KR20220047096A - A method for determining personal skin type and method and system for personal tailored cosmetic based on the same - Google Patents

Abstract

Provided is a method for determining a personal skin type to determine a personal skin type, which is the most important in providing personalized cosmetics, in the most sophisticated way on the basis of big data analysis, and a method and system for personalized cosmetics based on the same. According to the present invention, the method for determining personal skin type comprises: a step of acquiring skin measurement values, single nucleotide polymorphism measurement values, and questionnaire values for each of n (n >= 3) or more skin phenotypes from an individual; a step of coding the skin measurement value, the single nucleotide polymorphism measurement value, and the questionnaire value with at least one of three values with respect to each of the skin phenotypes; and a step of generating a personal skin type code according to an algorithm which adds a coding value of the skin measurement value, a coding value of the single nucleotide polymorphism measurement value, and a coding value of the questionnaire value, converts the sum value into any one of at least three step values to generate a personal skin type code which is any one of 3^n or more skin type and formed of n-digits.

Description

Method and system for determining personal skin type and providing customized cosmetics based on it

The present disclosure relates to a method for determining a personal skin type and a method and system for providing customized cosmetics based on the method, and in particular, to a system for providing personalized cosmetics that can improve customer satisfaction.

Until now, it has been common for consumers to manually select and use cosmetics manufactured by the manufacturer according to the manufacturer's standards. There is a problem in using the product depending on the general efficacy/effect in a state where individual characteristics are not accurately reflected. In light of the recent trend of the cosmetics market changing from producer-centered 'what will you sell?' to consumer-centered 'what do you want to buy?' It is necessary to provide customized cosmetics tailored to the individual skin type of consumers.

The present disclosure relates to a method for determining the skin type of an individual consumer.

The present disclosure relates to a method of providing a personalized cosmetic tailored to the skin type of an individual consumer.

The present disclosure relates to a system for providing personalized cosmetics according to the skin type of individual consumers.

The method for determining an individual skin type according to the embodiments is to obtain a skin measurement value, a mononucleotide polymorphism measurement value, and a questionnaire value for each of n (n≥3) skin phenotypes from an individual, the skin measurement value, Coding the single nucleotide polymorphism measured value and the questionnaire value as one of at least three values for each of the skin phenotypes, the coding value of the skin measurement value, the coding value of the mononucleotide polymorphism measurement value, and the coding of the questionnaire value Generate a personal skin type code according to an algorithm that generates an n-digit personal skin type code that is any one of 3 ⁿ or more skin types by adding the values and then converting the summed value back to one of the minimum three-step values;

After obtaining the skin measurement value and questionnaire value for each of n (n≥3) skin phenotypes from an individual, the skin measurement value and the questionnaire value are converted into one of at least three values for each of the skin phenotypes. Coding, summing the coding value of the skin measurement value and the coding value of the questionnaire value, and then converting the summed value back to any one of the minimum three-step values, is any one of 3 ⁿ or more skin types, consisting of at least n digits A personal skin type code is generated according to an algorithm for generating a personal skin type code.

The system for providing personalized cosmetics according to the embodiments obtains a skin measurement value, a measurement value for mononucleotide polymorphism, and a questionnaire value for each of n (n≥3) skin phenotypes from an individual, and the skin measurement value, a single base Coding the polymorphism measurement value and the questionnaire value into any one of the minimum three-step values for each of the skin phenotypes, the coding value of the skin measurement value, the coding value of the single nucleotide polymorphism measurement value, and the coding value of the questionnaire value After summing, the individual skin type code is generated according to the algorithm for generating a personal skin type code consisting of n digits and any one of at least 3 ⁿ skin types by converting the summed value back to any one of the minimum three-step values,

After obtaining the skin measurement values and questionnaire values for each of at least n (n≥3) skin phenotypes from an individual, the skin measurement values and the questionnaire values are set to any of the minimum three-step values for each of the skin phenotypes. Coding into one, summing the coding value of the skin measurement value and the coding value of the questionnaire value, and then converting the summed value back to any one of the minimum three-step values, is any one of 3 ⁿ or more skin types and at least n digits an algorithm for generating a personal skin type code according to an algorithm for generating the configured personal skin type code;

a customized cosmetic prescription calculator configured to determine a customized cosmetic recipe according to the personal skin type code; and

and a personalized cosmetics providing unit that provides personalized cosmetics according to the determined recipe.

In the embodiments of the present disclosure, after determining an accurate individual skin type by comprehensively evaluating individual skin measurement information, genetic information, and questionnaire/questionnaire information or personal skin measurement information and questionnaire/questionnaire information, the determined individual skin Personal satisfaction can be increased by providing personalized cosmetics according to the type. In the provision of customized cosmetics, the most important skin type is determined by the summation of personal skin measurement information, genetic information, and questionnaire/questionnaire information based on big data analysis, so that individual needs can be fully satisfied. It becomes possible to provide personalized cosmetics with

In addition, in the embodiments of the present disclosure, after a certain period of time has elapsed after providing personalized cosmetics, measurement information and questionnaire/questionnaire information of personal skin are re-evaluated to determine a change in an individual's skin type after applying the customized cosmetics, and a change in skin type In the case of skin irritation, customized cosmetics that reflect the true needs of individuals can be provided by providing customized cosmetics with a new recipe according to the changed skin type.

1 is a schematic diagram of a system for providing personalized cosmetics according to an embodiment.
2 is a schematic diagram for explaining an algorithm for determining an individual's skin type in a system for providing personalized cosmetics according to an embodiment.

Hereinafter, embodiments will be described in detail so that those of ordinary skill in the art can easily implement it. The embodiment may be implemented in various different forms, and is not limited to the specific embodiment described herein.

