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:
Jan. 24, 2023

Filed:

Apr. 30, 2019
Applicant:

Qualtrics, Llc, Provo, UT (US);

Inventors:

Jeffrey Whiting, Salem, UT (US);

David Patty, Orem, UT (US);

Cutler (C J) Campbell, Lehi, UT (US);

P J Tatlow, Spanish Fork, UT (US);

Caius Worthen, Sandy, UT (US);

Gregory Burnham, Springville, UT (US);

Assignee:

Qualtrics, LLC, Provo, UT (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/02 (2012.01); G06N 20/00 (2019.01); G06F 16/9032 (2019.01); G06F 40/35 (2020.01);
U.S. Cl.
CPC ...
G06Q 30/0203 (2013.01); G06F 16/90332 (2019.01); G06F 40/35 (2020.01); G06N 20/00 (2019.01);
Abstract

This disclosure covers methods, systems, and computer-readable media that select answer choices from potential answer choices for a digital question based on responses to other digital questions and/or embedded user data. In certain embodiments, the disclosed systems select answer choices from potential answer choices for a digital question based on a multiple choice response. Furthermore, in some embodiments, the disclosed systems select answer choices from potential answer choices for a digital question based on keywords and/or sentiment values identified by analyzing a text response. In some embodiments, the disclosed systems select answer choices for a digital question from a dynamic choice reference dataset that comprises potential answer choices. Additionally, in one or more embodiments, the disclosed systems train and/or utilize a machine-learning model to select answer choices from potential answer choices for a digital question based on a response.


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