Patents by Inventor Daniel Brim

Daniel Brim 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).

  • Publication number: 20190281076
    Abstract: A corpus of documents (and other data objects) stored for an entity can be analyzed to determine one or more topics for each document. Elements of the documents can be analyzed to also assign a risk score. The types of topics and security elements, and the associated risk scores, can be learned and adapted over time using, for example, a topic model and random forest regressor. Activity with respect to the documents is monitored, and expected behavior for a user determined using a trained recurrent neural network. Ongoing user activity is processed to determine whether the activity excessively deviates from the expected user activity. The activity can also be compared against the activity of user peers to determine whether the activity is also anomalous among the user peer group. For anomalous activity, risk scores of the accessed documents can be analyzed to determine whether to generate an alert.
    Type: Application
    Filed: May 30, 2019
    Publication date: September 12, 2019
    Inventors: Alexander Watson, Daniel Brim, Christopher Simmons, Paul Radulovic, Tyler Stuart Bray, Jennifer Anne Brinkley, Eric Johnson, Victor Chin, Jack Rasgaitis, Nai Qin Cai, Michael Gough, Max Anger
  • Patent number: 10320819
    Abstract: A corpus of documents (and other data objects) stored for an entity can be analyzed to determine one or more topics for each document. Elements of the documents can be analyzed to also assign a risk score. The types of topics and security elements, and the associated risk scores, can be learned and adapted over time using, for example, a topic model and random forest regressor. Activity with respect to the documents is monitored, and expected behavior for a user determined using a trained recurrent neural network. Ongoing user activity is processed to determine whether the activity excessively deviates from the expected user activity. The activity can also be compared against the activity of user peers to determine whether the activity is also anomalous among the user peer group. For anomalous activity, risk scores of the accessed documents can be analyzed to determine whether to generate an alert.
    Type: Grant
    Filed: February 27, 2017
    Date of Patent: June 11, 2019
    Assignee: Amazon Technologies, Inc.
    Inventors: Alexander Watson, Daniel Brim, Christopher Simmons, Paul Radulovic, Tyler Stuart Bray, Jennifer Anne Brinkley, Eric Johnson, Victor Chin, Jack Rasgaitis, Nai Qin Cai, Michael Gough, Max Anger
  • Publication number: 20180248895
    Abstract: A corpus of documents (and other data objects) stored for an entity can be analyzed to determine one or more topics for each document. Elements of the documents can be analyzed to also assign a risk score. The types of topics and security elements, and the associated risk scores, can be learned and adapted over time using, for example, a topic model and random forest regressor. Activity with respect to the documents is monitored, and expected behavior for a user determined using a trained recurrent neural network. Ongoing user activity is processed to determine whether the activity excessively deviates from the expected user activity. The activity can also be compared against the activity of user peers to determine whether the activity is also anomalous among the user peer group. For anomalous activity, risk scores of the accessed documents can be analyzed to determine whether to generate an alert.
    Type: Application
    Filed: February 27, 2017
    Publication date: August 30, 2018
    Inventors: Alexander Watson, Daniel Brim, Christopher Simmons, Paul Radulovic, Tyler Stuart Bray, Jennifer Anne Brinkley, Eric Johnson, Victor Chin, Jack Rasgaitis, Nai Qin Cai, Michael Gough, Max Anger