# US 11,993,269 B2 · Smart ring system for measuring driver impairment levels and using machine learning techniques to predict high risk driving behavior

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 11,993,269 B2
- Document ID: US11993269B2
- Status: Granted
- Kind: B2
- Application number: 18/056,537
- Filed: 2022-11-17
- Published: 2023-03-09
- Granted: 2024-05-28
- Earliest claimed: 2019-07-23
- Assignees: Blueowl, LLC
- Applicants: BlueOwl, LLC
- Inventors: Kenneth Jason Sanchez
- Web page: https://patentgrep.com/patent/US11993269B2

## CPC

- B60W 40/08: related to drivers or passengers
- A44C 9/0053: {having special functions}
- A61B 5/01: Measuring temperature of body parts {; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue} (clinical contact thermometers G01K13/20)
- A61B 5/6826: {Finger}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- A61B 5/746: {Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms}
- B60K 28/066: {actuating a signalling device (B60K28/063 takes precedence)}
- B60W 50/0097: {Predicting future conditions}
- B60W 50/14: Means for informing the driver, warning the driver or prompting a driver intervention
- G01S 19/42: Determining position
- G06N 5/04: Inference or reasoning models
- G06N 20/00: Machine learning
- H02J 7/02: for charging batteries from AC mains by converters
- H02J 7/40: characterised by the exchange of charge or discharge related data
- H02J 7/70: characterised by the mechanical construction
- H02J 7/80: including monitoring or indicating arrangements
- A61B 5/0205: Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
- A61B 5/02141: {Details of apparatus construction, e.g. pump units or housings therefor, cuff pressurising systems, arrangements of fluid conduits or circuits (A61B5/02233, A61B5/0235 take precedence)}
- A61B 5/02438: {with portable devices, e.g. worn by the patient}
- A61B 5/1114: {Tracking parts of the body}
- A61B 5/1125: {Grasping motions of hands}
- A61B 5/14517: {for sweat}
- A61B 5/1477: non-invasive
- A61B 5/165: {Evaluating the state of mind, e.g. depression, anxiety}
- A61B 5/441: {Skin evaluation, e.g. for skin disorder diagnosis}
- A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor}
- A61B 2560/0242: adapted to measure environmental factors, e.g. temperature, pollution
- B60W 2040/0818: {Inactivity or incapacity of driver}
- B60W 2040/0836: {due to alcohol}
- B60W 2040/0845: {due to drugs}
- B60W 2040/0872: {Driver physiology}
- B60W 2540/221: Physiology, e.g. weight, heartbeat, health or special needs
- B60W 2540/24: Drug level, e.g. alcohol
- B60W 2540/26: Incapacity
- B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- A44C: PERSONAL ADORNMENTS, e.g. JEWELLERY; COINS
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- B60K: ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY

## Abstract

The described systems and methods determine a driver's fitness to safely operate a moving vehicle based at least in part upon observed impairment patterns. A smart ring, wearable on a user's finger, continuously monitors impairment levels. This impairment data, representing impairment patterns, can be utilized, in combination with driving data, to train a machine learning model, which will predict the user's level of risk exposure based at least in part upon observed impairment patterns. The user can be warned of this risk to prevent them from driving or to encourage them to delay driving. In some instances, the disclosed smart ring system may interact with the user's vehicle to prevent it from starting while the user is in a state of impairment induced by substance intoxication.

## Claims

### Claim 1

A method for implementing a machine learning model to predict driving risk exposure based at least in part upon observed impairment patterns, the method comprising:
receiving a first set of data collected via a smart ring associated with a user;
analyzing, via a machine learning (ML) model, the first set of data collected by the smart ring associated with the user, wherein:
the ML model is trained using the first set of first data indicative of:
an impairment pattern collected via an impairment monitoring device, wherein the impairment monitoring device collects sleep data; and
a second set of data indicative of a high-risk driving pattern collected via one or more driving monitor devices, wherein the analyzing includes:
determining that the first set of data represents the impairment pattern correlated with the high-risk driving pattern; and
in response to the determining that the first set of data represents the impairment pattern, predicting, based at least in part upon the ML model, a level of risk exposure for the user during driving;
determining a remediating action to reduce or eliminate the level of risk exposure;
generating a notification to alert the user of the level of risk exposure or the remediating action to reduce or eliminate the level of risk exposure; and
providing the notification to at least one of the smart ring or a display in a vehicle.

### Claim 2 (depends on 1)

The method of claim 1, wherein the smart ring includes a physiological and biochemical sensor at least partially disposed on a surface of the smart ring.

### Claim 3 (depends on 1)

The method of claim 1, wherein the set of first data comprises physiological and biochemical data collected via one or more physiological and biochemical sensors of a first set of smart rings.

### Claim 4 (depends on 1)

The method of claim 1, wherein the ML model is trained to discover one or more relationships between the impairment pattern and the high-risk driving pattern, wherein the one or more relationships include a relationship representing a correlation between the impairment pattern and the high-risk driving pattern.

