Abstract: An AR/VR input device include a processor(s), an internal measurement unit (IMU), and a plurality of sensors configured to detect emissions received from a plurality of remote emitters. The processor(s) can be configured to: determine a time-of-flight (TOF) of the detected emissions, determine a first estimate of a position and orientation of the input device based on the TOF of a subset of the detected emissions and the particular locations of each of the plurality of sensors on the input device that are detecting the detected emissions, determine a second estimate of the position and orientation of the input device based on the measured acceleration and velocity from the IMU, and continuously update a calculated position and orientation of the input device within the AR/VR environment in real-time based on a Beyesian estimation (e.g., Extended Kalman filter) that utilizes the first estimate and second estimate.

Abstract: A wearable multi-sensor device for measuring physiological properties includes a plurality of non-invasive sensors, such as a single-sided electrocardiography sensor, a bioimpedance and electrodermal activity sensor, a skin temperature sensor, and a photoplethysmography sensor. The device is configured to secure a skin-facing side of the sensors to exposed skin of a user and includes a communication module configured to receive signals from the plurality of non-invasive sensors and output data from the device, the being suitable for use in predicting glycemic events in the user.

Abstract: Enhanced diarization systems and methods of use are provided herein. Some exemplary methods may include applying one or more rules that affect separation of the call audio data into segments, the rules being associated with the at least one call schema, separating the call audio data into segments according to the one or more rules, grouping segments of call audio data associated with a speaker, and storing in a storage media an identifier and the grouped segments for the speaker.

Abstract: Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Abstract: Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Abstract: Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Abstract: Systems and methods for authenticating callers are disclosed that may obtain identifying data and a voice print, and compare the voice print to one or more stored voice prints. Further, one or more initial scores may be calculated based on the data and the comparison, and a confidence interval score may also be calculated. The systems and methods may determine whether to authenticate based on the one or more initial scores and the confidence interval score.

Abstract: Disclosed is a method for generating a fraud risk score representing a fraud risk associated with an individual, the method comprising: a) determining a telephony channel risk score from at least one of audio channel data and non-audio channel data of the individual; and b) generating the fraud risk score based on at least one of the telephony channel risk score, the audio channel data, and the non-audio channel data.

Abstract: Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Abstract: Disclosed is a method for generating a fraud risk score representing a fraud risk associated with an individual, the method comprising: a) determining a telephony channel risk score from at least one of audio channel data and non-audio channel data of the individual; and b) generating the fraud risk score based on at least one of the telephony channel risk score, the audio channel data, and the non-audio channel data.

Abstract: According to one aspect of the invention there is provided a method, comprising collecting voiceprints of callers; identifying which of the collected voiceprints are associated with fraud; and generating a whitelist comprising voiceprints corresponding to the collected voiceprints not identified as associated with fraud.

Abstract: Systems, methods, and media for generating fused risk scores for determining fraud in call data are provided herein. Some exemplary methods include generating a fused risk score used to determine fraud from call data by generating a fused risk score for a leg of call data, via a fuser module of an analysis system, the fused risk score being generated by fusing together two or more uniquely calculated fraud risk scores, each of the uniquely calculated fraud risk scores being generated by a sub-module of the analysis system; and storing the fused risk score in a storage device that is communicatively couplable with the fuser module.

Abstract: Systems and methods for authenticating callers are disclosed that may obtain identifying data and a voice print, and compare the voice print to one or more stored voice prints. Further, one or more initial scores may be calculated based on the data and the comparison, and a confidence interval score may also be calculated. The systems and methods may determine whether to authenticate based on the one or more initial scores and the confidence interval score.

Abstract: Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Abstract: Disclosed is a method for enrolling a voiceprint in a fraudster database, the method comprising: a) defining a fraud model comprising at least one hypothesis indicative of a fraudulent transaction; b) processing audio data based on the fraud model to identify at least one suspect voiceprint in the audio data suspected of belonging to a fraudster; and c) enrolling the at least one suspect voiceprint in the fraudster database.

Abstract: Disclosed is a method for enrolling a voiceprint in a fraudster database, the method comprising: a) defining a fraud model comprising at least one hypothesis indicative of a fraudulent transaction; b) processing audio data based on the fraud model to identify at least one suspect voiceprint in the audio data suspected of belonging to a fraudster; and c) enrolling the at least one suspect voiceprint in the fraudster database.

Abstract: Systems, methods, and media for generating fused risk scores for determining fraud in call data are provided herein. Some exemplary methods include generating a fused risk score used to determine fraud from call data by generating a fused risk score for a leg of call data, via a fuser module of an analysis system, the fused risk score being generated by fusing together two or more uniquely calculated fraud risk scores, each of the uniquely calculated fraud risk scores being generated by a sub-module of the analysis system; and storing the fused risk score in a storage device that is communicatively couplable with the fuser module.

Abstract: Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Abstract: Disclosed is method for screening an audio for fraud detection, the method comprising: providing a User Interface (UI) control capable of: a) receiving an audio; b) comparing the audio with a list of fraud audios; c) assigning a risk score to the audio based on the comparison with a potentially matching fraud audio of the list of fraud audios; and d) displaying an audio interface on a display screen, wherein the audio interface is capable of playing the audio along with the potentially matching fraud audio, and wherein the display screen further displays metadata for each of the audio and the potentially matching fraud audio thereon, wherein the metadata includes location and incident data of each of the audio and the potentially matching fraud audio.