# US 11,992,318 B2 · Diagnostic lab-on-a-chip device

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- Number: US 11,992,318 B2
- Document ID: US11992318B2
- Status: Granted
- Kind: B2
- Application number: 17/812,327
- Filed: 2022-07-13
- Published: 2024-01-18
- Granted: 2024-05-28
- Earliest claimed: 2022-07-13
- Applicants: Celestin B. Bitjonck
- Inventors: Celestin B. Bitjonck
- Web page: https://patentgrep.com/patent/US11992318B2

## CPC

- A61B 5/207: {Sensing devices adapted to collect urine}
- A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet}
- A61B 5/02055: {Simultaneously evaluating both cardiovascular condition and temperature}
- A61B 5/6891: {Furniture}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- 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 10/0038: {Devices for taking faeces samples; Faecal examination devices (anal receptacles A61F5/451)}
- A61B 10/007: {for taking urine samples (measuring urological functions A61B5/20; genital receptacles A61F5/451; urinals for bed-ridden persons A61G9/006)}
- G16H 10/40: for data related to laboratory analysis, e.g. patient specimen analysis
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 40/67: for remote operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 80/00: ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
- A61B 5/02108: {from analysis of pulse wave characteristics}
- A61B 5/02416: {using photoplethysmograph signals, e.g. generated by infrared radiation (A61B5/14552 takes precedence)}
- A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)}
- A61B 5/14542: {for measuring blood gases (A61B5/14551 takes precedence)}
- A61B 2090/063: {for measuring volume}
- A61B 2560/0242: adapted to measure environmental factors, e.g. temperature, pollution
- A61B 2560/0462: Apparatus with built-in sensors
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A diagnostic hub located in a toilet receives a wireless signal from a mobile device of a user and determines that the user is proximate to the toilet based on the wireless signal. An identification of the user is determined based on the wireless signal. A geolocation of the diagnostic hub is determined. Sensor data generated by biometric sensors and environmental sensors is received. The diagnostic intakes a mixture of water and urine from the toilet into an intake chamber of the diagnostic hub through an inlet line or one or more openings in a housing of the diagnostic hub. A lab-on-a-chip of the diagnostic hub executes a biochemical assay on a fluid volume of the mixture using a reagent. Substance levels present in the urine are determined based on the biochemical assay. Results based on the substance levels are determined, indicating a diagnosis of one or more diseases or medical conditions.

## Claims

### Claim 1

A computer-implemented method of operating a diagnostic hub located in a toilet, comprising:
receiving, by a wireless transceiver of the diagnostic hub, a wireless signal from a mobile device;
determining a user identification and that the mobile device is proximate to the toilet based on the wireless signal;
determining a geolocation of the diagnostic hub by at least one of:
analyzing location signals received by a geolocation receiver of the diagnostic hub; or
analyzing location information in the wireless signal;
receiving sensor data generated by a plurality of biometric sensors and environmental sensors communicably coupled to the diagnostic hub;
causing the diagnostic hub to intake a mixture of water and at least one of urine or feces from the toilet into an intake chamber of the diagnostic hub through an inlet line or one or more openings in a housing of the diagnostic hub;
biosensing, by a lab-on-a-chip of the diagnostic hub, one or more micro ribonucleic acid (miRNA) biomarkers in the mixture using one or more biosensors of the lab-on-a-chip;
performing a biochemical assay on a fluid volume of the mixture, using a biochemical reagent, to produce one or more measurements of:
cellular processes or metabolic reactions; and
substances or functional activity of biomolecules;
performing an analysis, by one or more computer processors implemented in the diagnostic hub, by:
extracting a feature vector from the one or more miRNA biomarkers and the one or more measurements; and
determining, using at least one machine learning model, from the feature vector, at least one of:
a diagnosis of one or more diseases or medical conditions of a user;
a likelihood that the user will develop the one or more diseases or medical conditions;
a likelihood that the user will develop a more severe or more mild form of one more diseases or medical conditions;
pre-existing conditions or risk factors for other diseases or medical conditions; or
a change in severity of the one or more diseases or medical conditions in the user;
correlating the analysis to the sensor data; and
transmitting, by the wireless transceiver, the correlated analysis to the mobile device or a cloud server, wherein the correlated analysis is tagged by the user identification and the geolocation.