1 is a schematic diagram of a system for providing customized customer service according to an embodiment.

Referring to FIG. 1 , the customized cosmetics providing system includes an information receiving unit 100 , a data collection/management/analysis unit 200 , an input information processing unit 300 , a customized cosmetic prescription calculation unit 300 , and a customized cosmetic providing unit 400 . ), a feedback information providing unit 500 and a feedback information calculating unit 600 .

The information receiving unit 100 receives data from the questionnaire/questionnaire 101 , the smart skin diagnosis device 103 , the skin genetic test kit 105 , and the like.

The questionnaire/questionnaire 101 data may include consumer response data to various items for identifying skin types and skin concerns.

The smart skin diagnosis device 103 is a device for identifying skin types and skin concerns through skin measurement. The smart skin diagnosis device 103 may be a camera of a mobile phone or a separate precision skin diagnosis device, etc., and acquires and transmits skin diagnosis data such as skin image information and skin moisture content information.

The skin genetic test kit 105 is a kit for testing an individual's genetic information, and uses it to acquire and transmit individual genetic information data.

The big data collection/management/analysis unit 200 includes a living environment/beauty information database 10 , a personal skin/genetic information database 30 , and a cosmetic raw material/formulation database 50 .

Living environment/beauty information database 10 stores living environment information, such as temperature, humidity, ultraviolet rays, fine dust, and various beauty information.

The personal skin/genetic information database 30 stores questionnaire/questionnaire, precise skin diagnosis and genetic analysis information.

The cosmetic raw material/formulation database 50 stores customized cosmetic raw material and customized cosmetic formulation information.

The input information processing unit 300 determines the personal skin type and skin concerns by using the data input through the information receiving unit 100 and referring to the living environment/beauty information database 10 and the personal skin/genetic information database 30 . do.

The input information processing unit 300 may be installed in one device together with the information receiving unit 100 or may be installed in a separate device. For example, the smart skin diagnosis device 103 includes both the information receiving unit 100 and the input information processing unit 300 to acquire skin image information and determine skin type and skin concerns based on these values. there is. On the other hand, in the case of the camera of a mobile phone, the acquired image information is transmitted to the input information processing unit 300 installed in the cloud computer, etc. together with the big data collection/management/analysis unit 200, and the cloud computer analyzes it You can also decide on the type and skin concerns. Even in the case of genetic information processing, the input information processing unit 300 may be installed in one device together with the information receiving unit 100 or may be installed in a separate device.

The customized cosmetic prescription calculation unit 350 determines a customized cosmetic recipe with reference to the cosmetic raw material/formulation database 50 based on the personal skin type determined by the input information processing unit 300 .

The personalized cosmetics providing unit 400 provides the personalized cosmetics to the consumer according to the customized cosmetics recipe determined by the customized cosmetics prescription calculation unit 350 . The personalized cosmetic providing unit 400 may manufacture customized cosmetics according to a recipe in a smart factory and then apply smart packaging technology to complete packaged customized cosmetics.

In some cases, the personalized cosmetics providing unit 400 may be a smart device that receives customized recipe data and immediately provides customized cosmetics to consumers. The smart device may be a device installed in a store, and in some cases may be a home device individually installed in an individual consumer's home.

2 is a schematic diagram for explaining an algorithm for determining an individual's skin type in a system for providing personalized cosmetics according to an embodiment. This algorithm may be included in the input information processing unit 300 .

Wrinkles of the skin are caused by various causes, but it is known that it is mainly caused by aging such as degeneration or atrophy of elastic fibers, connective fibers, and muscle fibers in the dermis. At first, fine wrinkles appear, but it is common to gradually become large and deep wrinkles, and wrinkles caused by degeneration, degeneration, and atrophy of the elastic fibers or connective fibers of the dermis are usually not healed. The causes of wrinkles include intrinsic aging accompanying aging of other organs and extrinsic aging due to external factors.

The skin is kept elastic and soft by the moisture present in the stratum corneum. It has been known that in order to maintain the elasticity of the stratum corneum due to moisture, it is essential that the skin contains 10% or more of moisture. By controlling the moisture content, the skin protects the dry, easily dries skin surface from damage. Moisturizing the skin is affected by moisture diffusion from the lower stratum corneum as described above, the natural moisturizing factors present in the upper skin layer, stratum corneum lipids and sebum, and external factors such as relative humidity and cosmetics.

Skin pigmentation refers to a skin symptom in which the color is black or brown due to an increase in melanin on the surface or mucous membrane of the skin. Skin pigmentation can be caused by genetic diseases, drugs, inflammation, trauma, sun exposure, hyperpigmented skin disease, etc. In particular, when the amount of melanin in the body is physiologically abnormal or there is an abnormality in the place where it appears, there are cases where a pigment that does not exist physiologically occurs under pathological conditions.

The skin is kept elastic and soft by the oil and moisture present in the stratum corneum. In order to maintain the elasticity of the stratum corneum due to oil and moisture, it has been known that moisture content of 10% or more and adequate oil content in the skin are essential. By controlling the oil content (oil content), the skin protects the dry, easily dries skin surface from damage. The skin's ability to retain oil and water depends on the lipid bilayer between the skin keratinocytes. Insufficient or excessive oil in the skin can cause skin troubles, which can lead to dry skin, an inflammatory reaction, or acne or pimples. The amount of this oil varies from person to person, and it also varies depending on the type of skin commonly referred to as oily, dry, etc.

The sensitivity of the skin refers to the degree of reaction of the skin to external factors such as the environment, and the sensitive skin generally refers to the skin that responds quickly and sensitively to changes in the environment. The sensitivity of the skin varies from person to person, and it also varies depending on the type of skin commonly referred to as oily or dry. In the case of sensitive skin, symptoms such as redness, itching, and rash may occur easily, so caution is required.

