KR102692814B1 - Personalized content determining method based on multiple factors and service apparatus - Google Patents

Abstract

The method of determining personalized content based on complex factors includes the following steps: a service device receives genome analysis results, health status information, survey results, and life log data for a subject, and the service device derives a first genetic analysis result for a representative phenotype. A step of, wherein the service device derives a second genetic analysis result for a sub-genetic factor related to the representative phenotype, wherein the service device derives a third genetic analysis result for a physical response genetic factor related to the representative phenotype. Step and the service device includes determining customized content for the subject using the first genetic analysis result, the second genetic analysis result, and the third genetic analysis result.

Description

Method and service device for determining personalized content based on complex factors {PERSONALIZED CONTENT DETERMINING METHOD BASED ON MULTIPLE FACTORS AND SERVICE APPARATUS}

The technology described below relates to a technique for providing personalized content by complexly using an individual's genetic test results and various information.

Direct to consumer (DTC) genetic testing service is a type of service that identifies biological characteristics through genetic analysis of various phenotypes and delivers an interpretation of the results to the consumer. A representative method used to identify biological characteristics of a phenotype is single nucleotide polymorphism (SNP). Currently, most DTC genetic testing services analyze the genotypes of SNPs that have been reported to have effects and influences on the phenotype. It is a form of presentation.

Korean Patent Publication No. 10-2019-0141460

In most cases, the phenotype presented through DTC genetic testing consists of multiple physiological or genetic pathways. Therefore, DTC provides results that do not take into account the individual's physical/health status, lifestyle, and purpose of test use.

The technology described below seeks to provide a technique for providing personalized content based on complex information consisting of not only genetic test results, but also various additional genetic information, health status information, and life log information related to a specific representative phenotype desired by the user. do.

A method of determining personalized content based on complex factors includes receiving, at a service device, source data including genome analysis results, health status information, survey results, and life log data for a subject, and the service device receiving at least one of the source data. Deriving a first genetic analysis result for a representative phenotype using data, the service device generating a second genetic analysis result for a sub-genetic factor related to the representative phenotype using at least one of the source data Deriving a third genetic analysis result for a physical response genetic factor related to the representative phenotype, by the service device, using at least one of the source data, and the service device deriving a third genetic analysis result of the first genetic analysis result , and determining customized content for the subject using the second genetic analysis result and the third genetic analysis result.

The service device that provides personalized content based on complex factors is an input device that receives source data including the subject's genome analysis results, health status information, survey results, and life log data, a storage device that stores a health activity content library, and A first genetic analysis result for a representative phenotype using at least one of the source data, a second genetic analysis result for a sub-genetic factor related to the representative phenotype, and a third genetic analysis result for a physical response genetic factor related to the representative phenotype. It includes a computing device that derives genetic analysis results and determines customized content for the subject using the first genetic analysis results, the second genetic analysis results, and the third genetic analysis results.

The technology described below can provide accurate content by considering the individual's genetic information, health status, life log, etc. to achieve the purpose of individual testing.

Figure 1 is an example of a system for calculating personal health indicators and providing personalized content considering complex factors.
Figure 2 is an example of the process of deriving representative phenotype-related gene analysis results.
Figure 3 is an example of representative phenotype-related gene analysis results.
Figure 4 is an example of a process for determining customized content.
Figure 5 is an example of outputting personalized content.
Figure 6 is an example of a service device that determines personalized content considering complex factors.

The technology described below may be subject to various changes and may have various embodiments, and specific embodiments will be illustrated in the drawings and described in detail. However, this is not intended to limit the technology described below to specific embodiments, and should be understood to include all changes, equivalents, and substitutes included in the spirit and scope of the technology described below.

Terms such as first, second, A, B, etc. may be used to describe various components, but the components are not limited by the terms, and are only used for the purpose of distinguishing one component from other components. It is used only as For example, a first component may be named a second component without departing from the scope of the technology described below, and similarly, the second component may also be named a first component. The term and/or includes any of a plurality of related stated items or a combination of a plurality of related stated items.

In terms used in this specification, singular expressions should be understood to include plural expressions, unless clearly interpreted differently from the context, and terms such as “including” refer to the described features, numbers, steps, operations, and components. , it means the existence of parts or a combination thereof, but should be understood as not excluding the possibility of the presence or addition of one or more other features, numbers, step operation components, parts, or combinations thereof.

