# US 12,593,990 B2 · System and methods utilizing artificial intelligence algorithms to analyze wearable activity tracker data

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- Number: US 12,593,990 B2
- Document ID: US12593990B2
- Status: Granted
- Kind: B2
- Application number: 16/916,004
- Filed: 2020-06-29
- Published: 2021-12-30
- Granted: 2026-04-07
- Earliest claimed: 2020-06-29
- Assignees: Aetna Inc.
- Applicants: Aetna Inc.
- Inventors: Naiqian Zhi; Benjamin Wanamaker; Rajiv Bhan; Sumeet Kumar
- Web page: https://patentgrep.com/patent/US12593990B2

## CPC

- A61B 5/0205: Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
- A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet}
- A61B 5/1118: {Determining activity level}
- A61B 5/14551: {for measuring blood gases}
- A61B 5/681: {Wristwatch-type devices}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- G06F 16/24522: {Translation of natural language queries to structured queries}
- G06N 3/04: Architecture, e.g. interconnection topology
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 20/30: relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
- G16H 40/67: for remote operation
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- A61B 5/02438: {with portable devices, e.g. worn by the patient}
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- 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

A system and method are disclosed for monitoring health conditions based on data collected by a wearable device such as an activity tracker or a smart watch. Deep learning algorithms are configured to process an input vector that includes monitored parameter data collected by the wearable device as well as embedding data obtained from health records corresponding to a user account registered to the wearable device. In some embodiments, the input vector can also include social determinants data and/or demographic data. The output of the deep learning algorithms provides classifiers that represent probabilities that the user of the wearable device has an underlying health condition. If any underlying health condition is detected, then the user can be notified directly, via the wearable device or an associated application or technology, or indirectly, via a primary care provider associated with the user.

## Claims

### Claim 1

A system for performing health monitoring, the system comprising:
a wearable device that includes a wireless interface; and
a server device including a memory and one or more processors configured to:
receive monitored parameter data from the wearable device via the wireless interface;
determine an artificial intelligence algorithm to use for processing the monitored parameter data;
discard one or more types of the monitored parameter data from the wearable device based on the determined artificial intelligence algorithm to generate filtered monitored parameter data;
obtain embedding data by processing health records corresponding to a user registered to the wearable device using a natural language processing algorithm, wherein the embedding data comprises an n-dimensional vector of dimensionless features obtained based on codes included in the health records corresponding to the user, and wherein the codes included in the health records comprise at least one of ICD (International Statistical Classification of Diseases) 9/10 diagnostic codes, CPT (Current Procedural Terminology) procedure codes, GPI (Generic Product Identifier) drug codes, or LOINC (Logical Observation Identifiers Names and Codes) lab codes;
process the filtered monitored parameter data and the embedding data to generate an input vector, wherein the input vector comprises a first portion corresponding to the filtered monitored parameter data and a second portion corresponding to the embedding data;
process the input vector by the artificial intelligence algorithm to generate an output vector; and
schedule an appointment with a healthcare provider based on the output vector.

### Claim 2 (depends on 1)

The system of claim 1, wherein the wearable device is an activity tracker, and wherein the monitored parameter data includes data points related to one or more of:
a heart rate;
an oxygen level;
an activity level including at least one of a number of steps, a number of flights climbed, or a duration of exercise; or
a number of calories burned.

### Claim 3 (depends on 2)

The system of claim 2, wherein the monitored parameter data further includes information logged by the user manually.

### Claim 4 (depends on 1)

The system of claim 1, wherein the health records are stored in a database and comprise at least one of:
claims records received from a health care provider;
prescription records received from a pharmacy; or
laboratory results received from a laboratory or other health care provider.

### Claim 5 (depends on 1)

The system of claim 1, wherein the input vector further includes social determinants data including one or more of:
economic information;
neighborhood information;
education information;
nutritional information; or
other environmental information.

### Claim 6 (depends on 1)

The system of claim 1, wherein the input vector further includes demographic data including one or more of:
age information;
gender information;
neighborhood type information;
family size information; or
employment indicator information.

### Claim 7 (depends on 1)

The system of claim 1, wherein the artificial intelligence algorithm comprises one or more of:
a multi-layer perceptron (MLP) algorithm;
a convolution neural network (CNN); or
a recurrent neural network (RNN).

### Claim 8 (depends on 1)

The system of claim 1, wherein the server device is further configured to generate a notification message based on the output vector, wherein the notification message is transmitted to one of the wearable device or a mobile device associated with the wearable device to provide the user of the wearable device with a suggested action.

