KR102556981B1 - A system for personal tailored cosmetic with improved customer satisfaction - Google Patents

Abstract

Consumers' feedback information on personalized cosmetics is input, and after evaluating the accuracy of the primarily determined skin type and skin concern information and satisfaction and efficacy of the provided customized cosmetics, maintenance, improvement, or change of the customized cosmetics is determined. A system for providing customized cosmetics that reflects the true needs of consumers by providing them to consumers is provided.

Description

Personalized cosmetics provision system to improve consumer satisfaction {A SYSTEM FOR PERSONAL TAILORED COSMETIC WITH IMPROVED CUSTOMER SATISFACTION}

The present description relates to a system for providing personalized cosmetics, and in particular, to a system for providing personalized cosmetics that can improve consumer satisfaction.

Until now, it has been common for consumers to passively select and use cosmetics manufactured by manufacturers according to the manufacturer's standards. This has a problem of using the product depending on the general efficacy/effect without accurately reflecting individual characteristics. In view of the recent trend in the cosmetics market, which is changing from producer-centered 'what to sell?' to consumer-centered 'what do you want to buy?' There is a need to provide customized cosmetics tailored to each consumer's skin type and characteristics.

The present disclosure is intended to provide a method of providing customized cosmetics tailored to the skin type and characteristics of an individual consumer by reflecting the consumer's satisfaction.

The present disclosure aims to provide a system that provides customized cosmetics tailored to the skin type and characteristics of individual consumers by reflecting consumer satisfaction.

A method for providing customized cosmetics according to embodiments determines an individual's skin type using a set of questionnaire questionnaire values, skin measurement values, and single nucleotide polymorphism measurement values obtained from an individual, or a set of questionnaire questionnaire values and skin measurement values, and the Personalized cosmetics are provided using the skin type, and the accuracy of the determined skin type using the newly measured skin measurement value and questionnaire questionnaire value after applying the customized cosmetic product to the individual's skin for a certain period of time, the provided customized cosmetics Evaluate the satisfaction or efficacy of the provided customized cosmetics, or evaluate the objective satisfaction with the provided customized cosmetics or the efficacy of the provided customized cosmetics using the individual's emotional index measurement value for the customized cosmetics to provide the provided After a decision is made to maintain, improve or change the customized cosmetic, it is provided to the individual.

A system for providing customized cosmetics according to embodiments includes an information receiver configured to receive a set of a questionnaire questionnaire, a skin measurement value, and a single nucleotide polymorphism measurement value provided by an individual, or a set of questionnaire questionnaire and skin measurement values, and the information receiver An input information processing unit that determines a personal skin type using the values inputted through, a customized cosmetics prescription calculation unit that determines a customized cosmetics recipe using the skin type, and a personalized cosmetics according to the customized cosmetics recipe determined by the customized cosmetics prescription calculation unit. A personalized cosmetics providing unit that provides consumers with a newly measured skin measurement value and a survey questionnaire value after applying the provided customized cosmetics to the skin for a certain period of time, or an index of the individual's sensitivity to the customized cosmetics Includes a feedback information receiving unit into which feedback information on the customized cosmetics including measurement values is input, and a feedback information calculating unit which determines maintenance, improvement, or change of the customized cosmetics based on the feedback information input into the feedback information receiving unit do.

The specific details of other aspects of the present invention are included in the detailed description below.

In the embodiments of the present description, after the consumer uses the customized cosmetics, emotional evaluation such as feeling of use and satisfaction with the customized cosmetics and / or actual skin improvement efficacy are evaluated, and then the results are fed back to the manufacturer, so that individual consumers' skin diagnosis results and It can improve the reliability of customized cosmetics recipe and provide the basis for the customized cosmetics provision system to be continuously maintained and successful.

1 is a schematic diagram illustrating a customized cosmetics solution system in a personalized cosmetics providing system according to an embodiment.
2 is a schematic diagram of a system for providing customized cosmetics for implementing the customized cosmetics solution system disclosed in FIG. 1 .
3 is a schematic diagram illustrating an algorithm for determining an individual's skin type in a personalized cosmetics providing system according to an embodiment.
Figure 4 is a detailed schematic diagram of the feedback information receiver of Figure 2;
5 shows the results of measuring the brain waves before and after application of cosmetics containing a nutmeg scent using an brain wave measuring device (EEG).

Hereinafter, the embodiments will be described in detail so that those skilled in the art can easily implement them. Embodiments may be implemented in many different forms, and are not limited to specific embodiments described herein.

1 is a schematic diagram illustrating a customized cosmetics solution system in a personalized cosmetics providing system according to an embodiment.

Personalized cosmetic solutions can be composed of three things: personal demand, skin condition, and constitution/genetic characteristics.

Personal (customer) demand customization can be performed in real time or daily through questionnaires, etc.