Before providing a detailed description of the drawings, it would be clarified that the division of components in this specification is merely a division according to the main function each component is responsible for. That is, two or more components, which will be described below, may be combined into one component, or one component may be divided into two or more components for more detailed functions. In addition to the main functions it is responsible for, each of the components described below may additionally perform some or all of the functions handled by other components, and some of the main functions handled by each component may be performed by other components. Of course, it can also be carried out exclusively by .

In addition, when performing a method or operation method, each process that makes up the method may occur in a different order from the specified order unless a specific order is clearly stated in the context. That is, each process may occur in the same order as specified, may be performed substantially simultaneously, or may be performed in the opposite order.

The following explains that the service device calculates health indicators by considering complex factors for a specific target and provides personalized content. A service device can be implemented as a variety of devices capable of processing data. For example, a service device may be implemented as a PC, a server on a network, a smart device, or a chipset with a dedicated program embedded therein.

Figure 1 is an example of a system 100 for calculating personal health indicators and providing personalized content considering complex factors. Figure 1 is an example showing the form of a server as a service device. The service server 150 collects various information about the subject, calculates a personal health index, and provides personalized content based on the information or index.

The genome analysis device 110 analyzes a subject's sample and generates genome data. Genomic data refers to genetic information calculated by analyzing samples. For example, genomic data includes base sequences obtained from deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or protein from cells, tissues, etc., gene expression data, genetic variation with standard genomic data, and DNA methylation. ), etc. may be included. The genome analysis device 110 may be a next generation sequencing (NGS) device.

EMR (Electronic Medical Record, 120) holds medical information (clinical information, test results, etc.) collected about subjects by medical institutions. It is assumed that the EMR 120 holds the subject's health checkup results.

The user terminal 130 transmits questionnaire information or questionnaire information for a specific individual. Questionnaire information and survey information include items that the service provider wishes to confirm in advance.

The wearable device 140 transmits life log data of a specific individual. Various devices, such as smart devices and IoT devices, can collect and transmit lifelog data. Lifelog data refers to information related to lifestyle habits. For example, lifestyle information may include (i) physical activity information, (ii) food intake information, (iii) exercise information, etc. Physical activity information may include sleep time, wake-up time, work time, rest time, etc. Food intake information may include daily calories consumed, amount of nutrients consumed, etc. Exercise information may include information such as whether or not to exercise and exercise intensity.

The service server 150 can calculate health indicators for representative phenotypes based on the subject's genome analysis results, medical information (health examination results), survey information, and life log data. The representative phenotype may be a phenotype of a region of interest selected from the user or user terminal. For example, representative phenotypes may be information such as muscle development, diet, diabetes level management, etc.

The service server 150 may determine personalized content from the content DB 160 based on complex factors generated from information collected from various sources. The service server 150 can deliver personalized content derived from the content to the individual.

Explains the various factors used in calculating health indicators. The service device can calculate health indicators using various factors below.

(1) Genetic factors

For any genetic item, in g _ij (i=1,...,m, j=1,...,p), i is a genetic factor, and j represents the gene of the phenotype for that genetic factor. That is, there may be multiple phenotypic genes for a particular genetic factor. is called the Genetic Risk Score (GRS) derived from each gene. At this time can be calculated as follows: In other words, g ^grs corresponds to a SNP-based genetic risk score. Table 1 below is an example of a case where C is a risk allele in a specific SNP. The GRS score may be the sum of the genotype scores of the relevant genes.

target gene Reference SNP Genotype score 0 One 2 SNP1 TT TC CC SNP2 TT TC CC SNP3 TT TC CC SNP4 TT TC CC

Using the genetic risk score derived in this way, matrix A ⁽¹⁾ (i=1,...,N) related to genetic factors can be expressed as Equation 1 below. The matrix value in Equation 1 is an example.

In the m×p matrix, m is the number of genetic factors, and p is the number of related genes related to the genetic factor.