In the Baumann type, which classifies the well-known skin types, only 4 items of wrinkles, pigmentation, sebum secretion, and sensitivity were judged, and the skin type was also limited to 16 types, so the classification was not sufficient to provide personalized cosmetics. .

Accordingly, the personal skin type determination method according to embodiments of the present invention is a combination of values coded as high (H), medium (M), and low (L) for n (n≥3) or more skin phenotype items, respectively. After classifying into 3 ⁿ skin types consisting of Even when n=3, which has the smallest skin phenotype, 27 (=3*3*3) skin types, which are more diverse than the Baumann type, can be distinguished.

In addition, when using the five skin phenotype items, wrinkle, moisturizing (moisture), pigmentation (whitening), oily, and sensitive, it is possible to classify into 243 (=3*3*3*3*3) skin types. .

As a result of analyzing the skin measurement data of more than 1,000 people, it is possible to determine the individual skin type more accurately if the sebum secretion is divided into oil and moisture in order to accurately reflect the skin characteristics of Asians, especially Koreans, while maintaining the items of wrinkles, pigmentation, and sensitivity. And through this, it was found that it is possible to provide more accurate customized cosmetics.

As shown in FIG. 2 , the personal skin/genetic information database 30 has big data consisting of more than 1000 gene (single nucleotide polymorphism (SNP)) data, 1000 or more skin measurement data, and 1000 or more questionnaire data. The analysis result is stored, and an algorithm for classifying 243 skin types based on five skin phenotype items through multiple regression analysis as described below is stored in the input information processing unit 300. . The algorithm for classifying 243 skin types was derived through the following experimental example.

Derivation of coding values (first coding values) of skin measurements for skin phenotype items

sample population

The sample cohort consisted of a total of 1,079 Korean women collected at the P&K Skin Research Center (Seoul) from January 2019 to November 2019. All participants had no skin-related diseases and the mean age was 40.81 years. All participants gave written informed consent to the study. This population was approved by the institutional review board of Theragen Etex Bio Institute (IRB No.: 700062-20190819-GP-006-01).

Derivation of measurement values for each skin phenotype using skin measurement equipment

To measure skin properties, various measuring devices were used.

For skin wrinkles, the average roughness of the skin around the eyes, the maximum wrinkle depth of the skin around the eyes, the average roughness of the skin between the eyebrows, and the maximum wrinkle depth of the skin between the eyes were measured using a Primos CR (Canfield Scientific, Parsippany, NJ, USA) device.

For skin moisturizing (moisture), corneometer®CM-825 (EnviroDerm Services Ltd., Hedworth, Grange Court, UK) device was used to measure the moisture content of the skin between the glabellar and right cheeks.

For pigmentation (whitening), melanin and skin brightness were measured using a Mexameter and a CM-700d device, respectively.

For the skin oil content (oil content), Sebumeter®SM 815 (Courage+Khazaka electronic GmbH., Germany) was used to measure the oil content (oil content) of the skin between the forehead and the right cheek.

In the case of skin sensitivity, the skin was treated with 10% (v/v) lactic acid without a measuring device and measured according to the degree of reaction.

As a result, it was possible to obtain raw data of the skin measurements of the sample group. Based on the skin measurement data, the name of the variable for each skin measurement value, the measurement site, and the description of the variable value were compiled into a codebook.

Conversion of metrics

In the case of measurement by phenotype using skin measurement equipment, it was necessary to uniformly convert each measurement value before performing GWAS analysis because different measurement equipment was used for each phenotype.

Therefore, the present inventors assigned a code to each measurement item, and divided it into three groups of 1, 2, and 3 according to the size of each measurement value, and for each group, 1 point (low (L)), 2 points (medium) (M)) and 3 points (high (H)) were given.

Then, codes were assigned to each measurement site, and each code was divided into an upper group, an intermediate group, and a lower group, and 3 points were sequentially assigned to the upper group and 1 point to the lower group for quantification.

As a result of quantification, in the case of skin wrinkles, a total of 4 to 12 points were converted according to the measured values, with 4 to 6 points being the low group (L), 7 to 9 points being the middle group (M), and 10 to 12 points. was classified as a high group (H) and the measured values could be quantified.

As a result of quantification, in the case of skin moisturizing, a total of 2 to 6 points were converted according to the measured values, with 2 points being the low group (L), 3 to 5 points being the middle group (M), and 6 points being the high group ( H) to quantify the measured values.

As a result of quantification, in the case of skin pigmentation, a total of 4 to 12 points were converted according to the measured values, with 4 to 6 points being the low group (L), 7 to 8 points being the middle group (M), and 9 to 12 points. was classified as a high group (H) and the measured values could be quantified.

As a result of quantification, in the case of skin oil, a total of 2 to 6 points were converted according to the measured value, with 2 points being the low group (L), 3 to 4 points being the middle group (M), and 5 to 6 points being the high points. It was possible to quantify the measured values by classifying them into groups (H).

As a result of quantification, in the case of skin sensitivity, a total of 1 or 2 points were converted according to the measured value, and 1 point was classified into the low group (L) and 2 points were classified into the middle group (M), so that the measured value could be quantified.

A skin measurement value (a first coding value) for each skin phenotype item may be derived by the method described above.