Skin condition matching can be performed at regular intervals (eg, seasonally, yearly cycle, or customized cosmetics supply cycle) through skin measurement.

Constitution and genetic characteristics matching can be performed through genetic testing, etc. In this case, the test may be performed once in a lifetime or once over several years.

2 is a schematic diagram of a system for providing customized cosmetics for implementing the customized cosmetics solution system disclosed in FIG. 1 .

Referring to FIG. 2, the system for providing customized cosmetics includes an information receiving unit 100, a data collection/management/analysis unit 200, an input information processing unit 300, a customized cosmetics prescription calculation unit 300, and a customized cosmetics providing unit 400. ), a feedback information providing unit 500 and a feedback information calculating unit 600 are included.

The information receiving unit 100 receives data from a questionnaire/questionnaire 101, a smart skin diagnostic device 103, a kit for skin genetic testing 105, and the like.

The smart skin diagnosis device 101 is a device for finding out skin types and skin concerns through skin measurement. The smart skin diagnosis device 101 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 information.

The skin genetic test kit 103 is a kit for examining individual genetic information, and by using the kit, individual genetic information data is obtained and transmitted.

Questionnaire/questionnaire 105 data may include consumer response data for various items for finding out skin type and skin trouble.

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.

The 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/interview, precision skin diagnosis, and genetic analysis information.

In the cosmetic raw material/formulation database 50, customized cosmetic raw materials and customized cosmetic formulation information are stored.

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

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, in the case of the smart skin diagnosis device 103, both the information receiving unit 100 and the input information processing unit 300 are included to obtain image information of the skin and simultaneously determine the skin type and skin concern based on these values. there is. On the other hand, in the case of a camera of a mobile phone, the acquired image information is transmitted to the input information processing unit 300 installed in the cloud computer together with the big data collection/management/analysis unit 200, and the cloud computer analyzes the acquired image information. You can also determine the type and skin concern. 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 by referring 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 personalized cosmetics to consumers according to the customized cosmetics recipe determined by the customized cosmetics prescription calculation unit 350 . The personalized cosmetics 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 accordingly. The smart device may be a device installed in a store or, in some cases, may be a home device individually installed in each consumer's home.

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

Skin wrinkles occur for various reasons, but it is known that they are mainly caused by aging, such as degeneration or atrophy of elastic fibers, connective tissue fibers, and muscle fibers in the dermis. At first, fine wrinkles form, but it is common that they gradually become large and deep wrinkles, and wrinkles caused by degeneration, degeneration, and atrophy of elastic fibers or connective tissue fibers in the dermis are usually not healed. Causes of wrinkles include endogenous aging accompanied by aging of other organs and extrinsic aging caused by external factors. The most important factors of extrinsic aging include sunlight, facial expressions, or skin dryness.

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 to contain 10% or more of water in the skin. By regulating the water content, the skin protects the dry, easily drying skin surface from damage. Skin moisturizing is influenced by internal factors such as moisture diffusion from the lower stratum corneum, natural moisturizing factors stratum corneum lipids and sebum present in the upper layer of the skin, and external factors such as relative humidity and cosmetics.

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

The skin is kept elastic and soft by the oil and moisture present in the stratum corneum. It has been known that in order to maintain the elasticity of the stratum corneum due to oil and moisture, it is essential to contain 10% or more of water and appropriate oil in the skin. By controlling the oil content (oil content), the skin protects the dry, easily drying skin surface from damage. The ability of the skin to retain oil and moisture depends on the lipid bilayer between the keratinocytes of the skin. Insufficient or excessive oil in the skin can cause skin troubles, such as dryness of the skin, inflammatory reactions, or the formation of acne or pimples. The amount of oil varies from person to person, and also varies greatly depending on the skin type, such as oily or dry skin.

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

In the Bauman type, which classifies skin types that are widely known in the past, only four items of wrinkles, pigment, sebum secretion, and sensitivity are judged, and skin types are also limited to 16 types, so classification is not sufficient to provide personalized cosmetics. .

Therefore, the personal skin type determination method according to the embodiments of the present invention is a combination of high (H), medium (M), and low (L) coded values for n (n≥3) or more skin phenotype items, respectively. After classifying into ³ⁿ skin types consisting of , the individual's skin type is diagnosed with one skin type among them. Even when the skin phenotype is n=3, which is the smallest, 27 (= 3*3*3) skin types that are more diverse than the Bauman type can be distinguished.

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

As a result of analyzing the skin measurement data of more than 1,000 people, wrinkles, pigmentation, and sensitivity are maintained, but in order to accurately reflect the skin characteristics of Asians, especially Koreans, more accurate individual skin type determination is possible if sebum secretion is divided into oil and moisture. Through this, it was found that more accurate customized cosmetics could be provided.

