公开(公告)号：US20240249728A1

公开(公告)日：2024-07-25

申请号：US18422523

申请日：2024-01-25

Applicant: Pindrop Security, Inc.

Inventor： Elie KHOURY ,  Matthew GARLAND

IPC: G10L17/08 ,  G06N3/04 ,  G06N3/08 ,  G10L15/16 ,  G10L17/02 ,  G10L17/04 ,  G10L17/18 ,  G10L17/22

CPC classification number: G10L17/08 ,  G06N3/04 ,  G06N3/08 ,  G10L15/16 ,  G10L17/02 ,  G10L17/04 ,  G10L17/18 ,  G10L17/22

Abstract: The present invention is directed to a deep neural network (DNN) having a triplet network architecture, which is suitable to perform speaker recognition. In particular, the DNN includes three feed-forward neural networks, which are trained according to a batch process utilizing a cohort set of negative training samples. After each batch of training samples is processed, the DNN may be trained according to a loss function, e.g., utilizing a cosine measure of similarity between respective samples, along with positive and negative margins, to provide a robust representation of voiceprints.

公开(公告)号：US12022024B2

公开(公告)日：2024-06-25

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 ,  G06F18/214 ,  G06N20/00 ,  G06N3/08 ,  G06N20/10 ,  G06N20/20 ,  H04M3/42 ,  H04M3/51

CPC classification number: H04M3/2281 ,  G06F18/214 ,  G06N20/00 ,  G06N3/08 ,  G06N20/10 ,  G06N20/20 ,  H04M3/42042 ,  H04M3/51 ,  H04M2203/551 ,  H04M2203/556 ,  H04M2203/6027

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：US20240153510A1

公开(公告)日：2024-05-09

申请号：US18394300

申请日：2023-12-22

Applicant: PINDROP SECURITY, INC.

Inventor： Tianxiang CHEN ,  Elie KHOURY

IPC: G10L17/18 ,  G10L17/02 ,  G10L17/04 ,  G10L17/08 ,  G10L17/22

CPC classification number: G10L17/18 ,  G10L17/02 ,  G10L17/04 ,  G10L17/08 ,  G10L17/22

Abstract: Embodiments described herein provide for systems and methods for implementing a neural network architecture for spoof detection in audio signals. The neural network architecture contains a layers defining embedding extractors that extract embeddings from input audio signals. Spoofprint embeddings are generated for particular system enrollees to detect attempts to spoof the enrollee's voice. Optionally, voiceprint embeddings are generated for the system enrollees to recognize the enrollee's voice. The voiceprints are extracted using features related to the enrollee's voice. The spoofprints are extracted using features related to features of how the enrollee speaks and other artifacts. The spoofprints facilitate detection of efforts to fool voice biometrics using synthesized speech (e.g., deepfakes) that spoof and emulate the enrollee's voice.

公开(公告)号：US20240064152A1

公开(公告)日：2024-02-22

申请号：US18235321

申请日：2023-08-17

Applicant: Pindrop Security, Inc.

Inventor： MohammedAli MERCHANT ,  Payas GUPTA

IPC: H04L9/40

CPC classification number: H04L63/123 ,  H04L63/0861 ,  H04L63/102

Abstract: Embodiments include a computing device that executes software routines and/or one or more machine-learning architectures providing improved omni-channel authentication solutions. Embodiments include one or more computing devices that provide an authentication interface by which various communication channels may deposit contact or session data received via a first-channel session into a non-transitory storage medium of an authentication database for another channel to obtain and employ (e.g., verify users). This allows the customer to access an online data channel and enter the contact center through a telephony communication channel, but further allows the enterprise contact center systems to passively maintain access to various types of information about the user's identity captured from each contact channel, allowing the call center to request or capture authenticating information (e.g., voice biometrics) from both channels to employ authentication processes for one or both channels, such as voice biometrics authentication processes or other types of authentication functions.

公开(公告)号：US11889024B2

公开(公告)日：2024-01-30

申请号：US17948991

申请日：2022-09-20

Applicant: Pindrop Security, Inc.

Inventor： John Cornwell ,  Terry Nelms, II

IPC: H04M3/00 ,  H04M5/00 ,  H04L12/66 ,  H04M3/51 ,  H04M3/22 ,  H04M3/42 ,  G06F18/214

CPC classification number: H04M3/5175 ,  G06F18/214 ,  H04M3/2218 ,  H04M3/2281 ,  H04M3/42059 ,  G06V2201/10

Abstract: Embodiments described herein provide for passive caller verification and/or passive fraud risk assessments for calls to customer call centers. Systems and methods may be used in real time as a call is coming into a call center. An analytics server of an analytics service looks at the purported Caller ID of the call, as well as the unaltered carrier metadata, which the analytics server then uses to generate or retrieve one or more probability scores using one or more lookup tables and/or a machine-learning model. A probability score indicates the likelihood that information derived using the Caller ID information has occurred or should occur given the carrier metadata received with the inbound call. The one or more probability scores be used to generate a risk score for the current call that indicates the probability of the call being valid (e.g., originated from a verified caller or calling device, non-fraudulent).

公开(公告)号：US20240022662A1

公开(公告)日：2024-01-18

申请号：US18221802

申请日：2023-07-13

Applicant: Pindrop Security, Inc.

