Patent ID: 11887733
Assignee: nan
Field: Measurement (Instruments)
Classification: CPC G | IPC G

Claim 1:
2. A computer implemented method for evaluating one of a plurality of unique diagnostic devices, the method comprising:
a. capturing a digital image of each one of the plurality of unique diagnostic devices to produce a corresponding unique digital image for each one of the plurality of unique diagnostic devices, wherein each one of the plurality of unique diagnostic devices includes a plurality of spatially separated optically detectable signal (SSODS) test strips, and wherein each one of the plurality of unique diagnostic devices includes a diagnostic device identifier, wherein capturing a digital image of each one of the plurality of unique diagnostic devices includes taking a digital image of the each one of the plurality of spatially separated optically detectable signal test strips to produce corresponding unique digital image of each one of the plurality of spatially separated optically detectable signal test strips,
b. grouping the plurality of unique digital images of the plurality of unique diagnostic devices to generate a plurality of groups of unique digital images of the unique diagnostic devices, wherein each group of digital images of unique diagnostic devices is limited to related diagnostic devices,
c. generating a unique machine learning model for each group of digital images of unique diagnostic devices,
d. assigning a distinct machine learning model group identifier for each unique machine learning model for each group of digital images of unique diagnostic devices,
e. generating a plurality of groups of digital images of the plurality of spatially separated optically detectable signal test strips, wherein each group of digital images of spatially separated optically detectable signal test strips is limited to related diagnostic devices,
f. generating a unique machine learning model for each group of digital images of spatially separated optically detectable signal test strips,
g. separating each unique machine learning model for each group of digital images of spatially separated optically detectable signal test strips into a predetermined grid, wherein each grid represents a specific drug control variable,
h. using a camera enabled device to capture a digital image of an active diagnostic device, wherein the active diagnostic device is of similar construction to at least one of the plurality of unique diagnostic devices, wherein the active diagnostic device to be read includes a unique active diagnostic device encoded identifier, wherein the active diagnostic device includes multiple spatially separated optically detectable signal test strips,
i. capturing a digital image of the active diagnostic device multiple spatially separated optically detectable signal test strips, wherein a portion of the digital image of the active diagnostic device includes a digital image of the active diagnostic device multiple spatially separated optically detectable signal test strips,
j. selecting at least one of the unique machine learning models for at least one of the groups of digital images of unique diagnostic devices by matching the encoded active diagnostic device identifier to at least one of the distinct machine learning model group identifiers,
k. separating the digital image of the active diagnostic device multiple spatially separated optically detectable signal test trips into a grid, wherein each module of the multiple spatially separated optical detectable signal test strips grid is a digital representation of at least a portion of each one of the multiple spatially separated optically detectable signal test strips, and
l. comparing at least one module of the digital image of the active diagnostic device multiple spatially separated optically detectable signal test strips to at least one of the modules of grids including specific drug control variables to determine the concentration of analytes in at least one of the active diagnostic device multiple spatially separated optically detectable test strips.