公开(公告)号：US20240323270A1

公开(公告)日：2024-09-26

申请号：US18680327

申请日：2024-05-31

申请人： Pindrop Security, Inc.

发明人： Nick Gaubitch ,  Scott Strong ,  John Cornwell ,  Hassan Kingravi ,  David Dewey

IPC分类号： H04M1/56 ,  G10L25/51 ,  H04L25/02 ,  H04M3/22 ,  H04M3/493 ,  H04M7/12 ,  H04Q1/45 ,  H04Q3/70

CPC分类号： H04M1/56 ,  H04L25/0202 ,  H04M3/2281 ,  H04M3/493 ,  H04M7/1295 ,  H04Q1/45 ,  H04Q3/70 ,  G10L25/51 ,  H04M2201/18 ,  H04M2203/60 ,  H04Q2213/13139 ,  H04Q2213/13405 ,  H04Q2213/13515

摘要： 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：US12015731B2

公开(公告)日：2024-06-18

申请号：US17857618

申请日：2022-07-05

申请人： Pindrop Security, Inc.

发明人： Nick Gaubitch ,  Scott Strong ,  John Cornwell ,  Hassan Kingravi ,  David Dewey

IPC分类号： H04M1/56 ,  H04L25/02 ,  H04M3/22 ,  H04M3/493 ,  H04M7/12 ,  H04M15/06 ,  H04Q1/45 ,  H04Q3/70 ,  G10L25/51

CPC分类号： H04M1/56 ,  H04L25/0202 ,  H04M3/2281 ,  H04M3/493 ,  H04M7/1295 ,  H04Q1/45 ,  H04Q3/70 ,  G10L25/51 ,  H04M2201/18 ,  H04M2203/60 ,  H04Q2213/13139 ,  H04Q2213/13405 ,  H04Q2213/13515

摘要： 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：US11895264B2

公开(公告)日：2024-02-06

申请号：US17365970

申请日：2021-07-01

申请人： PINDROP SECURITY, INC.

发明人： Kedar Phatak ,  Jayaram Raghuram

IPC分类号： H04M3/22 ,  G06N20/00 ,  H04L9/40

CPC分类号： H04M3/2281 ,  G06N20/00 ,  H04L63/1483

摘要： Embodiments described herein provide for a fraud detection engine for detecting various types of fraud at a call center and a fraud importance engine for tailoring the fraud detection operations to relative importance of fraud events. Fraud importance engine determines which fraud events are comparative more important than others. The fraud detection engine comprises machine-learning models that consume contact data and fraud importance information for various anti-fraud processes. The fraud importance engine calculates importance scores for fraud events based on user-customized attributes, such as fraud-type or fraud activity. The fraud importance scores are used in various processes, such as model training, model selection, and selecting weights or hyper-parameters for the ML models, among others. The fraud detection engine uses the importance scores to prioritize fraud alerts for review. The fraud importance engine receives detection feedback, which contacts involved false negatives, where fraud events were undetected but should have been detected.

公开(公告)号：US20230284016A1

公开(公告)日：2023-09-07

申请号：US18117134

申请日：2023-03-03

申请人： PINDROP SECURITY, INC.

发明人： MohammedAli Merchant ,  Yitao Sun

IPC分类号： H04W12/069 ,  H04W12/40 ,  H04W12/72

CPC分类号： H04W12/069 ,  H04W12/40 ,  H04W12/72

摘要： Embodiments described herein provide for evaluating call metadata and certificates of inbound calls for authentication. The computer identifies a service provider indicated by the SPID and/or the ANI (or other identifier) of the metadata and identifies a service provider indicated by the SPID and/or ANI (or other identifier) of the certificate, then compares identities of the service providers and/or compares the data values associated with the service providers (e.g., SPIDs, ANIs). Based on this comparison, the computer determines whether the service provider that signed the certificate is first-party signer (e.g., carrier) for the ANI or a third-party signer that is signing certificates as the first-party signer for the ANI.

公开(公告)号：US11748463B2

公开(公告)日：2023-09-05

申请号：US17157837

申请日：2021-01-25

申请人： PINDROP SECURITY, INC.

