Skin condition estimation method, information processing device, and program

Abstract

To provide a skin condition estimation method.SOLUTION: A skin condition estimation method comprises: a step for acquiring psychosomatic data related to the body and mind of a subject of skin condition estimation; and a step for estimating the subject's skin condition using a skin model in which the correlation between the psychosomatic data and skin conditions is described. The psychosomatic data includes at least one of a liver function-related variable, a muscle / skeleton-related variable, a heart rate / blood pressure-related variable, a blood oxidation-related variable, a blood count-related variable, a lipid metabolism-related variable, an autonomic function-related variable, a personal attribute-related variable, a renal function-related variable, a body composition-related variable, a glucose tolerance-related variable, an electrolyte-related variable, a cognitive function-related variable, an immunometabolism-related variable, and a depression / fatigue / drowsiness-related variable.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present invention relates to a skin condition estimation method, an information processing apparatus, and a program.

Background Art

[0002] Methods for estimating skin conditions based on various human body information are known.

[0003] For example, International Publication No. WO2022-030300 discloses a technique for estimating future skin conditions based on hormonal balance.

[0004] For example, Japanese Unexamined Patent Application Publication No. 2020-014710 discloses a technique for estimating skin conditions (at least one of pores, wrinkles, pigmentation, and skin color) using muscle mass (muscle weight per body weight, or muscle volume per volume, muscle mass related to the trunk (chest and abdomen), lower body, or the total of the trunk and lower body) as an index.

Summary of the Invention

Problems to be Solved by the Invention

[0005] Various factors are related to and affect skin conditions. However, since the hormonal balance of International Publication No. WO2022-030300 and the muscle mass of Japanese Unexamined Patent Application Publication No. 2020-014710 are only part of the human body indices, estimations based on hormonal balance or muscle mass are not necessarily appropriate.

[0006] An object of the present invention is to more appropriately estimate skin conditions and the factors that define the skin conditions.

Means for Solving the Problems

[0007] One aspect of the present invention includes a step of acquiring psychosomatic data related to the body and mind of the subject for whom the skin condition is to be estimated, A step of estimating the skin condition of the subject is provided by using a skin model in which the correlation between the psychosomatic data and the skin condition is described. The psychosomatic data includes at least one of a liver function-related variable, a muscle and bone-related variable, a heart rate and blood pressure-related variable, a blood oxidation-related variable, a blood sugar-related variable, a lipid metabolism-related variable, an autonomic nerve function-related variable, a personal attribute-related variable, a kidney function-related variable, a body composition-related variable, a glucose tolerance-related variable, an electrolyte-related variable, a cognitive function-related variable, an immune metabolism-related variable, and a depression, fatigue, and sleepiness-related variable. It is a skin condition estimation method.

Brief Description of Drawings

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Mode for Carrying Out the Invention

[0009] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. In the drawings for explaining the embodiment, the same reference numerals are generally given to the same components, and the repeated description thereof will be omitted.

[0010] (1) Configuration of the Information Processing System The configuration of the information processing system will be described. FIG. 1 is a block diagram showing the configuration of the information processing system of the present embodiment. FIG. 2 is a functional block diagram of the information processing system of FIG. 1.

[0011] As shown in FIG. 1, the information processing system 1 includes a client device 10 and a server 30. The client device 10 and the server 30 are connected via a network (for example, the Internet or an intranet) NW.

[0012] The client device 10 is a computer (an example of an "information processing device") that transmits a request to the server 30. The client device 10 is, for example, a smartphone, a tablet terminal, or a personal computer.

[0013] The server 30 is a computer (an example of an "information processing device") that provides a response corresponding to a request transmitted from the client device 10 to the client device 10. The server 30 is, for example, a web server.

[0014] (1-1) Configuration of the client device The configuration of the client device 10 will be described.

[0015] As shown in FIG. 2, the client device 10 includes a storage device 11, a processor 12, an input / output interface 13, and a communication interface 14.

[0016] The storage device 11 is configured to store programs and data. The storage device 11 is, for example, a combination of a ROM (Read Only Memory), a RAM (Random Access Memory), and a storage (e.g., a flash memory or a hard disk).

[0017] The program includes, for example, the following programs. · Program of the OS (Operating System) · Program of an application (e.g., a web browser) that executes information processing

[0018] The data includes, for example, the following data. · Database referred to in information processing · Data obtained by executing information processing (i.e., the execution result of information processing)

[0019] The processor 12 is configured to realize the functions of the client device 10 by starting the programs stored in the storage device 11. The processor 12 is, for example, a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof.

[0020] The input / output interface 13 is configured to obtain a user instruction from an input device connected to the client device 10 and output information to an output device connected to the client device 10. The input device is, for example, a keyboard, a pointing device, a touch panel, or a combination thereof. The output device is, for example, a display.

[0021] The communication interface 14 is configured to control communication between the client device 10 and the server 30.

[0022] (1-2) Configuration of the server The configuration of the server 30 will be described.