As shown in FIG. 3, the personal skin/genetic information database 30 includes big data consisting of more than 1000 genes (single nucleotide polymorphism (SNP)) data, more than 1000 skin measurement data, and more than 1000 questionnaire data. The result of analyzing is stored, and an algorithm for classifying 243 skin types based on five skin phenotypic 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 examples.

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

sample population

The sample population 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 were free of skin-related diseases and the mean age was 40.81 years. All participants gave written consent to the study. The 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 measurement devices have been 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 glabellars, and the maximum depth of wrinkles between the glabellar skin were measured using a Primos CR (Canfield Scientific, Parsippany, NJ, USA) device.

For skin moisturization (moisture), the moisture content of the glabella and right cheek skin was measured using a Corneometer®CM-825 (EnviroDerm Services Ltd., Hedworth, Grange Court, UK) device.

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

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

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

As a result, raw data of skin measurement values of the sample group could be obtained. Based on the skin measurement data, the variable names, measurement sites, and variable value descriptions for each skin measurement value were organized in a codebook.

Conversion of measurement indicators

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

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

Subsequently, a code was 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 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 value, with 4 to 6 points as low group (L), 7 to 9 points as medium group (M), and 10 to 12 points was classified as a high group (H) to quantify the measured values.

As a result of quantification, skin moisturizing was converted into a total of 2 to 6 points according to the measured value, with 2 points as the low group (L), 3 to 5 points as the medium group (M), and 6 points as the high group ( H) to quantify the measured value.

As a result of quantification, skin pigment was converted into a total of 4 to 12 points depending on the measured value, with 4 to 6 points as low group (L), 7 to 8 points as medium group (M), and 9 to 12 points was classified as a high group (H) to quantify the measured value.

As a result of quantification, skin oil was converted into a total of 2 to 6 points depending on the measured value, with 2 points as the low group (L), 3 to 4 points as the medium group (M), and 5 to 6 points as the high group. It was possible to quantify the measured value by classifying into group (H).

As a result of quantification, skin sensitivity was converted into a total of 1 or 2 points depending on the measured value, and 1 point was classified as a low group (L) and 2 points were classified as a medium group (M) to quantify the measured value.

Skin measurement values (first coding values) for each skin phenotype item can be derived by the method described above.

Derivation of coding value (second coding value) of genetic risk (single nucleotide polymorphism) measurement value for skin phenotypic items

Preparation for selection of genetic polymorphic markers

To select genetic polymorphic markers for skin phenotypes, we obtained samples with cotton swabs from the oral cavity of a sample population and detected DNA using ExgeneTM Tissue SV (GeneAll, Seoul, Korea). All DNA samples were amplified into 25-125 bp fragments, randomly fractionated, and the DNA was subjected to a custom assay based on the Asian Precision Medicine Research Array (Thermofisher Scientific, Waltham, Massachusetts, USA), Theragen Precision Medicine Research Array (Theragen PMRA). assay) were used to sequentially purify, resuspend and hybridize. After hybridization, the DMA was washed under stringent conditions to remove background to minimize noise. Next, Thaeragen PMRA analysis was used to analyze 820,000 SNPs as per guidelines. In order to reduce errors that may occur due to association, etc., a strict quality control method was applied to select SNPs to be used in the invention and control the data set. 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 above filtering, 560,795 polymorphic SNPs within chromosomes 1 to 22 were analyzed.

GWAS analysis of skin phenotype

Genome-wide association study (GWAS) analysis was performed on the sample group. As a result, a total of 23 SNPs were selected, and the SNPs are reference databases (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 skin phenotypes consistent with the minor allele frequency (MAF) of . Specific genetic polymorphisms are described below.

Genetic polymorphisms by skin phenotype

Skin wrinkle-associated polymorphism

Regarding 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 skin phenotypic changes for wrinkles, and five SNPs around ± 100 kb showed a significant correlation of P < 0.05 in GWAS analysis. showed up

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

As a result, rs117381658 is located downstream of the FCRL5 gene and could affect FCRL5 by forming a significant SNP cluster around chronic inflammatory conditions. In addition, expression of FCRL5 can affect the inflammatory response and NF-κ pathway, suggesting that it can disrupt tissue constancy modulators and affect skin aging.

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

Skin moisturizing (moisture) related gene polymorphism

Regarding skin moisturizing, single nucleotide polymorphisms of rs9873353, rs34567709, rs1362404, rs7855290, and rs143938096 were selected. Among them, rs9873353 showed a high correlation (β = -0.567, P = 1.47x10-6).

Internal factors related to skin moisture content include oil content of the stratum corneum, natural moisturizing factors, and moisture content due to external factors, and it is known that the above factors cause differences (Iizaka 2017).

