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.

公开(公告)号：US20240363119A1

公开(公告)日：2024-10-31

申请号：US18646375

申请日：2024-04-25

Applicant: Pindrop Security, Inc.

Inventor： Elie KHOURY ,  Ganesh SIVARAMAN ,  Tianxiang CHEN ,  Nikolay GAUBITCH ,  David LOONEY ,  Amit GUPTA ,  Vijay BALASUBRAMANIYAN ,  Nicholas KLEIN ,  Anthony STANKUS

IPC: G10L17/00

CPC classification number: G10L17/00

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.

公开(公告)号：US20240363100A1

公开(公告)日：2024-10-31

申请号：US18646228

申请日：2024-04-25

Applicant: Pindrop Security, Inc.

Inventor： Elie KHOURY ,  Ganesh SIVARAMAN ,  Tianxiang CHEN ,  Nikolay GAUBITCH ,  David LOONEY ,  Amit GUPTA ,  Vijay BALASUBRAMANIYAN ,  Nicholas KLEIN ,  Anthony STANKUS

IPC: G10L15/02

CPC classification number: G10L15/02

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.

公开(公告)号：US20220392452A1

公开(公告)日：2022-12-08

申请号：US17832146

申请日：2022-06-03

Applicant: Pindrop Security, Inc.

Inventor： Payas GUPTA ,  Elie KHOURY ,  Terry NELMS, II ,  Vijay BALASUBRAMANIYAN

IPC: G10L17/04 ,  G06F21/32 ,  G10L17/22

Abstract: Disclosed are systems and methods including computing-processes executing machine-learning architectures extract vectors representing disparate types of data and output predicted identities of users accessing computing services, without express identity assertions, and across multiple computing services, analyzing data from multiple modalities, for various user devices, and agnostic to architectures hosting the disparate computing service. The system invokes the identification operations of the machine-learning architecture, which extracts biometric embeddings from biometric data and context embeddings representing all or most of the types of metadata features analyzed by the system. The context embeddings help identify a subset of potentially matching identities of possible users, which limits the number of biometric-prints the system compares against an inbound biometric embedding for authentication. The types of extracted features originate from multiple modalities, including metadata from data communications, audio signals, and images. In this way, the embodiments apply a multi-modality machine-learning architecture.