[0023] As shown in FIG. 2, the server 30 includes a storage device 31, a processor 32, an input / output interface 33, and a communication interface 34.

[0024] The storage device 31 is configured to store programs and data. The storage device 31 is, for example, a combination of a ROM, a RAM, and a storage (e.g., a flash memory or a hard disk).

[0025] The program includes, for example, the following programs. · Program of the OS · Program of an application that executes information processing

[0026] The data includes, for example, the following data. · Database referred to in information processing · Execution result of information processing

[0027] The processor 32 is configured to realize the functions of the server 30 by starting the program stored in the storage device 31. The processor 32 is, for example, a CPU, an ASIC, an FPGA, or a combination thereof.

[0028] The input / output interface 33 is configured to obtain a user's instruction from an input device connected to the server 30 and output information to an output device connected to the server 30. The input device is, for example, a keyboard, a pointing device, a touch panel, or a combination thereof. The output device is, for example, a display.

[0029] The communication interface 34 is configured to control communication between the server 30 and the client device 10.

[0030] (2) Outline of the Embodiment The outline of this embodiment will be described. FIG. 3 is an explanatory diagram of the outline of this embodiment.

[0031] As shown in FIG. 3, the skin model M of this embodiment describes the correlation between psychosomatic data X and skin condition Y. When the psychosomatic data X of the subject is given to the skin model M, the skin condition Y corresponding to the psychosomatic data X is estimated. The estimated skin condition Y is presented to the subject.

[0032] (3) Database The data structure of the database of this embodiment will be described.

[0033] (3-1) Subject Database The data structure of the subject database of this embodiment will be described. FIG. 4 is a diagram showing the data structure of the subject database of this embodiment.

[0034] As shown in FIG. 4, subject information is stored in the subject database. The subject information is information about the subject. The subject database includes a "Subject ID" field, a "Subject Name" field, and a "Subject Attribute" field. Each field is associated with each other.

[0035] The "Subject ID" field stores subject identification information. The subject identification information is information for identifying the subject.

[0036] The "Subject Name" field stores subject name information. The subject name information is information regarding the subject name.

[0037] The "Subject Attribute" field stores subject attribute information. The subject attribute information is information regarding the attributes of the subject (an example of "mind and body data"). The "Subject Attribute" field includes a "Gender" field and a "Date of Birth" field.

[0038] The "Gender" field stores gender information. The gender information is information regarding the gender of the subject.

[0039] The "Date of Birth" field stores date of birth information. The date of birth information is information regarding the date of birth of the subject. Based on the combination of the date of birth information and the execution date of the information processing, the age of the subject at the execution date of the information processing can be specified.

[0040] (3-2) Estimation Log Database The data structure of the estimation log database of this embodiment will be described. FIG. 5 is a diagram showing the data structure of the estimation log database of this embodiment.

[0041] As shown in FIG. 5, the estimation log database stores estimation log information. The estimation log information is information regarding the history of the estimation results of the skin condition of the subject. The estimation log database includes an "Estimation Log ID" field, an "Estimation Date and Time" field, an "Estimation Result" field, and an "Advice" field. Each field is associated with each other. The estimated log database is associated with the subject identification information.

[0042] The "estimated log ID" field stores the estimated log identification information. The estimated log identification information is the information that identifies the history of the estimation results. The "estimated date and time" field stores the estimated date and time information. The estimated date and time information is the information regarding the date and time when the estimation was executed.

[0043] The "estimation result" field stores the estimation result information. The estimation result information is the information regarding the estimation result of the skin condition. The "estimation result" field includes a plurality of skin index fields. The skin index fields include, for example, the following. · "Moisture" field: Stores the estimated result of the moisture of the skin. · "Barrier strength" field: Stores the estimated result of the barrier strength of the skin. · "Brightness" field: Stores the estimated result of the brightness of the skin. · "Yellowishness" field: Stores the estimated result of the yellowishness of the skin. · "Firmness" field: Stores the estimated result of the firmness of the skin. · "Melanin" field (not shown): Stores the estimated result of the melanin of the skin. · "Hemoglobin" field (not shown): Stores the estimated result of the hemoglobin of the skin. · "Vividness" field (not shown): Stores the estimated result of the vividness of the skin. · "Redness" field (not shown): Stores the estimated result of the redness of the skin. · "Texture" field (not shown): Stores the estimated result of the texture of the skin. · "Wrinkles" field (not shown): Stores the estimated result of the wrinkles of the skin. · "Spots" field (not shown): Stores the estimated result of the spots of the skin.

[0044] Advice information is stored in the "Advice" field. The advice information is advice provided to the target person and is information regarding advice determined according to the estimation result of the skin condition.

[0045] (4) Skin model The skin model of this embodiment will be described.

[0046] (4-1) Configuration of the skin model The configuration of the skin model will be described. FIG. 6 is a schematic diagram showing the configuration of the skin model of this embodiment.

