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:
Jul. 08, 2025

Filed:

Dec. 29, 2020
Applicant:

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

Inventors:

David J. Fogarty, Old Greenwich, CT (US);

Yee Wah Eva Lee, South Windsor, CT (US);

Chun Ho Chan, Hong Kong, HK;

Ho Fai Yau, Hong Kong, HK;

Xiao Xiao, Hong Kong, HK;

Hei Fung, Hong Kong, HK;

Nicholas F. Nett, Vernon, CT (US);

Mahendra Bisht, Bangalore, IN;

Chit Ming Yip, Hong Kong, HK;

Yifei Luo, Hong Kong, HK;

Assignee:

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

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 18/2134 (2023.01); G06F 18/243 (2023.01); G06F 18/2433 (2023.01); G06F 18/25 (2023.01); G06Q 40/08 (2012.01); G16H 10/60 (2018.01); G16H 50/30 (2018.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 18/21342 (2023.01); G06F 18/24323 (2023.01); G06F 18/2433 (2023.01); G06F 18/251 (2023.01); G06Q 40/08 (2013.01); G16H 10/60 (2018.01); G16H 50/30 (2018.01);
Abstract

A computerized method of automatic distributed communication includes training a first and second machine learning models with historical feature vector inputs to generate a likelihood output and a mean count output, respectively. For each entity in a set, the method includes processing a likelihood feature vector input with the first machine learning model to generate a likelihood output indicative of a likelihood that the entity will have an avoidable negative health event within a specified first time period, and processing a mean count feature vector input with the second machine learning model to generate a mean count output indicative of an expected number of avoidable negative health events that the entity will have within a specified second time period. The method includes automatically distributing structured campaign data to at least a subset of entities in the set according to the likelihood output or the mean count output.


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