Patent ID: 11901066
Assignee: LABORATORY CORPORATION OF AMERICA HOLDINGS
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
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.