### Claim 5 (depends on 1)

The method of claim 1, wherein the one or more driving monitor devices includes a vehicle computer or a dedicated electronic driving tracker device wherein acquiring the second set of data comprises acquiring the second set of data via one or more of the vehicle computer or the dedicated electronic driving tracker device.

### Claim 6 (depends on 1)

The method of claim 1, further comprising:
comparing the level of risk exposure to a known threshold to determine the level of risk exposure exceeds the known threshold; and
in response to determining that the level of risk exposure exceeds the known threshold, generating a system action that prevents the user from operating a vehicle, wherein:
the system action includes preventing the user from starting the vehicle.

### Claim 7 (depends on 1)

The method of claim 1, further comprising:
comparing the level of risk exposure to a known threshold to determine the level of risk exposure exceeds the known threshold; and
in response to determining that the level of risk exposure exceeds the known threshold, generating a system action that prevents the user from operating a vehicle, wherein:
the system action includes overtaking control of the vehicle while the vehicle is in operation.

### Claim 8 (depends on 1)

The method of claim 1, further comprising:
utilizing the first set of data representing the impairment pattern to further train the ML model.

### Claim 9

A system for acquiring data indicative of an impairment pattern, and utilizing the data to predict driving risk exposure, comprising:
one or more processors configured to:
receive a first set of data collected via a smart ring associated with a user;
analyze, via a machine learning (ML) model, the first set of data collected by the smart ring associated with the user, wherein:
the ML model is trained using the first set of data indicative of:
an impairment pattern collected via an impairment monitoring device, wherein the impairment monitoring device collects sleep data; and
a second set of data indicative of a high-risk driving pattern collected via one or more driving monitor devices, wherein the one or more processors:
determine that the first set of data represents the impairment pattern correlated with the high-risk driving pattern; and
in response to determining that the first set of data represents the impairment pattern, predict, via the ML model, a level of risk exposure for the user during driving;
determine a remediating action to reduce or eliminate the level of risk exposure;
generate a notification to alert the user of the predicted level of risk exposure or the remediating action to reduce or eliminate the level of risk exposure; and
provide the notification to at least one of the smart ring or a display in a vehicle.

### Claim 10 (depends on 9)

The system of claim 9, wherein the smart ring includes a physiological and biochemical sensor at least partially disposed on a surface of the smart ring.

### Claim 11 (depends on 9)

The system of claim 9, wherein the first set of first data comprises physiological and biochemical data collected via one or more physiological and biochemical sensors of a first set of smart rings.

### Claim 12 (depends on 9)

The system of claim 9, wherein the ML model is trained to discover one or more relationships between the impairment pattern and the high-risk driving pattern, wherein the one or more relationships include a relationship representing a correlation between the impairment pattern and the high-risk driving pattern.

### Claim 13 (depends on 9)

The system of claim 9, wherein the one or more processors are further configured to generate the notification to alert the user of the level of risk exposure by way of generating the notification via the smart ring, a vehicle computer, or a mobile device in communication with the one or more processors.

### Claim 14 (depends on 9)

The system of claim 9, wherein the first set of data includes impairment pattern data for users other than the user associated with the smart ring.

### Claim 15 (depends on 9)

The system of claim 9, wherein the one or more processors are further configured to:
compare the level of risk exposure to a known threshold to determine the level of risk exposure exceeds the known threshold; and
in response to determining that the level of risk exposure exceeds the known threshold, generate a system action and transmit the system action to a vehicle computer for a vehicle to cause the vehicle computer to prevent the user from operating the vehicle.

### Claim 16

A server for implementing a machine learning (ML) model to predict driving risk exposure based at least in part upon acquired impairment patterns, 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:
analyze, via the ML model, a first set of data collected by a smart ring associated with a user, wherein:
the ML model is trained using the first set of data indicative of:
an impairment pattern collected via an impairment monitoring device, wherein the impairment monitoring device collects sleep data; and
a second set of data indicative of a high-risk driving pattern collected via one or more driving monitor devices, wherein analyzing includes:
determining that the first set of data represents the impairment pattern corresponding to a given impairment pattern correlated with the high-risk driving pattern; and
predicting, based at least in part upon the ML model, a level of risk exposure for the user during driving;
determine a remediating action to reduce or eliminate the level of risk exposure;
generate a notification to alert the user of the predicted level of risk exposure or the remediating action to reduce or eliminate the level of risk exposure; and
provide the notification to at least one of the smart ring or a display in a vehicle.

### Claim 17 (depends on 16)

The server of claim 16, wherein the smart ring includes a physiological and biochemical sensor at least partially disposed on a surface of the smart ring.

### Claim 18 (depends on 16)

The server of claim 16, wherein the level of risk exposure includes a binary or ternary parameter.

### Claim 19 (depends on 16)

The server of claim 16, wherein the instructions further cause the one or more processors to:
compare the level of risk exposure to a threshold; and
when the level of risk exposure exceeds the threshold, generate a system action and transmit the system action to a vehicle computer for a vehicle to cause the vehicle computer to prevent the user from operating the vehicle.

### Claim 20 (depends on 16)

The server of claim 16, wherein the first set of data includes impairment pattern data for users other than the user associated with the smart ring.