Derivation of the coding value (second coding value) of the genetic risk (single nucleotide polymorphism) measurement value for the skin phenotype item

Preparation for screening of genetic polymorphic markers

To select genetic polymorphism markers for skin phenotypes, the present inventors obtained samples from the oral cavity of the sample population with a cotton swab, and detected DNA using Exgene™ Tissue SV (GeneAll, Seoul, Korea). All DNA samples were amplified into 25-125 bp fragments, randomly fractionated, and the DNA was analyzed using the Theragen Precision Medicine Research Array (Theragen PMRA), a customized analysis based on the Asian Precision Medicine Research Array (Thermofisher Scientific, Waltham, Massachusetts, USA). assay) were sequentially purified, resuspended and hybridized. After hybridization, the DMA was washed under stringent conditions to remove the background to minimize noise. Next, 820,000 SNPs were analyzed using Thaeragen PMRA analysis as instructed. In order to reduce errors that may occur due to association, strict quality control methods were applied, SNPs to be used in the invention were selected and the data set was controlled. In addition, quality control procedures were performed for 820,000 SNPs. SNP sets were filtered based on the genotype call rates (≥ 0.95) and MAF (≥ 0.10), and Hardy-Weinberg equilibrium (HWE) was calculated for each SNP.

As a result, all SNPs showed HWE p values > 0.01, and after the filtering, 560,795 polymorphic SNPs in chromosomes 1-22 were analyzed.

GWAS analysis of skin phenotypes

A genome-wide association study (GWAS) analysis was performed on the sample population. As a result, a total of 23 SNPs were selected, and the SNPs are a reference database (KRGDB, http://coda.nih go.kr/coda/KRGDB/index.jsp; Ensembl DB, https://asia.ensembl.org) A significant p-value (P<1.0x10 ^-5 ) was shown by GWAS analysis using a skin phenotype consistent with the minor allele frequency (MAF) of . Specific genetic polymorphisms are described below.

Genetic polymorphisms by skin phenotype

Skin wrinkle-related gene polymorphism

In relation to skin wrinkles, single nucleotide polymorphisms (SNPs) of rs117381658, rs1961184, rs1929013, and rs7042102 were selected. Among them, rs117381658 showed the highest correlation (β = 0.952, P = 1.52x10-8) with changes in skin phenotype for wrinkles, and 5 SNPs near ± 100 kb showed a significant correlation of P < 0.05 in GWAS analysis. showed

Aging accumulates B-cell receptors and increases the risk of inducing chronic inflammation through them, which is known to induce transcription of FCRL5 that triggers an inflammatory response (Damdinsuren et al. 2016).

Consequently, in the case of rs117381658, it exists downstream of the FCRL5 gene and could affect FCRL5 by forming a significant SNP cluster around chronic inflammatory conditions. In addition, the expression of FCRL5 can affect the inflammatory response and the NF-κ pathway, suggesting that it can destroy tissue constancy modulators and affect skin aging.

In addition, another SNP, rs7042102, is a mutation present downstream of the SPTLC1 gene, and it was confirmed that the expression difference according to the genotype of the skin tissue appeared in the eQTL database.

Skin moisturizing (moisture) related gene polymorphism

With regard to skin moisturizing, single nucleotide polymorphisms of rs9873353, rs34567709, rs1362404, rs7853290, and rs143938096 were selected. Among them, rs9873353 showed a high correlation (β = -0.567, P = 1.47x10-6).

The internal factors related to skin moisture content are moisture content due to oil in the stratum corneum, natural moisturizing factors, and external factors, and it is known that differences appear due to the above factors (Iizaka 2017).

The expression differences according to SNPs in the eQTL database showed that only adipose tissue was presented to single-tissue eQTLs, whereas multi-tissue eQTLs showed that differences in expression of CEMIP2 by the genotype of rs7853290 were also expressed in skin tissues. These results suggest that the SNPs can moisturize and functionally control the barrier.

Gene polymorphism associated with skin pigmentation (whitening)

Regarding skin pigmentation, single-nucleotide polymorphisms of rs74653330, rs34466224, rs11685354, rs4653497, rs59784607, and rs76548385 were selected. In particular, rs74653330 showed a high significance (β = -1.092, P = 1.04x10-8) correlation in the GWAS data.

The amino acid substitution (His615Arg) in Asian races is highly related to skin whitening and pigmentation changes, and the rs74653330 is one of the missense mutations (Ala481Thr), and it was confirmed that it is related to pigmentation in the East Asian population.

Genetic polymorphisms related to skin oiliness (oil content)

Regarding skin oil content, single base polymorphisms of rs308971, rs151209785, rs9577919, rs147804495, rs8107564 and rs6490805 were screened. Among them, rs308971 showed a high correlation (β = -0.325, P = 4.60x10-6).

The potential functions of rs308971, rs9577919, rs8107564 and rs6490805 can change the skin. More specifically, rs9577919 was located in intron 1 of the GAS6 gene, a gene that mediates the inflammatory response and influences the development of psoriasis. In addition, rs8107564 was located downstream of the INSR gene. In addition, analysis results of rs308971 and rs6490805 also suggested a correlation with skin oil content.

Skin sensitization-related gene polymorphisms

Regarding skin sensitivity, single base polymorphisms of rs7334780 and rs41308 were selected. Among them, rs7334780 showed high significance (OR = 0.635, P = 2.82x10-6).

Derivation of conversion formula for gene polymorphism by skin phenotype

Skin phenotype prediction genetic indicator algorithm

Linear regression analysis was performed to understand the SPSS statistical program for SNP biomarkers by phenotype. In the linear correlation analysis, the dependent variable is the phenotype of each skin type, and the independent variable is the SNP marker of each phenotype, the age, and sex of the individual. Through the linear regression analysis, a weight constant (regression analysis beta value) for each phenotype marker was derived.

As a result, as shown in Tables 1 to 5 below, it was possible to derive a conversion formula for the skin phenotype. Table 1 shows the conversion formula of the skin wrinkle phenotype, Table 2 shows the conversion formula of the skin moisturizing (moisture) phenotype, Table 3 shows the conversion formula of the skin pigmentation (whitening) phenotype, and Table 4 shows the skin oil (oil) content) shows the conversion formula for the phenotype, and Table 5 shows the conversion formula for the skin sensitivity phenotype.

