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:
Sep. 02, 2025

Filed:

Mar. 29, 2021
Applicant:

Oracle International Corporation, Redwood Shores, CA (US);

Inventors:

Mark Edward Johnson, Castle Cove, AU;

Thanh Long Duong, Seabrook, AU;

Vishal Vishnoi, Redwood City, CA (US);

Balakota Srinivas Vinnakota, Sunnyvale, CA (US);

Tuyen Quang Pham, Springvale, AU;

Cong Duy Vu Hoang, Wantima South, AU;

Assignee:

Oracle International Corporation, Redwood Shores, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 16/3329 (2025.01); G06F 18/21 (2023.01); G06F 18/211 (2023.01); G06N 3/08 (2023.01); H04L 51/02 (2022.01);
U.S. Cl.
CPC ...
G06F 16/3329 (2019.01); G06F 18/211 (2023.01); G06F 18/217 (2023.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); H04L 51/02 (2013.01);
Abstract

Techniques are disclosed for tuning hyperparameters of a model. Datasets are obtained for training the model and metrics are selected for evaluating performance of the model. Each metric is assigned a weight specifying an importance to the performance of the model. A function is created that measures performance based on the weighted metrics. Hyperparameters are tuned to optimize the model performance. Tuning the hyperparameters includes: (i) training the model that is configured based on a current values for the hyperparameters; (ii) evaluating a performance of the model using the function; (iii) determining whether the model is optimized for the metrics; (iv) in response to the model not being optimized, searching for a new values for the hyperparameters, reconfiguring the model with the new values, and repeating steps (i)-(iii) using the reconfigured model; and (v) in response to the model being optimized for the metrics, providing a trained model.


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