公开(公告)号：US20180152561A1

公开(公告)日：2018-05-31

申请号：US15880287

申请日：2018-01-25

Applicant: PINDROP SECURITY, INC.

Inventor： Scott STRONG ,  Kailash PATIL ,  David DEWEY ,  Raj BANDYOPADHYAY ,  Telvis CALHOUN ,  Vijay BALASUBRAMANIYAN

IPC: H04M3/527 ,  G06N99/00 ,  H04M15/00 ,  H04M7/00 ,  H04W12/12

CPC classification number: H04M3/527 ,  G06F21/32 ,  G06F21/552 ,  G06N20/00 ,  H04M3/493 ,  H04M7/0078 ,  H04M15/41 ,  H04M2203/551 ,  H04M2203/6027 ,  H04W12/12

Abstract: Systems and methods for call detail record (CDR) analysis to determine a risk score for a call and identify fraudulent activity and for fraud detection in Interactive Voice Response (IVR) systems. An example method may store information extracted from received calls. Queries of the stored information may be performed to select data using keys, wherein each key relates to one of the received calls, and wherein the queries are parallelized. The selected data may be transformed into feature vectors, wherein each feature vector relates to one of the received calls and includes a velocity feature and at least one of a behavior feature or a reputation feature. A risk score for the call may be generated during the call based on the feature vectors.

公开(公告)号：US20170111515A1

公开(公告)日：2017-04-20

申请号：US15294576

申请日：2016-10-14

Applicant: PINDROP SECURITY, INC.

Inventor： Raj BANDYOPADHYAY ,  Kailash PATIL ,  David DEWEY ,  Scott STRONG ,  Telvis CALHOUN ,  Vijay BALASUBRAMANIYAN

IPC: H04M15/00 ,  G06N99/00 ,  H04M3/527

CPC classification number: H04M3/527 ,  G06F21/32 ,  G06F21/552 ,  G06N99/005 ,  H04M3/493 ,  H04M7/0078 ,  H04M15/41 ,  H04M2203/551 ,  H04M2203/6027 ,  H04W12/12

Abstract: Systems and methods for call detail record (CDR) analysis to determine a risk score for a call and identify fraudulent activity and for fraud detection in Interactive Voice Response (IVR) systems. An example method may store information extracted from received calls. Queries of the stored information may be performed to select data using keys, wherein each key relates to one of the received calls, and wherein the queries are parallelized. The selected data may be transformed into feature vectors, wherein each feature vector relates to one of the received calls and includes a velocity feature and at least one of a behavior feature or a reputation feature. A risk score for the call may be generated during the call based on the feature vectors.

公开(公告)号：US20240355336A1

公开(公告)日：2024-10-24

申请号：US18439049

申请日：2024-02-12

Applicant: PINDROP SECURITY, INC.

Inventor： Umair ALTAF ,  Sai Pradeep PERI ,  Lakshay PHATELA ,  Payas GUPTA ,  Yitao SUN ,  Svetlana AFANASEVA ,  Kailash PATIL ,  Elie KHOURY ,  Bradley MAGNETTA ,  Vijay BALASUBRAMANIYAN ,  Tianxiang CHEN

IPC: G10L17/18 ,  G10L15/26

CPC classification number: G10L17/18 ,  G10L15/26

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

公开(公告)号：US20230254403A1

公开(公告)日：2023-08-10

申请号：US18301897

申请日：2023-04-17

Applicant: PINDROP SECURITY, INC.

Inventor： Ricardo CASAL ,  Theo WALKER ,  Kailash PATIL ,  John CORNWELL

IPC: H04M3/22 ,  G06N20/00 ,  G06F18/214

CPC classification number: H04M3/2281 ,  G06N20/00 ,  G06F18/214 ,  H04M2203/551 ,  H04M2203/556

Abstract: Embodiments described herein provide for performing a risk assessment using graph-derived features of a user interaction. A computer receives interaction information and infers information from the interaction based on information provided to the computer by a communication channel used in transmitting the interaction information. The computer may determine a claimed identity of the user associated with the user interaction. The computer may extract features from the inferred identity and claimed identity. The computer generates a graph representing the structural relationship between the communication channels and claimed identities associated with the inferred identity and claimed identity. The computer may extract additional features from the inferred identity and claimed identity using the graph. The computer may apply the features to a machine learning model to generate a risk score indicating the probability of a fraudulent interaction associated with the user interaction.

公开(公告)号：US20200321009A1

公开(公告)日：2020-10-08

申请号：US16907951

申请日：2020-06-22

Applicant: PINDROP SECURITY, INC.

Inventor： Elie KHOURY ,  Parav NAGARSHETH ,  Kailash PATIL ,  Matthew GARLAND

IPC: G10L17/02 ,  G10L17/04 ,  G10L25/24 ,  G10L17/18 ,  G10L19/02 ,  G10L17/06 ,  G10L17/00

Abstract: An automated speaker verification (ASV) system incorporates a first deep neural network to extract deep acoustic features, such as deep CQCC features, from a received voice sample. The deep acoustic features are processed by a second deep neural network that classifies the deep acoustic features according to a determined likelihood of including a spoofing condition. A binary classifier then classifies the voice sample as being genuine or spoofed.

公开(公告)号：US20170111506A1

公开(公告)日：2017-04-20

申请号：US15294538

申请日：2016-10-14

Applicant: PINDROP SECURITY, INC.

Inventor： Scott STRONG ,  Kailash PATIL ,  David DEWEY ,  Raj BANDYOPADHYAY ,  Telvis CALHOUN ,  Vijay BALASUBRAMANIYAN

IPC: H04M3/527 ,  G06N99/00 ,  H04M15/00

CPC classification number: H04M3/527 ,  G06F21/32 ,  G06F21/552 ,  G06N99/005 ,  H04M3/493 ,  H04M7/0078 ,  H04M15/41 ,  H04M2203/551 ,  H04M2203/6027 ,  H04W12/12

Abstract: Systems and methods for call detail record (CDR) analysis to determine a risk score for a call and identify fraudulent activity and for fraud detection in Interactive Voice Response (IVR) systems. An example method may store information extracted from received calls. Queries of the stored information may be performed to select data using keys, wherein each key relates to one of the received calls, and wherein the queries are parallelized. The selected data may be transformed into feature vectors, wherein each feature vector relates to one of the received calls and includes a velocity feature and at least one of a behavior feature or a reputation feature. A risk score for the call may be generated during the call based on the feature vectors.