SNP B percent reference value wrinkle (a constant) 7.45 LOW 7.434 (39.2%) 0<=X<=7.434 rs117381658 0.857 MEDIUM 7.773 (33.3%) 7.434<X<=7.773 rs1961184 0.699 HIGH 10.509 (27.5%) 7.773<X<=10.509 rs1929013 -0.339 rs7042102 0.323 FORMULA 7.45+(rs117381658)*(0.857)+(rs1961184)*(0.699)+(rs1929013)*(-0.339)+(rs7042102)*(0.323)

SNP B percent reference value Moisturizing (a constant) 3.968 LOW 3.65 (30.13%) 0<=X<=3.65 rs9873353 -0.492 MEDIUM 4.056 (31.01%) 3.65<X<=4.056 rs34567709 0.406 HIGH 5.562 (38.86%) 4.056<X<=5.562 rs1362404 -0.318 rs7853290 0.482 rs143938096 -0.521 rs12955989 0.353 FORMULA 3.968+(rs1362404)*(-0.318)+(rs7853290)*(0.482)+(rs143938096)*(-0.521)+(rs12955989)*(0.353)

SNP B percent reference value melanin (a constant) 7.939 LOW 7.547 (30.03%) 0<=X<=7.547 rs74653330 -1.006 MEDIUM 8.189 (30.31%) 7.547<X<=8.189 rs34466224 0.532 HIGH 10.427 (39.66%) 8.189<X<=10.427 rs11685354 -0.419 rs4653497 0.419 rs59784607 -0.462 rs76548385 0.712 FORMULA 7.939+(rs74653330)*(-1.006)+(rs34466224)*(0.532)+(rs11685354)*(-0.419)+(rs4653497)*(0.419)+(rs59784607)*(-0.462)+(rs76548385)*( 0.712)

SNP B percent reference value oil (a constant) 3.901 LOW 3.873 (27.47%) 0<=X<=3.873 rs308971 -0.28 MEDIUM 4.211 (34.37%) 3.873<X<=4.211 rs151209785 -0.558 HIGH 5.824 (38.16%) 4.211<X<=5.824 rs9577919 0.5 rs147804495 0.649 rs8107564 0.31 rs6490805 0.315 FORMULA 3.901+(rs308971)*(-0.28)+(rs151209785)*(-0.558)+(rs9577919)*(0.5)+(rs147804495)*(0.649)+(rs8107564)*(0.31)+(rs6490805)*(0.315 )

SNP B percent reference value sensitive (a constant) 1.479 LOW 1.384 (26.07%) 0<=X<=1.384 rs7334780 -0.104 MEDIUM 1.488 (40.41%) 1.384<X<=1.488 rs41308 0.113 HIGH 1.705 (33.53%) 1.488<X<=1.705 FORMULA 1.479+(rs7334780)*(-0.104)+(rs41308)*(0.113)

Genetic risk for each skin phenotype can be predicted using the conversion formulas in Tables 1 to 5 above. Hereinafter, a method of predicting the risk for each skin phenotype will be described using a specific conversion formula.

Skin Wrinkle Prediction

That is, predicting skin wrinkles derives a converted value using the following Equation 1, and when the converted value is greater than 7.773 to 10.509 or less, predicts that the risk of skin wrinkles is high (H), and the converted value is greater than 7.434 to 7.773 If it is less than or equal to, it is predicted that the risk of skin wrinkles is medium (M), and when the converted value is 0 or more to 7.434 or less, it is predicted that the risk of skin wrinkles is low (L).

[Equation 1]

converted value

=7.45+(rs117381658)*(0.857)+(rs1961184)(0.699)*(rs1929013)(0.339)+(rs7042102)*(0.323)

In the above formula, (SNP) means a value of 0 if the genotype of each SNP is WILDTYPE, 1 if it is a heterotype, and 2 if it is a mutant type.

Predicting skin moisture (moisture)

Predicting skin hydration is to derive a converted value using Equation 2 below, and when the converted value is greater than 4.056 to 5.562 or less, the skin moisture content is high and the risk of skin moisturizing is predicted to be low, and the converted value is greater than 3.65 to 4.056 In the case of less than or equal to, the skin moisture content is medium and the risk of skin moisturizing is predicted to be medium, and when the converted value is 0 to 3.65 or less, the skin moisture content is low and the risk of skin moisturizing is predicted to be high.

[Equation 2]

converted value

=3.968+(rs1362404)*(-0.318)+(rs7853290)*(0.482)+(rs143938096)(0.521)

+(rs12955989)*(0.353)

In the above formula, (SNP) means a value of 0 if the genotype of each SNP is WILDTYPE, 1 if it is a heterotype, and 2 if it is a mutant type.

Predicting skin pigmentation (whitening)

Predicting skin pigmentation is derived using Equation 3 below, and when the converted value is more than 8.189 to 10.427 or less, it is predicted that the risk of skin pigmentation is high (H), and when the converted value is more than 7.547 to 8.189 or less, the skin It is predicted that the risk of pigmentation is medium (M), and when the converted value is 0 or more to 7.547 or less, it is predicted that the risk of skin pigmentation is low (L).

