Granted Patent B2

US 12,458,283 · App. 18/109,720 · Granted Nov 4, 2025

Inventors: Keith Harry Nothacker (San Francisco, CA); Kimberly Annette Neely (San Francisco, CA)

Assignee: KHN Solutions, LLC

A61B5/4845A61B5/0008A61B5/0022A61B5/1112A61B5/681A61B5/6843A61B5/7465G01N33/48714

Abstract

Claims (48)

1 . A method for remote intoxication monitoring of a user with a wearable transdermal alcohol sensing device, the method comprising:

providing the wearable transdermal alcohol sensing device comprising:

a set of sensors comprising an alcohol sensor configured to collect transdermal samples, and supplementary sensors comprising a temperature sensor and a motion sensor, and

a wristband configured to position the alcohol sensor at a wrist of the user;

at a computing system, collecting a set of data from the set of sensors of the wearable transdermal alcohol sensing device, the set of data comprising:

temperature data associated with a skin surface of the user, the temperature data generated from the temperature sensor;

motion data associated with the user, the motion data generated from the motion sensor of the set of sensors; and

intoxication data associated with the user, the intoxication data generated from the alcohol sensor;

at the computing system, processing the data with:

a removal detection process, the removal detection process comprising:

with a first trained machine learning model, processing the set of data to determine a removal metric associated with the wearable transdermal alcohol sensing device relative to the user;

a tamper detection process, the tamper detection process separate and distinct from the removal detection process, the tamper detection process comprising:

with a programmed process, processing the intoxication data to determine a set of tamper features, a and

processing the set of tamper features to determine a tamper metric;

an intoxication detection process configured to determine an intoxication metric based on the intoxication data; and

at a computing system, in response to at least one of the removal metric or the tamper metric exceeding a predetermined threshold, automatically triggering a first action comprising prompting the user to perform a video check operation.

2 . The method of claim 1 , wherein the tamper detection process is configured to detect a physical obstruction of an alcohol sensor onboard the wearable transdermal alcohol sensing device.

3 . The method of claim 2 , wherein the physical obstruction comprises a placement of an object between the alcohol sensor and a skin surface of the user.

4 . The method of claim 1 , wherein the intoxication detection process is performed with a second trained machine learning model, the second trained machine learning model separate and distinct from the first trained machine learning model.

5 . The method of claim 1 , wherein the tamper detection process is performed using the motion data associated with the user.

6 . The method of claim 1 , wherein the set of data is collected at a processing subsystem, wherein at least a portion of the processing subsystem is arranged offboard the wearable transdermal alcohol sensing device.

7 . The method of claim 1 , further comprising checking for receipt of the set of data at the processing subsystem, wherein in an event that the set of data is not received within a threshold period of time relative to receipt of a prior set of data, the method further comprises triggering a notification to the user, wherein the notification prompts the user to open a client application at a user device in communication with the wearable transdermal alcohol sensing device.

8 . The method of claim 7 , wherein opening the client application automatically triggers a foregrounding of the client application.

9 . The method of claim 1 , wherein the programmed process comprises a feature analysis of an intoxication curve, the intoxication curve comprising the intoxication data.

10 . The method of claim 9 , wherein the feature analysis is performed using a rule-based process.

11 . A system for remote intoxication monitoring of a user, the system comprising:

a wearable transdermal alcohol sensing device comprising:

a set of sensors comprising an alcohol sensor configured to collect transdermal samples, and supplementary sensors comprising a motion sensor and a temperature sensor, and

a wristband configured to position the alcohol sensor at a wrist of the user; and

a processing subsystem comprising a computer-readable medium storing computer-readable instructions for:

collecting a set of data from a set of sensors of the wearable transdermal alcohol sensing device, the set of data comprising:

temperature data associated with a skin surface of the user;

motion data associated with the user; and

intoxication data associated with the user;

processing the data with:

a removal detection process, comprising: with a first trained machine learning model, processing the set of data to determine a removal metric associated with the wearable transdermal alcohol sensing device relative to the user;

a tamper detection process separate and distinct from the removal detection process, comprising: with a programmed process, processing the intoxication data to determine a set of tamper features, and processing the set of tamper features to determine a tamper metric;

an intoxication detection process configured to determine an intoxication metric based on the intoxication data; and

in response to at least one of the removal metric or the tamper metric exceeding a predetermined threshold, automatically triggering a first action comprising prompting the user to perform a video check operation.

12 . The system of claim 11 , further comprising a client application in communication with the processing subsystem.

13 . The system of claim 12 , wherein the client application executed onboard a user device associated with the user, wherein the first and second actions are triggered at the client application.

14 . The system of claim 13 , wherein the first and second actions comprise automatically initiating a video call at the client application, the video call utilizing a camera onboard the user device.

15 . The system of claim 11 , wherein the intoxication detection process is performed with a second trained machine learning model, the second trained machine learning model separate and distinct from the first trained machine learning model.

16 . The system of claim 11 , wherein the tamper detection process is performed using the intoxication data associated with the user.

17 . The system of claim 11 , further comprising checking for receipt of the set of data at the processing subsystem, wherein in an event that the set of data is not received within a threshold period of time relative to receipt of a prior set of data, the method further comprises triggering a notification to the user, wherein the notification prompts the user to open a client application at a user device in communication with the wearable transdermal alcohol sensing device.

18 . The system of claim 17 , wherein opening the client application automatically triggers a foregrounding of the client application.

19 . The system of claim 11 , wherein the programmed process comprises a feature analysis of an intoxication curve, the intoxication curve comprising the intoxication data.

20 . The system of claim 19 , wherein the feature analysis is performed using a rule-based process.

Assignments (2)

Continuity (4)

References Cited (288)