(2) Current health status (survey information) factors

Current health status information includes user survey information about the individual's health status, health examination result information, etc. For example, the current health status information may be composed of a questionnaire including survey results such as 'strength exercise habits', health examination results regarding blood sugar levels, etc., and the type of health status result may be multiple choice.

s _ij (i=1,...,m, j=1,...,p) represents question i and the gene j of the phenotype related to the answer to question and answer i. is said to be the risk score of the gene for the phenotype that directly corresponds to each question and answer. Matrix A ⁽²⁾ (i=1,...,N) related to the results of user feedback (survey information) can be expressed as Equation 2 below. The matrix value in Equation 2 is an example.

In the m×p matrix, m is the number of questions and answers, and p is the number of genes related to the questions and answers.

(3) Lifelog factors

Lifelog data can be composed of detailed factors such as heart rate, amount of sleep, calories consumed, and body temperature. l _ij (i=1,...m, j=1,...,p) represents lifelog data i and gene j of the phenotype related to the lifelog data i. is said to be the risk score of the gene for the phenotype that directly corresponds to the corresponding lifelog data.

Matrix A ⁽³⁾ (i=1,...,N) related to the lifelog can be expressed as Equation 3 below. The matrix value in Equation 3 is an example.

In the m×p matrix, m is the number of lifelog data, and p is the number of genes related to the lifelog data.

Furthermore, the matrix corresponding to the health index may be a combination of the genetic risk score of the phenotype related to genetic factors, the genetic risk score of the phenotype related to the current health state, and the genetic risk score of the lifelog-related phenotype into one matrix. Alternatively, the health indicator may be a value obtained by uniformly processing the indicators expressed by Equation 1 to Equation 3 through matrix operations.

The service device can calculate health indicators for the aforementioned genetic factors, current health status factors, and life log factors. The service device can calculate a comprehensive score that takes into account complex factors. Alternatively, the service device may calculate individual scores for individual factors as shown in Table 2 below. Users can check the score to determine weak or strong factors.

Genetic factor score Presence Score Health/Lifestyle Score 80 points 50 points 30 points

Figure 2 is an example of a process 200 for deriving gene analysis results related to representative phenotypes. The order in Figure 2 is for convenience of explanation and to explain factors that can be used in the analysis process, but the order in which the factors are applied is not important. Therefore, the types of factors used in the analysis process and the order based on the factors may be different.

For convenience of explanation, the explanation will focus on an example of analysis using 'muscle development ability' as a representative phenotype. It is assumed that genetic factor analysis, current health status-related genetic analysis, and lifelog-related genetic analysis have been performed for a specific subject in advance.

The service device receives the subject's genome analysis results, health status information, survey results, and life log data in advance.

The service device performs genetic analysis on ‘muscle development ability’, a representative phenotype (210). At this time, the service device can calculate a certain score by combining the analysis results of genetic markers for representative phenotypes. Table 3 below is an example of markers related to muscle development ability. The service device can determine the GRS for the marker.

gene ACTN3 IL-15RA IL-15RA BMP2 LEPR SNP Rs1815739 Rs2296135 Rs2208059 Rs15705 Rs1137100 risk allele T C C A A

The service device determines whether there is information on the user's health status for the representative phenotype (215). Health status information may include health status-related health examination results and/or health status-related survey results for representative phenotypes.

If the health status information does not contain information (related health status information) related to the representative phenotype (No in 215), the service device may output the representative phenotype genetic analysis result analyzed in 210 (218).

For example, health checkup results may include blood sugar, triglyceride, cholesterol, blood pressure, BMI, and other checkup results. Survey results may include gender, age, height, weight, BMI, physical activity, exercise level, eating habits, medical history, family history, etc.

If there is relevant health status information (Yes in 215), the service device may reevaluate the genetic analysis results particularly related to the representative phenotype based on the health status information related to the representative phenotype (220). For example, in the case of muscle development ability, there may be differences in genetic markers depending on gender. In this case, the service device can determine the GRS by resetting the marker based on the subject's gender information. For example, if the gender is female, the genetic analysis results of the representative phenotype can be determined based on the markers in Table 4 below.

gene ACTN3 IL-15RA IL-15RA LEPR SNP Rs1815739 Rs2296135 Rs2208059 Rs1137100 risk allele T C C A

Alternatively, the service device may further analyze the analysis results for genes corresponding to sub-factors of the representative phenotype based on health status information (220). For example, if the health status information is blood sugar, the service device can additionally derive analysis results (GRS) for genes related to metabolic functions such as insulin secretion control ability and in vivo carbohydrate metabolism ability. Alternatively, if the health status information is an ankle injury, the service device may additionally derive analysis results for genes related to the collagen composition of the tendon, intracellular recovery ability, etc.

