in association with each other, the degree of fraud risk corresponding to the voiceprint of the calling party, and determines whether the degree of fraud risk exceeds a predetermined threshold; and a notification unit 15 that notifies that the calling party is dangerous when the degree of fraud risk exceeds the threshold.

A method for detecting a denial of service attach on a call center, the method including automated means for detecting at least one anomaly in calls made to the call center from at least one source, determining if a detected anomaly has a match in a historical file of previously detected anomalies, and filtering calls received from the at least one source if the detected anomaly does not have a match in the historical file of previously detected anomalies.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing menu-based communication are disclosed. In one aspect, a method includes the actions of receiving, from a first computing device, a communication and data identifying a second computing device as a recipient of the communication. The actions may further include determining a first and second candidate response to the communication. The actions may further include providing, to the second computing device, the communication, the first and second candidate responses, and instructions to provide, for output by the second computing device, the first and second candidate responses as selectable responses to the communication. The actions may further include receiving, from the second computing device, the selection of the first or second candidate response. The actions may further include providing, for output to the first computing device, data indicating the selection of the first or second candidate response.

According to an embodiment of the disclosure, a toll-free telecommunications validation system determines a confidence value that an incoming phone call to an enterprises' toll-free number is originating from the station it purports to be, i.e., is not a spoofed call by incorporating one or more layers of signals and data in determining said confidence value, the data and signals including, but not limited to, toll-free call routing logs, service control point (SCP) signals and data, service data point (SDP) signals and data, dialed number information service (DNIS) signals and data, automatic number identification (ANI) signals and data, session initiation protocol (SIP) signals and data, carrier identification code (CIC) signals and data, location routing number (LRN) signals and data, jurisdiction information parameter (JIP) signals and data, charge number (CN) signals and data, billing number (BN) signals and data, and originating carrier information (such as information derived from the ANI, including, but not limited to, alternative service provider ID (ALTSPID), service provider ID (SPID), or operating company number (OCN)). In certain configurations said enterprise provides an ANI and DNIS associated with said incoming toll-free call, which is used to query a commercial toll-free telecommunications routing platform for any corresponding log entries. The existence of any such log entries, along with the originating carrier information in the event log entries do exist, is used to determine a confidence value that said incoming toll-free call is originating from the station it purports to be. As a result, said entities or enterprises operating a toll-free number may be provided a confidence value regarding an incoming telephone call, and using that confidence value, further determine whether or not to accept the authenticity of the incoming telephone call and/or based on said confidence value, service the incoming call differently.

Methods and systems are described for processing calls. An example method may comprise receiving a message for establishing a call. Identification information in the message may be compared to screening data. If a match is found, the message may be forwarded to a screening server. The screening server may establish a call based on the session and provide information indicative of a level of trust associated with the call.

Methods and apparatus for call traffic anomaly mitigation are described herein. One or more embodiments include receiving a scoring request including a telephone number associated with a telephone call at a scoring device from a call processing entity, receiving a violator list of telephone numbers and their corresponding severity values at the scoring device from an anomaly analyzer, determining a severity value associated with the telephone number by performing a lookup operation in the violator list, performing a random simulation using the severity value as a probability to determine an indicator value, and inputting the indicator value into a model to determine a call reputation score.

A vehicle includes a communication device configured to communicate with a user terminal. The vehicle further includes an input processor configured to recognize a voice of a telephone call received by the user terminal and generate a voice recognition result on the voice of the telephone call. The vehicle also includes a dialogue manager configured to determine telephone call contents based on the voice recognition result and determine whether or not to provide the telephone call contents to a user based on the telephone call contents.

An example method is described. The method includes receiving, by a computing system, data indicative of a call into an interactive voice response (IVR) system from a user device and determining, by the computing system and based on the data, a set of actions performed by the user device within the IVR system and a corresponding set of results performed by the IVR system during the call. Additionally, the method includes converting, by the computing system, the set of actions and the corresponding set of results into a sequence of code pairs using a dictionary established based on training data, determining, by the computing system, an activity pattern during the call based on the sequence of code pairs; and calculating, by the computing system, a probability that the call is fraudulent based on the activity pattern during the call.