US 11,901,066 B2
Express tracking for patient flow management in a distributed environment
Michael Demick, Wake Forest, NC (US); and Mark Wright, Tobaccoville, NC (US)
Assigned to Laboratory Corporation of America Holdings, Burlington, NC (US)
Filed by LABORATORY CORPORATION OF AMERICA HOLDINGS, Burlington, NC (US)
Filed on Jul. 27, 2022, as Appl. No. 17/874,656.
Application 17/874,656 is a continuation of application No. 16/669,099, filed on Oct. 30, 2019, granted, now 11,429,934.
Claims priority of provisional application 62/752,723, filed on Oct. 30, 2018.
Prior Publication US 2023/0079032 A1, Mar. 16, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 10/10 (2023.01); G16H 40/20 (2018.01); G06N 3/08 (2023.01); G06Q 10/1093 (2023.01); G06Q 40/08 (2012.01); G06V 30/416 (2022.01); G06V 30/19 (2022.01); G06V 10/82 (2022.01); G06V 10/44 (2022.01); G06V 10/22 (2022.01); G06V 10/24 (2022.01); G06V 10/32 (2022.01); G06V 10/30 (2022.01)
CPC G16H 40/20 (2018.01) [G06N 3/08 (2013.01); G06Q 10/1095 (2013.01); G06Q 40/08 (2013.01); G06V 10/22 (2022.01); G06V 10/454 (2022.01); G06V 10/82 (2022.01); G06V 30/19173 (2022.01); G06V 30/416 (2022.01); G06V 10/243 (2022.01); G06V 10/30 (2022.01); G06V 10/32 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
obtaining a set of training input image elements, wherein each of the training input image elements includes a digital image depicting an identifier, each of the training input image elements is associated with one or more labels that identify an interpretation of a first piece of information, a second piece of information, or both from the identifier, and the first piece of information is different from the second piece of information;
training a multi-task convolutional neural network architecture using the set of training input image elements, wherein the training comprises:
extracting, by a first machine-learning model in the multi-task convolutional neural network architecture, a first set of features from the set of training input image elements for the first piece of information on the identifier, wherein the first set of features are specific to a first task of classifying the identifier;
extracting, by a second machine-learning model in the multi-task convolutional neural network architecture, a second set of features from the set of training input image elements for the second piece of information on the identifier, wherein the second set of features are specific to a second task of predicting a location of the second piece of information on the identifier;
classifying, by the multi-task convolutional neural network architecture, the identifier based on the first set of features and the second set of features;
predicting, by the multi-task convolutional neural network architecture, the location of the second piece of information on the identifier based on the first set of features and the second set of features;
optimizing parameters of the first machine-learning model based on the classification of the identifier and the one or more labels that identify the interpretation of the first piece of information; and
optimizing parameters of the second machine-learning model based on the prediction of the location and the one or more labels that identify the interpretation of the second piece of information; and
providing the multi-task convolutional neural network architecture comprising the first machine-learning model and the second machine-learning model.