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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：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：US11470194B2
公开(公告)日：2022-10-11
申请号：US16992789
申请日：2020-08-13
发明人： John Cornwell , Terry Nelms, II
摘要： 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：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：US11870932B2
公开(公告)日：2024-01-09
申请号：US17706398
申请日：2022-03-28
发明人： Akanksha , Terry Nelms, II , Kailash Patil , Chirag Tailor , Khaled Lakhdhar
CPC分类号： H04M3/42102 , G06N20/00 , H04M2203/556 , H04M2203/558
摘要： 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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公开(公告)号：US20230014180A1
公开(公告)日：2023-01-19
申请号：US17948991
申请日：2022-09-20
发明人： John Cornwell , Terry Nelms, II
摘要： 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：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：US11019201B2
公开(公告)日：2021-05-25
申请号：US16784071
申请日：2020-02-06
发明人： Akanksha , Terry Nelms, II , Kailash Patil , Chirag Tailor , Khaled Lakhdhar
摘要： 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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公开(公告)号：US11889024B2
公开(公告)日：2024-01-30
申请号：US17948991
申请日：2022-09-20
发明人： John Cornwell , Terry Nelms, II
CPC分类号： H04M3/5175 , G06F18/214 , H04M3/2218 , H04M3/2281 , H04M3/42059 , G06V2201/10
摘要： 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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