IP Library Granted Patent US 11,610,682
Granted Patent B2
US 11,610,682 · App. 17/807,934 · Granted Mar 21, 2023

Systems, methods, and devices for non-human readable diagnostic tests

Inventors: Michael W. Ferro, Jr. (Palm Beach, FL); Zachary Carl Nienstedt (Wilton Manors, FL); Sam Miller (Hollywood, FL); Marco Magistri (Miami, FL); Nicholas Atkinson Kramer (Wilton Manors, FL); James Thomas Heising (Richard, WA)
Assignee: EMED LABS, LLC
G16H50/20G16H30/20G16H40/67G16H50/30
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Quick Facts
Patent No.
US 11,610,682
App. No.
17/807,934
Granted
Mar 21, 2023
Kind
B2
Abstract

Systems and methods for ensuring medical diagnostic test integrity are disclosed. In particular, systems and methods herein can be used to ensure compliance with testing procedures. Some embodiments provide systems and methods for verifying test results. According to some embodiments, test results can be non-human-readable results that can be interpreted by a computing system.

Claims (41)

1. Computer-implemented method of measuring compliance with a medical diagnostic testing procedure comprising:

providing, by a computing system, a prompt to a user to present a medical diagnostic component to a camera of a user device, wherein the medical diagnostic component is included in a test kit;

receiving, by the computing system, a first image captured by the camera;

prompting, by the computing system, the user to present the medical diagnostic component to the camera of the user device;

receiving, by the computing system, a second image captured by the camera;

detecting, by the computing system, a first set of features associated with the first image and a second set of features associated with the second image;

based on the first set of features and the second set of features, determining, by the computing system, that the medical diagnostic component in the first image is the same as the medical diagnostic component in the second image by:

determining, by the computing system, a first plurality of identifiers based on the first set of features,

determining, by the computing system, a second plurality of identifiers based on the second set of features, and

mapping each identifier of the first plurality of identifiers and the second plurality of identifiers to a hash space comprising hash values, wherein mapping each identifier to a hash space comprises performing a transformation on at least one identifier of the first and second pluralities of identifiers, and wherein the transformation comprises color matching, a scale-invariant feature transform, a convolution transform against a template, thresholding, color matching, edge detection, or overlap detection; and

based at least in part on the determining, assessing, by the computing system, that the user remained in compliance with the medical diagnostic testing procedure.

2. The computer-implemented method of claim 1 , wherein the medical diagnostic test component is a test strip.

3. The computer-implemented method of claim 2 , wherein the test strip includes an identifier comprising one or more of: a QR code, a pattern, a graphic, an image, or a bar code.

4. The method of claim 3 , wherein the identifier is unique to a particular test strip.

5. The computer-implemented method of claim 3 , wherein the pattern comprises a linear pattern, a color gradient, a grayscale gradient, a sine wave, a triangle wave, a square wave, or an arrangement of shapes.

6. The computer-implemented method of claim 1 , wherein the medical diagnostic test component comprises a pattern region.

7. The computer-implemented method of claim 6 , wherein the determining comprises:

determining, by the computing system, a first average color value of the first set of features and a second average color value of the second set of features; and

comparing the first average color value and the second average color value.

8. The computer-implemented method of claim 1 , further comprising:

determining, by the computing system based on the first set of features, a first identifier;

determining, by the computing system based on the second set of features, a second identifier; and

determining, by the computing system, that the first identifier is the same as the second identifier.

9. The computer-implemented method of claim 1 , wherein the determining comprises:

determining a difference in the hash values of the first plurality of identifiers and the hash values of the second plurality of identifiers.

10. The computer-implemented method of claim 9 , wherein the difference in hash values is a Hamming distance or a Levenshtein distance.

11. The computer-implemented method of claim 1 , wherein the medical diagnostic test component comprises a reference region.

12. The computer-implemented method of claim 11 , wherein the reference region comprises grayscale reference values.

13. The computer-implemented method of claim 11 , wherein the reference region comprises color reference values.

14. The computer-implemented method of claim 1 , wherein the medical diagnostic component comprises:

a first region having a first pattern;

a second region having a second pattern; and

a third region having a third pattern,

wherein the determining comprises comparing the first, second, and third regions of the medical diagnostic component of the first image to the first, second, and third regions of the medical diagnostic component the second image.

15. The computer-implemented method of claim 14 , wherein the first pattern has a first frequency,

wherein the second pattern has a second frequency that is different from the first frequency, and

wherein the third pattern has a third frequency that is different from the first frequency and the second frequency.

16. The computer-implemented method of claim 15 , wherein the first frequency is larger than the second frequency and the third frequency,

wherein the second frequency is larger than the third frequency, and

wherein at least one of the ratio of the first frequency to the second frequency, the ratio of the first frequency to the third frequency, and the ratio of the second frequency to the third frequency is not an integer.

17. The computer-implemented method of claim 16 , wherein at least one of the ratios is an irrational number.

Assignments (3)
CHANGE OF NAME Recorded Aug 13, 2025
From: EMED POPULATION HEALTH, LLC
To: EMED POPULATION HEALTH, INC.
Reel/Frame 072435/0043 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 23, 2025
From: EMED LABS, LLC
To: EMED POPULATION HEALTH, LLC
Reel/Frame 071208/0446 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 25, 2022
From: FERRO, MICHAEL W., JR.; NIENSTEDT, ZACHARY CARL; MILLER, SAM; MAGISTRI, MARCO; KRAMER, NICHOLAS ATKINSON; HEISING, JAMES THOMAS
To: EMED LABS, LLC
Reel/Frame 061533/0681 →
Continuity (7)
Provisional Application 63202731 · Jun 22, 2021
Provisional Application 63268663 · Feb 28, 2022
Provisional Application 63271996 · Oct 26, 2021
Provisional Application 63264328 · Nov 19, 2021
Provisional Application 63265472 · Dec 15, 2021
Provisional Application 63268407 · Feb 23, 2022
Related Publication 20220406458A1 · Dec 22, 2022
Cited By (2)
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