Patent ID: 11887307
Assignee: BIO-MARKETING-T, LTD. (BMT)
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method comprising:
receiving, by a processor, at least one first image of a first device;
training, by the processor, a deep learning module comprising a plurality of neural networks to:
identify the first device in the at least one first image and one of a presence or absence of a first test region of the first device in the at least one first image based on classifying a plurality of device features and a plurality of test region features identified in the at least one first image compared with a first reference image of the first device;
when the absence of the first test region in the at least one first image is identified or when the classifying of the plurality of test region features is below a base-level confidence:
generate at least one imaging directing command to capture at least one second image of at least one of the first device or the first test region of the first device;

wherein at least one neural network of the plurality of neural networks is configured based on at least one computer vision technique;
receiving, by the processor, the at least one second image based on the at least one imaging directing command;
inputting, by the processor, the at least one second image of the first device into the deep learning module to:
classify the plurality of device features and the plurality of test region features of the first test region identified in the at least one second image compared with a second reference image of a visual indicator indicative of a first test result in the first test region of the first device;
when the presence of the first test region in the at least one second image is identified or when the classifying of the plurality of test region features is higher than the base-level confidence:
retrain the deep learning module for improved identification of the first device based on the plurality of device features and the plurality of test region features identified in the at least one first image and the at least one second image; and
train the deep learning module for identification of the first test result in the first test region based on the plurality of device features and the plurality of test region features identified in the at least one first image and the at least one second image;

receiving, by the processor, at least one third image of a second device; and
inputting, by the processor, the at least one third image into the deep learning module to identify a second test result in a second test region of the second device in the at least one third image.