### Claim 9 (depends on 1)

The system of claim 1, wherein the server device is further configured to generate a notification message based on the output vector, wherein the notification message is transmitted to the healthcare provider to suggest treatment options that are applicable to a patient.

### Claim 10

A method for performing health monitoring, the method comprising:
receiving monitored parameter data from a wireless interface of a wearable device;
determining an artificial intelligence algorithm to use for processing the monitored parameter data;
discarding one or more types of the monitored parameter data from the wearable device based on the determined artificial intelligence algorithm to generate filtered monitored parameter data;
obtaining embedding data by processing health records corresponding to a user registered to the wearable device using a natural language processing algorithm, wherein the embedding data comprises an n-dimensional vector of dimensionless features obtained based on codes included in the health records corresponding to the user, and wherein the codes included in the health records comprise at least one of ICD (International Statistical Classification of Diseases) 9/10 diagnostic codes, CPT (Current Procedural Terminology) procedure codes, GPI (Generic Product Identifier) drug codes, or LOINC (Logical Observation Identifiers Names and Codes) lab codes;
processing the filtered monitored parameter data and the embedding data to generate an input vector, wherein the input vector includes a first portion corresponding to the filtered monitored parameter data and a second portion corresponding to the embedding data;
processing the input vector by the artificial intelligence algorithm to generate an output vector; and
scheduling an appointment with a healthcare provider based on the output vector.

### Claim 11 (depends on 10)

The method of claim 10, wherein the wearable device is an activity tracker, and wherein the monitored parameter data includes data points related to one or more of:
a heart rate;
an oxygen level;
an activity level including at least one of a number of steps, a number of flights climbed, or a duration of exercise; or
a number of calories burned.

### Claim 12 (depends on 10)

The method of claim 10, wherein the health records are stored in a database and comprise at least one of:
claims records received from the health provider;
prescription records received from a pharmacy; or
laboratory results received from a laboratory or other health provider.

### Claim 13 (depends on 10)

The method of claim 10, wherein the input vector further includes social determinants data including one or more of:
economic information;
neighborhood information;
education information;
nutritional information; or
other environmental information.

### Claim 14 (depends on 10)

The method of claim 10, wherein the input vector further includes demographic data including one or more of:
age information;
gender information;
neighborhood type information;
family size information; or
employment indicator information.

### Claim 15 (depends on 10)

The method of claim 10, wherein the method further comprises generating a notification message based on the output vector, wherein the notification message is transmitted to the wearable device to provide a user of the wearable device with a suggested action.

### Claim 16 (depends on 10)

The method of claim 10, wherein the method further comprises generating a notification message based on the output vector, wherein the notification message is transmitted to the healthcare provider to suggest treatment options that may be applicable to a patient.

### Claim 17

A non-transitory computer-readable medium having processor-executable instructions stored thereon for performing health monitoring, wherein the processor-executable instructions, when executed, facilitate:
receiving monitored parameter data from a wireless interface of a wearable device;
determining an artificial intelligence algorithm to use for processing the monitored parameter data;
discarding one or more types of the monitored parameter data from the wearable device based on the determined artificial intelligence algorithm to generate filtered monitored parameter data;
obtaining embedding data by processing health records corresponding to a user registered to the wearable device using a natural language processing algorithm, wherein the embedding data comprises an n-dimensional vector of dimensionless features obtained based on codes included in the health records corresponding to the user, and wherein the codes included in the health records comprise at least one of ICD (International Statistical Classification of Diseases) 9/10 diagnostic codes, CPT (Current Procedural Terminology) procedure codes, GPI (Generic Product Identifier) drug codes, or LOINC (Logical Observation Identifiers Names and Codes) lab codes;
processing the filtered monitored parameter data and the embedding data to generate an input vector, wherein the input vector includes a first portion corresponding to the filtered monitored parameter data and a second portion corresponding to the embedding data;
processing the input vector by the artificial intelligence algorithm to generate an output vector; and
scheduling an appointment with a healthcare provider based on the output vector.

### Claim 18 (depends on 17)

The non-transitory computer-readable medium of claim 17, wherein the monitored parameters include at least one of:
a heart rate;
an oxygen level;
an activity level including at least one of a number of steps, a number of flights climbed, or a duration of exercise; or
a number of calories burned.

### Claim 19 (depends on 17)

The non-transitory computer-readable medium of claim 17, wherein the wearable device comprises a wearable activity tracker.

### Claim 20 (depends on 17)

The non-transitory computer-readable medium of claim 17, wherein the processor-executable instructions, when executed, further facilitate generating a notification message based on the output vector, wherein the notification message is transmitted to one of the wearable device or a mobile device associated with the wearable device to provide the user of the wearable device with a suggested action.