[0047] As shown in FIG. 6, the skin model Mi is generated for each index of the skin condition (hereinafter referred to as "skin index"). The skin model Mi is stored in the storage device 31. The skin index is classified into, for example, the following skin index groups. · Index group related to skin texture (hereinafter referred to as "texture index group") · Index group related to skin structure (hereinafter referred to as "structure index group") · Index group related to skin color (hereinafter referred to as "color index group")

[0048] The texture index group includes, for example, the following indexes. · Index related to skin moisture (hereinafter referred to as "moisture-related index") · Index related to skin surface morphology (hereinafter referred to as "surface morphology-related index")

[0049] The moisture-related index includes, for example, the following indexes. · Skin moisture · Skin barrier force

[0050] The surface morphology-related index includes, for example, skin texture.

[0051] The structure index group includes, for example, the following indexes. · Index related to skin elasticity (hereinafter referred to as "elasticity-related index") · Index related to skin wrinkles (hereinafter referred to as "wrinkle-related index")

[0052] Elasticity-related indicators include, for example, the firmness of the skin.

[0053] Wrinkle-related indicators include, for example, the wrinkles of the skin.

[0054] The color indicator group includes, for example, the following indicators. · Indicators related to skin color (hereinafter referred to as "skin color-related indicators") · Indicators related to spots (hereinafter referred to as "spot-related indicators")

[0055] Skin color-related indicators include, for example, the following indicators. · Melanin in the skin · Hemoglobin in the skin · Brightness of the skin · Vividness of the skin · Redness of the skin · Yellowness of the skin

[0056] Spot-related indicators include, for example, spots on the skin.

[0057] As shown in FIG. 6, in the skin model Mi, an explanatory variable Xij, an influence degree Kij, and an objective variable Yi are defined. The argument i (i is a natural number) is an identifier of a skin index. The argument j (j is a natural number) is an identifier of a variable related to psychosomatic data.

[0058] The explanatory variable Xij is a variable related to psychosomatic data.

[0059] The influence degree Kij is a parameter of a variable related to psychosomatic data. The influence degree Kij indicates the influence degree of psychosomatic data on the skin state.

[0060] The objective variable Yi is the skin state. The skin state is expressed, for example, in at least one of the following forms. · Numerical value · Level · Classification

[0061] (4-2) Psychosomatic data Describe the psychosomatic data of this embodiment.

[0062] Psychosomatic data is data related to the human body and mind. Variables related to psychosomatic data are classified into any of the following related variables. A related variable is a set of variables related to psychosomatic data. · Liver function related variables · Muscle and bone related variables · Heart rate and blood pressure related variables · Blood oxidation related variables · Blood count related variables · Lipid metabolism related variables · Autonomic nerve function related variables · Personal attribute related variables · Kidney function related variables · Body composition related variables · Glucose tolerance related variables · Electrolyte related variables · Cognitive function related variables · Immune metabolism related variables · Depression, fatigue, sleepiness related variables

[0063] Liver function related variables are variables related to liver function. Liver function related variables preferably include · ALT (GPT) in blood · AST (GOT) in blood · ALP in blood · γ-GTP in blood · Indirect bilirubin in blood · Total bilirubin in blood · Direct bilirubin in blood and so on.

[0064] Muscle and bone related variables are variables related to muscles and bones. Muscle and bone related variables preferably include · Grip strength · Bone density and so on.

[0065] Heart rate and blood pressure related variables are variables related to heart rate and blood pressure. Heart rate and blood pressure related variables preferably include · Diastolic blood pressure · Systolic blood pressure · Heart Rate (HR) including the like.

[0066] Blood oxidation-related variables are variables related to blood oxidation. Blood oxidation-related variables are preferably · Biological Anti-oxidant Potential (BAP) in blood · Reactive oxygen metabolites (d-ROMs) in blood · Oxidation Stress Index (OSI) including the like.

[0067] Blood count-related variables are variables related to blood count. Blood count-related variables are preferably · Hematocrit in blood · Hemoglobin in blood · Platelets in blood · Red blood cells in blood · White blood cells in blood · Mean corpuscular hemoglobin concentration in blood · Mean corpuscular hemoglobin in blood · Mean corpuscular volume in blood including the like.

[0068] Lipid metabolism-related variables are variables related to lipid metabolism. Lipid metabolism-related variables are preferably · HDL cholesterol in blood · LDL cholesterol in blood · Ratio of LDL cholesterol in blood to HDL cholesterol in blood · Total cholesterol in blood · Triglycerides in blood including the like.

[0069] Autonomic nerve function-related variables are variables related to autonomic nerve function. Autonomic nerve function-related variables are preferably · Heart rate variability high frequency power spectrum component of the autonomic nerve index HF · Autonomic nerve index LF (low-frequency component of the power spectrum in the time series variation of the heartbeat interval) · Ratio of the autonomic nerve index HF to the autonomic nerve index LF etc. are included.

