Patents by Inventor Spencer Bror Koehler

Spencer Bror Koehler 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).

  • Using a Machine Learning System to Process a Corpus of Documents Associated With a User to Determine a User-Specific and/or Process-Specific Consequence Index
    Publication number: 20230104176
    Abstract: Aspects of the disclosure relate to using a machine learning system to process a corpus of documents associated with a user to determine a user-specific consequence index. A computing platform may load a corpus of documents associated with a user. Subsequently, the computing platform may create a first plurality of smart groups based on the corpus of documents, and then may generate a first user interface comprising a representation of the first plurality of smart groups. Next, the computing platform may receive user input applying one or more labels to a plurality of documents associated with at least one smart group. Subsequently, the computing platform may create a second plurality of smart groups based on the corpus of documents and the received user input. Then, the computing platform may generate a second user interface comprising a representation of the second plurality of smart groups.
    Type: Application
    Filed: November 17, 2022
    Publication date: April 6, 2023
    Inventors: Daniel Wallace Rapp, Brian Sanford Jones, Spencer Bror Koehler
  • Using a machine learning system to process a corpus of documents associated with a user to determine a user-specific and/or process-specific consequence index
    Patent number: 11537668
    Abstract: Aspects of the disclosure relate to using a machine learning system to process a corpus of documents associated with a user to determine a user-specific consequence index. A computing platform may load a corpus of documents associated with a user. Subsequently, the computing platform may create a first plurality of smart groups based on the corpus of documents, and then may generate a first user interface comprising a representation of the first plurality of smart groups. Next, the computing platform may receive user input applying one or more labels to a plurality of documents associated with at least one smart group. Subsequently, the computing platform may create a second plurality of smart groups based on the corpus of documents and the received user input. Then, the computing platform may generate a second user interface comprising a representation of the second plurality of smart groups.
    Type: Grant
    Filed: April 13, 2020
    Date of Patent: December 27, 2022
    Assignee: Proofpoint, Inc.
    Inventors: Daniel Wallace Rapp, Brian Sanford Jones, Spencer Bror Koehler
  • Using a Machine Learning System to Process a Corpus of Documents Associated With a User to Determine a User-Specific and/or Process-Specific Consequence Index
    Publication number: 20210049516
    Abstract: Aspects of the disclosure relate to using a machine learning system to process a corpus of documents associated with a user to determine a user-specific consequence index. A computing platform may load a corpus of documents associated with a user. Subsequently, the computing platform may create a first plurality of smart groups based on the corpus of documents, and then may generate a first user interface comprising a representation of the first plurality of smart groups. Next, the computing platform may receive user input applying one or more labels to a plurality of documents associated with at least one smart group. Subsequently, the computing platform may create a second plurality of smart groups based on the corpus of documents and the received user input. Then, the computing platform may generate a second user interface comprising a representation of the second plurality of smart groups.
    Type: Application
    Filed: April 13, 2020
    Publication date: February 18, 2021
    Inventors: Daniel Wallace Rapp, Brian Sanford Jones, Spencer Bror Koehler