# US 11,298,054 B2 · Systems and methods for analyzing, interpreting, and acting on continuous glucose monitoring data

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- Number: US 11,298,054 B2
- Document ID: US11298054B2
- Status: Granted
- Kind: B2
- Application number: 17/226,702
- Filed: 2021-04-09
- Published: 2021-09-23
- Granted: 2022-04-12
- Earliest claimed: 2020-03-20
- Assignees: Welldoc, Inc.
- Applicants: Welldoc, Inc.
- Inventors: Shiping Liu; Mansur Shomali; Abhimanyu Kumbara; Anand Iyer; Malinda Peeples; Michelle Dugas; Kenyon Crowley; Guodong Gao
- Web page: https://patentgrep.com/patent/US11298054B2

## CPC

- A61B 5/14532: {for measuring glucose, e.g. by tissue impedance measurement}
- A61B 5/1118: {Determining activity level}
- A61B 5/48: {Other medical applications}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- A61B 5/7267: {involving training the classification device}
- 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 5/7282: {Event detection, e.g. detecting unique waveforms indicative of a medical condition (cough events A61B5/0823; seizures A61B5/4094; sleep apnoea A61B5/4818)}
- A61B 5/7475: {User input or interface means, e.g. keyboard, pointing device, joystick}
- G06N 20/00: Machine learning
- G16H 10/40: for data related to laboratory analysis, e.g. patient specimen analysis
- G16H 20/00: ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H 20/17: delivered via infusion or injection
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet}
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Methods and devices include automated coaching for management of glucose states by receiving a user's glucose levels using a continuous glucose monitoring (CGM) device, determining a time in range (TIR) value, determining a TIR state, receiving a glucose variability (GV) value, determining a GV state, determining a starting state based on the TIR state and the GV state, determining that the starting state corresponds to a non-ideal state, generating an optimized pathway to reach an ideal state based on one or more account vectors such as addressing self-management behavior including food, activity, and medication use. The optimized pathway may further be based on computer detection and classification of significant events of interest over time.

## Claims

### Claim 1

A system for treating a glucose condition of a user, the system comprising:
a continuous glucose monitoring (CGM) device configured to output a plurality of glucose readings based on analyzing a bodily fluid over a period of time;
a memory configured to store the plurality of glucose readings; and
a processor configured to:
generate a CGM trace based on the plurality of glucose readings over the period of time;
identify a severity score of the CGM trace, wherein the severity score is based on a height of the CGM trace and a duration of time that the CGM trace stays above a target value;
identify a starting state based on the severity score, the starting state being indicative of a glucose health of the user;
generate an optimized pathway to reach an ideal state based on one or more user vectors and the starting state, the optimized pathway comprising one or more adjustments to the one or more user vectors wherein the one or more adjustments comprise a medication adjustment, a food consumption adjustment, and an exercise value; and
provide the optimized pathway to the user via a graphical user interface (GUI) for treatment of the glucose condition using the adjustments to the one or more user vectors, wherein:
the optimized pathway is provided as a machine learning model output, the machine learning model output comprising a user vector change based on machine learning inputs comprising the severity score of the CGM trace;
the machine learning model inputs further comprising user attributes, wherein the user attributes comprise a medical attribute, a user preference, a metabolic attribute, and a user demographic;
the optimized pathway is further based on a habit index score of the user, determined based on a cohort of users with one or more user attributes in common with the user;
receiving an updated CGM trace over a second period of time using the CGM device by sensing an updated concentration of glucose within bodily fluid obtained during the second period of time;
generating an updated optimized pathway to reach the ideal state based on the updated CGM trace, the updated optimized pathway comprising insulin intake information; and
providing the updated optimized pathway to the user via the GUI.

### Claim 2 (depends on 1)

The system of claim 1, further comprising:
identifying a beginning parameter, wherein the beginning parameter is a scaled value determined based on a beginning point of the CGM trace in comparison to a target range; and
generating the optimized pathway based further on the beginning parameter.

### Claim 3 (depends on 2)

The system of claim 2, wherein the beginning parameter is selected form one of a very high parameter, a high parameter, an in range parameter, a low parameter, and a very low parameter.

### Claim 4 (depends on 1)

The system of claim 1, further comprising:
identifying an ending parameter, wherein the ending parameter is a scaled value determined based on an ending point of the CGM trace in comparison to a target range; and
generating the optimized pathway based further on the ending parameter.

### Claim 5 (depends on 1)

The system of claim 1, wherein the severity score is determined by multiplying a height of the CGM trace by a duration that the CGM trace is above the target value.

### Claim 6 (depends on 5)

The system of claim 5, wherein the height of the CGM trace is given a first weight and the duration that the CGM trace is above the target value is given a second weight different than the first weight.

### Claim 7 (depends on 1)

The system of claim 1, wherein a lower severity score corresponds to a starting state closer to the ideal state when compared to a higher severity score.

