US 12,465,208 B2
Systems and methods for using machine learning to predict contact lens compatibility
Ramesh Sarangapani, Coppell, TX (US); and Kevin Baker, Fort Worth, TX (US)
Assigned to Alcon Inc., Fribourg (CH)
Filed by Alcon Inc., Fribourg (CH)
Filed on Feb. 22, 2024, as Appl. No. 18/584,409.
Application 18/584,409 is a continuation of application No. 16/896,006, filed on Jun. 8, 2020, granted, now 11,944,379.
Claims priority of provisional application 62/867,362, filed on Jun. 27, 2019.
Prior Publication US 2024/0197167 A1, Jun. 20, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. A61B 3/00 (2006.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01)
CPC A61B 3/0025 (2013.01) [G06N 5/04 (2013.01); G06N 20/00 (2019.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a non-transitory memory; and
one or more hardware processors coupled with the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
receiving, from a first device associated with a first eye-care professional (ECP), a request for selecting a contact lens for a consumer, wherein the request comprises biometric information associated with the consumer;
obtaining a performance metric associated with the first ECP, wherein the performance metric represents compatibilities between contact lenses and first consumers of the first ECP relative to compatibilities between contact lenses and second consumers of at least a second ECP;
determining, using a machine learning model and based on the performance metric, a compatibility index indicating a compatibility between a particular contact lens and the consumer for the first ECP; and
presenting a report indicating the compatibility index on the first device.