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    DETECTION OF CALLS FROM VOICE ASSISTANTS
    1.
    发明申请
    DETECTION OF CALLS FROM VOICE ASSISTANTS 审中-公开

    公开(公告)号：US20200312313A1

    公开(公告)日：2020-10-01

    申请号：US16829705

    申请日：2020-03-25

    Applicant: PINDROP SECURITY, INC.

    Inventor： Vinay MADDALI ,  David LOONEY ,  Kailash PATIL

    IPC: G10L15/197 ,  H04M3/51 ,  G10L15/18 ,  G10L15/02 ,  G10L15/04 ,  G10L25/21 ,  G10L15/06 ,  G10L15/22 ,  G10L25/84 ,  G06N20/00 ,  G06N5/04

    Abstract: Embodiments described herein provide for automatically classifying the types of devices that place calls to a call center. A call center system can detect whether an incoming call originated from voice assistant device using trained classification models received from a call analysis service. Embodiments described herein provide for methods and systems in which a computer executes machine learning algorithms that programmatically train (or otherwise generate) global or tailored classification models based on the various types of features of an audio signal and call data. A classification model is deployed to one or more call centers, where the model is used by call center computers executing classification processes for determining whether incoming telephone calls originated from a voice assistant device, such as Amazon Alexa® and Google Home®, or another type of device (e.g., cellular/mobile phone, landline phone, VoIP).

    DEEPFAKE DETECTION
    4.
    发明公开
    DEEPFAKE DETECTION 审中-公开

    公开(公告)号：US20240355334A1

    公开(公告)日：2024-10-24

    申请号：US18388457

    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: G10L17/06

    CPC classification number: G10L17/06

    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.

    DYNAMIC ACCOUNT RISK ASSESSMENT FROM HETEROGENEOUS EVENTS
    6.
    发明申请
    DYNAMIC ACCOUNT RISK ASSESSMENT FROM HETEROGENEOUS EVENTS 有权

    公开(公告)号：US20210240837A1

    公开(公告)日：2021-08-05

    申请号：US17159748

    申请日：2021-01-27

    Applicant: PINDROP SECURITY, INC.

    Inventor： Hung Wei TSENG ,  Kailash PATIL

    IPC: G06F21/57 ,  G06F21/55 ,  G06F11/30

    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.

    BEHAVIORAL BIOMETRICS USING KEYPRESS TEMPORAL INFORMATION
    8.
    发明公开
    BEHAVIORAL BIOMETRICS USING KEYPRESS TEMPORAL INFORMATION 审中-公开

    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.

    SYSTEMS AND METHODS EMPLOYING GRAPH-DERIVED FEATURES FOR FRAUD DETECTION
    9.
    发明申请
    SYSTEMS AND METHODS EMPLOYING GRAPH-DERIVED FEATURES FOR FRAUD DETECTION 有权

    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.

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