公开(公告)号：US20210152897A1

公开(公告)日：2021-05-20

申请号：US17157848

申请日：2021-01-25

Applicant: PINDROP SECURITY, INC.

Inventor： Nick GAUBITCH ,  Scott STRONG ,  John CORNWELL ,  Hassan KINGRAVI ,  David DEWEY

IPC: H04Q3/70 ,  H04M3/22 ,  H04M7/12 ,  H04Q1/45 ,  G10L25/51 ,  H04L25/02 ,  H04M3/493

Abstract: Systems, methods, and computer-readable media for call classification and for training a model for call classification, an example method comprising: receiving DTMF information from a plurality of calls; determining, for each of the calls, a feature vector including statistics based on DTMF information such as DTMF residual signal comprising channel noise and additive noise; training a model for classification; comparing a new call feature vector to the model; predicting a device type and geographic location based on the comparison of the new call feature vector to the model; classifying the call as spoofed or genuine; and authenticating a call or altering an IVR call flow.

公开(公告)号：US20190238956A1

公开(公告)日：2019-08-01

申请号：US16378286

申请日：2019-04-08

Applicant: PINDROP SECURITY, INC.

Inventor： Nick GAUBITCH ,  Scott STRONG ,  John CORNWELL ,  Hassan KINGRAVI ,  David DEWEY

IPC: H04Q3/70 ,  H04L25/02 ,  H04M3/493 ,  H04M3/22 ,  H04M7/12 ,  H04Q1/45 ,  G10L25/51

CPC classification number: H04Q3/70 ,  G10L25/51 ,  H04L25/0202 ,  H04M3/2281 ,  H04M3/493 ,  H04M7/1295 ,  H04M2201/18 ,  H04M2203/60 ,  H04Q1/45 ,  H04Q2213/13139 ,  H04Q2213/13405 ,  H04Q2213/13515

Abstract: Systems, methods, and computer-readable media for call classification and for training a model for call classification, an example method comprising: receiving DTMF information from a plurality of calls; determining, for each of the calls, a feature vector including statistics based on DTMF information such as DTMF residual signal comprising channel noise and additive noise; training a model for classification; comparing a new call feature vector to the model; predicting a device type and geographic location based on the comparison of the new call feature vector to the model; classifying the call as spoofed or genuine; and authenticating a call or altering an IVR call flow.

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：US20180041823A1

公开(公告)日：2018-02-08

申请号：US15600625

申请日：2017-05-19

Applicant: PINDROP SECURITY, INC.

Inventor： Nick GAUBITCH ,  Scott STRONG ,  John CORNWELL ,  Hassan KINGRAVI ,  David DEWEY

IPC: H04Q3/70 ,  H04L25/02 ,  G10L25/51 ,  H04M3/493

CPC classification number: H04Q3/70 ,  G10L25/51 ,  H04L25/0202 ,  H04M3/2281 ,  H04M3/493 ,  H04M7/1295 ,  H04M2201/18 ,  H04M2203/60 ,  H04Q1/45 ,  H04Q2213/13139 ,  H04Q2213/13405 ,  H04Q2213/13515

Abstract: Systems, methods, and computer-readable media for call classification and for training a model for call classification, an example method comprising: receiving DTMF information from a plurality of calls; determining, for each of the calls, a feature vector including statistics based on DTMF information such as DTMF residual signal comprising channel noise and additive noise; training a model for classification; comparing a new call feature vector to the model; predicting a device type and geographic location based on the comparison of the new call feature vector to the model; classifying the call as spoofed or genuine; and authenticating a call or altering an IVR call flow.

公开(公告)号：US20240171680A1

公开(公告)日：2024-05-23

申请号：US18423858

申请日：2024-01-26

Applicant: Pindrop Security, Inc.

Inventor： John CORNWELL ,  Terry NELMS, II

IPC: H04M3/51 ,  G06F18/214 ,  H04M3/22 ,  H04M3/42

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：US20240169040A1

公开(公告)日：2024-05-23

申请号：US18515128

申请日：2023-11-20

Applicant: PINDROP SECURITY, INC.

Inventor： Hrishikesh RAO ,  Ricky CASAL ,  Elie KHOURY ,  Eric LORIMER ,  John CORNWELL ,  Kailash PATIL

IPC: G06F21/31

CPC classification number: G06F21/316

Abstract: Embodiments include a computing device that executes software routines and/or one or more machine-learning architectures including a neural network-based embedding extraction system that to produce an embedding vector representing a user's behavior's keypresses, where the system extracts the behaviorprint embedding vector using the keypress features that the system references later for authenticating users. Embodiments may extract and evaluate keypress features, such as keypress sequences, keypress pressure or volume, and temporal keypress features, such as the duration of keypresses and the interval between keypresses, among others. Some embodiments employ a deep neural network architecture that generates a behaviorprint embedding vector representation of the keypress duration and interval features that is used for enrollment and at inference time to authenticate users.

公开(公告)号：US20220070292A1

公开(公告)日：2022-03-03

申请号：US17201849

申请日：2021-03-15

Applicant: PINDROP SECURITY, INC.

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

IPC: H04M3/22 ,  G06K9/62 ,  G06N20/00

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：US20210058507A1

公开(公告)日：2021-02-25

申请号：US16992789

申请日：2020-08-13

Applicant: PINDROP SECURITY, INC.

Inventor： John CORNWELL ,  Terry NELMS, II

IPC: H04M3/22 ,  H04M3/51 ,  H04M3/42 ,  G06K9/62

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).