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公开(公告)号：AU2019200722A1
公开(公告)日：2019-02-28
申请号：AU2019200722
申请日：2019-02-04
Applicant: PINDROP SECURITY INC
Inventor： BANDYOPADHYAY RAJ , CALHOUN TELVIS , BALASUBRAMANIYAN VIJAY , STRONG SCOTT , PATIL KAILASH , DEWEY DAVID
Abstract: Abstract Method for determining a risk score for a call; receiving a call from a particular phone number; retrieving pre-stored information relating to the particular phone number to derive a reputation feature and a velocity feature; including the reputation feature and the velocity feature in a feature vector; and generating a risk score for the call based on the feature vector.
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公开(公告)号：CA3054063A1
公开(公告)日：2018-09-07
申请号：CA3054063
申请日：2018-03-02
Applicant: PINDROP SECURITY INC
Inventor： KHOURY ELIE , NAGARSHETH PARAV , PATIL KAILASH , GARLAND MATTHEW
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：AU2016338689B2
公开(公告)日：2019-06-13
申请号：AU2016338689
申请日：2016-10-14
Applicant: PINDROP SECURITY INC
Inventor： BANDYOPADHYAY RAJ , CALHOUN TELVIS , BALASUBRAMANIYAN VIJAY , STRONG SCOTT , PATIL KAILASH , DEWEY DAVID
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：AU2021239845A1
公开(公告)日：2022-09-08
申请号：AU2021239845
申请日：2021-03-15
Applicant: PINDROP SECURITY INC
Inventor： CASAL RICARDO , WALKER THEO , PATIL KAILASH , CORNWELL JOHN
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：AU2019229365A1
公开(公告)日：2019-10-03
申请号：AU2019229365
申请日：2019-09-11
Applicant: PINDROP SECURITY INC
Inventor： BANDYOPADHYAY RAJ , CALHOUN TELVIS , BALASUBRAMANIYAN VIJAY , STRONG SCOTT , PATIL KAILASH , DEWEY DAVID
Abstract: The invention proposes a computer-implemented method for determining a risk score for a call, the method comprising: querying, by a computer, call detail records to retrieve source automatic number identifications (ANIs) and destination ANIs of the corresponding calls; generating, by the computer, feature vectors based on the source ANIs and the destination ANIs of the calls; training, by the computer, a machine learning model using the feature vectors; receiving, by the computer, a call from a particular phone number; generating, by the computer, a feature vector of the call comprising the particular phone number; executing, by the computer during the call, the machine learning model applying the feature vector for the call to generate a risk score for the call, the risk score indicating a likelihood of the call being fraudulent; and triggering, by the computer during the call, computer operation based on the risk score for the calls.
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Patent Agency Ranking
公开(公告)号：ES2993990T3
公开(公告)日：2025-01-15
申请号：ES18712052
申请日：2018-03-02
Applicant: PINDROP SECURITY INC
Inventor： KHOURY ELIE , NAGARSHETH PARAV , PATIL KAILASH , GARLAND MATTHEW
Abstract: Un sistema de verificación automática de hablantes (ASV) incorpora una primera red neuronal profunda para extraer características acústicas profundas, como características CQCC profundas, de una muestra de voz recibida. Las características acústicas profundas son procesadas por una segunda red neuronal profunda que clasifica las características acústicas profundas según una probabilidad determinada de incluir una condición de suplantación. A continuación, un clasificador binario clasifica la muestra de voz como genuina o suplantada. (Traducción automática con Google Translate, sin valor legal)
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公开(公告)号：AU2021277642A1
公开(公告)日：2021-12-23
申请号：AU2021277642
申请日：2021-11-30
Applicant: PINDROP SECURITY INC
Inventor： KHOURY ELIE , NAGARSHETH PARAV , PATIL KAILASH , GARLAND MATTHEW
Abstract: 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：AU2019200722B2
公开(公告)日：2019-07-11
申请号：AU2019200722
申请日：2019-02-04
Applicant: PINDROP SECURITY INC
Inventor： BANDYOPADHYAY RAJ , CALHOUN TELVIS , BALASUBRAMANIYAN VIJAY , STRONG SCOTT , PATIL KAILASH , DEWEY DAVID
Abstract: Abstract Method for determining a risk score for a call; receiving a call from a particular phone number; retrieving pre-stored information relating to the particular phone number to derive a reputation feature and a velocity feature; including the reputation feature and the velocity feature in a feature vector; and generating a risk score for the call based on the feature vector.
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公开(公告)号：CA3001839C
公开(公告)日：2018-10-23
申请号：CA3001839
申请日：2016-10-14
Applicant: PINDROP SECURITY INC
Inventor： BANDYOPADHYAY RAJ , CALHOUN TELVIS , BALASUBRAMANIYAN VIJAY , STRONG SCOTT , PATIL KAILASH , DEWEY DAVID
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：AU2016338689A1
公开(公告)日：2018-04-26
申请号：AU2016338689
申请日：2016-10-14
Applicant: PINDROP SECURITY INC
Inventor： BANDYOPADHYAY RAJ , CALHOUN TELVIS , BALASUBRAMANIYAN VIJAY , STRONG SCOTT , PATIL KAILASH , DEWEY DAVID
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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