发明人： Scott Strong ,  Kailash Patil ,  David Dewey ,  Raj Bandyopadhyay ,  Telvis Calhoun ,  Vijay Balasubramaniyan

IPC分类号： G06F21/32 ,  G06N20/00 ,  G06F21/55 ,  H04M3/493 ,  H04W12/128 ,  H04M3/527 ,  H04M15/00 ,  H04W12/12 ,  H04M7/00

CPC分类号： G06F21/32 ,  G06F21/552 ,  G06N20/00 ,  H04M3/493 ,  H04M3/527 ,  H04M15/41 ,  H04W12/12 ,  H04W12/128 ,  H04M7/0078 ,  H04M2203/551 ,  H04M2203/6027

摘要： 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：US20230262161A1

公开(公告)日：2023-08-17

申请号：US18109095

申请日：2023-02-13

申请人： Pindrop Security, Inc.

发明人： Mohammed Ali Merchant ,  Yitao Sun

IPC分类号： H04M3/42 ,  H04M3/22

CPC分类号： H04M3/42059 ,  H04M3/2281 ,  H04M3/42357 ,  H04M2203/6027

摘要： Embodiments described herein provide for systems and methods for verifying authentic JIPs associated with ANIs using CLLIs known to be associated with the ANIs, allowing a computer to authenticate calls using the verified JIPs, among various factors. The computer builds a trust model for JIPs by correlating unique CLLIs to JIPs. A malicious actor might spoof numerous ANIs mapped to a single CLLI, but the malicious actor is unlikely to spoof multiple CLLIs due to the complexity of spoofing the volumes of ANIs associated with multiple CLLIs, so the CLLIs can be trusted when determining whether a JIP is authentic. The computer identifies an authentic JIP when the trust model indicates that a number of CLLIs associated with the JIP satisfies one or more thresholds. A machine-learning architecture references the fact that the JIP is authentic as an authentication factor for downstream call authentication functions.

公开(公告)号：US20230254403A1

公开(公告)日：2023-08-10

申请号：US18301897

申请日：2023-04-17

申请人： PINDROP SECURITY, INC.

发明人： Ricardo CASAL ,  Theo WALKER ,  Kailash PATIL ,  John CORNWELL

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

CPC分类号： H04M3/2281 ,  G06N20/00 ,  G06F18/214 ,  H04M2203/551 ,  H04M2203/556

摘要： 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：US20230015189A1

公开(公告)日：2023-01-19

申请号：US17953156

申请日：2022-09-26

申请人： Pindrop Security, Inc.

发明人： David Looney ,  Nikolay D. Gaubitch

IPC分类号： G10L25/51 ,  G10L25/90 ,  G10L15/06 ,  G10L15/22

摘要： A computer may train a single-class machine learning using normal speech recordings. The machine learning model or any other model may estimate the normal range of parameters of a physical speech production model based on the normal speech recordings. For example, the computer may use a source-filter model of speech production, where voiced speech is represented by a pulse train and unvoiced speech by a random noise and a combination of the pulse train and the random noise is passed through an auto-regressive filter that emulates the human vocal tract. The computer leverages the fact that intentional modification of human voice introduces errors to source-filter model or any other physical model of speech production. The computer may identify anomalies in the physical model to generate a voice modification score for an audio signal. The voice modification score may indicate a degree of abnormality of human voice in the audio signal.

公开(公告)号：US11495244B2

公开(公告)日：2022-11-08

申请号：US16375785

申请日：2019-04-04

申请人： PINDROP SECURITY, INC.

发明人： David Looney ,  Nikolay D. Gaubitch

IPC分类号： G10L25/00 ,  G10L25/51 ,  G10L25/90 ,  G10L15/06 ,  G10L15/22 ,  H04M3/436

摘要： A computer may train a single-class machine learning using normal speech recordings. The machine learning model or any other model may estimate the normal range of parameters of a physical speech production model based on the normal speech recordings. For example, the computer may use a source-filter model of speech production, where voiced speech is represented by a pulse train and unvoiced speech by a random noise and a combination of the pulse train and the random noise is passed through an auto-regressive filter that emulates the human vocal tract. The computer leverages the fact that intentional modification of human voice introduces errors to source-filter model or any other physical model of speech production. The computer may identify anomalies in the physical model to generate a voice modification score for an audio signal. The voice modification score may indicate a degree of abnormality of human voice in the audio signal.

公开(公告)号：US11488605B2

公开(公告)日：2022-11-01

申请号：US16907951

申请日：2020-06-22

申请人： PINDROP SECURITY, INC.

发明人： Elie Khoury ,  Parav Nagarsheth ,  Kailash Patil ,  Matthew Garland

IPC分类号： G10L17/02 ,  G10L17/04 ,  G10L25/24 ,  G10L17/18 ,  G10L19/02 ,  G10L17/06 ,  G10L17/00 ,  G10L25/51 ,  G10L25/30

摘要： 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.