The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 06, 2024

Filed:

Nov. 14, 2022
Applicant:

Snowflake Inc., Bozeman, MT (US);

Inventors:

Monica J. Holboke, Toronto, CA;

Justin Langseth, Kailua, HI (US);

Stuart Ozer, Oakland, CA (US);

William L. Stratton, Jr., Atlanta, GA (US);

Assignee:

Snowflake Inc., Stockholm, SE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/00 (2023.01); G06N 20/00 (2019.01); G06F 21/62 (2013.01); G06F 16/25 (2019.01); G06F 16/28 (2019.01); G06F 18/214 (2023.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 16/256 (2019.01); G06F 16/283 (2019.01); G06F 18/214 (2023.01); G06F 21/6227 (2013.01);
Abstract

Disclosed are systems, methods, and non-transitory computer-readable media for sharing, on a distributed database, a database application to a first user of the distributed database, the database application generated by a second user of the distributed database. The training dataset includes a first database training dataset from the first user of the distributed database and a second database training dataset from the second user of the distributed database, the first database training dataset and the second database training dataset including non-overlapping dataset features. The database application further identifies a query from the second user to train the machine learning model on the training dataset and generates a trained machine learning model by training the machine learning model on a joined dataset according to the query. The database application generates outputs from the trained machine learning model by applying the trained machine learning model on new data.


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