Expression differences according to SNPs in the eQTL database show that only adipose tissue is provided for single-tissue eQTL, but multi-tissue eQTL shows that CEMIP2 expression differences also appear in skin tissues due to the genotype of rs7855290. These results suggest that the SNPs can moisturize and functionally control the intestinal wall.

Genetic polymorphisms related to skin pigmentation (lightening)

Regarding skin pigment, single nucleotide polymorphisms of rs74655330, rs34466224, rs11685554, rs4655497, rs59784607, and rs76548385 were selected. In particular, rs74655330 showed a highly significant (β = -1.092, P = 1.04x10-8) correlation in the GWAS data.

In Asians, an amino acid substitution (His615Arg) is highly associated with skin whitening and pigmentation changes, and the rs74655330 is one of the missense mutations (Ala481Thr), which is associated with pigmentation in East Asian populations.

Genetic polymorphisms related to skin oil content (oil content)

Regarding skin oil content, single nucleotide 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).

Potential functions of rs308971, rs9577919, rs8107564 and rs6490805 may change the skin. More specifically, rs9577919 was located in intron 1 of the GAS6 gene, a gene that mediates the inflammatory response and affects the development of psoriasis. Also, rs8107564 was located downstream of the INSR gene. In addition, analysis of rs308971 and rs6490805 suggested a correlation with skin oil content.

Skin sensitivity-related genetic polymorphisms

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

Derivation of genetic polymorphism conversion formula for each skin phenotype

Skin phenotype prediction genetic marker algorithm

Linear regression analysis was performed on the SNP biomarkers for each phenotype using the SPSS statistical program. The dependent variable of the linear correlation analysis is the phenotype of each skin, and the independent variables are the SNP marker of each phenotype, the age of the individual, and the gender. Through the linear regression analysis, weight constants (regression analysis beta values) for each phenotypic marker were derived.

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

SNPs 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)

SNPs 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 rs7855290 0.482 rs143938096 -0.521 rs12955989 0.353 FORMULA 3.968+(rs1362404)*(-0.318)+(rs7855290)*(0.482)+(rs143938096)*(-0.521)+(rs12955989)*(0.353)

SNPs B percent reference value melanin (a constant) 7.939 LOW 7.547 (30.03%) 0<=X<=7.547 rs74655330 -1.006 MEDIUM 8.189 (30.31%) 7.547<X<=8.189 rs34466224 0.552 HIGH 10.427 (39.66%) 8.189<X<=10.427 rs11685554 -0.419 rs4655497 0.419 rs59784607 -0.462 rs76548385 0.712 FORMULA 7.939+ (RS74655330)*(-1.006)+(RS34466224)*(0.552)+(RS11685554)*(RS4655497)*(RS4655497)*(RS59784607) 8385)*( 0.712)

SNPs 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 )

SNPs 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)

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

Prediction of skin wrinkles

That is, to predict skin wrinkles, a conversion value is derived using Equation 1 below, and when the conversion value is greater than 7.773 to 10.509, the risk of skin wrinkles is predicted to be high (H), and the conversion value is greater than 7.434 to 7.773 The risk of skin wrinkles is predicted to be medium (M) in the case of the following, and the risk of skin wrinkles is predicted to be low (L) when the conversion value is 0 or more to 7.434 or less.

[Equation 1]

conversion value

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

+(rs7042102)*(0.323)

In the above formula, (SNP) means 0 if the genotype of each SNP is WILDTYPE, 1 if it is Heterotype, and 2 if it is Mutant type.

Skin Moisture (Moisture) Prediction

To predict skin moisturizing, a conversion value is derived using Equation 2 below, and when the conversion value is greater than 4.056 to 5.562, the skin moisture content is high and the risk of skin moisturizing is predicted to be low, and the conversion 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 skin moisturizing risk is predicted to be medium, and when the conversion value is 0 or more to 3.65 or less, the skin moisture content is low and the skin moisturizing risk is predicted to be high.

[Equation 2]

conversion value

= 3.968+(rs1362404)*(-0.318)+(rs7855290)*(0.482)+(rs143938096)(0.521)

+(rs12955989)*(0.353)

In the above equation, (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.

Prediction of skin pigmentation (lightening)

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

[Equation 3]

conversion value

=7.939+(rs74655330)*(1.006)+(rs34466224)(0.552)+(rs11685554)*(0.419)+(rs4655497)*(0.419)+(rs59784607)*(-0.462)+(rs76548385)*(0.7 12)

In the above equation, (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 conversion value is more than 4.211 to 5.824, the risk of skin oil is predicted to be high, and when the conversion value is more than 3.873 to 4.211, the risk of oil skin is medium. It is predicted that the risk of skin oil is low when the conversion value is 0 or more and 3.873 or less.

