Zachary D Taylor

20220309286, Sep 29, 2022

Jan 13, 2022

17/575533

- San Francisco CA, US
Meera Rao - San Francisco CA, US
Rick Wolf - San Francisco CA, US
Peyton Rose - San Francisco CA, US
Stephen Martin - San Francisco CA, US
Sameer Soi - San Francisco CA, US
Zachary Taylor - San Francisco CA, US
Ye Wang - San Francisco CA, US

Includede Health, Inc. - San Francisco CA

G06K 9/62
G06N 20/00
G06F 16/242
G06F 16/2458

Methods, systems, and computer-readable media for generating a statistically covaried machine learning model for performance measurement of service providers. The method receives a configuration file that includes one or more parameters associated with a plurality of individuals and parses it to generate and executing the database query on input data to generate sets of tabulated data of individuals of the plurality of individuals. The method next determines one or more measures of service providers listed in the configuration file using two or more tabulated data of individuals from the sets of tabulated data of individuals. The method finally generates a covaried machine learning model by training a machine learning model by statistically covarying measures and using them as training data.

20230004801, Jan 5, 2023

Aug 30, 2022

17/898887

- Santa Clara CA, US
John Zedlewski - San Francisco CA, US
Zachary Taylor - Santa Cruz CA, US
Greg Heinrich - Nice, FR
Claire Delaunay - Menlo Park CA, US
Mark Daly - Eagle ID, US
Matthew Campbell - Surf City NC, US
Curtis Beeson - Irwin PA, US
Gary Hicok - Mesa CA, US
Michael Cox - Menlo Park CA, US
Rev Lebaredian - Los Gatos CA, US
Tony Tamasi - Portola Valley CA, US
David Auld - Saratoga CA, US

G06N 3/08
G06N 20/00
G06F 9/455
G06N 3/063
G06K 9/62
G06N 3/04
G06V 10/44
G06V 20/56

In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment—in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack—to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.

20190303759, Oct 3, 2019

Mar 27, 2019

16/366875

- San Jose CA, US
John Zedlewski - San Francisco CA, US
Zachary Taylor - Santa Cruz CA, US
Greg Heinrich - Nice, FR
Claire Delaunay - Menlo Park CA, US
Mark Daly - Eagle ID, US
Matthew Campbell - Surf City NC, US
Curtis Beeson - Irwin PA, US
Gary Hicok - Mesa AZ, US
Michael Cox - Menlo Park CA, US
Rev Lebaredian - Los Gatos CA, US
Tony Tamasi - Portola Valley CA, US
David Auld - Saratoga CA, US

G06N 3/08
G06F 9/455
G06N 20/00

In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment—in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack—to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.