Douglas C Merrill

US Patent:

20130091050, Apr 11, 2013

Filed:

Apr 24, 2012

Appl. No.:

13/454970

Inventors:

DOUGLAS MERRILL - LOS ANGELES CA, US
SHAWN BUDDLE - EVANSTON IL, US
KASIA CHMIELINKSI - LOS ANGELES CA, US

International Classification:

G06Q 40/02

US Classification:

705 38

Abstract:

A preferred method for providing credit to an underserved borrower can include generating a borrower dataset at a first computer in response to receipt of a borrower profile; formatting the borrower dataset into a plurality of variables; and independently processing each of the plurality of variables using one of a statistical algorithm or a machine learning algorithm to generate a plurality of independent decision sets. The preferred method can further include ensembling the plurality of independent decision sets to generate a model question set; and transmitting the model question set to a second computer from which a user can direct one or more questions in the model question set to a borrower.

US Patent:

20140081832, Mar 20, 2014

Filed:

Sep 18, 2012

Appl. No.:

13/622260

Inventors:

Douglas Merrill - Los Angeles CA, US
John Merrill - Redmond WA, US
Shawn M. Budde - Evanston IL, US
Lingyun Gu - Thousand Oaks CA, US
James P. McGuire - Long Beach CA, US
Manoj Pinnamaneni - Los Angeles CA, US

International Classification:

G06Q 40/02

US Classification:

705 38

Abstract:

This invention relates generally to the personal finance and banking field, and more particularly to the field of credit scoring methods and systems. Preferred embodiments of the present invention provide systems and methods for building and validating a credit scoring function based on a creditor's target information from non-traditional sources using specific algorithms.

US Patent:

20200302524, Sep 24, 2020

Filed:

Mar 18, 2020

Appl. No.:

16/822908

Inventors:

- Los Angeles CA, US
Michael Egan Van Veen - Los Angeles CA, US
Feng Li - Los Angeles CA, US
Marc Frederick Eberstein - Los Angeles CA, US
Jose Efrain Valentin - Los Angeles CA, US
Jerome Louis Budzik - Altadena CA, US
Douglas C. Merrill - Los Angeles CA, US
John Wickens Lamb Merrill - Redmond WA, US

International Classification:

G06Q 40/02
G06K 9/62
G06N 3/04
G06N 7/00

Abstract:

Systems and methods for training models to improve fairness.

US Patent:

20180365765, Dec 20, 2018

Filed:

Aug 22, 2018

Appl. No.:

16/109545

Inventors:

- Los Angeles CA, US
Shawn M. Budde - Evanston IL, US
John Candido - Burbank CA, US
Lingyun Gu - Thousand Oaks CA, US
Farshad Kheiri - Woodland Hills CA, US
James P. McGuire - Long Beach CA, US
Douglas C. Merrill - Los Angeles CA, US
Manoj Pinnamaneni - Los Angeles CA, US
Marick Sinay - Hawthorne CA, US

International Classification:

G06Q 40/02

Abstract:

This invention relates generally to the personal finance and banking field, and more particularly to the field of lending and credit notification methods and systems. Preferred embodiments of the present invention provide systems and methods for automatically generating high quality adverse action notifications based on identifying variations between a declined borrower's profile and that of approved applicants, both with simple and sophisticated credit scoring systems, using specific algorithms.

US Patent:

20160155193, Jun 2, 2016

Filed:

Nov 30, 2015

Appl. No.:

14/954825

Inventors:

- Los Angeles CA, US
Shawn M. Budde - Evanston IL, US
John Candido - Burbank CA, US
Lingyun Gu - Thousand Oaks CA, US
Farshad Kheiri - Woodland Hills CA, US
James P. McGuire - Long Beach CA, US
Douglas C. Merrill - Los Angeles CA, US
Manoj Pinnamaneni - Los Angeles CA, US
Marick Sinay - Hawthorne CA, US

International Classification:

G06Q 40/02

Abstract:

This invention relates generally to the personal finance and banking field, and more particularly to the field of lending and credit notification methods and systems. Preferred embodiments of the present invention provide systems and methods for automatically generating high quality adverse action notifications based on identifying variations between a declined borrower's profile and that of approved applicants, both with simple and sophisticated credit scoring systems, using specific algorithms.

US Patent:

20140214648, Jul 31, 2014

Filed:

Jan 31, 2014

Appl. No.:

14/169400

Inventors:

- Los Angeles CA, US
Shawn M. Budde - Evanston IL, US
Lingyun Gu - Thousand Oaks CA, US
Farshad Kheiri - Woodland Hills CA, US
James P. McGuire - Long Beach CA, US
Douglas C. Merrill - Los Angeles CA, US
Manoj Pinnamaneni - Los Angeles CA, US
Marick Sinay - Hawthorne CA, US

Assignee:

ZESTFINANCE, INC. - Los Angeles CA

International Classification:

G06Q 40/02

US Classification:

705 38

Abstract:

This invention relates generally to the personal finance and banking field, and more particularly to the field of lending and credit notification methods and systems. Preferred embodiments of the present invention provide systems and methods for automatically generating high quality adverse action notifications based on identifying variations between a declined borrower's profile and that of approved applicants, both with simple and sophisticated credit scoring systems, using specific algorithms.

Author:

Douglas T. Merrill

ISBN #:

0898676002