[0070] The person attribute-related variable is a variable related to the person attribute. The person attribute-related variable is preferably · Exercise habit (as an example, average number of steps per day) · Sleep duration value · Fatigue duration · Average working hours · Medical history · Smoking experience · Regular medications · Marriage history etc. are included.

[0071] The renal function-related variable is a variable related to renal function. The renal function-related variable is preferably · Glomerular filtration value (eGFR: estimated glomerular filtration rate) · Blood urea nitrogen etc. are included.

[0072] The body composition-related variable is a variable related to body composition. The body composition-related variable is preferably · BMI (Body Mass Index) · Muscle mass · Height · Body fat percentage · Body weight · Skeletal muscle mass etc. are included.

[0073] The glucose tolerance-related variable is a variable related to glucose tolerance. The glucose tolerance-related variable is preferably · Glycated hemoglobin A1c (HbA1c) in blood · Blood glucose etc. are included.

[0074] The electrolyte-related variable is a variable related to electrolytes. The electrolyte-related variable is preferably · Potassium in blood · Chloride in blood · Sodium in blood · Ratio of sodium in blood to potassium in blood etc. are included.

[0075] Cognitive function-related variables are variables related to cognitive function and are variables obtained from the task performance of cognitive function tests. Cognitive function-related variables are preferably · Reaction time of cognitive tasks · Correct answer rate of cognitive tasks · Incorrect answer rate of cognitive tasks · Number of tasks completed in cognitive tasks etc. are included.

[0076] Immune metabolism-related variables are variables related to immune metabolism. Immune metabolism-related variables are preferably · High-sensitivity CRP (C-reactive protein) in blood · Amylase in blood · Albumin in blood · Creatine kinase in blood · Cortisol in blood · Total protein in blood · Lactate dehydrogenase in blood · Uric acid in blood · IgE in blood · IgG in blood · IgA in blood etc. are included.

[0077] Depression·fatigue·drowsiness-related variables are variables related to depression·fatigue·drowsiness and are variables obtained by questionnaire surveys. Depression·fatigue·drowsiness-related variables are preferably · Fatigue evaluation scale · Depression·anxiety evaluation scale · Motivation evaluation scale · Drowsiness evaluation scale · Life Performance Status evaluation scale etc. are included.

[0078] (4-3) Method for generating muscle model The method for generating the skin model of this embodiment will be described.

[0079] In this embodiment, using the combinations of the psychosomatic data and skin conditions of a plurality of observers as teacher data, for example, the skin model is generated using any of the following methods. As an example, simple regression analysis, multiple regression analysis, Elastic NET, PLS (Partial Least Squares), neural network, DNN (Deep Neural Network).

[0080] (4-4) Specific example of the skin model The specific example of the skin model of this embodiment will be described.

[0081] (4-4-1) Specific example of the skin model of the texture index group The specific example of the skin model of the texture index group of this embodiment will be described.

[0082] As an example of the skin model of the moisture-related index of the texture index group, the skin model M1 for estimating the skin index "skin moisture" Y1 includes at least one of the following variables. · Fatigue evaluation scale · Sleepiness evaluation scale · Depression / anxiety evaluation scale · Body weight · BMI: Body Mass Index · Body fat percentage · Exercise habit · Duration of fatigue · Uric acid in blood · Cortisol in blood · Reaction time of cognitive task · Potassium in blood · Sodium in blood · Heart rate (HR: Heart rate) · LDL cholesterol in blood · Triglyceride in blood · Red blood cell in blood · White blood cell in blood · Platelet in blood · Oxidative stress value in blood (d-ROMs: Reactive oxygen metabolites) · Antioxidant capacity value in blood (BAP: Biological Anti-oxidant Potential) · Oxidative stress degree (OSI: Oxidation Stress Index) · Total bilirubin in blood · Indirect bilirubin in blood · Hematocrit in blood · Grip strength · Immunoglobulin A (IgA) in blood · Glycated hemoglobin A1c (HbA1c) in blood

[0083] As an example, the skin index "skin moisture" Y1 is affected by each variable as follows. · Hematocrit in blood shows a positive correlation with the skin index "skin moisture" Y1 (that is, the higher the hematocrit in blood, the better the skin index "skin moisture" Y1). · Fatigue evaluation scale shows a negative correlation with the skin index "skin moisture" Y1 (that is, the higher the fatigue, the worse the skin index "skin moisture" Y1). · Depression and anxiety evaluation scale shows a negative correlation with the skin index "skin moisture" Y1 (that is, the stronger the depression and anxiety, the worse the skin index "skin moisture" Y1).