Inventor： Ricky Casal ,  Vinay Maddali ,  Payas Gupta ,  Kailash Patil

IPC: H04M3/42 ,  H04M3/51

CPC classification number: H04M3/42357 ,  H04M3/51 ,  H04M2203/6027

Abstract: Disclosed are systems and methods including computing-processes, which may include layers of machine-learning architectures, for assessing risk for calls directed to call center systems using carrier signaling metadata. A computer evaluates carrier signaling metadata to perform various new risk-scoring techniques to determine riskiness of calls and authenticate calls. When determining a risk score for an incoming call is received at a call center system, the computer may obtain certain metadata values from inbound metadata, prior call metadata, or from third-party telecommunications services and executes processes for determining the risk score for the call. The risk score operations include several scoring components, including appliance print scoring, carrier detection scoring, ANI location detection scoring, location similarity scoring, and JIP-ANI location similarity scoring, among others.

公开(公告)号：US11810559B2

公开(公告)日：2023-11-07

申请号：US17833674

申请日：2022-06-06

Applicant: Pindrop Security, Inc.

Inventor： Hrishikesh Rao

IPC: G10L15/197 ,  G10L15/04 ,  G10L15/30 ,  G10L15/22 ,  G10L15/08

CPC classification number: G10L15/197 ,  G10L15/04 ,  G10L15/22 ,  G10L15/30 ,  G10L2015/088 ,  G10L2015/223

Abstract: Embodiments described herein provide for a computer that detects one or more keywords of interest using acoustic features, to detect or query commonalities across multiple fraud calls. Embodiments described herein may implement unsupervised keyword spotting (UKWS) or unsupervised word discovery (UWD) in order to identify commonalities across a set of calls, where both UKWS and UWD employ Gaussian Mixture Models (GMM) and one or more dynamic time-warping algorithms. A user may indicate a training exemplar or occurrence of call-specific information, referred to herein as “a named entity,” such as a person's name, an account number, account balance, or order number. The computer may perform a redaction process that computationally nullifies the import of the named entity in the modeling processes described herein.

公开(公告)号：US11715460B2

公开(公告)日：2023-08-01

申请号：US17066210

申请日：2020-10-08

Applicant: PINDROP SECURITY, INC.

Inventor： Elie Khoury ,  Ganesh Sivaraman ,  Tianxiang Chen ,  Amruta Vidwans

IPC: G10L15/16 ,  G10L25/51 ,  G10L17/04 ,  G10L15/06

CPC classification number: G10L15/16 ,  G10L15/063 ,  G10L17/04 ,  G10L25/51

Abstract: Described herein are systems and methods for improved audio analysis using a computer-executed neural network having one or more in-network data augmentation layers. The systems described herein help ease or avoid unwanted strain on computing resources by employing the data augmentation techniques within the layers of the neural network. The in-network data augmentation layers will produce various types of simulated audio data when the computer applies the neural network on an inputted audio signal during a training phase, enrollment phase, and/or testing phase. Subsequent layers of the neural network (e.g., convolutional layer, pooling layer, data augmentation layer) ingest the simulated audio data and the inputted audio signal and perform various operations.

公开(公告)号：US11670304B2

公开(公告)日：2023-06-06

申请号：US16895750

申请日：2020-06-08

Applicant: PINDROP SECURITY, INC.

Inventor： Elie Khoury ,  Matthew Garland

IPC: G10L17/00 ,  H04M1/27 ,  G10L17/24 ,  G10L15/19 ,  G10L17/08 ,  G06N7/01 ,  G10L15/07 ,  G10L15/26 ,  G10L17/04

CPC classification number: G10L17/00 ,  G06N7/01 ,  G10L15/07 ,  G10L15/19 ,  G10L15/26 ,  G10L17/04 ,  G10L17/08 ,  G10L17/24 ,  H04M1/271 ,  H04M2203/40

Abstract: Utterances of at least two speakers in a speech signal may be distinguished and the associated speaker identified by use of diarization together with automatic speech recognition of identifying words and phrases commonly in the speech signal. The diarization process clusters turns of the conversation while recognized special form phrases and entity names identify the speakers. A trained probabilistic model deduces which entity name(s) correspond to the clusters.

公开(公告)号：US11659082B2

公开(公告)日：2023-05-23

申请号：US16983967

申请日：2020-08-03

Applicant: PINDROP SECURITY, INC.

Inventor： Payas Gupta

IPC: H04M3/38 ,  H04M3/51 ,  H04L9/40 ,  H04M3/42 ,  H04M3/493

CPC classification number: H04M3/382 ,  H04L63/0838 ,  H04L63/18 ,  H04M3/5166 ,  H04M3/42008 ,  H04M3/493 ,  H04M2203/6072 ,  H04M2203/6081

Abstract: A method of obtaining and automatically providing secure authentication information includes registering a client device over a data line, storing information and a changeable value for authentication in subsequent telephone-only transactions. In the subsequent transactions, a telephone call placed from the client device to an interactive voice response server is intercepted and modified to include dialing of a delay and at least a passcode, the passcode being based on the unique information and the changeable value, where the changeable value is updated for every call session. The interactive voice response server forwards the passcode and a client device identifier to an authentication function, which compares the received passcode to plural passcodes generated based on information and iterations of a value stored in correspondence with the client device identifier. Authentication is confirmed when a generated passcode matches the passcode from the client device.