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:
Dec. 24, 2024

Filed:

Jun. 28, 2021
Applicant:

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

Inventors:

Yi-An Lai, Bellevue, WA (US);

Yi Zhang, Sammamish, WA (US);

Roger Scott Jenke, Seattle, WA (US);

Meghana Puvvadi, Seattle, WA (US);

Shang-Wen Daniel Li, Somerville, MA (US);

Peng Zhang, Santa Clara, CA (US);

Jason P. Krone, Pacific Grove, CA (US);

Garima Lalwani, Delhi, IN;

Niranjhana Nayar, Seattle, WA (US);

Kartik Natarajan, Shoreline, WA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G10L 15/22 (2006.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G10L 15/06 (2013.01); G10L 15/18 (2013.01); H04L 51/02 (2022.01); G10L 15/16 (2006.01);
U.S. Cl.
CPC ...
G10L 15/063 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G10L 15/18 (2013.01); H04L 51/02 (2013.01); G10L 2015/0636 (2013.01); G10L 2015/0638 (2013.01); G10L 15/16 (2013.01); G10L 15/1815 (2013.01); G10L 15/1822 (2013.01); G10L 15/22 (2013.01);
Abstract

Techniques for updating a machine learning model based on user interactions are described. In particular, in some examples, user interactions with a chatbot provide aspects of a data set to be used to train or fine-tune a ML model. In some examples, this is accomplished by collecting data from a first plurality of interactions with a machine learning (ML) model; generating a variant of the ML model using the collected data by: filtering the collected data to create a first data set, training the ML model based on the first data set to generate an adapted ML model, and fine-tuning the adapted ML model on a second data set, different than the first data set to generate the variant of the ML model.


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