[0084] The skin model M2 for estimating the skin index "skin barrier strength" Y2 of the moisture-related index in the texture index group includes at least one of the following variables. Here, the barrier strength means an index representing the strength of the skin's moisturizing function, such as the reciprocal of the transepidermal water loss. · Fatigue evaluation scale · Sleepiness evaluation scale · Depression and anxiety evaluation scale · Muscle mass · Exercise habit · Fatigue persistence period · Uric acid in blood · Reaction time of cognitive tasks · Potassium in blood · Hemoglobin A1c (HbA1c) in blood · Blood glucose in blood · Systolic blood pressure · White blood cells in blood · Platelets in blood · Oxidative stress value in blood (d-ROMs: Reactive oxygen metabolites) · Antioxidant capacity value in blood (BAP: Biological Anti-oxidant Potential) · Degree of oxidative stress (OSI: Oxidation Stress Index) · γ-GTP in blood · Direct bilirubin in blood · Chloride in blood · ALT (GPT) in blood

[0085] As an example, the skin index "skin barrier function" Y2 is affected by each variable as follows. · The fatigue evaluation scale shows a negative correlation with the skin index "skin barrier function" Y2 (that is, the stronger the fatigue, the weaker the "skin barrier function" Y2). · The depression / anxiety evaluation scale shows a negative correlation with the skin index "skin barrier function" Y2 (that is, the stronger the depression / anxiety, the weaker the "skin barrier function" Y2).

[0086] As an example of a skin model for the surface morphology-related index of the texture index group, the skin model M3 for estimating the skin index "skin texture" Y3 includes at least one of the following variables, for example. · Body weight · Lactate dehydrogenase in blood · Amylase in blood · Autonomic nerve index LF · Mean corpuscular volume in blood · Platelets in blood · ALT (GPT) in blood · ALP in blood · Ratio of sodium to potassium in blood · IgE in blood · Ratio of LDL cholesterol to HDL cholesterol in blood · Skeletal muscle mass

[0087] (4-4-2) Specific examples of skin models in the structural index group Specific examples of the skin models in the structural index group of this embodiment will be described.

[0088] As an example of the skin model of the elasticity-related index in the structural index group, the skin model M4 for estimating the skin index "skin firmness" Y4 includes at least one of the following variables, for example. · Fatigue evaluation scale · Sleepiness evaluation scale · Depression / anxiety evaluation scale · Body weight · Muscle mass · Sleep duration numerical value · Exercise habit · Lactate dehydrogenase in blood · Glycated hemoglobin A1c (HbA1c) in blood · Blood glucose · Systolic blood pressure · Estimated glomerular filtration rate (eGFR) · Ratio of the autonomic nerve index HF to the autonomic nerve index LF · Hemoglobin in blood · Hematocrit in blood · White blood cells in blood · Platelets in blood · Oxidative stress value in blood (d-ROMs: Reactive oxygen metabolites) · AST (GOT) in blood · Direct bilirubin in blood · Grip strength · ALP in blood · Oxidative stress value in blood (d-ROMs: Reactive oxygen metabolites) · Body fat percentage

[0089] As an example, the skin index "skin firmness" Y4 is affected by each variable as follows. · AST in the blood shows a positive correlation with the skin index "skin firmness" Y4 (that is, the higher the AST in the blood, the better the skin index "skin firmness" Y4). · The fatigue evaluation scale shows a positive correlation with the skin index "skin firmness" Y4 (that is, the higher the fatigue, the better the skin index "skin firmness" Y4). · The depression / anxiety evaluation scale shows a positive correlation with the skin index "skin firmness" Y4 (that is, the higher the depression / anxiety, the better the skin index "skin firmness" Y4).

[0090] As an example of a skin model of the structure-related index in the structural index group, the skin model M5 for estimating the skin index "skin wrinkles" Y5 includes at least one of the following variables, for example. · Body weight · Systolic blood pressure · Average working hours

[0091] (4-4-3) Specific examples of skin models in the color index group Specific examples of the skin models in the color index group of this embodiment will be described.

[0092] As an example of a skin model of the skin color-related index in the color index group, the skin model M6 for estimating the skin index "skin melanin" Y6 includes at least one of the following variables, for example. · Fatigue evaluation scale · Sleepiness evaluation scale · Depression / anxiety evaluation scale · Body weight · Exercise habit · Uric acid in the blood · Reaction time of cognitive tasks · Potassium in the blood · Glycated hemoglobin A1c (HbA1c) in the blood · Blood glucose · Autonomic nerve index HF · Hemoglobin in the blood · Hematocrit in the blood · Mean corpuscular hemoglobin in the blood · Mean corpuscular hemoglobin concentration in the blood · White blood cells in the blood · Platelets in the blood · d-ROMs (Reactive oxygen metabolites) value in blood · AST (GOT) in blood · γ-GTP in blood · ALT (GPT) in blood · Total bilirubin in blood · Direct bilirubin in blood · Ratio of HF to LF, an autonomic nerve index · Red blood cells in blood

[0093] As an example of a skin model of skin color-related indices in the color index group, the skin model M7 for estimating the skin index "hemoglobin in skin" Y7 includes at least one of the following variables, for example. · Fatigue evaluation scale · Sleepiness evaluation scale · Depression / anxiety evaluation scale · BMI: Body Mass Index · Body fat percentage · Exercise habit · Duration of fatigue · Reaction time for cognitive tasks · Potassium in blood · Blood glucose in blood · Systolic blood pressure · Total cholesterol in blood · Triglyceride in blood · Hematocrit in blood · Mean corpuscular volume in blood · White blood cells in blood · Platelets in blood · d-ROMs (Reactive oxygen metabolites) value in blood · Biological Anti-oxidant Potential (BAP) value in blood · Oxidation Stress Index (OSI) · γ-GTP in blood · Direct bilirubin in blood

