John S Candido, Age 623016 96Th Pl SE, Everett, WA 98208

2019034, Nov 7, 2019

Apr 25, 2019

16/394651

- Los Angeles CA, US
Armen Avedis Donigian - Los Angeles CA, US
Eran Dvir - Los Angeles CA, US
Sean Javad Kamkar - Los Angeles CA, US
Evan George Kriminger - Los Angeles CA, US
Michael Edward Ruberry - Los Angeles CA, US
Ozan Sayin - Los Angeles CA, US
Yachen Yan - Los Angeles CA, US
Derek Wilcox - Los Angeles CA, US
John Candido - Los Angeles CA, US
Benjamin Anthony Solecki - Los Angeles CA, US
Jiahuan He - Los Angeles CA, US
Jerome Louis Budzik - Los Angeles CA, US
Michael Hartman - Los Angeles CA, US
John Wickens Lamb Merrill - Los Angeles CA, US
Esfandiar Alizadeh - Los Angeles CA, US
Liubo Li - Los Angeles CA, US
Carlos Alberta Huertas Villegas - Los Angeles CA, US
Feng Li - Los Angeles CA, US

G06N 5/02
G06N 20/00
G06K 9/62
G06F 16/908
G06F 17/28

Systems and methods for generating and processing modeling workflows.

2019027, Sep 12, 2019

Mar 8, 2019

16/297099

- Los Angeles CA, US
Michael Edward Ruberry - Los Angeles CA, US
Ozan Sayin - Los Angeles CA, US
Bojan Tunguz - Greencastle IN, US
Lin Song - Sugarland TX, US
Esfandiar Alizadeh - Venice CA, US
Melanie Eunique DeBruin - Northridege CA, US
Yachen Yan - Los Angeles CA, US
Derek Wilcox - Los Angeles CA, US
John Candido - Burbank CA, US
Benjamin Anthony Solecki - Los Angeles CA, US
Jiahuan He - Los Angeles CA, US
Jerome Louis Budzik - Altadena CA, US
Armen Avedis Donigian - Los Angeles CA, US
Eran Dvir - Valley Village CA, US
Sean Javad Kamkar - Los Angeles CA, US
Evan George Kriminger - Los Angeles CA, US

G06N 20/20
G06N 5/00
B60Q 9/00

Systems and methods for model evaluation. A model is evaluated by performing a decomposition process for a model output, relative to a baseline input data set.

2019004, Feb 7, 2019

Aug 1, 2018

16/052293

- Los Angeles CA, US
Michael Edward Ruberry - Los Angeles CA, US
Ozan Sayin - West Hollywood CA, US
Bojan Tunguz - Greencastle IN, US
Lin Song - Sugar Land TX, US
Esfandiar Alizadeh - Venice CA, US
Melanie Eunique DeBruin - Northridge CA, US
Yachen Yan - Los Angeles CA, US
Derek Wilcox - Los Angeles CA, US
John Candido - Burbank CA, US
Benjamin Anthony Solecki - Los Angeles CA, US
Jiahuan He - Los Angeles CA, US
Jerome Louis Budzik - Altadena CA, US
Sean Javad Kamkar - Los Angeles CA, US

G06Q 30/02
G06N 5/04
G06N 99/00
G06F 9/54

Systems and methods for model evaluation. A protected class model that satisfies an accuracy threshold is built by using: data sets for use by a modeling system being evaluated, and protected class membership information for each data set. A target for the protected class model is a protected class membership variable indicating membership in a protected class. Each predictor of the protected class model is a predictor of an evaluated model used by the modeling system. A target of the evaluated model is different from the target of the protected class model. Each predictor is a set of one or more variables of the data sets. For each predictor of the protected class model, a protected class model impact ranking value and a modeling system impact ranking value are determined.

2018032, Nov 8, 2018

May 3, 2018

15/970626

- Los Angeles CA, US
Michael Edward Ruberry - Los Angeles CA, US
Ozan Sayin - West Hollywood CA, US
Bojan Tunguz - Greencastle IN, US
Lin Song - Sugar Land TX, US
Esfandiar Alizadeh - Venice CA, US
Melanie Eunique DeBruin - Northridge CA, US
Yachen Yan - Los Angeles CA, US
Derek Wilcox - Los Angeles CA, US
John Candido - Burbank CA, US
Benjamin Anthony Solecki - Los Angeles CA, US
Jiahuan He - Los Angeles CA, US
Jerome Louis Budzik - Altadena CA, US

G06N 5/04
G06N 99/00

Systems and methods for generating explanation information for a result of an application system. Explanation configuration is generated based on received user input. Responsive to an explanation generation event, a plurality of modified input variable value sets are generated for a first applicant by using the explanation configuration. For each modified input variable value set: a request is provided to a first application system for generation of a result for the modified input variable value set, and a result is received for the modified input variable value set. At least one input variable value is selected based on a comparison between a first result of a first input variable value set of the first applicant and results for the modified input variable value set. Explanation information is generated for the first result by using human-readable description information for each selected input variable value, in accordance with the explanation configuration.