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. 05, 2023

Filed:

Dec. 09, 2022
Applicant:

Cigna Intellectual Property, Inc., Wilmington, DE (US);

Inventor:

Jeffrey R. McCormick, Cheshire, CT (US);

Assignee:

Cigna Intellectual Property, Inc., Wilmington, DE (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/02 (2023.01); G06Q 30/0282 (2023.01); G06Q 30/0204 (2023.01); G06N 20/00 (2019.01); G06F 16/2457 (2019.01); G06Q 30/0203 (2023.01);
U.S. Cl.
CPC ...
G06Q 30/0282 (2013.01); G06F 16/24578 (2019.01); G06N 20/00 (2019.01); G06Q 30/0203 (2013.01); G06Q 30/0205 (2013.01);
Abstract

A computer-implemented method includes determining whether historical profile data structures are stored in a database with corresponding structured supplemental data and selected health care plan option identifiers of a set of health care plan option identifiers. The method includes generating historical feature vectors using the historical data structures stored in the database or generating the historical feature vectors using created sample profile data structures. The method includes training machine learning models using the generated historical feature vectors, selecting one of the machine learning models for use in generating recommendation outputs, presenting an interactive voice interface to an entity to generate audio questions and prompts for obtaining response data from the entity, classifying voice survey responses of the entity, and generating feature vectors. The method includes processing, using the selected machine learning model, the feature vectors to generate the recommendation outputs, and transforming a user interface to display the recommendation output.


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