[0094] As an example of a skin model for skin color-related indicators of a color index group, a skin model M8 for estimating the skin index "skin brightness" Y8 includes at least one of the following variables, for example. · Motivation evaluation scale · Body weight · Muscle mass · BMI: Body Mass Index · Body fat percentage · Sleep duration numerical value · Average working hours · Exercise habit · Lactate dehydrogenase in blood · Uric acid in blood · High-sensitivity CRP (C-reactive protein) in blood · Reaction time of cognitive task · Potassium in blood · Systolic blood pressure · Diastolic blood pressure · Estimated glomerular filtration rate (eGFR) · Blood urea nitrogen · Ratio of autonomic nerve index HF to autonomic nerve index LF · HDL cholesterol in blood · LDL cholesterol in blood · Total cholesterol in blood · Triglyceride in blood · Mean corpuscular hemoglobin concentration in blood · White blood cells in blood · Platelets in blood · Oxidative stress value in blood (d-ROMs: Reactive oxygen metabolites) · Antioxidant capacity value in blood (BAP: Biological Anti-oxidant Potential) · Oxidation stress degree (OSI: Oxidation Stress Index) · Grip strength

[0095] As an example, the skin index "skin brightness" Y8 is affected by each variable as follows. · The hematocrit in the blood shows a negative correlation with the skin index "skin brightness" Y8 (that is, the higher the hematocrit in the blood, the worse the skin index "skin brightness" Y8).

[0096] As an example of the skin model of the skin color-related index in the color index group, the skin model M9 for estimating the skin index "skin vividness" Y9 includes at least one of the following variables, for example. · Fatigue evaluation scale · Sleepiness evaluation scale · Depression / anxiety evaluation scale · Exercise habit · Duration of fatigue · Uric acid in the blood · Reaction time of cognitive tasks · Potassium in the blood · Glycated hemoglobin A1c (HbA1c) in the blood · Blood glucose · Hemoglobin in the blood · Hematocrit in the blood · White blood cells in the blood · Platelets in the blood · Oxidative stress value in the blood (d-ROMs: Reactive oxygen metabolites) · AST (GOT) in the blood · γ-GTP in the blood · ALT (GPT) in the blood · Direct bilirubin in the blood

[0097] As an example of the skin model of the skin color-related index in the color index group, the skin model M10 for estimating the skin index "skin redness" Y10 includes at least one of the following variables, for example. · Fatigue evaluation scale · Sleepiness evaluation scale · Depression / anxiety evaluation scale · Body fat percentage · Exercise habit · Duration of fatigue · Uric acid in the blood · Reaction time of cognitive tasks · Potassium in the blood · Glycated hemoglobin A1c (HbA") in the blood · Blood glucose in blood · Systolic blood pressure · Hematocrit in blood · White blood cells in blood · Platelets in blood · Oxidative stress value in blood (d-ROMs: Reactive oxygen metabolites) · Antioxidant capacity value in blood (BAP: Biological Anti-oxidant Potential) · Degree of oxidative stress (OSI: Oxidation Stress Index) · γ-GTP in blood · ALT (GPT) in blood · Direct bilirubin in blood · AST (GOT) in blood

[0098] As an example, the skin index "skin redness" Y10 is affected by each variable as follows. · ALT in blood shows a positive correlation with the skin index "skin redness" Y10 (that is, the higher the ALT in blood, the better the skin index "skin redness" Y10). · The fatigue evaluation scale shows a positive correlation with the skin index "skin redness" Y10 (that is, the stronger the fatigue, the better the skin index "skin redness" Y10). · The depression / anxiety evaluation scale shows a positive correlation with the skin index "skin redness" Y10 (that is, the stronger the depression / anxiety, the better the skin index "skin redness" Y10).

[0099] As an example of the skin model of the skin color-related index in the color index group, the skin model M11 for estimating the skin index "skin yellowness" Y11 includes at least one of the following variables, for example. · Body weight · BMI: Body Mass Index · Body fat percentage · Exercise habit · Grip strength · Total protein in blood · Uric acid in blood · High-sensitivity CRP (C-reactive protein) in blood · Albumin in blood · Reaction time for cognitive tasks · Potassium in blood · Glycated hemoglobin A1c (HbA1c) in blood · Systolic blood pressure · Diastolic blood pressure · Heart rate variability index HF · LDL cholesterol in blood · Total cholesterol in blood · Triglycerides in blood · Hemoglobin in blood · Mean corpuscular hemoglobin in blood · Mean corpuscular hemoglobin concentration in blood · Red blood cells in blood · White blood cells in blood · Platelets in blood · Oxidative stress value in blood (d-ROMs: Reactive oxygen metabolites) · Oxidative stress degree (OSI: Oxidation Stress Index) · AST (GOT) in blood · γ-GTP in blood · ALT (GPT) in blood · Total bilirubin in blood · Direct bilirubin in blood · Indirect bilirubin in blood · Hematocrit in blood

