Patents by Inventor Sanny Xiao Yang Liao
Sanny Xiao Yang Liao has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11824870Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.Type: GrantFiled: November 4, 2019Date of Patent: November 21, 2023Assignee: Abnormal Security CorporationInventors: Sanjay Jeyakumar, Jeshua Alexis Bratman, Dmitry Chechik, Abhijit Bagri, Evan James Reiser, Sanny Xiao Yang Liao, Yu Zhou Lee, Carlos Daniel Gasperi, Kevin Lau, Kai Jing Jiang, Su Li Debbie Tan, Jeremy Kao, Cheng-Lin Yeh
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Patent number: 11743294Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.Type: GrantFiled: June 28, 2021Date of Patent: August 29, 2023Assignee: Abnormal Security CorporationInventors: Sanjay Jeyakumar, Jeshua Alexis Bratman, Dmitry Chechik, Abhijit Bagri, Evan James Reiser, Sanny Xiao Yang Liao, Yu Zhou Lee, Carlos Daniel Gasperi, Kevin Lau, Kai Jing Jiang, Su Li Debbie Tan, Jeremy Kao, Cheng-Lin Yeh
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Patent number: 11431738Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.Type: GrantFiled: July 13, 2020Date of Patent: August 30, 2022Assignee: Abnormal Security CorporationInventors: Sanjay Jeyakumar, Jeshua Alexis Bratman, Dmitry Chechik, Abhijit Bagri, Evan James Reiser, Sanny Xiao Yang Liao, Yu Zhou Lee, Carlos Daniel Gasperi, Kevin Lau, Kai Jing Jiang, Su Li Debbie Tan, Jeremy Kao, Cheng-Lin Yeh
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Patent number: 11381581Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.Type: GrantFiled: July 13, 2020Date of Patent: July 5, 2022Assignee: Abnormal Security CorporationInventors: Sanjay Jeyakumar, Jeshua Alexis Bratman, Dmitry Chechik, Abhijit Bagri, Evan James Reiser, Sanny Xiao Yang Liao, Yu Zhou Lee, Carlos Daniel Gasperi, Kevin Lau, Kai Jing Jiang, Su Li Debbie Tan, Jeremy Kao, Cheng-Lin Yeh
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Patent number: 11336666Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.Type: GrantFiled: July 13, 2020Date of Patent: May 17, 2022Assignee: Abnormal Security CorporationInventors: Sanjay Jeyakumar, Jeshua Alexis Bratman, Dmitry Chechik, Abhijit Bagri, Evan James Reiser, Sanny Xiao Yang Liao, Yu Zhou Lee, Carlos Daniel Gasperi, Kevin Lau, Kai Jing Jiang, Su Li Debbie Tan, Jeremy Kao, Cheng-Lin Yeh
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Publication number: 20210329035Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.Type: ApplicationFiled: June 28, 2021Publication date: October 21, 2021Inventors: Sanjay Jeyakumar, Jeshua Alexis Bratman, Dmitry Chechik, Abhijit Bagri, Evan James Reiser, Sanny Xiao Yang Liao, Yu Zhou Lee, Carlos Daniel Gasperi, Kevin Lau, Kai Jing Jiang, Su Li Debbie Tan, Jeremy Kao, Cheng-Lin Yeh
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Publication number: 20210297444Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.Type: ApplicationFiled: June 7, 2021Publication date: September 23, 2021Inventors: Sanjay Jeyakumar, Jeshua Alexis Bratman, Dmitry Chechik, Abhijit Bagri, Evan James Reiser, Sanny Xiao Yang Liao, Yu Zhou Lee, Carlos Daniel Gasperi, Kevin Lau, Kai Jing Jiang, Su Li Debbie Tan, Jeremy Kao, Cheng-Lin Yeh
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Patent number: 11050793Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.Type: GrantFiled: July 13, 2020Date of Patent: June 29, 2021Assignee: Abnormal Security CorporationInventors: Sanjay Jeyakumar, Jeshua Alexis Bratman, Dmitry Chechik, Abhijit Bagri, Evan James Reiser, Sanny Xiao Yang Liao, Yu Zhou Lee, Carlos Daniel Gasperi, Kevin Lau, Kai Jing Jiang, Su Li Debbie Tan, Jeremy Kao, Cheng-Lin Yeh
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Patent number: 11032312Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.Type: GrantFiled: July 13, 2020Date of Patent: June 8, 2021Assignee: Abnormal Security CorporationInventors: Sanjay Jeyakumar, Jeshua Alexis Bratman, Dmitry Chechik, Abhijit Bagri, Evan James Reiser, Sanny Xiao Yang Liao, Yu Zhou Lee, Carlos Daniel Gasperi, Kevin Lau, Kai Jing Jiang, Su Li Debbie Tan, Jeremy Kao, Cheng-Lin Yeh
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Publication number: 20200396258Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.Type: ApplicationFiled: July 13, 2020Publication date: December 17, 2020Inventors: Sanjay Jeyakumar, Jeshua Alexis Bratman, Dmitry Chechik, Abhijit Bagri, Evan James Reiser, Sanny Xiao Yang Liao, Yu Zhou Lee, Carlos Daniel Gasperi, Kevin Lau, Kai Jing Jiang, Su Li Debbie Tan, Jeremy Kao, Cheng-Lin Yeh
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Publication number: 20200389486Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.Type: ApplicationFiled: July 13, 2020Publication date: December 10, 2020Inventors: Sanjay Jeyakumar, Jeshua Alexis Bratman, Dmitry Chechik, Abhijit Bagri, Evan James Reiser, Sanny Xiao Yang Liao, Yu Zhou Lee, Carlos Daniel Gasperi, Kevin Lau, Kai Jing Jiang, Su Li Debbie Tan, Jeremy Kao, Cheng-Lin Yeh
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Publication number: 20200344251Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.Type: ApplicationFiled: July 13, 2020Publication date: October 29, 2020Inventors: Sanjay Jeyakumar, Jeshua Alexis Bratman, Dmitry Chechik, Abhijit Bagri, Evan James Reiser, Sanny Xiao Yang Liao, Yu Zhou Lee, Carlos Daniel Gasperi, Kevin Lau, Kai Jing Jiang, Su Li Debbie Tan, Jeremy Kao, Cheng-Lin Yeh
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Publication number: 20200204572Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.Type: ApplicationFiled: November 4, 2019Publication date: June 25, 2020Inventors: Sanjay Jeyakumar, Jeshua Alexis Bratman, Dmitry Chechik, Abhijit Bagri, Evan James Reiser, Sanny Xiao Yang Liao, Yu Zhou Lee, Carlos Daniel Gasperi, Kevin Lau, Kai Jing Jiang, Su Li Debbie Tan, Jeremy Kao, Cheng-Lin Yeh