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公开(公告)号：US10715660B2
公开(公告)日：2020-07-14
申请号：US16595168
申请日：2019-10-07
发明人： Payas Gupta , Terry Nelms, II
摘要： In an illustrative embodiment, a user device may block all the phone numbers used by an enterprise. When an enterprise wants to call the user, the enterprise may notify the user device through a separate secure channel that an enterprise phone number is in the process of making a phone call to the user device. The secure channel may include an authentication server that may request the user device to unblock the enterprise phone number. An incoming phone call from the enterprise phone number therefore can be trusted. After the phone call is terminated, the user device may again block the enterprise phone number. An attacker may not have access to the authentication server and a phone call from the attacker with a spoofed enterprise phone number (now blocked) may be dropped by the user device.
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公开(公告)号：US20240355337A1
公开(公告)日：2024-10-24
申请号：US18388364
申请日：2023-11-09
发明人： Umair Altaf , Sai Pradeep Peri , Lakshay Phatela , Payas Gupta , Yitao Sun , Svetlana Afanaseva , Kailash Patil , Elie Khoury , Bradley Magnetta , Vijay Balasubramaniyan , Tianxiang Chen
IPC分类号： G10L17/24
CPC分类号： G10L17/24
摘要： 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：US10440178B2
公开(公告)日：2019-10-08
申请号：US16289957
申请日：2019-03-01
发明人： Payas Gupta , Terry Nelms, II
摘要： In an illustrative embodiment, a user device may block all the phone numbers used by an enterprise. When an enterprise wants to call the user, the enterprise may notify the user device through a separate secure channel that an enterprise phone number is in the process of making a phone call to the user device. The secure channel may include an authentication server that may request the user device to unblock the enterprise phone number. An incoming phone call from the enterprise phone number therefore can be trusted. After the phone call is terminated, the user device may again block the enterprise phone number. An attacker may not have access to the authentication server and a phone call from the attacker with a spoofed enterprise phone number (now blocked) may be dropped by the user device.
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公开(公告)号：US11783839B2
公开(公告)日：2023-10-10
申请号：US17491363
申请日：2021-09-30
发明人： Payas Gupta , Terry Nelms, II
CPC分类号： G10L17/04 , G06F21/32 , G10L25/27 , H04M3/2281 , H04M3/42068 , H04M3/51 , H04M3/5141 , H04M3/5166 , H04M2203/6045 , H04M2203/6054
摘要： 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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公开(公告)号：US10665244B1
公开(公告)日：2020-05-26
申请号：US16240602
申请日：2019-01-04
发明人： Payas Gupta , Terry Nelms, II
摘要： Disclosed herein are embodiments of systems, methods, and products comprises an authentication server for authentication leveraging multiple audio channels. The server receives an authentication request regarding a user upon the user interacting with a first electronic device. The server requests the first device to transmit a first audio file of an audio sample to the server. The audio sample may be the user's audio command or a machine-generated audio signal. The server requests a second electronic device to transmit a second audio file that is the recording of the same audio sample to the server. The second electronic device is a trusted device in proximity of the first device and executes an authentication function to enable the recording and transmitting of the audio sample. The server determines a similarity score between the first audio file and the second audio file and authenticates the user based on the similarity score.
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公开(公告)号：US10027816B2
公开(公告)日：2018-07-17
申请号：US15445569
申请日：2017-02-28
发明人： Payas Gupta , David Dewey
摘要： The invention may verify calls to a telephone device by activating call forwarding to redirect calls for the telephone device to a prescribed destination; receiving a message from a server verifying the call; deactivating call forwarding to receive the call; and reactivating call forwarding when the call is concluded. In another embodiment, the invention may, in response to a telephone device initiating a call to a second telephone device installed with a particular application or software, transmit a message to a server causing it to instruct the second telephone device to deactivate call forwarding. In yet another embodiment, the invention may cause a server to receive a message from a prescribed location indicating that a call was received via call forwarding, and in response to the message, transmit an instruction to the intended recipient to deactivate the call forwarding if the call is verified as legitimate.
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公开(公告)号：US20240363099A1
公开(公告)日：2024-10-31
申请号：US18388466
申请日：2023-11-09
发明人： Umair Altaf , Sai Pradeep Peri , Lakshay Phatela , Payas Gupta , Yitao Sun , Svetlana Afanaseva , Kailash Patil , Elie Khoury , Bradley Magnetta , Vijay Balasubramaniyan , Tianxiang Chen
摘要： 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：US20240355319A1
公开(公告)日：2024-10-24
申请号：US18388385
申请日：2023-11-09
发明人： Umair Altaf , Sai Pradeep Peri , Lakshay Phatela , Payas Gupta , Yitao Sun , Svetlana Afanaseva , Kailash Patil , Elie Khoury , Bradley Magnetta , Vijay Balasubramaniyan , Tianxiang Chen
摘要： 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：US20220392453A1
公开(公告)日：2022-12-08
申请号：US17832404
申请日：2022-06-03
摘要： Disclosed are systems and methods including computing-processes executing machine-learning architectures extract vectors representing disparate types of data and output predicted identities of users accessing computing services, without express identity assertions, and across multiple computing services, analyzing data from multiple modalities, for various user devices, and agnostic to architectures hosting the disparate computing service. The system invokes the identification operations of the machine-learning architecture, which extracts biometric embeddings from biometric data and context embeddings representing all or most of the types of metadata features analyzed by the system. The context embeddings help identify a subset of potentially matching identities of possible users, which limits the number of biometric-prints the system compares against an inbound biometric embedding for authentication. The types of extracted features originate from multiple modalities, including metadata from data communications, audio signals, and images. In this way, the embodiments apply a multi-modality machine-learning architecture.
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公开(公告)号：US10735581B2
公开(公告)日：2020-08-04
申请号：US16551327
申请日：2019-08-26
发明人： Payas Gupta
摘要： A method of obtaining and automatically providing secure authentication information includes registering a client device over a data line, storing information and a changeable value for authentication in subsequent telephone-only transactions. In the subsequent transactions, a telephone call placed from the client device to an interactive voice response server is intercepted and modified to include dialing of a delay and at least a passcode, the passcode being based on the unique information and the changeable value, where the changeable value is updated for every call session. The interactive voice response server forwards the passcode and a client device identifier to an authentication function, which compares the received passcode to plural passcodes generated based on information and iterations of a value stored in correspondence with the client device identifier. Authentication is confirmed when a generated passcode matches the passcode from the client device.
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