[0100] As an example, the skin index "skin yellowness" Y11 is affected by each variable as follows. · ALT in blood shows a positive correlation with the skin index "skin yellowness" Y11 (that is, the higher the ALT in blood, the better the skin index "skin yellowness" Y11). · AST in blood shows a positive correlation with the skin index "skin yellowness" Y11 (that is, the higher the AST in blood, the better the skin index "skin yellowness" Y11). · Hematocrit in blood shows a negative correlation with the skin index "skin yellowness" Y11 (that is, the higher the hematocrit in blood, the worse the skin index "skin yellowness" Y11).

[0101] As an example of a skin model of skin color-related indicators in a color index group, the skin model M12 for estimating the skin index "skin spots" Y12 includes, for example, at least one of the following variables. · Sleepiness evaluation scale · BMI: Body Mass Index · Creatine kinase in blood · High-sensitivity CRP (C-reactive protein) in blood · Amylase in blood · Reaction time of cognitive task · Systolic blood pressure · HDL cholesterol in blood · Red blood cells in blood · IgG in blood

[0102] For example, in the case of water-related indicators (as an example, the skin indicators "skin moisture" Y1 and "skin barrier strength" Y2), the following can be cited as highly influential variables.

[0103] In the skin index "skin moisture" Y1, BMI: Body Mass Index, antioxidant power value in blood, body fat percentage, hematocrit in blood, sleepiness evaluation scale, IgA in blood, potassium in blood, hemoglobin A1c (HbA1c) in blood, fatigue evaluation scale, and cortisol in blood have a high degree of influence. The skin model M1 preferably includes any one or more of BMI: Body Mass Index, antioxidant power value in blood, body fat percentage, hematocrit in blood, sleepiness evaluation scale, IgA in blood, potassium in blood, hemoglobin A1c (HbA1c) in blood, fatigue evaluation scale, and cortisol in blood.

[0104] In the skin index "skin barrier strength" Y2, chloride in blood, ALT (GPT) in blood, hemoglobin A1c (HbA1c) in blood, oxidative stress value in blood (d-ROMs: Reactive oxygen metabolites), γ-GTP in blood, platelets in blood, blood glucose, and sleepiness evaluation scale have a high degree of influence. Muscle model M2 preferably includes any one or more of blood chlorine, blood ALT (GPT), blood hemoglobin A1c (HbA1c), blood oxidative stress value (d-ROMs: Reactive oxygen metabolites), blood γ-GTP, blood platelets, blood glucose, and sleepiness evaluation scale.

[0105] For example, in surface morphology-related indicators (e.g., muscle indicator "skin texture" Y3), the following can be cited as highly influential variables.

[0106] In the muscle indicator "skin texture" Y3, blood IgE, the ratio of blood sodium to blood potassium, autonomic nerve index LF, and blood ALP have a high degree of influence. Muscle model M3 preferably includes any one or more of blood IgE, the ratio of blood sodium to blood potassium, autonomic nerve index LF, and blood ALP.

[0107] For example, in elasticity-related indicators (e.g., muscle indicator "skin firmness" Y4), the following can be cited as highly influential variables.

[0108] In the muscle indicator "skin firmness" Y4, blood oxidative stress value (d-ROMs: Reactive oxygen metabolites), blood direct bilirubin, blood AST (GOT), blood ALP, blood lactate dehydrogenase, blood oxidative stress value (d-ROMs: Reactive oxygen metabolites), body fat percentage, the ratio of autonomic nerve index HF to LF, blood glucose, blood white blood cells, and grip strength have a high degree of influence. Muscle model M4 preferably includes any one or more of blood oxidative stress value (d-ROMs: Reactive oxygen metabolites), blood direct bilirubin, blood AST (GOT), blood ALP, blood lactate dehydrogenase, blood oxidative stress value (d-ROMs: Reactive oxygen metabolites), body fat percentage, the ratio of autonomic nerve index HF to LF, blood glucose, blood white blood cells, and grip strength.

[0109] For example, in skin color-related indicators (as an example, skin indicators "skin melanin" Y6, "skin brightness" Y8, and "skin redness" Y10), the following can be cited as highly influential variables.

[0110] In the skin indicator "skin melanin" Y6, the ratios of the autonomic nerve indicators HF and LF, the depression / anxiety evaluation scale, the sleepiness evaluation scale, blood uric acid, blood glucose, total bilirubin in blood, ALT (GPT) in blood, white blood cells in blood, red blood cells in blood, and the oxidative stress value in blood (d-ROMs: Reactive oxygen metabolites) have a high degree of influence. The skin model M4 preferably includes any one or more of the ratios of the autonomic nerve indicators HF and LF, the depression / anxiety evaluation scale, the sleepiness evaluation scale, blood uric acid, blood glucose, total bilirubin in blood, ALT (GPT) in blood, white blood cells in blood, red blood cells in blood, and the oxidative stress value in blood (d-ROMs: Reactive oxygen metabolites).