### Claim 2 (depends on 1)

The method of claim 1, comprising:
determining, from the biochemical assay, a substance level of a controlled substance present in the at least one of urine or feces that is greater than a threshold substance level.

### Claim 3 (depends on 1)

The method of claim 1, comprising:
increasing a frequency of transmitting the correlated analysis to the mobile device or the cloud server responsive to determining at least one of a particular indication in the analysis or a particular correlation of the analysis to the sensor data.

### Claim 4 (depends on 1)

The method of claim 1, wherein the sensor data describes at least one of a heart rate, a blood pressure, an amount of dissolved oxygen, a respiratory rate, a composition of sweat, a stress level, or a body temperature of the user.

### Claim 5 (depends on 1)

The method of claim 1, wherein the housing is located on a surface of a bowl of the toilet and proximate to a water level in the bowl or at least partially submerged in water.

### Claim 6 (depends on 1)

The method of claim 1, comprising storing the analysis in a user health database comprising trends recorded over time of:
a plurality of substance levels;
diagnoses of the one or more diseases or medical conditions;
likelihoods that the user will develop the one or more diseases or medical conditions;
the pre-existing conditions or risk factors for other diseases or medical conditions; or
changes in the severity of the one or more diseases or medical conditions in the user.

### Claim 7 (depends on 1)

The method of claim 1, wherein the analysis is performed by the lab-on-a-chip on the mixture using a biochemical reagent.

### Claim 8 (depends on 1)

The method of claim 1, wherein the biosensing is performed using at least one of a paper-based microfluidic device (μPAD), a digital microfluidic (DMF) device, or a lateral flow biosensor in the lab-on-a-chip.

### Claim 9 (depends on 1)

The method of claim 1, wherein the biosensing is performed using a DNA sensor in the lab-on-a-chip and the one or more computer processors execute a pharmacogenetics application that considers drug metabolic pathways affecting one or more user's individual responses to a drug based on genetic information obtained using the DNA sensor.

### Claim 10 (depends on 1)

The method of claim 1, wherein the lab-on-a-chip comprises at least one of a complementary metal oxide semiconductor (CMOS)-based DNA sensor, an AC electro-osmosis (ACEO) chip, or a label-free impedimetric DNA sensing chip.

### Claim 11

A diagnostic hub located in a toilet, consisting of:
one or more computer processors; and
a non-transitory computer-readable storage medium storing computer instructions, which, when executed by the one or more computer processors, cause the diagnostic hub to:
receive, by a wireless transceiver, a wireless signal from a mobile device;
determine a user identification and that the mobile device is proximate to the toilet based on the wireless signal;
determine a geolocation by at least one of:
analyzing location signals received by a geolocation receiver; or
analyzing location information embedded in the wireless signal;
receive sensor data generated by a plurality of biometric sensors and environmental sensors communicably coupled to the diagnostic hub;
intake a mixture of water and at least one of urine or feces from the toilet into an intake chamber through an inlet line or one or more openings in a housing of the diagnostic hub;
perform an analysis, by a lab-on-a-chip, of the mixture by:
biosensing one or more micro ribonucleic acid (miRNA) biomarkers in the mixture using one or more biosensors; and
performing a biochemical assay to produce one or more measurements of:
cellular processes or metabolic reactions; and
substances or functional activity of biomolecules;
extract a feature vector from the one or more miRNA biomarkers and the one or more measurements;
determine, from the feature vector, one of:
a diagnosis of one or more diseases or medical conditions of a user;
a likelihood that the user will develop the one or more diseases or medical conditions; or
a change in severity of the one or more diseases or medical conditions in the user;
correlate the analysis to the sensor data; and
transmit, by the wireless transceiver, the correlated analysis to the mobile device or a cloud server, wherein the correlated analysis is tagged by the user identification and the geolocation.