### Claim 8 (depends on 1)

The system of claim 1, wherein the user vectors comprise one or more of medications, food consumption, exercise value, psycho-social parameters, or social-determinant parameters.

### Claim 9 (depends on 1)

The system of claim 1, further comprising:
determining a time in range (TIR) value of the CGM trace, wherein the TIR value is based on an amount of time the CGM trace is within a threshold band over a base time period;
determining a TIR state based on the TIR value;
receiving a glucose variability (GV) value based at least on the CGM trace, wherein the GV value is one of a standard deviation or a coefficient of variance (CV), wherein a CV indicates a variability of the glucose readings in view of a standard deviation of the glucose readings over the base time period;
determining a GV state based on the GV value; and
determining the starting state further based on the TIR state and the GV state.

### Claim 10 (depends on 1)

The system of claim 1, wherein the CGM device is configured to obtain the bodily fluid via a skin penetrating component, the glucose readings being determined by sensing a concentration of analytes within the obtained bodily fluid.

### Claim 11 (depends on 1)

The system of claim 1, wherein the CGM device is configured to obtain the glucose reading in increments of five minutes or less.

### Claim 12 (depends on 1)

The system of claim 1, further comprising identifying a CGM trace shape of the CGM trace using a machine learning model.

### Claim 13 (depends on 1)

The system of claim 1, wherein the starting state is identified further based on the CGM trace shape.

### Claim 14

A system for treating a glucose condition of a user, the system comprising:
a continuous glucose monitoring (CGM) device configured to output a plurality of glucose readings based on analyzing a bodily fluid over a period of time;
a memory configured to store the plurality of glucose readings; and
a processor configured to:
generate a CGM trace based on the plurality of glucose readings over the period of time;
identify a severity score of the CGM trace, wherein the severity score is based on a height of the CGM trace and a duration of time that the CGM trace stays above a target value;
identify a starting state based on the severity score, the starting state being indicative of a glucose health of the user;
generate an optimized pathway to reach an ideal state based on one or more user vectors and the starting state, the optimized pathway comprising one or more adjustments to the one or more user vectors wherein the one or more adjustments comprise a medication adjustment, a food consumption adjustment, and an exercise value; and
provide the optimized pathway to the user via a graphical user interface (GUI) for treatment of the glucose condition using the adjustments to the one or more user vectors, wherein:
the optimized pathway is provided as a machine learning model output, the machine learning model output comprising a user vector change based on machine learning inputs comprising the severity score of the CGM trace;
the machine learning model inputs further comprising user attributes, wherein the user attributes comprise a medical attribute, a user preference, a metabolic attribute, and a user demographic;
the optimized pathway is further based on a habit index score of the user, determined based on a cohort of users with one or more user attributes in common with the user;
receiving an updated CGM trace over a second period of time using the CGM device by sensing an updated concentration of glucose within bodily fluid obtained during the second period of time;
generating an updated optimized pathway to reach the ideal state based on the updated CGM trace; and
providing the updated optimized pathway to the user via the GUI.

### Claim 15 (depends on 14)

The system of claim 14, wherein the CGM device is configured to obtain the bodily fluid via a skin penetrating component, the glucose readings being determined by sensing a concentration of analytes within the obtained bodily fluid.

### Claim 16

A system for treating a glucose condition of a user, the system comprising:
a continuous glucose monitoring (CGM) device configured to output a plurality of glucose readings based on analyzing a bodily fluid over a period of time;
a memory configured to store the plurality of glucose readings; and
a processor configured to:
generate a CGM trace based on the plurality of glucose readings over the period of time;
identify a severity score of the CGM trace, wherein the severity score is based on a height of the CGM trace and a duration of time that the CGM trace stays above a target value;
identify a starting state based on the severity score, the starting state being indicative of a glucose health of the user;
generate an optimized pathway to reach an ideal state based on one or more user vectors and the starting state, the optimized pathway comprising one or more adjustments to the one or more user vectors; and
provide the optimized pathway to the user via a graphical user interface (GUI) for treatment of the glucose condition using the adjustments to the one or more user vectors, wherein:
the optimized pathway is provided as a machine learning model output, the machine learning model output comprising a user vector change based on machine learning inputs comprising the severity score of the CGM trace;
the machine learning model inputs further comprising user attributes, wherein the user attributes comprise a medical attribute, a user preference, a metabolic attribute, and a user demographic;
the optimized pathway is further based on a habit index score of the user, determined based on a cohort of users with one or more user attributes in common with the user;
receiving an updated CGM trace over a second period of time using the CGM device by sensing an updated concentration of glucose within bodily fluid obtained during the second period of time;
generating an updated optimized pathway to reach the ideal state based on the updated CGM trace, the updated optimized pathway comprising insulin intake information; and
providing the updated optimized pathway to the user via the GUI.

### Claim 17 (depends on 16)

The system of claim 16, wherein the CGM device is configured to obtain the bodily fluid via a skin penetrating component, the glucose readings being determined by sensing a concentration of analytes within the obtained bodily fluid.