The service device determines whether interactive genetic factors associated with the representative phenotype exist (225). If there is no interacting genetic factor (No in 225), the service device can output a genetic analysis result reflecting the health status information analyzed at 230 (228).

If there is an interactive genetic factor (Yes in 225), the service device can derive analysis results for the interactive genetic factor (230). For example, interactive genetic factors related to muscle development include analysis results of 'strength exercise suitability (strength development for strength exercise)' and 'connection between muscles and tendons'. The service device can derive analysis results about strength exercise suitability or the connection between muscles and tendons based on the markers in Table 5 below.

muscular strength
work out
compatibility
male gene ACTN3 CCL2 CCR2 FAT2 SNP Rs1815739 Rs1024610 Rs3918358 Rs10072841 risk allele T A C T
female gene ACTN3 CCL2 CCR2 IL-15RA SLC10A2 TMX1 SNP Rs1815739 Rs1024610 Rs3918358 Rs2296135 Rs1341439 Rs7154161 risk allele T A C A G C
muscles and
connection
male/
female gene PTK2 PTK2 SNP Rs7843014 Rs4760 risk allele C T

The service device determines whether a physical response genetic factor related to the representative phenotype exists (235). If there is no genetic factor for physical response (No in 235), the service device can output a genetic analysis result reflecting the health status information analyzed at 250 (238).

If there is an interactive genetic factor (Yes in 235), the service device can derive analysis results for the physical response genetic factor. For example, a genetic factor related to physical response related to muscle development may be 'caffeine response'. The service device can determine additional analysis results for caffeine response based on the markers in Table 6 below (240).

Caffeine reaction
male/female gene CYP1A2 SNP Rs762551 risk allele A

The service device determines whether survey results related to the representative phenotype exist (245). For example, the results of a survey related to muscle development could become the 'purpose of strength training'. The purpose of strength training may be one of muscle development, health management, slim fit, or diet. If there are no survey results (No in 245), the service device can output the genetic analysis results for the physical response genetic factors analyzed in 270 (248).

If there is a survey result (Yes in 245), the service device can provide an exercise method (customized content) considering the survey results (250).

The service device determines whether lifelog data related to the representative phenotype exists (255). Lifelog data related to muscle development may include strength exercise items, exercise performance level, exercise volume, etc. Lifelog data can be obtained from smartphones, wearable devices, etc. The service device can constantly update user-customized content (exercise information) targeting the actually measured life log data as its main function (260).

Figure 3 is an example of representative phenotype-related gene analysis results. A representative phenotype is muscle development. Figure 3 displays scores according to genetic analysis results for the aforementioned genetic factors, current health status factors, and life log factors. In addition, the customized analysis results determined the individual's tendency to exert muscle force as 'Big Muscle', but customized content is recommended that appropriate exercise may be necessary depending on the individual's exercise purpose. The customized analysis results explain genetic tendencies based on downstream related genetic factors (connection between muscles and tendons) and include advice based on physical response genetic factors (caffeine response).

Figure 4 is an example of a process for determining customized content. The service device 150 provides customized content (health content) to a specific user based on analysis results and information about the specific user. The service device 150 searches the content DB for matching content based on the input variable.

Input variables may include analysis result codes, current status codes, genetic factor codes, lifelog codes, and B2B codes.

The analysis result code may include results analyzed in the process described in FIG. 2. In other words, the analysis result code may include at least one of the following: representative phenotype analysis results, health status-related gene or sub-gene analysis results, interactive genetic factor analysis results, physical response genetic factor analysis results, survey results, and lifelog data-based results. there is.

The current status code is information related to health status. The current status code may include at least one of a health examination result and a survey result.

The genetic factor code may include gene expression information related to a representative phenotype. In this case, the genetic factor code may be composed of information about genes that were not used in the above-mentioned analysis result code. Furthermore, genetic factor codes show associations with representative phenotypes.

Lifelog code may be composed of information obtained as lifelog data.

B2B code is information that can be used optionally. B2B codes may consist of affiliate services, products, etc. In other words, B2B code can be used when an affiliate recommends content in consideration of a product or service in providing personalized content.