[Equation 3]

converted value

=7.939+(rs74653330)*(1.006)+(rs34466224)(0.532)+(rs11685354)*(0.419)+(rs4653497)*(0.419)+(rs59784607)*(-0.462)+(rs76548385)*(0.712)

In the above formula, (SNP) means a value of 0 if the genotype of each SNP is WILDTYPE, 1 if it is a heterotype, and 2 if it is a mutant type.

skin oil prediction

Predicting skin oil is derived using Equation 4 below, and when the converted value is more than 4.211 to 5.824 or less, the risk of skin oil is predicted to be high, and when the converted value is more than 3.873 to 4.211 or less, the risk of skin oil is medium It is predicted that the risk of skin oiliness is low when the converted value is 0 or more to 3.873 or less.

[Equation 4]

converted value

= 3.901+(rs308971)*(-0.28)+(rs151209785)(0.558)*(rs9577919)(0.5)+ (rs147804495)*(0.649)+(rs8107564)*(0.31)+(rs6490805)*(0.315)

Predicting skin sensitivity

Predicting skin sensitivity is derived using Equation 5 below, and when the converted value is more than 1.488 to 1.705 or less, it is predicted that the skin sensitivity risk is high (H), and when the converted value is more than 1.384 to 1.488 or less, the skin sensitivity risk is predicted to be medium (M), and when the converted value is 0 or more to 1.384 or less, it is predicted that the skin sensitivity risk is low (L).

[Equation 5]

converted value

=1.479+(rs7334780)*(-0.104)+(rs41308)*(0.113)

Derivation of coding value (third coding value) of questionnaire questionnaire measurement value for skin phenotype item

After big data analysis of the results of more than 1000 questionnaires for 88 questionnaires, a step-by-step regression analysis was performed on the measured values (dependent variables) and the entire questionnaire (independent variables) for 5 skin phenotype items, and measured values out of a total of 88 questionnaires Only the minimum questionnaires that had an impact on people were calculated. Table 6 shows the calculated questionnaire list.

skin phenotype items Questionnaire name survey Wrinkle Survey Combination Q47S1 T zone U zone trouble Q02S1 Skin Concerns: Hyperpigmentation Q14S1 blemishes, spots, freckles Q07S1 pore condition Q13S1 thread Q33S1 greasy and sweet food Q10S1 weather skin redness Q01S4 neutrality Q02S4 Skin concerns: wrinkles Q02S5 Skin concerns: pores Moisture (moisture) questionnaire combination Q35S1 sunscreen Q31S2 Cosmetic feeling: feeling quickly absorbed Q18S1 pigmentation Q24S1 skin aging Q42S1 Skin deterioration (pregnancy, childbirth) Pigmentation (whitening) questionnaire combination Q15S1 dullness Q17S1 sun sensitivity Q28S1 face volume Q07S1 pore condition Q02S3 Skin Concern: Elasticity Q36S1 Skin deterioration (diet) Q19S1 fine lines Q31S2 Cosmetic feeling: feeling quickly absorbed Q13S1 thread Q04S2 Makeup looks good Q03S1 pulling after washing Q23S1 skin marks Oil Survey Combination Q26S1 Pore size and shape Q48S1 stress trouble Q18S1 pigmentation Q07S1 pore condition Q05S1 T zone dry Q08S2 excessive skin shine Q24S1 skin aging Q04S1 Makeup is easily removed Sensitive Survey Combination Q05S1 T zone dry Q42S1 Skin deterioration (pregnancy, childbirth) Q33S1 greasy and sweet food

Linear regression analysis was performed for each question combination in a similar way to the formula for calculating the genetic risk conversion value. A weight constant (regression beta value) for each questionnaire was derived through linear regression analysis. If the beta value is positive, the risk is high, and if the beta value is negative, it is predicted that the risk is low.

After calculating the converted value according to the conversion formula, if the converted value is within the upper 40% range, the expression risk of each skin phenotype is predicted to be high (H), and if it is within the middle 30% range, the expression risk is considered to be medium (M). It is predicted, and if it is within the lower 30% range, the expression risk is predicted to be low (L).

Taking moisturizing (moisture) prediction as an example, a converted value is derived using Equation 6 below.

[Equation 6]

converted value

=5.065+(Q35S1)*(-0.509)+(Q31S2)*(0.288)

+(Q18S1)*(-0.294)+(Q24S1)*(0.513)+(Q42S1)*(0.246)

Skin measurement value (first coding value) for skin phenotype item derived according to the method described above, genetic risk measurement value for skin phenotype item (second coding value), questionnaire questionnaire measurement value for skin phenotype item (third coding value) coding values). If the combined value is within the upper 40% range, the expression risk of each skin phenotype is predicted to be high (H), if it is within the middle 30% range, the expression risk is predicted to be medium (M), and if it is within the lower 30% range, the expression risk is predicted to be low (L). As a result, for each of the five skin phenotype items, 243 (=3*3*3*3*3) The skin type is derived.

When summing the first coding value, the second coding value, and the third coding value, the sum can be obtained by applying the same weight, adding them, and then 1/3 to obtain the sum value. can For example, 0.5 for the first coding value of the skin measurement that most reflects the customer's current condition, 0.3 for the second coding value measured using genes (SNP), and 0.2 for the first coding value of the questionnaire It may be more desirable to obtain the most rational and objective summation value by multiplying and summing by .

In this way, the 243 skin type classification and determination algorithms built by the big data collection/management/analysis unit 200 through information processing on big data are the big data collection/management/analysis unit 200 as well as the input information processing unit 300 ) is stored in

An individual consumer uses the smart skin diagnosis device 101, the skin genetic test kit 103, and the questionnaire/questionnaire 105 of the information receiving unit 100 to input each individual's skin measurement value, genetic measurement value, and questionnaire questionnaire value According to the algorithm described above, the skin measurement value for the skin phenotype item (first coding value), the genetic risk measurement value for the skin phenotype item (second coding value), and the questionnaire questionnaire measurement value for the skin phenotype item (third coding) values) and calculating the range of the summed value, predicting that the risk of expression of each skin phenotype is high (H) if it is within the first range (eg, top 40% range), and the second range (eg, top 40% range) If it is within the middle 30% range), the expression risk is predicted to be medium (M), and if it is within the third range (eg, the lower 30% range), the expression risk is predicted to be low (L). According to the value thus obtained, a 5-digit coded skin type can be obtained.