### Claim 12 (depends on 11)

The diagnostic hub of claim 11, wherein the computer instructions cause the diagnostic hub to
determine, from the biochemical assay, a substance level of a controlled substance present in the at least one of urine or feces that is greater than a threshold substance level.

### Claim 13 (depends on 11)

The diagnostic hub of claim 11, wherein the computer instructions cause the diagnostic hub to:
increase a frequency of transmitting the correlated analysis to the mobile device or the cloud server responsive to determining at least one of a particular indication in the analysis or a particular correlation of the analysis to the sensor data.

### Claim 14 (depends on 11)

The diagnostic hub of claim 11, wherein the sensor data describes at least one of a heart rate, a blood pressure, an amount of dissolved oxygen, a respiratory rate, a composition of sweat, a stress level, or a body temperature of the user.

### Claim 15 (depends on 11)

The diagnostic hub of claim 11, wherein the housing is located on a surface of a bowl of the toilet and proximate to a water level in the bowl or at least partially submerged in water.

### Claim 16 (depends on 11)

The diagnostic hub of claim 11, wherein the computer instructions cause the diagnostic hub to store the analysis in a user health database consisting of trends recorded over time of:
diagnoses of the one or more diseases or medical conditions;
likelihoods that the user will develop the one or more diseases or medical conditions; or
changes in the severity of the one or more diseases or medical conditions in the user.

### Claim 17 (depends on 11)

The diagnostic hub of claim 11, wherein the biosensing is performed using a lateral flow biosensor in the lab-on-a-chip.

### Claim 18 (depends on 11)

The diagnostic hub of claim 11, wherein the biosensing is performed using a paper-based microfluidic device (μPAD) in the lab-on-a-chip.

### Claim 19 (depends on 11)

The diagnostic hub of claim 11, wherein the biosensing is performed using a digital microfluidic (DMF) device in the lab-on-a-chip.

### Claim 20

A non-transitory computer-readable storage medium storing computer instructions, which, when executed by one or more computer processors, cause the one or more computer processors to:
receive, by a wireless transceiver, a wireless signal from a mobile device;
determine a user identification and that the mobile device is proximate to a toilet based on the wireless signal;
determine a geolocation by at least one of:
analyzing location signals received by a geolocation receiver; or
analyzing location information embedded in the wireless signal;
receive sensor data generated by a plurality of biometric sensors and environmental sensors communicably coupled to a diagnostic hub;
intake a mixture of water and at least one of urine or feces from the toilet into an intake chamber through an inlet line or one or more openings in a housing of the diagnostic hub;
biosense, by a lab-on-a-chip, one or more micro ribonucleic acid (miRNA) biomarkers in the mixture using one or more biosensors;
perform a biochemical assay on a fluid volume of the mixture, using a biochemical reagent, to produce one or more measurements of:
cellular processes or metabolic reactions;
and substances or functional activity of biomolecules;
perform an analysis, by the one or more computer processors implemented in the diagnostic hub, by:
extracting a feature vector from the one or more miRNA biomarkers and the one or more measurements; and
determining, using at least one machine learning model, from the feature vector, at least one of:
a diagnosis of one or more diseases or medical conditions of a user;
a likelihood that the user will develop the one or more diseases or medical conditions;
a likelihood that the user will develop a more severe or more mild form of one more diseases or medical conditions;
pre-existing conditions or risk factors for other diseases or medical conditions; or
a change in severity of the one or more diseases or medical conditions in the user;
correlate the analysis to the sensor data; and transmit, by the wireless transceiver, the correlated analysis to the mobile device or a cloud server, wherein the correlated analysis is tagged by the user identification and the geolocation.