[Equation 4]

conversion value

= 3.901+(rs308971)*(-0.28)+(rs151209785)(0.558)*(rs9577919)(0.5)

+(rs147804495)*(0.649)+(rs8107564)*(0.31)+(rs6490805)*(0.315)

skin sensitivity prediction

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

[Equation 5]

conversion value

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

Derivation of coding values (third coding values) of survey questionnaire measurement values for skin phenotype items

After big data analysis of the survey results of more than 1,000 people for 88 surveys, stepwise regression analysis was performed on the measurement values (dependent variable) and the entire survey (independent variable) for the 5 skin phenotype items, Only the minimum questionnaire that had an impact on was calculated. Table 6 shows the calculated questionnaire list.

Skin phenotype items Survey name survey Wrinkle Questionnaire Q47S1 T zone U zone trouble Q02S1 Skin Concerns: Hyperpigmentation Q14S1 spots, dots, freckles Q07S1 Pore condition Q13S1 bloodline 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 Feeling of applying cosmetics: feeling of being absorbed quickly Q18S1 pigmentation Q24S1 skin aging Q42S1 skin aggravation (pregnancy, childbirth) Pigmentation (whitening) questionnaire combination Q15S1 dullness Q17S1 sun sensitivity Q28S1 face volume Q07S1 condition of pores Q02S3 Skin trouble: Elasticity Q36S1 skin deterioration (diet) Q19S1 fine wrinkles Q31S2 Feeling of applying cosmetics: feeling of being absorbed quickly Q13S1 bloodline Q04S2 Make-up looks good Q03S1 Tightening after face wash Q23S1 skin marks Oil questionnaire combination Q26S1 Pore size and shape Q48S1 stress trouble Q18S1 pigmentation Q07S1 condition of pores Q05S1 dry T zone Q08S2 Excessive skin shine Q24S1 skin aging Q04S1 Makeup comes off easily Sensitive Questionnaire Q05S1 dry T zone Q42S1 skin aggravation (pregnancy, childbirth) Q33S1 greasy and sweet food

Linear regression analysis was performed for each questionnaire combination in a similar way to the genetic risk conversion value calculation formula. The weight constant (regression analysis beta value) for each question was derived through linear regression analysis. A positive beta value predicts a high risk, and a negative beta predicts a low risk.

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

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

[Equation 6]

conversion 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 (second coding value) for skin phenotype item, questionnaire questionnaire measurement value for skin phenotype item (third coding value) coding values). If the combined value is within the upper 40% range, the risk of occurrence of each skin phenotype is predicted to be high (H), if it is within the middle 30% range, the risk of occurrence is predicted to be medium (M), and if it is within the lower 30% range, the risk of occurrence is predicted is predicted to be low (L). As a result, for each of the five skin phenotype items, 243 (= 3*3*3*3*3) combinations of values coded as high (H), medium (M), and low (L) were obtained. Skin type is derived.

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

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

An individual consumer uses the smart skin diagnosis device 101 of the information receiving unit 100, the skin genetic test kit 103, and the questionnaire/questionnaire 105 to input each individual's skin measurement value, gene measurement value, and questionnaire questionnaire value. Then, 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 value) value) and calculates the range of the summed value, predicts that the risk of developing each skin phenotype is high (H) if it is within the first range (eg, top 40% range), and predicts that the second range (eg, If it is within the middle 30% range), the risk of occurrence is predicted to be medium (M), and if it is within the third range (eg, the lower 30% range), the risk of occurrence is predicted to be low (L). According to the values obtained in this way, a skin type coded with 5 digits can be obtained.

For example, among 243 codes, one skin type code as shown below may be given as a personal 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 exemplified, but when determining an individual skin type, five or more types of skin phenotypes to be measured, for example, sebum and pore skin phenotypes, can be further added to classify skin types. . Sebum and pores are also examples, and it goes without saying that various types of skin phenotypes can be added. When determining an individual's skin type, the skin measurement value, the single nucleotide polymorphism measurement value, and the questionnaire value are coded as one of three levels of high, medium, or low for each skin phenotype, and then, after summing these values, again high, Although the conversion to one of the three stages of medium and low has been described as an example, depending on the skin phenotype, it is further subdivided and coded as one of the five stages, and then the values are summed and converted to one of the five stages again. may be In this case, it may be divided into 5*5*5*5*5 skin types. Although five steps have been exemplified, it can be further subdivided if necessary.

In addition, in the case of a skin phenotype with many consumers' concerns, the coding step may be subdivided into 3 or more steps, and in the case of a skin phenotype with little consumer concern, the skin type may be determined by using only 3 coding steps.

As described above, by diversifying the types of skin phenotypes, classification of coding stages, and combinations thereof, it is possible to slightly increase consumer satisfaction.

