Granted Patent B2

US 12,441,332 · App. 18/635,628 · Granted Oct 14, 2025

Inventor: Kenneth Jason Sanchez (San Francisco, CA)

Assignee: QUANATA, LLC

B60W40/08A44C9/0053B60W50/0097B60W50/14G01S19/42G06N5/04G06N20/00H02J7/00032H02J7/0042H02J7/0047H02J7/02B60W2040/0836B60W2040/0845B60W2040/0872B60W2540/221B60W2540/24B60W2540/26

Abstract

Claims (50)

1. A method for predicting risk exposure, the method comprising:

receiving a particular set of data acquired via a sensor;

analyzing, via a machine learning (ML) model, the particular set of data, wherein the analyzing comprises:

determining that the particular set of data represents a particular light exposure pattern corresponding to a light exposure pattern correlated with a risk pattern, wherein:

the ML model is trained with a first set of data and a second set of data to identify a correlation between the light exposure pattern and the risk pattern;

the first set of data is indicative of the light exposure pattern acquired via a light exposure monitoring device; and

the second set of data is indicative of the risk pattern acquired via a monitoring device;

predicting a risk exposure for a user based on the analyzing the particular set of data; and

providing a notice indicating the risk exposure.

2. The method of claim 1 , wherein providing the notice indicating the risk exposure comprises providing the notice indicating the risk exposure to a smart ring worn by the user.

3. The method of claim 1 , wherein the sensor comprises a smart ring worn by the user.

4. The method of claim 1 , wherein the notice indicating the risk exposure comprises an exposure risk score.

5. The method of claim 1 , wherein the risk pattern comprises a high-risk pattern.

6. The method of claim 1 , wherein the monitoring device comprises a vehicle computer.

7. The method of claim 1 , wherein the monitoring device comprises an electronic tracker.

8. The method of claim 1 , wherein providing the notice indicating the risk exposure comprises providing the notice indicating the risk exposure to a display of a vehicle driven by the user.

9. The method of claim 1 , further comprising:

comparing the risk exposure to a known threshold to determine whether the risk exposure exceeds the known threshold; and

in response to the risk exposure exceeding the known threshold, generating a system action to prevent the user from operating a vehicle.

10. A system for predicting risk exposure, comprising:

a server configured to:

receive a particular set of data acquired via a sensor;

analyze, via a machine learning (ML) model, the particular set of data, by:

determining that the particular set of data represents a particular light exposure pattern corresponding to a light exposure pattern correlated with a risk pattern, wherein:

the ML model is trained with a first set of data and a second set of data to identify a correlation between the light exposure pattern and the risk pattern;

the first set of data is indicative of the light exposure pattern acquired via a light exposure monitoring device; and

the second set of data is indicative of the risk pattern acquired via a monitoring device;

predict a risk exposure for a user based on the particular set of data; and

provide a notice indicating the risk exposure.

11. The system of claim 10 , wherein the first set of data comprises radiation data.

12. The system of claim 10 , wherein the second set of data comprises data acquired via a GPS receiver.

13. The system of claim 10 , wherein the sensor is disposed within a smart ring.

14. The system of claim 10 , wherein the server is configured to provide the notice indicating the risk exposure by providing a notification to a mobile device in communication with the server.

15. The system of claim 10 , wherein the first set of data comprises light exposure pattern data for users other than the user.

16. The system of claim 10 , wherein the second set of data comprises pattern data for users other than the user.

17. A non-transitory computer-readable medium storing instructions for implementing a machine learning model to predict risk exposure, wherein the instructions, when executed by one or more processors, cause the one or more processors to:

receive a particular set of data acquired via a sensor;

analyze, via a machine learning (ML) model, the particular set of data by:

determining that the particular set of data represents a particular light exposure pattern corresponding to a light exposure pattern correlated with a risk pattern, wherein:

the ML model is trained with a first set of data and a second set of data to identify a correlation between the light exposure pattern and the risk pattern;

the first set of data is indicative of the light exposure pattern acquired via a light exposure monitoring device; and

the second set of data is indicative of the risk pattern acquired via a monitoring device;

predict a risk exposure for a user based on the analyze the particular set of data; and

provide a notice indicating the risk exposure.

18. The non-transitory computer-readable medium of claim 17 , wherein to provide the notice indicate the risk exposure comprises to transmit the notice to a smart ring, a vehicle computer, or a mobile device.

19. The non-transitory computer-readable medium of claim 17 , wherein the notice indicating the risk exposure compares an estimated amount of vitamin D of the user to a recommended daily amount of vitamin D.

20. The non-transitory computer-readable medium of claim 17 , wherein the instructions further cause the one or more processors to:

compare the risk exposure to a threshold;

generate a system action if the risk exposure exceeds the threshold; and

transmit the system action to a vehicle computer for a vehicle to prevent the user from operating the vehicle.

Assignments (2)

Continuity (4)

References Cited (400)

Cited By (2)

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