The service device 150 may determine customized content matched in the content DB based on the above-mentioned input variables and provide it to the user.

Customized content may include at least one of various types of content. For example, customized content may include knowledge-type information or activity-type information. Knowledge-type information is information corresponding to knowledge for personal health, and activity-type information is information containing certain active content, such as exercise information. Additionally, customized content can be provided in various forms such as text, video, and images. Figure 5 is an example of outputting personalized content. Figure 5 is an example mainly composed of exercise methods, recommended exercises, etc.

Figure 6 is an example of a service device that determines personalized content considering complex factors. The service device 300 corresponds to the service device 150 described above. The service device 300 may be physically implemented in various forms. For example, the service device 300 may take the form of a computer device such as a PC, a network server, or a chipset dedicated to data processing.

The service device 300 may include a storage device 310, a memory 320, an arithmetic device 330, an interface device 340, a communication device 350, and an output device 360.

The storage device 310 can store various information about the subject or collected by the subject. The storage device 310 can store the subject's genomic information, medical information (health health results), survey information, life log data, etc. Such information (raw data) can be called source data.

The storage device 310 may store complex factors for representative phenotypes extracted from source data. As described above, complex factors may include genetic factors, current health status factors, and life log factors.

The storage device 310 can store analysis results (health indicators, personalized content).

The storage device 310 may store a health activity content library held in the content DB.

The storage device 310 may store algorithms or programs for the process of determining complex factors from source data, the process of calculating health indicators based on complex factors, and the process of determining personalized content.

The memory 320 may store data and information generated in the process of the service device 300 calculating health indicators and determining personalized content.

The interface device 340 is a device that receives certain commands and data from the outside.

The interface device 340 may receive the subject's source data from a physically connected input device or an external storage device.

The interface device 340 can receive information about representative phenotypes.

The interface device 340 can receive personalized content from a content DB or storage device based on individual complex factors.

The interface device 340 may transmit analysis results (health indicators, personalized content) to an external object.

The communication device 350 refers to a configuration that receives and transmits certain information through a wired or wireless network. The communication device 350 may receive the subject's source data from an external object.

The communication device 350 can receive information about representative phenotypes.

The communication device 350 can receive personalized content from a content DB based on complex personal factors.

The communication device 350 may transmit analysis results (health indicators, personalized content) to an external object such as a user terminal.

Since the interface device 340 and the communication device 350 are components that exchange certain data with a user or other physical object, they can also be collectively referred to as input/output devices. If limited to the function of receiving source data from a subject, the interface device 340 and communication device 350 may be referred to as input devices.

The output device 360 is a device that outputs certain information. The output device 360 can output interfaces, analysis results, etc. required for the data processing process.

The computing device 330 may determine health indicators and personalized content for the subject using programs and data stored in the storage device 310.

The computing device 330 may determine complex factors for a representative phenotype in which an individual is interested from source data through the above-described process. Through this, the computing device 330 can calculate an individual's health index. Additionally, the computing device 330 can perform representative phenotype gene analysis, health status information-based gene analysis, interactive genetic factor gene analysis, and physical response genetic factor gene analysis, similar to the process described in FIG. 2 . In other words, the computing device 330 can determine an individual's health index and customized content by utilizing various available information, starting with genetic analysis according to the representative phenotype.

As described in FIG. 4, the computing device 330 may determine personalized content based on various information collected about the individual (analysis result code, current status code, genetic factor code, lifelog code, etc.).

The computing device 330 may be a device such as a processor that processes data and performs certain operations, an AP, or a chip with an embedded program.

Additionally, the method for calculating personalized indicators and providing content using complex factors as described above may be implemented as a program (or application) that includes an executable algorithm that can be executed on a computer. The program may be stored and provided in a temporary or non-transitory computer readable medium.

A non-transitory readable medium refers to a medium that stores data semi-permanently and can be read by a device, rather than a medium that stores data for a short period of time, such as registers, caches, and memories. Specifically, the various applications or programs described above include CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM (read-only memory), PROM (programmable read only memory), and EPROM (Erasable PROM, EPROM). Alternatively, it may be stored and provided in a non-transitory readable medium such as EEPROM (Electrically EPROM) or flash memory.

