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公开(公告)号：US12250344B2
公开(公告)日：2025-03-11
申请号：US18423858
申请日：2024-01-26
Applicant: Pindrop Security, Inc.
Inventor： John Cornwell , Terry Nelms, II
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).
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公开(公告)号：US20250037506A1
公开(公告)日：2025-01-30
申请号：US18918928
申请日：2024-10-17
Applicant: Pindrop Security, Inc.
Inventor： Tianxiang CHEN , Elie KHOURY
Abstract: The embodiments execute machine-learning architectures for biometric-based identity recognition (e.g., speaker recognition, facial recognition) and deepfake detection (e.g., speaker deepfake detection, facial deepfake detection). The machine-learning architecture includes layers defining multiple scoring components, including sub-architectures for speaker deepfake detection, speaker recognition, facial deepfake detection, facial recognition, and lip-sync estimation engine. The machine-learning architecture extracts and analyzes various types of low-level features from both audio data and visual data, combines the various scores, and uses the scores to determine the likelihood that the audiovisual data contains deepfake content and the likelihood that a claimed identity of a person in the video matches to the identity of an expected or enrolled person. This enables the machine-learning architecture to perform identity recognition and verification, and deepfake detection, in an integrated fashion, for both audio data and visual data.
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公开(公告)号：US12175983B2
公开(公告)日：2024-12-24
申请号：US18436911
申请日：2024-02-08
Applicant: PINDROP SECURITY, INC.
Inventor： Ellie Khoury , Matthew Garland
Abstract: Utterances of at least two speakers in a speech signal may be distinguished and the associated speaker identified by use of diarization together with automatic speech recognition of identifying words and phrases commonly in the speech signal. The diarization process clusters turns of the conversation while recognized special form phrases and entity names identify the speakers. A trained probabilistic model deduces which entity name(s) correspond to the clusters.
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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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公开(公告)号：US12159633B2
公开(公告)日：2024-12-03
申请号：US17491292
申请日：2021-09-30
Applicant: PINDROP SECURITY, INC.
Inventor： Payas Gupta , Terry Nelms, II
Abstract: Embodiments described herein provide for a voice biometrics system execute machine-learning architectures capable of passive, active, continuous, or static operations, or a combination thereof. Systems passively and/or continuously, in some cases in addition to actively and/or statically, enrolling speakers. The system may dynamically generate and update profiles corresponding to end-users who contact a call center. The system may determine a level of enrollment for the enrollee profiles that limits the types of functions that the user may access. The system may update the profiles as new contact events are received or based on certain temporal triggering conditions.
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Patent Agency Ranking
公开(公告)号：US20240363119A1
公开(公告)日：2024-10-31
申请号：US18646375
申请日：2024-04-25
Applicant: Pindrop Security, Inc.
Inventor： Elie KHOURY , Ganesh SIVARAMAN , Tianxiang CHEN , Nikolay GAUBITCH , David LOONEY , Amit GUPTA , Vijay BALASUBRAMANIYAN , Nicholas KLEIN , Anthony STANKUS
IPC: G10L17/00
CPC classification number: G10L17/00
Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.
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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：US20240363100A1
公开(公告)日：2024-10-31
申请号：US18646228
申请日：2024-04-25
Applicant: Pindrop Security, Inc.
Inventor： Elie KHOURY , Ganesh SIVARAMAN , Tianxiang CHEN , Nikolay GAUBITCH , David LOONEY , Amit GUPTA , Vijay BALASUBRAMANIYAN , Nicholas KLEIN , Anthony STANKUS
IPC: G10L15/02
CPC classification number: G10L15/02
Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.
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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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150.发明公开公开(公告)号：US20240267459A1
公开(公告)日：2024-08-08
申请号：US18413524
申请日：2024-01-16
Applicant: Pindrop Security, Inc.
Inventor： MohammedAli MERCHANT
CPC classification number: H04M3/42059 , H04M3/436
Abstract: Disclosed are systems and methods including processes executed by a server that executes software routines for machine-learning architectures that receive call-invite messages containing data from a terminating carrier. The server a caller ANI and types of call data. The server further requests data from a telephony database. The server applies and executes the software programming of the machine-learning architecture on the call data (from the terminating carrier) and the portability data (from the telephony database) to generate risk scores. The server stores the data and the risk scores into a request database, until a provider server requests the risk scores in a threat assessment request. The server returns a threat assessment message to the provider server in response to the threat assessment request. The threat assessment message includes information about the caller or caller device, and the risk scores, but not the caller ANI.
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