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:
Nov. 19, 2024

Filed:

Nov. 27, 2020
Applicant:

Amazon Technologies, Inc., Seattle, WA (US);

Inventors:

Barath Balasubramanian, Bothell, WA (US);

Rahul Bhotika, Bellevue, WA (US);

Niels Brouwers, New York, NY (US);

Ranju Das, Seattle, WA (US);

Prakash Krishnan, Oakland, NJ (US);

Shaun Ryan James McDowell, Great Neck, NY (US);

Anushri Mainthia, Seattle, WA (US);

Rakesh Madhavan Nambiar, Seattle, WA (US);

Anant Patel, Seattle, WA (US);

Avinash Aghoram Ravichandran, Shoreline, WA (US);

Joaquin Zepeda Salvatierra, Mercer Island, WA (US);

Gurumurthy Swaminathan, Redmond, WA (US);

Assignee:

Amazon Technologies, Inc., Seattle, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/091 (2023.01); G06F 16/23 (2019.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06N 3/088 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 16/2379 (2019.01); G06F 18/214 (2023.01); G06F 18/2178 (2023.01); G06N 3/088 (2013.01); G06N 3/091 (2023.01); G06N 5/04 (2013.01); G06T 7/0004 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30108 (2013.01);
Abstract

Techniques for feedback-based training may include selecting a scoring machine learning model based at least in part on a test metric, and applying the model on an unlabeled dataset to generate, per dataset item of the unlabeled dataset, a prediction and an importance ranking score for the prediction. Techniques for feedback-based training may further include selecting, based on the importance ranking scores, a result of the application of the model on the unlabeled dataset, providing the result and requesting feedback on the result via a graphical user interface, receiving the feedback via the graphical user interface, adding data from the unlabeled dataset into a training dataset when the feedback indicates a verified result, and retraining the model using the training dataset with the data added from the unlabeled dataset to generate a retrained model.


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