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US 12,643,553 B2Granted
Smart ring system for measuring driver impairment levels and using machine learning techniques to predict high risk driving behavior
AssigneeQuanata, LLC
InventorKenneth Jason Sanchez
Life of this patent
Estimating the term…
Abstract
A method for predicting risk exposure can include receiving a set of data collected via a smart ring. The method also can include analyzing, via a trained machine learning (ML) model, the set of data collected via the smart ring to determine whether the set of data collected via the smart ring (a) represents an impairment pattern or (b) correlates to a high-risk pattern, wherein at least one of the impairment pattern or the high-risk pattern correlates to a risk exposure. The method for predicting risk exposure further can include generating a notification to alert a user of the risk exposure. Other embodiments are disclosed.
Claims20
A method for predicting a risk exposure, comprising:
- sensing a set of data before and during driving using:
- a first sensor of a smart ring of a user, wherein the first sensor is configured to detect a sleep pattern of the user;
- and
- a second sensor of the smart ring of the user, wherein the second sensor is configured to integrate with a microfluidic device;
- and
- determining, by a trained machine learning (ML) model:
- whether the set of data correlates to a risk score;
- a first remediating action to reduce the risk score prior to a driving session based on the sleep pattern of the user;
- and
- a second remediating action to reduce the risk score during the driving session based on the microfluidic device of the user;
- and
- in response to determining that the set of data correlates to the risk score:
- generating a notification to alert the user of the risk score and the first remediating action to reduce the risk score;
- generating a notification to alert the user of the risk score and the second remediating action to reduce the risk score;
- and
- transmitting the notification to display the risk score at a display of a mobile device of the user.
13 more elements
A system for predicting a risk exposure, comprising:
- sensors sensing a set of data before and during driving using:
- a first sensor of a smart ring of a user, wherein the first sensor is configured to detect a sleep pattern of the user;
- and
- a second sensor of the smart ring of the user, wherein the second sensor is configured to integrate with a microfluidic device;
- one or more processors;
- and
- one or more non-transitory computer readable media comprising computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising:
- determining, by a trained machine learning (ML) model:
- whether the set of data of the user correlates to a risk score;
- a first remediating action to reduce the risk score prior to a driving session based on the sleep pattern of the user;
- a second remediating action to reduce the risk score during the driving session based on the microfluidic device of the user;
- and
- in response to determining that the set of data correlates to the risk score:
- generating a notification to alert the user of the risk score and the first remediating action to reduce the risk score;
- generating a notification to alert the user of the risk score and the second remediating action to reduce the risk score;
- and
- transmitting the notification to display the risk score at a display of a mobile device of the user.
14 more elements
One or more non-transitory computer-readable media comprising computing instructions that, when executed on one or more processors, cause the one or more processors to perform operations comprising:
- sensing a set of data before and during driving using:
- a first sensor of a smart ring of a user, wherein the first sensor is configured to detect a sleep pattern of the user;
- and
- a second sensor of the smart ring of the user, wherein the second sensor is configured to integrate with a microfluidic device;
- and
- determining, by a trained machine learning (ML) model:
- whether the set of data correlates to a risk score;
- a first remediating action to reduce the risk score prior to a driving session based on the sleep pattern of the user;
- and
- a second remediating action to reduce the risk score during the driving session based on the microfluidic device of the user;
- and
- in response to determining that the set of data correlates to the risk score:
- generating a notification to alert the user of the risk score and the first remediating action to reduce the risk score;
- generating a notification to alert the user of the risk score and the second remediating action to reduce the risk score;
- and
- transmitting the notification to display the risk score at a display of a mobile device of the user.
13 more elements
A server for implementing a machine learning (ML) model to predict a risk exposure, the server comprising:
- a communication interface;
- one or more processors coupled to the communication interface;
- and
- a memory coupled to the one or more processors and storing instructions that, when implemented, cause the one or more processors to:
- sense a set of data before and during driving using:
- a first sensor of a smart ring of a user, wherein the first sensor is configured to detect a sleep pattern of the user;
- and
- a second sensor of the smart ring of the user, wherein the second sensor is configured to integrate with a microfluidic device;
- and
- determine, by a trained machine learning (ML) model:
- whether the set of data correlates to a risk score;
- a first remediating action to reduce the risk score prior to a driving session based on the sleep pattern of the user;
- a second remediating action to reduce the risk score during the driving session based on the microfluidic device of the user;
- and
- in response to determining that the set of data correlates to the risk score:
- generate a notification to alert the user of the risk score and the first remediating action to reduce the risk score;
- generate a notification to alert the user of the risk score and the second remediating action to reduce the risk score;
- and
- transmit the notification to display the risk score at a display of a mobile device of the user.
16 more elements
Dependencies are inferred from the claim text.