Temporarily readable media include Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), and Enhanced SDRAM (Enhanced RAM). It refers to various types of RAM, such as SDRAM (ESDRAM), synchronous DRAM (Synclink DRAM, SLDRAM), and Direct Rambus RAM (DRRAM).

This embodiment and the drawings attached to this specification only clearly show some of the technical ideas included in the above-described technology, and those skilled in the art can easily understand them within the scope of the technical ideas included in the specification and drawings of the above-described technology. It is self-evident that all inferable modifications and specific embodiments are included in the scope of rights of the above-described technology.

Claims (10)

The service device receives genome analysis results, health status information, and life log data for the subject;
Deriving, by the service device, a first genetic analysis result for a representative phenotype from the genome analysis results;
Deriving, by the service device, a second genetic analysis result for a lower genetic factor related to the representative phenotype from the genome analysis result based on state information related to the representative phenotype among the health status information;
Deriving, by the service device, a third genetic analysis result from the genome analysis result based on physical response genetic factors related to the representative phenotype; and
The service device uses the first genetic analysis result, the second genetic analysis result, the third genetic analysis result, the health status information, and the life log data to determine customized content for the subject. How to determine personalized content based on complex factors. According to paragraph 1,
The results of the first genetic analysis are
For the subject, a complex factor including a genetic risk score calculated from genetic markers for the representative phenotype, a risk score of a gene of the phenotype related to the health status information, and a risk score of a gene related to the phenotype of the lifelog data How to determine personalized content according to your needs. According to paragraph 1,
The first genetic analysis result is a genetic risk score calculated from genetic markers for the representative phenotype, a risk score of a gene of the phenotype related to the health status information, and a risk score of a gene related to the phenotype of the lifelog data. A method of determining personalized content based on complex factors whose values are composed of a matrix. According to paragraph 1,
The first genetic analysis result is a first matrix composed of genetic risk scores calculated from genetic markers for the representative phenotype, a second matrix composed of risk scores of genes of the phenotype related to the health status information, and the lifelog data. A method for determining personalized content based on complex factors where the third matrix consisting of the risk scores of genes related to the phenotype is a value processed through matrix operations. According to paragraph 1,
The service device is
Determine initial customized content for the subject based on survey results related to the representative phenotype included in the health status information, and determine the initial customized content based on data related to the representative phenotype among the lifelog data. A method for determining personalized content based on complex factors that are updated. An input device that receives genome analysis results, health status information, and life log data for the subject;
A storage device for storing a health activity content library; and
First genetic analysis results for representative phenotypes extracted from the genome analysis results,
Based on the state information related to the representative phenotype among the health status information, the second genetic analysis result for the sub-genetic factor related to the representative phenotype extracted from the genome analysis result, and the physical response genetic factor related to the representative phenotype. A third gene analysis result extracted from the genome analysis result is derived, and the first gene analysis result, the second gene analysis result, the third gene analysis result, the health status information, and the lifelog data are used to derive the above-mentioned gene analysis result. A service device that provides personalized content based on complex factors, including a computing device that determines customized content for the target audience. According to clause 6,
The results of the first genetic analysis are
For the subject, a complex factor including a genetic risk score calculated from genetic markers for the representative phenotype, a risk score of a gene of the phenotype related to the health status information, and a risk score of a gene related to the phenotype of the lifelog data A service device that provides personalized content according to your needs. According to clause 6,
The first genetic analysis result is a genetic risk score calculated from genetic markers for the representative phenotype, a risk score of a gene of the phenotype related to the health status information, and a risk score of a gene related to the phenotype of the lifelog data. A service device that provides personalized content based on complex factors, which are values composed of a matrix. According to clause 6,
The first genetic analysis result is a first matrix composed of genetic risk scores calculated from genetic markers for the representative phenotype, a second matrix composed of risk scores of genes of the phenotype related to the health status information, and the lifelog data. A service device that provides personalized content based on complex factors, where the third matrix consisting of the risk scores of genes related to the phenotype is a value processed through matrix operations. According to clause 6,
The computing device is
Determine initial customized content for the subject based on survey results related to the representative phenotype included in the health status information, and determine the initial customized content based on data related to the representative phenotype among the lifelog data. A service device that provides personalized content based on complex factors that are updated.

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