For example, one skin type code (Table 7) as follows among 243 codes may be given as an individual skin type.

wrinkle moisturizing (moisture) Pigmentation (whitening) oil sensitive H M L H M

In the above description, five types of skin phenotypes have been described as examples, but five or more types of skin phenotypes measured when determining an individual skin type, for example, sebum and pore skin phenotypes, may be further added to classify skin types. . Of course, sebum and pores are also exemplary, and various types of skin phenotypes may be added. When determining an individual skin type, the skin measurement value, the single nucleotide polymorphism measurement value, and the questionnaire value are coded into any one of three levels of high, medium, and low for each skin phenotype, and after summing these values, it is high again, Conversion to any one of the three levels of medium and low has been illustrated and explained, but depending on the skin phenotype, it is further subdivided and coded into any of the five levels, then these values are summed and converted back to any one of the five levels. may be In this case, it may be divided into 5*5*5*5*5 skin types. Although five steps have been exemplified, it goes without saying that it can be further subdivided if necessary.

In addition, in the case of a skin phenotype with many consumer concerns, the coding step may be subdivided into three or more steps, and in the case of a skin phenotype with few consumer concerns, the skin type may be determined by using only three coding steps.

As described above, by diversifying the types of skin phenotypes, classification of coding steps, and combinations thereof, customer satisfaction can be slightly increased.

The customized cosmetic prescription calculation unit 350 determines a customized cosmetic recipe by referring to the cosmetic raw material/formulation database 50 based on the personal skin type determined by the input information processing unit 300, and prepares personalized cosmetics according to the determined customized cosmetic recipe In the study 400, personalized cosmetics are provided to consumers.

After a certain period of time has elapsed, information on the customer's satisfaction with the customized cosmetic provided to the consumer or the efficacy of the provided customized cosmetic may be provided to the feedback information receiving unit 500 .

Unlike the input information processing unit 300 , the feedback information receiving unit 500 does not include a skin genetic test kit, and may be composed of a smart skin diagnosis device and questionnaire/questionnaire. This is because there is no need to measure the genetic information again because the previously measured values are stored in the personal skin/genetic information DB 30 . After using the customized cosmetics for a certain period of time, the skin is measured with a smart skin diagnostic device and the questionnaire/questionnaire is rewritten and then this value is input. there is. If the skin type is the same as the existing one, the previously prescribed customized recipe can be applied as it is, but if the skin type is changed (HMLHM → HMHLL), customized cosmetics with a new recipe can be provided according to the changed skin type. That is, after receiving feedback information from consumers and evaluating the accuracy of the previously determined skin type, satisfaction with the provided customized cosmetic and/or the efficacy of the provided customized cosmetic, it is decided to maintain, improve, or change the customized cosmetic, and then provide it to the consumer. It is possible to provide customized cosmetics that reflect the true needs of consumers.

Of course, if necessary, the function of the feedback information calculating unit 600 may be integrated and performed by the input information processing unit 300 .

In the preceding description, the case of summing the coding value of the skin measurement value, the coding value of the single nucleotide polymorphism measurement value, and the coding value of the questionnaire value was explained as an example. After obtaining the skin measurement value and questionnaire value for each of more than one skin phenotype (n≥3), the skin measurement value and the questionnaire value are coded as any one of the minimum three-step values for each of the skin phenotypes, After summing the coding value of the skin measurement value and the coding value of the questionnaire value, the summed value is converted back to any one of the minimum three-step values, and is any one of 3 ⁿ or more skin types and an individual skin type consisting of at least n digits A personal skin type code can be generated according to the code generating algorithm.

Although the present invention has been described with reference to various embodiments, it will be understood that various modifications and variations are included within the spirit and scope of the present invention.

Claims (17)

