# US 12,758,148 B2 · User clustering and analysis method using body composition big data, and system thereof

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- Number: US 12,758,148 B2
- Document ID: US12758148B2
- Status: Granted
- Kind: B2
- Application number: 18/198,741
- Filed: 2023-05-17
- Published: 2023-11-23
- Granted: 2026-10-13
- Earliest claimed: 2022-05-18
- Assignees: Inbodyhealthcare Co., Ltd.
- Applicants: InBodyHealthcare Co., Ltd.
- Inventors: Ki Chul Cha; Won Young Jang; Soo Cheol Lim
- Web page: https://patentgrep.com/patent/US12758148B2

## CPC

- A61B 5/4872: {Body fat}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- A61B 5/742: {using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339)}
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)

## Abstract

A user clustering and analysis method using body composition big data and a system includes analyzing, by a body composition analyzer, a user's body composition, and transmitting analyzed body composition data of the user to a user terminal, receiving, by the user terminal, the user's body composition data, and transmitting the body composition data to a service provider server providing services related to body composition analysis or executing a body composition data analysis application thereof, and analyzing, by the service provider server or the body composition data analysis application of the user terminal, the user's body composition data accumulated for a predetermined period of time, assigning a cluster that is a group of similar body composition, and providing the cluster to the user terminal or displaying the cluster on a display screen.

## Claims

### Claim 1

A user clustering and analysis method using body composition big data, the method comprising:
analyzing, by a body composition analyzer comprising a sensor unit provided with an electrode sensor and an automatic zero-point compensation load cell, and a handle unit provided with a pair of thumb electrodes for contacting both thumbs of a user, a body composition of the user as the user uses the body composition analyzer;
transmitting analyzed body composition data of the user to a user terminal;
receiving, by the user terminal, the analyzed body composition data from the body composition analyzer;
analyzing the analyzed body composition data accumulated for a predetermined period of time; and
assigning a cluster that is a group of similar body composition by:
generating a big data map by extracting body composition big data corresponding to a country, a gender and an age of the user from entire body composition big data,
dividing a Body Mass Index (BMI) section into a plurality of stages on the big data map,
dividing a Percentage Body Fat (PBF) from the big data map included in each divided BMI stage into a plurality of regions to generate a cluster classification map comprising a plurality of coordinate regions each having a unique address, wherein each coordinate region is mapped to one of a plurality of clusters,
retrieving the cluster classification map corresponding to a gender and an age of the user,
determining a coordinate region of the plurality of coordinate regions to which the user belongs based on the analyzed body composition data, and
assigning the cluster mapped to the coordinate region to the user; and
displaying, on a display screen, based on the cluster being assigned, a cluster template including a position of the user on the big data map, a cluster name and an illustration corresponding to the cluster assigned to the user, wherein the cluster template is set in advance in response to the cluster, wherein the cluster name is assigned differently depending on the age and the gender of the user even when a same cluster address is present,
wherein the position of the user on the big data map included in the cluster template is displayed by:
distributing all users having the gender and the age of the user on the big data map having BMI and PBF as axes;
displaying BMI mode bands and PBF mode bands on the big data map, and
providing position information of the user by displaying the analyzed body composition data of the user on the big data map.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
displaying, on the display screen, the cluster and a description of the cluster.

### Claim 3 (depends on 2)

The method of claim 2, wherein the description of the cluster comprises: the cluster name, the illustration of the cluster, a naming reason for the cluster name, and a histogram.

### Claim 4 (depends on 1)

The method of claim 1, wherein the cluster classification map depends on data parameters of each gender and each age group, and is determined differently for the each gender and the each age group.

### Claim 5 (depends on 4)

The method of claim 4, wherein the cluster is assigned differently when the gender and the age are different even when the unique address is identical, based on the cluster classification map being determined differently for the each gender and the each age group.

### Claim 6

A user clustering and analysis system using body composition big data, the system comprising:
a body composition analyzer comprising:
a sensor unit provided with an electrode sensor and an automatic zero-point compensation load cell, and
a handle unit provided with a pair of thumb electrodes for contacting both thumbs of a user;
wherein the body composition analyzer is configured to analyze a body composition of the user as the user uses the body composition analyzer, and transmit analyzed body composition data of the user to a user terminal;
the user terminal configured to receive the analyzed body composition data from the body composition analyzer; and
a service provider server configured to (i) analyze the analyzed body composition data accumulated for a predetermined period of time, (ii) assign a cluster that is a group of similar body composition by:
generating a big data map by extracting body composition big data corresponding to a country, a gender, and an age of the user from entire body composition big data,
dividing a Body Mass Index (BMI) section into a plurality of stages on the big data map,
dividing a Percentage Body Fat (PBF) from the big data map included in each divided BMI stage into a plurality of regions to generate a cluster classification map comprising a plurality of coordinate regions each having a unique address, wherein each coordinate region is mapped to one of a plurality of clusters,
retrieving the cluster classification map corresponding to the gender and the age of the user,
determining a coordinate region of the plurality of coordinate regions to which the user belongs based on the analyzed body composition data, and
assigning the cluster mapped to the coordinate region to the user, and
(iii) provide the cluster to the user terminal; and
wherein the user terminal is further configured to:
display, on a display screen of the user terminal, based on the cluster being assigned, a cluster template including a position of the user on the big data map, a cluster name and an illustration corresponding to the cluster assigned to the user, wherein the cluster template is set in advance in response to the cluster, wherein the cluster name is assigned differently depending on the age and the gender of the user even when a same cluster address is present,
wherein the position of the user on the big data map included in the cluster template is displayed by:
distributing all users having the gender and the age of the user on the big data map having BMI and PBF as axes,
displaying BMI mode bands and PBF mode bands on the big data map, and
providing position information of the user by displaying the analyzed body composition data of the user on the big data map.

### Claim 7 (depends on 6)

The system of claim 6, wherein the user terminal is further configured to:
display, on the display screen, the cluster and a description of the cluster.

### Claim 8 (depends on 7)

The system of claim 7, wherein the description of the cluster comprises: the cluster name, the illustration of the cluster, a naming reason for the cluster name, and a histogram.

### Claim 9 (depends on 6)

The system of claim 6, wherein the cluster classification map depends on data parameters of each gender and each age group, and is determined differently for the each gender and the each age group.

### Claim 10 (depends on 9)

The system of claim 9, wherein the cluster is assigned differently when the gender and the age are different even when the unique address is identical, based on the cluster classification map being determined differently for the each gender and the each age group.