US 12,340,886 B2
Methods and systems for selecting a machine learning algorithm
Sudipto Dey, Parsippany, NJ (US); Camille Patel, Baie Durfe (CA); Pulla Reddy P. Yeduru, Leander, TX (US); and Robert A. Seyss, Lafayette, NJ (US)
Assigned to Express Scripts Strategic Development, Inc., St. Louis, MO (US)
Filed by Express Scripts Strategic Development, Inc., St. Louis, MO (US)
Filed on Nov. 20, 2023, as Appl. No. 18/514,181.
Application 18/514,181 is a continuation of application No. 17/994,442, filed on Nov. 28, 2022, granted, now 11,848,086.
Application 17/994,442 is a continuation of application No. 16/272,090, filed on Feb. 11, 2019, granted, now 11,515,022, issued on Nov. 29, 2022.
Prior Publication US 2024/0087709 A1, Mar. 14, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 20/10 (2018.01); G06N 20/20 (2019.01); G16H 50/70 (2018.01)
CPC G16H 20/10 (2018.01) [G06N 20/20 (2019.01); G16H 50/70 (2018.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
identifying, by a machine learning algorithm selection subsystem implemented by a processor, one or more factors to be used by a machine learning algorithm in predicting a value of an element entered into a form;
training, by the machine learning algorithm selection subsystem, the machine learning algorithm to predict the value of the element using a first subset of previously received data by analyzing known element values within the first subset of previously received data and a relationship between the known element values and the one or more factors;
predicting, by the machine learning algorithm, respective known values of respective known elements for each of a second subset of previously received data, the second subset comprising a remainder of the previously received data not included within the first subset;
evaluating, by the machine learning algorithm selection subsystem, a success rate for the machine learning algorithm at predicting respective known values of respective known elements for each of the second subset by determining whether the machine learning algorithm correctly predicted the respective known values of respective known elements for each of the second subset;
receiving, by the machine learning algorithm, a new form subsequent to training and evaluating the machine learning algorithm;
predicting, by the machine learning algorithm, the value of the element in the new form; and
pre-populating, by the machine learning algorithm, the value of the element in the new form, as predicted.