# US 2017/0303818 A1 · State characterization based on multi-variate data fusion techniques

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- Number: US 2017/0303818 A1
- Document ID: US20170303818A1
- Status: Published application
- Kind: A1
- Application number: 15/469,052
- Filed: 2017-03-24
- Published: 2017-10-26
- Earliest claimed: 2013-03-15
- Assignees: Proteus Digital Health, Inc.
- Applicants: Proteus Digital Health, Inc.
- Inventors: Yashar Behzadi
- Web page: https://patentgrep.com/patent/US20170303818A1

## CPC

- G16H 20/30: relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
- A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet}
- A61B 5/07: Endoradiosondes
- A61B 5/117: Identification of persons (methods or arrangements for recognising patterns, e.g. fingerprints, G06F18/00, G06V40/00; identification of persons by analysing their voice or speech G10L17/00)
- A61B 5/4833: {Assessment of subject's compliance to treatment}
- G16H 10/65: stored on portable record carriers, e.g. on smartcards, RFID tags or CD
- 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 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H 20/60: relating to nutrition control, e.g. diets
- G16H 70/40: relating to drugs, e.g. their side effects or intended usage
- G16H 80/00: ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
- A61B 5/021: Measuring pressure in heart or blood vessels
- G06Q 10/40: Business processes related to social networking or social networking services
- G16H 40/67: for remote operation
- G16Y 20/40: relating to personal data, e.g. biometric data, records or preferences
- G16Y 40/10: Detection; Monitoring
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G16Y: INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]

## Abstract

The disclosed system includes an ingestible event marker device configured to collect Ingestible Event Marker (IEM) data from a body of an individual and transmit a signal including the IEM data, wherein the IEM data include information associated with an ingestion event, a receiver adapted to be associated with the body of the individual and configured to receive the signal including the IEM data via the body of the individual, a hub to receive the IEM data from the receiver, and at least one IEM data system to receive the IEM data from the hub. The at least one IEM data system analyzes the IEM data and generates at least one metric based on the IEM data. The at least one IEM data system may further generate predictive information based on the at least one metric, wherein the predictive information is related to prediction of a state of the individual.

## Claims

### Claim at position 1

1-16. (canceled)

### Claim 17

A system comprising:
an ingestible event marker device configured to collect Ingestible Event Marker (IEM) data from a body of an individual and transmit a signal comprising the IEM data, wherein the IEM data comprise information associated with an ingestion event;
a receiver adapted to be associated with the body of the individual, the receiver configured to receive the signal comprising the IEM data via the body of the individual associated with the receiver;
a hub to receive the IEM data from the receiver; and
at least one IEM data system to receive the IEM data from the hub;
wherein the at least one IEM data system analyzes the IEM data and generates at least one metric based on the IEM data; and
wherein the at least one IEM data system generates predictive information based on the at least one metric, wherein the predictive information is related to prediction of a state of the individual.

### Claim 18 (depends on 17)

The system of claim 17, wherein the receiver is selected from a group consisting of a patch receiver, an implantable receiver, a receiver configured to be worn on the body, apparel-configured receiver, and a receiver adapted to be associated with the hub.

### Claim 19 (depends on 18)

The system of claim 18, wherein the receiver adapted to be associated with the hub comprises a mobile phone attachment.

### Claim 20 (depends on 19)

The system of claim 19, wherein the hub is a mobile phone.

### Claim 21 (depends on 17)

The system of claim 17, wherein the at least one IEM data system further comprises software.

### Claim 22 (depends on 21)

The system of claim 21, wherein the software further comprises at least one of an analysis module, a metrics module, and a predictive information module.

### Claim 23 (depends on 22)

The system of claim 22, wherein the analysis module analyzes the IEM data.

### Claim 24 (depends on 22)

The system of claim 22, wherein the metrics modules generates the at least one metric based on the IEM data.

### Claim 25 (depends on 22)

The system of claim 22, wherein the predictive information module generates the predictive information based on the IEM data.

### Claim 26 (depends on 22)

The system of claim 22, wherein the IEM data further comprise physiologic data, the analysis module analyzes the physiologic data and generates the at least one metric based on the physiologic data, and the predictive module generates the predictive information based on the physiologic data.

### Claim 27 (depends on 17)

The system of claim 17, wherein the at least one IEM data system comprises at least one of a feedback loop system and a decision support system.

### Claim 28 (depends on 17)

The system of claim 17, wherein the receiver is configured to capture the IEM data at a predetermined rate.

### Claim 29 (depends on 17)

The system of claim 17, wherein the IEM data comprises information relating to a dosage of medication.

### Claim 30 (depends on 17)

The system of claim 17, wherein the receiver is configured to transmit additional data to the hub, and the at least one IEM data system to receive the additional data from the hub, wherein the additional data comprises data derived from the receiver.

### Claim 31 (depends on 30)

The system of claim 30, wherein the at least one IEM data system analyzes the additional data derived from the receiver and generates at least one metric based on the IEM data and the additional data derived from the receiver.

### Claim 32 (depends on 30)

The system of claim 30, wherein the additional data derived from the receiver comprises information regarding at least one of positional data, accelerometer data, dosing time, galvanic skin response, heat-flux, heart rate, or heart rate variability.

### Claim 33 (depends on 17)

The system of claim 17, wherein the at least one metric comprises a circadian pattern of the individual.

### Claim 34 (depends on 33)

The system of claim 33, wherein the at least one metric comprises a standard deviation of the circadian pattern of the individual across a predetermined number of days and an overall variability of the circadian pattern of the individual.

### Claim 35 (depends on 17)

The system of claim 17, wherein the at least one metric comprises blood pressure, blood pressure increase over time, and blood pressure increase compared to dosing and dose type.

### Claim 36 (depends on 17)

The system of claim 17, wherein the at least one IEM data system analyzes the IEM data such that the IEM data are time-normalized and interpolated to a time of day.

### Claim 37

A system comprising:
an Ingestible Event Marker (IEM) device configured to collect IEM data from a body of an individual associated with the IEM device and transmit a signal comprising the IEM data, wherein the IEM data comprise information associated with an ingestion event; and
at least one IEM data system to process the IEM data, the at least one IEM data system comprising a processor and a non-transitory machine readable medium, wherein the non-transitory machine readable medium comprises instructions that when executed by the processor cause the processor to:
analyze the IEM data and generate at least one metric based on the IEM data;
generate predictive information based on the at least one metric, wherein the predictive information is related to prediction of a state of the individual; and
wherein the IEM data is received from the body of the individual.

### Claim 38 (depends on 37)

The system of claim 37, wherein the predictive information is associated with a likelihood of adherence to a medication regimen.

### Claim 39 (depends on 37)

The system of claim 37, wherein the predictive information is associated with the prediction of occurrence of a future health event.

### Claim 40 (depends on 37)

The system of claim 37, wherein the predictive information is associated with a characterization of patient stability while on a medication regimen.

### Claim 41

A system comprising:
an ingestible event marker device configured to collect Ingestible Event Marker (IEM) data from a body of an individual and transmit a signal comprising the IEM data, wherein the IEM data comprise information associated with an ingestion event;
a receiver adapted to be associated with the body of the individual, the receiver configured to receive the signal comprising the IEM data via the body of the individual associated with the receiver;
a hub to receive the IEM data; and
at least one IEM data system to receive the IEM data from the hub;
wherein the at least one IEM data system analyzes the IEM data and generates at least one metric based on the IEM data;
wherein the at least one IEM data system generates predictive information based on the at least one metric; and
wherein the predictive information comprises an indication of a likelihood of adherence to a medication regimen.