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 a personalized cosmetic product according to the determined customized cosmetic recipe. In the study 400, personalized cosmetics are provided to the consumer. The customer's satisfaction with the customized cosmetics provided to the consumer in this way may be provided to the feedback information receiving unit 500.

The feedback information receiving unit 500 may receive feedback information on the customized cosmetics of the consumer immediately after the customized cosmetics are provided or after continuously using the customized cosmetics on the skin for a predetermined time.

The feedback information calculation unit 600 evaluates at least one of the skin type determined based on the feedback information input to the feedback information receiving unit 500, the efficacy of the customized cosmetics and the consumer's sensitivity to the customized cosmetics, and maintains the customized cosmetics, Decide on improvements or changes. This information is again interlocked with the customized cosmetics prescription calculation unit 350 and the customized cosmetics provider 400 so that the customized cosmetics reflecting the feedback information are delivered to the consumer again.

4 is a detailed schematic diagram of the feedback information receiving unit 500.

The feedback information receiving unit 500 may receive subjective feedback information and objective feedback information.

The subjective feedback information may include the consumer's subjective satisfaction with respect to the customized cosmetics provided to the consumer through the questionnaire/questionnaire 105 or the like or subjective evaluation data on efficacy.

The objective feedback information may include skin measurement data 530 or emotion index measurement data 550 .

After using the customized cosmetics for a certain period of time, skin measurement data 530 representing the efficacy of the customized cosmetics is collected using the smart skin diagnosis device 103, the self-diagnosis sensor or strip 532, or the microbiome 533.

The microbiome 533 obtains a sample containing a plurality of microorganisms from the skin or subcutaneous tissue of the consumer before using the customized cosmetic, and stores the first data characterizing the consumer's microbial community in advance, after using the customized cosmetic for a certain period of time. Again, a sample containing a plurality of microorganisms is obtained from the consumer's skin or subcutaneous tissue to obtain second data characterizing the consumer's microbial community, and then the efficacy of the customized cosmetic is obtained by comparing the first data and the second data. Skin measurement data 530 representing may be provided.

For example, unlike the input information processing unit 300, the feedback information receiver 500 does not include a kit for skin genetic testing, and may be composed of a smart skin diagnostic device 103 and a questionnaire/questionnaire 105. This is because the genetic information does not need to be measured again because already 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 the smart skin diagnostic device 103 and the questionnaire/paper questionnaire 105 is rewritten. When this value is input, the feedback information calculation unit (600 in FIG. 1) generates a 5-digit number according to the algorithm described above The skin type coded as can be recalculated. If the skin type is the same as before, the previously prescribed customized recipe can be applied as it is, but if the skin type has changed (HMLHM → HMHLL), customized cosmetics with a new recipe can be provided according to the changed skin type. That is, after receiving the consumer's feedback information, evaluating the accuracy of the previously determined skin type, satisfaction with the provided customized cosmetics, and/or efficacy of the provided customized cosmetics, maintenance, improvement, or change of the customized cosmetics is determined, and then the information is provided to the consumer. We can provide customized cosmetics that reflect the true needs of consumers.

Furthermore, the feedback information receiving unit 500 may receive the emotion index measurement value 550 . The emotional index may include a user's satisfaction with customized cosmetics, a feeling of use, texture, sensitivity, and the like. These emotional indices are inaccurate in the traditional subjective feedback method because of the wide variation among consumers.

Therefore, it is possible to more accurately measure consumers' emotions by applying various equipment capable of converting these emotional indicators into more objective indicators. When emotions (emotions) occur, physical changes can be induced by activating (or changing) brain cells or activating (or changing) the autonomic nervous system. More objective feedback information can be provided by applying emotion measurement technology that can measure this activation (or change) signal as an objective index.

Emotion measurement technologies include fNIRS (551), skin electrical response measurement device (552), facial expression measurement device (facial EMG) (553), pupil size measurement device (554), brain wave measurement device (EEG) (555), fMRI ( 556), an emotion expression display 557, and the like may be applied.

Functional Near Infrared Spectroscopy (fNIRS) (551) has low spatial resolution but relatively high temporal resolution, which is advantageous for measuring brain activation in real time. In addition, fNIRS does not require a cumbersome preparation process prior to measurement compared to EEG measurement, and has the advantage that it can be performed in an open space compared to fMRI (functional Magnetic Resonance Imaging), which measures in a closed space. Therefore, after allowing the consumer to use the customized cosmetics, the user's response to satisfaction, feeling of use, touch, and sensitivity can be measured in real time using the fNIRS 551.

The skin electrical response measuring device 552 is a GSR that measures, with a voltmeter, that psychological changes such as excitement and tension cause excitement in the autonomic nervous system, and fine sweat is secreted from the skin, causing minute changes in the electrical properties of the skin. (Galvanic Skin Response or EDR). For example, after having a consumer test a plurality of different formulations of customized cosmetics, it is possible to provide the most suitable customized cosmetics to consumers by measuring which prescription of customized cosmetics they feel most excited about.

