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:
Apr. 08, 2025

Filed:

Sep. 23, 2021
Applicant:

Scale Ai, Inc., San Francisco, CA (US);

Inventors:

Diego Ardila, Oakland, CA (US);

Russell Kaplan, San Francisco, CA (US);

Vinjai Saraj Vale, Exeter, NH (US);

Jihan Yin, San Francisco, CA (US);

Assignee:

SCALE AI, INC., San Francisco, CA (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06N 3/088 (2023.01); G06N 3/0895 (2023.01); G06N 3/09 (2023.01); G06N 20/00 (2019.01); G06V 10/70 (2022.01); G16B 40/20 (2019.01); G16B 40/30 (2019.01); G06F 16/24 (2019.01); G06F 16/28 (2019.01); G06F 16/332 (2019.01); G06F 16/3329 (2025.01); G06F 16/35 (2019.01); G06F 16/903 (2019.01); G06F 16/9032 (2019.01); G06F 16/906 (2019.01); G06F 18/23 (2023.01); G06N 3/091 (2023.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06F 18/2148 (2023.01); G06F 18/214 (2023.01); G06F 18/2155 (2023.01); G06F 18/2193 (2023.01); G06N 3/088 (2013.01); G06N 3/0895 (2023.01); G06N 3/09 (2023.01); G06N 20/00 (2019.01); G06V 10/70 (2022.01); G16B 40/20 (2019.02); G16B 40/30 (2019.02); G06F 16/24 (2019.01); G06F 16/285 (2019.01); G06F 16/3329 (2019.01); G06F 16/35 (2019.01); G06F 16/903 (2019.01); G06F 16/90332 (2019.01); G06F 16/906 (2019.01); G06F 18/217 (2023.01); G06F 18/23 (2023.01); G06N 3/091 (2023.01); G06V 10/82 (2022.01);
Abstract

One embodiment of the present invention sets forth a technique for curating a data sample set. The technique includes determining one or more data sampling criteria based on a sampling objective for a data sample set associated with the machine learning model. The technique also includes selecting, from a set of unlabeled data samples, at least one data sample to be labeled and added to a data sample set associated with the machine learning model based on the one or more data sampling criteria. The technique also includes, for each selected data sample, supplementing the data sample set with the selected data sample and at least one association with a label.


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