Patents by Inventor Thomas Dawes

Thomas Dawes 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).

  • Patent number: 12684004
    Abstract: Introduced here is a network-accessible platform (or simply “platform”) that is designed to monitor digital activities that are performed across different services to ascertain, in real time, threats to the security of an enterprise. In order to surface insights into the threats posed to an enterprise, the platform can apply machine learning models to data that is representative of digital activities performed on different services with respective accounts. Each model may be trained to understand what constitutes normal behavior for a corresponding employee with respect to a single service or multiple services. Not only can these models be autonomously trained for the employees of the enterprise, but they can also be autonomously applied to detect, characterize, and catalog those digital activities that are indicative of a threat.
    Type: Grant
    Filed: April 24, 2024
    Date of Patent: July 14, 2026
    Assignee: ABNORMAL AI, INC.
    Inventors: Sanjay Jeyakumar, Abhijit Bagri, David Hagar, Tanooj Parekh, Sanish Mahadik, Cheng-Lin Yeh, Yingkai Gao, Thomas Dawes, Lucas Sonnabend, Tejas Khot
  • Publication number: 20240356959
    Abstract: Introduced here is a network-accessible platform (or simply “platform”) that is designed to monitor digital activities that are performed across different services to ascertain, in real time, threats to the security of an enterprise. In order to surface insights into the threats posed to an enterprise, the platform can apply machine learning models to data that is representative of digital activities performed on different services with respective accounts. Each model may be trained to understand what constitutes normal behavior for a corresponding employee with respect to a single service or multiple services. Not only can these models be autonomously trained for the employees of the enterprise, but they can also be autonomously applied to detect, characterize, and catalog those digital activities that are indicative of a threat.
    Type: Application
    Filed: April 24, 2024
    Publication date: October 24, 2024
    Inventors: Sanjay Jeyakumar, Evan Reiser, Abhijit Bagri, Maritza Perez, Vineet Edupuganti, Yingkai Gao, Umut Gultepe, Cheng-Lin Yeh, Mark Philip, Tejas Khot, Thomas Dawes, Sanish Mahadik, Benjamin Snider, Cheng Li, Nirmal Balachundhar, Adithya Vellal, Lucas Sonnabend
  • Publication number: 20240356951
    Abstract: Introduced here is a network-accessible platform (or simply “platform”) that is designed to monitor digital activities that are performed across different services to ascertain, in real time, threats to the security of an enterprise. In order to surface insights into the threats posed to an enterprise, the platform can apply machine learning models to data that is representative of digital activities performed on different services with respective accounts. Each model may be trained to understand what constitutes normal behavior for a corresponding employee with respect to a single service or multiple services. Not only can these models be autonomously trained for the employees of the enterprise, but they can also be autonomously applied to detect, characterize, and catalog those digital activities that are indicative of a threat.
    Type: Application
    Filed: April 24, 2024
    Publication date: October 24, 2024
    Inventors: Sanjay Jeyakumar, Evan Reiser, Abhijit Bagri, Maritza Perez, Vineet Edupuganti, Yingkai Gao, Umut Gultepe, Cheng-Lin Yeh, Mark Philip, Tejas Khot, Thomas Dawes, Sanish Mahadik, Benjamin Snider, Cheng Li, Nirmal Balachundhar, Adithya Vellal, Lucas Sonnabend
  • Publication number: 20240356938
    Abstract: Introduced here is a network-accessible platform (or simply “platform”) that is designed to monitor digital activities that are performed across different services to ascertain, in real time, threats to the security of an enterprise. In order to surface insights into the threats posed to an enterprise, the platform can apply machine learning models to data that is representative of digital activities performed on different services with respective accounts. Each model may be trained to understand what constitutes normal behavior for a corresponding employee with respect to a single service or multiple services. Not only can these models be autonomously trained for the employees of the enterprise, but they can also be autonomously applied to detect, characterize, and catalog those digital activities that are indicative of a threat.
    Type: Application
    Filed: April 24, 2024
    Publication date: October 24, 2024
    Inventors: Sanjay Jeyakumar, Evan Reiser, Abhijit Bagri, Maritza Perez, Vineet Edupuganti, Yingkai Gao, Umut Gultepe, Cheng-Lin Yeh, Mark Philip, Tejas Khot, Thomas Dawes, Sanish Mahadik, Benjamin Snider, Cheng Li, Nirmal Balachundhar, Adithya Vellal, Lucas Sonnabend
  • Publication number: 20240354680
    Abstract: Introduced here is a network-accessible platform (or simply “platform”) that is designed to monitor digital activities that are performed across different services to ascertain, in real time, threats to the security of an enterprise. In order to surface insights into the threats posed to an enterprise, the platform can apply machine learning models to data that is representative of digital activities performed on different services with respective accounts. Each model may be trained to understand what constitutes normal behavior for a corresponding employee with respect to a single service or multiple services. Not only can these models be autonomously trained for the employees of the enterprise, but they can also be autonomously applied to detect, characterize, and catalog those digital activities that are indicative of a threat.
    Type: Application
    Filed: April 24, 2024
    Publication date: October 24, 2024
    Inventors: Sanjay Jeyakumar, Evan Reiser, Abhijit Bagri, Maritza Perez, Vineet Edupuganti, Yingkai Gao, Umut Gultepe, Cheng-Lin Yeh, Mark Philip, Tejas Khot, Thomas Dawes, Sanish Mahadik, Benjamin Snider, Cheng Li, Nirmal Balachundhar, Adithya Vellal, Lucas Sonnabend