The facial expression measurement device 553 is a device for measuring changes in facial expressions, which is a basic and direct method of expressing emotions that all humans have in common. When people feel uncomfortable, they slightly frown, tighten their chin, or close their mouths without realizing it, and when they feel good, a smile spreads around their eyes and mouth. Emotions trigger subtle changes in these facial muscles. A facial electromyography (Facial EMG) device may measure the activity of such facial muscles.

The pupil size measuring device 554 may measure a consumer's emotion index for customized cosmetics by measuring the change in pupil size according to emotion.

The electroencephalogram (EEG) 555 generates different types of brain waves according to a person's mental state (excitement, emotion, interest/interest, stress, etc.) Emotional indicators can be measured.

The fMRI 556 may measure the consumer's emotional index for the customized cosmetics by measuring cerebral blood flow after applying the customized cosmetics to the consumer.

The display 557 representing emotions is a device that allows the consumer to wear a smart wristband, applies customized cosmetics to the consumer, and digitizes the consumer's heartbeat, skin temperature, GSR measurement value, etc., and visually displays them. am. Through this, consumers' emotions can be converted into more visual indicators and confirmed.

The personalized provision system receives consumer feedback information on the provided personalized cosmetics and evaluates the accuracy of the primarily determined skin type and skin concern information and the satisfaction and efficacy of the provided customized cosmetics, and then maintains, improves, Alternatively, after determining the change, it can be provided to the consumer to provide customized cosmetics that reflect the true needs of the consumer. When the customized cosmetics providing system according to the present invention is used, various companies can be commercialized throughout the cosmetics value chain, and a new cosmetics industry ecosystem can be created. In other words, customized cosmetics are raw material companies, material companies, diagnostic device companies, genetic information analysis companies, microbiome information analysis companies, smart factory companies, AI-based big data information management companies, app development and providers, marketing sales and delivery It is possible to establish a variety of companies, such as companies.

Claims (20)

