Patents by Inventor Patrick Nathan Koch

Patrick Nathan Koch 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: 11151480
    Abstract: A visualization is presented while tuning a machine learning model. A model tuning process writes tuning data to a history table. The model tuning process is repeatedly training and scoring a model type with different sets of values of hyperparameters defined based on the model type. An objective function value is computed for each set of values of the hyperparameters. Data stored in the history table is accessed and used to identify the hyperparameters. (A) A page template is selected from page templates that describe graphical objects presented in the display. (B) The page template is updated with the accessed data. (C) The display is updated using the updated page template. (D) At the end of a refresh time period, new data stored in the history table by the model tuning process is accessed. (E) (B) through (D) are repeated with the accessed data replaced with the accessed new data.
    Type: Grant
    Filed: November 17, 2020
    Date of Patent: October 19, 2021
    Assignee: SAS Institute Inc.
    Inventors: Oleg Borisovich Golovidov, Brett Alan Wujek, Patrick Nathan Koch, Rajendra Prasad Singh
  • Publication number: 20210264287
    Abstract: Tuned hyperparameter values are determined for training a machine learning model. When a selected hyperparameter configuration does not satisfy a linear constraint, if a projection of the selected hyperparameter configuration is included in a first cache that stores previously computed projections is determined. When the projection is included in the first cache, the projection is extracted from the first cache using the selected hyperparameter configuration, and the selected hyperparameter configuration is replaced with the extracted projection in the plurality of hyperparameter configurations. When the projection is not included in the first cache, a projection computation for the selected hyperparameter configuration is assigned to a session. A computed projection is received from the session for the selected hyperparameter configuration.
    Type: Application
    Filed: October 27, 2020
    Publication date: August 26, 2021
    Inventors: Steven Joseph Gardner, Joshua David Griffin, Yan Xu, Patrick Nathan Koch, Brett Alan Wujek, Oleg Borisovich Golovidov
  • Patent number: 11093833
    Abstract: Tuned hyperparameter values are determined for training a machine learning model. When a selected hyperparameter configuration does not satisfy a linear constraint, if a projection of the selected hyperparameter configuration is included in a first cache that stores previously computed projections is determined. When the projection is included in the first cache, the projection is extracted from the first cache using the selected hyperparameter configuration, and the selected hyperparameter configuration is replaced with the extracted projection in the plurality of hyperparameter configurations. When the projection is not included in the first cache, a projection computation for the selected hyperparameter configuration is assigned to a session. A computed projection is received from the session for the selected hyperparameter configuration.
    Type: Grant
    Filed: October 27, 2020
    Date of Patent: August 17, 2021
    Assignee: SAS Institute Inc.
    Inventors: Steven Joseph Gardner, Joshua David Griffin, Yan Xu, Patrick Nathan Koch, Brett Alan Wujek, Oleg Borisovich Golovidov
  • Patent number: 10360517
    Abstract: A computing device automatically selects hyperparameter values based on objective criteria to train a predictive model. Each session of a plurality of sessions executes training and scoring of a model type using an input dataset in parallel with other sessions of the plurality of sessions. Unique hyperparameter configurations are determined using a search method and assigned to each session. For each session of the plurality of sessions, training of a model of the model type is requested using a training dataset and the assigned hyperparameter configuration, scoring of the trained model using a validation dataset and the assigned hyperparameter configuration is requested to compute an objective function value, and the received objective function value and the assigned hyperparameter configuration are stored. A best hyperparameter configuration is identified based on an extreme value of the stored objective function values.
    Type: Grant
    Filed: November 27, 2017
    Date of Patent: July 23, 2019
    Assignee: SAS INSTITUTE INC.
    Inventors: Patrick Nathan Koch, Brett Alan Wujek, Oleg Borisovich Golovidov, Steven Joseph Gardner, Joshua David Griffin, Scott Russell Pope, Yan Xu
  • Publication number: 20180240041
    Abstract: A computing device automatically selects hyperparameter values based on objective criteria to train a predictive model. Each session of a plurality of sessions executes training and scoring of a model type using an input dataset in parallel with other sessions of the plurality of sessions. Unique hyperparameter configurations are determined using a search method and assigned to each session. For each session of the plurality of sessions, training of a model of the model type is requested using a training dataset and the assigned hyperparameter configuration, scoring of the trained model using a validation dataset and the assigned hyperparameter configuration is requested to compute an objective function value, and the received objective function value and the assigned hyperparameter configuration are stored. A best hyperparameter configuration is identified based on an extreme value of the stored objective function values.
    Type: Application
    Filed: November 27, 2017
    Publication date: August 23, 2018
    Inventors: Patrick Nathan Koch, Brett Alan Wujek, Oleg Borisovich Golovidov, Steven Joseph Gardner, Joshua David Griffin, Scott Russell Pope, Yan Xu