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公开(公告)号：US11748463B2
公开(公告)日：2023-09-05
申请号：US17157837
申请日：2021-01-25
Applicant: PINDROP SECURITY, INC.
Inventor： Scott Strong , Kailash Patil , David Dewey , Raj Bandyopadhyay , Telvis Calhoun , Vijay Balasubramaniyan
IPC: G06F21/32 , G06N20/00 , G06F21/55 , H04M3/493 , H04W12/128 , H04M3/527 , H04M15/00 , H04W12/12 , H04M7/00
CPC classification number: G06F21/32 , G06F21/552 , G06N20/00 , H04M3/493 , H04M3/527 , H04M15/41 , H04W12/12 , H04W12/128 , H04M7/0078 , H04M2203/551 , H04M2203/6027
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.
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公开(公告)号：US11488605B2
公开(公告)日：2022-11-01
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 , G10L25/51 , G10L25/30
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.
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公开(公告)号：US20210150010A1
公开(公告)日：2021-05-20
申请号：US17157837
申请日：2021-01-25
Applicant: PINDROP SECURITY, INC.
Inventor： Scott Strong , Kailash Patil , David Dewey , Raj Bandyopadhyay , Telvis Calhoun , Vijay Balasubramaniyan
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.
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公开(公告)号：US10902105B2
公开(公告)日：2021-01-26
申请号：US16515823
申请日：2019-07-18
Applicant: PINDROP SECURITY, INC.
Inventor： Scott Strong , Kailash Patil , David Dewey , Raj Bandyopadhyay , Telvis Calhoun , Vijay Balasubramaniyan
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.
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公开(公告)号：US12174964B2
公开(公告)日：2024-12-24
申请号：US17159748
申请日：2021-01-27
Applicant: PINDROP SECURITY, INC.
Inventor： Hung Wei Tseng , Kailash Patil
Abstract: Embodiments described herein provide for performing a risk assessment. A computer identifies and stores heterogeneous events between a user and a provider system in which the user interacts with an account. The computer may store the heterogeneous events in a table. The stored event information normalizes the events associated with an account. The computer may determine static risk contributions associated with the event information of the account and store the static risk contributions in the table. The computer groups the static risk contributions into predetermined groups. The static risk contributions in each group are converted into dynamic risk contributions. The dynamic risk contributions of each group are aggregated, and the aggregate value of the dynamic risk contributions are fed to a machine learning model. The machine learning model determines a risk score associated with the account.
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Patent Agency Ranking
公开(公告)号：US20240363103A1
公开(公告)日：2024-10-31
申请号：US18388412
申请日：2023-11-09
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: G10L15/08
CPC classification number: G10L15/08
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.
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公开(公告)号：US20240355322A1
公开(公告)日：2024-10-24
申请号：US18388428
申请日：2023-11-09
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: G10L15/08
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.
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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
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.
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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
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.
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公开(公告)号：US20220224793A1
公开(公告)日：2022-07-14
申请号：US17706398
申请日：2022-03-28
Applicant: Pindrop Security, Inc.
Inventor： Akanksha , Terry Nelms , Kailash Patil , Chirag Tailor , Khaled Lakhdhar
Abstract: Embodiments described herein provide for detecting whether an Automatic Number Identification (ANI) associated with an incoming call is a gateway, according to rules-based models and machine learning models generated by the computer using call data stored in one or more databases.
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