determining a personal skin type code;
determining a recipe of a customized cosmetic according to the personal skin type code by a customized cosmetic prescription calculation unit; and
After applying the customized cosmetics to the skin of the individual for a certain period of time, the objective satisfaction with the provided customized cosmetics or the efficacy of the provided customized cosmetics is evaluated using the emotional index measurement value of the individual for the customized cosmetics, A method for providing personalized cosmetics, which determines the maintenance, improvement, or change of the provided customized cosmetics and then provides them to the individual,
The step of determining the personal skin type code is
Step 1-1 of obtaining, by a data collection/management/analysis unit, skin measurement values, single nucleotide polymorphism measurement values, and questionnaire values for the first skin phenotype;
Step 1-2 of encoding the skin measurement value, the single nucleotide polymorphism measurement value, and the questionnaire questionnaire value by an input information processing unit into one of at least three values; and
After 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, the sum value is converted into one of at least three values, and the converted value is calculated as the first skin value. Including steps 1-3 of assigning a code value to a phenotype,
Steps 1-1 to 1-3 are repeatedly performed for each of the 2nd to nth ((n≥3) skin phenotypes, and the personal skin type code is any one of 3 ⁿ or more skin types and consists of n digits. or create
Step 2-1 of obtaining skin measurement values and questionnaire values for the first skin phenotype by the data collection/management/analysis unit;
a 2-2 step of coding the skin measurement value and the survey questionnaire value into one of at least three values by an input information processing unit; and
After adding the coding values of the skin measurement values and the coding values of the survey questionnaire values, the sum value is converted into one of at least three steps, and the converted value is given as a code value to the first skin phenotype. Including 3 steps,
Steps 2-1 to 2-3 are repeatedly performed for each of the 2nd to nth ((n≥3) skin phenotypes, and the personal skin type code is any one of 3 ⁿ or more skin types and consists of n digits. As a step to determine
In step 1-2 or step 2-2, coding the measured skin value as one of the minimum 3-level values depends on the size of the measured skin value for each of at least one skin measurement item corresponding to the skin phenotype. After assigning a score of any one of the three-level values and summing all the values for each measurement item, it is classified again as one of the minimum three-level values to determine the coding value of the skin measurement value of the skin phenotype,
In step 1-2 or step 2-2, coding the single nucleotide polymorphism measurement value as one of the at least three-level values detects the single nucleotide polymorphism selected for each skin phenotype, but the beta value of the regression analysis of the single nucleotide polymorphism ( If beta) is positive, the risk is high, and if it is negative, the risk is predicted to be low. A conversion value is derived according to the regression conversion equation, and the conversion value is converted into one of at least three values to obtain the coding value of the single nucleotide polymorphism measurement value. is to determine
In step 1-2 or step 2-2, coding the questionnaire value to one of at least three values has a high risk when the beta value (beta) of the regression analysis of the questionnaire selected for each skin phenotype is a positive number, and a negative number Determining a skin type code that determines the coding value of the questionnaire value by deriving a conversion value according to a regression conversion equation that predicts that the risk is low in the case of How to provide personal customized cosmetics. delete delete delete delete According to claim 1,
In step 1-3, 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 are added, or in step 2-3, the coding value of the skin measurement value and The summing of the coding values of the survey questionnaire values is a method of providing personalized cosmetics by varying the weight of each coding value and summing them. According to claim 6,
The weight of the coding value of the skin measurement value: The 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. According to claim 1,
Wherein n is 5 and the skin phenotype is wrinkles, moisturizing (moisture), pigmentation (whitening), oil and sensitivity. According to claim 1,
The emotional index measurement value is a value measured from fNIRS, skin electrical response measuring device, facial expression measuring device, pupil size measuring device, brain wave measuring device, fMRI or emotional expression display Method for providing personalized cosmetics. delete A 1-1 step of obtaining a skin measurement value, a single nucleotide polymorphism measurement value, and a questionnaire survey value for a first skin phenotype from an individual;
1-2 steps of coding the skin measurement value, the single nucleotide polymorphism measurement value, and the questionnaire value into one of at least three values; and
After 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, the sum value is converted into one of at least three values, and the converted value is calculated as the first skin value. Including steps 1-3 of assigning a code value to a phenotype,
Steps 1-1 to 1-3 are repeatedly performed for each of the 2nd to nth ((n≥3) skin phenotypes, and the personal skin type code is any one of 3 ⁿ or more skin types and consists of n digits. or create
Step 2-1 of obtaining skin measurement values and questionnaire values for the first skin phenotype;
A 2-2 step of coding the skin measurement value and the questionnaire value into one of at least three levels; and
After adding the coding values of the skin measurement values and the coding values of the survey questionnaire values, the sum value is converted into one of at least three steps, and the converted value is given as a code value to the first skin phenotype. Including 3 steps,
For each of the 2nd to nth ((n≥3) skin phenotypes, steps 2-1 to 2-3 are repeatedly performed, and any one of 3 ⁿ or more skin types, consisting of n digits, is a personal skin type A big data collection/management/analysis unit and input information processing unit including an algorithm that generates codes,
In step 1-2 or step 2-2, coding the measured skin value as one of the minimum 3-level values depends on the size of the measured skin value for each of at least one skin measurement item corresponding to the skin phenotype. After assigning a score of any one of the three-level values and summing all the values for each measurement item, it is classified again as one of the minimum three-level values to determine the coding value of the skin measurement value of the skin phenotype,
In step 1-2 or step 2-2, coding the single nucleotide polymorphism measurement value as one of the at least three-level values detects the single nucleotide polymorphism selected for each skin phenotype, but the beta value of the regression analysis of the single nucleotide polymorphism ( If beta) is positive, the risk is high, and if it is negative, the risk is predicted to be low. A conversion value is derived according to the regression conversion equation, and the conversion value is converted into one of at least three values to obtain the coding value of the single nucleotide polymorphism measurement value. is to determine
In step 1-2 or step 2-2, coding the survey questionnaire value to one of at least three-step values has a high risk when the beta value (beta) of the regression analysis of the questionnaire selected for each skin phenotype is a positive number, and a negative number If , a big data collection/management/analysis unit for determining the coding value of the questionnaire value by deriving a conversion value according to a regression conversion equation that predicts that the risk is low and converting the conversion value back into one of at least three steps, and input information processing unit;
a customized cosmetics prescription calculation unit determining a recipe for customized cosmetics according to the personal skin type code;
a personalized cosmetics providing unit providing personalized cosmetics according to the determined recipe;
a feedback information receiving unit inputting feedback information about the customized cosmetics including a measurement value of the emotional index of the individual with respect to the customized cosmetics after applying the provided customized cosmetics to the skin of the individual for a predetermined period of time; and
and a feedback information calculation unit configured to determine maintenance, improvement, or change of the customized cosmetics based on the feedback information input to the feedback information receiving unit. delete delete delete delete delete According to claim 11,
The weight of the coding value of the skin measurement value: The 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. According to claim 11,
Wherein n is 5 and the skin phenotypes are wrinkles, moisturizing (moisture), pigmentation (whitening), oil and sensitive personal customized cosmetics providing system. According to claim 11,
The emotional index measurement value is data measured from fNIRS, skin electrical response measuring device, facial expression measuring device, pupil size measuring device, brain wave measuring device, fMRI or emotional expression display Personalized cosmetics providing system. delete

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