US 12,248,660 B2
Machine learning model automation of user interface transformation
Adithya Chowdary Boppana, Dublin, OH (US); Christopher R. Markson, Hawthorne, NJ (US); Pritesh J. Shah, Paramus, NJ (US); Jiawei Kuang, Kenmore, WA (US); and Keith L. Widmer, Grapevine, TX (US)
Assigned to Evernorth Strategic Development, Inc., St. Louis, MO (US)
Filed by Evernorth Strategic Development, Inc., St. Louis, MO (US)
Filed on Aug. 7, 2023, as Appl. No. 18/230,819.
Application 18/230,819 is a continuation of application No. 17/394,647, filed on Aug. 5, 2021, granted, now 11,720,228.
Prior Publication US 2023/0376173 A1, Nov. 23, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 20/00 (2019.01); G06F 3/0482 (2013.01); G06F 9/451 (2018.01); G06F 16/22 (2019.01); G16H 20/10 (2018.01)
CPC G06F 3/0482 (2013.01) [G06F 9/451 (2018.02); G06F 16/22 (2019.01); G06N 20/00 (2019.01); G16H 20/10 (2018.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
memory hardware configured to store processor-executable instructions, a persona, and a data structure associated with the persona; and
processor hardware configured to execute the processor-executable instructions, wherein the processor-executable instructions include:
generating a graphical user interface;
in response to a first condition:
inputting a first set of explanatory variables to a first trained machine learning model to generate a first metric, and
transforming the graphical user interface according to the persona and the first metric,
in response to a second condition:
inputting a second set of explanatory variables to a second trained machine learning model to generate a second metric, and
transforming the graphical user interface according to the persona and the second metric, and
wherein the first trained machine learning model is different from the second trained machine learning model, and
automatically approving a first prior authorization prescription in response to the first metric reaching a threshold value.