A skin measurement value, a mononucleotide polymorphism measurement value, and a questionnaire value for each of n (n≥3) skin phenotypes were obtained from an individual, and the skin measurement value, a mononucleotide polymorphism measurement value, and a questionnaire value were set to the skin phenotype. Code one of the minimum three-step values for each, and add up the coding value of the skin measurement value, the coding value of the single nucleotide polymorphism measurement value, and the coding value of the questionnaire value, and then return the summed value to the minimum three-step value Generates a personal skin type code according to an algorithm that converts to any one of 3 ⁿ or more skin types and generates an n-digit personal skin type code,
After obtaining the skin measurement value and questionnaire value for each of n (n≥3) skin phenotypes from an individual, the skin measurement value and the questionnaire value are converted into one of the minimum three-step values for each of the skin phenotypes. coding, summing the coding value of the skin measurement value and the coding value of the questionnaire value, then converting the summed value back to any one of the minimum three-step values, any one of 3 ⁿ or more skin types, consisting of at least n digits A method for determining a personal skin type for generating a personal skin type code according to an algorithm for generating a personal skin type code. According to claim 1,
The algorithm assigns a score of any one of the minimum three-step values according to the size of the skin measurement value for at least one skin measurement item corresponding to each skin phenotype, sums all the values for each measurement item, and then repeats it at least three steps A method for determining a personal skin type for classifying one of the values to determine a coding value of a skin measurement value of each skin phenotype. According to claim 1,
The algorithm detects the single nucleotide polymorphism selected for each skin phenotype, but a conversion value is derived according to a regression conversion formula that predicts that the risk is high when the beta value of the regression analysis of the single nucleotide polymorphism (beta) is positive and the risk is low when the number is negative. and converting the converted value back to any one of the minimum three-step values to determine the coding value of the single nucleotide polymorphism measurement value. According to claim 1,
The algorithm derives a conversion value according to a regression conversion formula that predicts that the risk is high when the regression analysis beta value of the questionnaire selected for each skin phenotype is positive, and that the risk is low when the number is negative. A personal skin type determination method for determining the coding value of the questionnaire value by converting it to any one of the values. According to claim 1,
When the algorithm sums the coding value of the skin measurement value, the coding value of the single nucleotide polymorphism measurement value, and the coding value of the questionnaire value, or summing the coding value of the skin measurement value and the coding value of the questionnaire value A method of determining an individual skin type in which the weight of each coding value is varied and summed. 6. The method of claim 5,
The weight of the coding value of the skin measurement value: the weight of the second coding value: the weight of the third coding value is 5:3:2. According to claim 1,
wherein n is 5 and the skin phenotype is wrinkle, moisturizing (moisture), pigmentation (whitening), oily and sensitive personal skin type. 8. The method according to any one of claims 1 to 7,
Determining a recipe for customized cosmetics according to the personal skin type code,
A method of providing personalized cosmetics that provides personalized cosmetics according to a determined recipe. 9. The method of claim 8,
After a certain period of time has elapsed after providing the personalized cosmetics, newly measured skin measurement values and questionnaire values are obtained for each of the n (n≥3) or more skin phenotypes,
The newly measured skin measurement value and questionnaire value are processed together with the previously measured single nucleotide polymorphism measurement value, or the newly measured skin measurement value and questionnaire value are processed with the algorithm to recalculate the skin type. After evaluating the accuracy of the skin type, satisfaction with the provided customized cosmetic and/or the efficacy of the provided customized cosmetic, determining maintenance, improvement or change of the provided customized cosmetic, and providing it to the consumer. . A skin measurement value, a mononucleotide polymorphism measurement value, and a questionnaire value for each of n (n≥3) skin phenotypes were obtained from an individual, and the skin measurement value, a mononucleotide polymorphism measurement value, and a questionnaire value were set to the skin phenotype. Code one of the minimum three-step values for each, and add up the coding value of the skin measurement value, the coding value of the single nucleotide polymorphism measurement value, and the coding value of the questionnaire value, and then return the summed value to the minimum three-step value Generates a personal skin type code according to an algorithm for generating an n-digit personal skin type code that is any one of at least 3 ⁿ or more skin types in terms of any one,
After obtaining the skin measurement values and questionnaire values for each of at least n (n≥3) skin phenotypes from an individual, the skin measurement values and the questionnaire values are set to any of the minimum three-step values for each of the skin phenotypes. Coding into one, summing the coding value of the skin measurement value and the coding value of the questionnaire value, and then converting the summed value back to any one of the minimum three-step values, is any one of 3 ⁿ or more skin types and at least n digits an algorithm for generating a personal skin type code according to an algorithm for generating the configured personal skin type code;
a customized cosmetic prescription calculator configured to determine a customized cosmetic recipe according to the personal skin type code; and
A system for providing personalized cosmetics including a personalized cosmetics providing unit that provides personalized cosmetics according to the determined recipe. 11. The method of claim 10,
The algorithm assigns a score of any one of the minimum three-step values according to the size of the skin measurement value for at least one or more skin measurement items corresponding to each skin phenotype, sums all the values for each measurement item, and then repeats this at least three steps A system for providing personalized cosmetics that classifies one of the values to determine the coding value of the skin measurement value of each skin phenotype. 11. The method of claim 10,
The algorithm detects the single nucleotide polymorphism selected for each skin phenotype, but a conversion value is derived according to a regression conversion formula that predicts that the risk is high when the beta value of the regression analysis of the single nucleotide polymorphism (beta) is positive and the risk is low when the number is negative. and a system for providing personalized cosmetics that converts the converted value back to any one of the minimum three-step values to determine the coding value of the measured value of single nucleotide polymorphism. 11. The method of claim 10,
The algorithm derives a conversion value according to a regression conversion formula that predicts that the risk is high when the regression analysis beta value of the questionnaire selected for each skin phenotype is positive, and that the risk is low when the number is negative. A system for providing personalized cosmetics that converts any one of the values to determine the coding value of the questionnaire value. 11. The method of claim 10,
The algorithm sums the coding value of the skin measurement value, the coding value of the single nucleotide polymorphism measurement value, and the coding value of the questionnaire value, or sums the coding value of the skin measurement value and the coding value of the questionnaire value A system for providing personalized cosmetics that differs in the weight of each coding value and sums them up. 15. The method of claim 14,
Weight of the coding value of the skin measurement value: Weight of the coding value of the single nucleotide polymorphism measurement value: The weight of the coding value of the questionnaire value is 5:3:2. 11. The method of claim 10,
wherein n is 5, and the skin phenotype is wrinkle, moisturizing (moisture), pigmentation (whitening), oily and sensitive personal cosmetics providing system. 11. The method of claim 10,
After a certain period of time has elapsed after providing the personalized cosmetics, newly measured skin measurement values and questionnaire values are obtained for each of the n (n≥3) or more skin phenotypes,
The newly measured skin measurement value and questionnaire value are processed together with the previously measured single nucleotide polymorphism measurement value, or the newly measured skin measurement value and questionnaire value are processed with the algorithm to recalculate the skin type. After evaluating the accuracy of the skin type, satisfaction with the provided customized cosmetic and/or the efficacy of the provided customized cosmetic, the personalized cosmetic further comprising a feedback information calculating unit for determining maintenance, improvement or change of the provided customized cosmetic delivery system.

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