[0111] In the skin indicator "skin brightness" Y8, BMI: Body Mass Index, white blood cells in blood, blood urea nitrogen, blood uric acid, body fat percentage, platelets in blood, systolic blood pressure, triglycerides in blood, potassium in blood, and the motivation evaluation scale have a high degree of influence. The model M8 preferably includes any one or more of BMI: Body Mass Index, white blood cells in blood, blood urea nitrogen, blood uric acid, body fat percentage, platelets in blood, systolic blood pressure, triglycerides in blood, potassium in blood, and the motivation evaluation scale.

[0112] In the skin indicator "skin redness" Y10, body fat percentage, fatigue evaluation scale, white blood cells in blood, depression / anxiety evaluation scale, AST (GOT) in blood, hematocrit in blood, platelets in blood, oxidative stress value in blood (d-ROMs: Reactive oxygen metabolites), blood uric acid, and sleepiness evaluation scale have a high degree of influence.

[0113] In addition, it is preferable that each skin model uses an index that can be acquired non-invasively and an index that can be acquired through health checkups or the like. The index that can be acquired non-invasively is not particularly limited as long as it can generally be acquired non-invasively. For example, it includes BMI (Body Mass Index), sleep evaluation scale, fatigue evaluation scale, grip strength, body fat percentage, depression / anxiety evaluation scale, motivation evaluation scale, systolic blood pressure, and the like. The index that can be acquired through health checkups or the like is not particularly limited as long as it can be acquired through general health checkups or medical checkups. For example, it includes the biological antioxidant potential (BAP) value in the blood, hematocrit in the blood, reactive oxygen metabolites (d-ROMs) value in the blood, potassium in the blood, hemoglobin in the blood, cortisol in the blood, IgE in the blood, autonomic nerve index LF, and the ratio of autonomic nerve index HF to LF.

[0114] (5) Information processing The information processing of this embodiment will be described. FIG. 7 is a sequence diagram of the information processing of this embodiment. FIG. 8 is a diagram showing an example of a screen displayed in the information processing of FIG. 7.

[0115] The trigger for the processing in FIG. 7 is that the user logs in to the skin condition estimation service provided by the server 30 using the user identification information.

[0116] As shown in FIG. 7, the client device 10 executes an estimation request (S1110). Specifically, the processor 12 displays the screen P1110 (FIG. 8) on the display. The screen P1110 includes an operation object B1110 and a field object F1110. The operation object B1110 is an object that receives a user instruction for determining an input to the field object F1110. The field object F1110 is an object that receives an input of psychosomatic data.

[0117] When the user inputs psychosomatic data into the field object F1110 and operates the operation object B1110, the processor 12 transmits estimation request data to the server 30. The estimation request data includes, for example, the following information. · Psychosomatic data input into the field object F1110

[0118] After step S1110, the server 30 executes the estimation of skin condition (S1130). Specifically, the processor 32 estimates the skin condition Y corresponding to the psychosomatic data X by giving the psychosomatic data X included in the estimation request data to the skin model M.

[0119] More specifically, the processor 32 estimates the skin condition Y1 of the skin index "skin moisture" by giving the psychosomatic data X included in the estimation request data to the skin model M1. The processor 32 estimates the skin condition Y2 of the skin index "barrier strength" by giving the psychosomatic data X included in the estimation request data to the skin model M2. The processor 32 estimates the skin condition Y3 of the skin index "skin texture" by giving the psychosomatic data X included in the estimation request data to the skin model M3. The processor 32 estimates the skin condition Y4 of the skin index "skin firmness" by giving the psychosomatic data X included in the estimation request data to the skin model M4. The processor 32 estimates the skin condition Y5 of the skin index "skin wrinkles" by giving the psychosomatic data X included in the estimation request data to the skin model M5. The processor 32 estimates the skin condition Y6 of the skin index "skin melanin" by giving the psychosomatic data X included in the estimation request data to the skin model M6. The processor 32 estimates the skin condition Y7 of the skin index "skin hemoglobin" by giving the psychosomatic data X included in the estimation request data to the skin model M7. The processor 32 estimates the skin state Y8 of the skin index "skin brightness" by giving the psychosomatic data X included in the estimated request data to the skin model M8. The processor 32 estimates the skin state Y9 of the skin index "skin vividness" by giving the psychosomatic data X included in the estimated request data to the skin model M9. The processor 32 estimates the skin state Y10 of the skin index "skin redness" by giving the psychosomatic data X included in the estimated request data to the skin model M10. The processor 32 estimates the skin state Y11 of the skin index "skin yellowness" by giving the psychosomatic data X included in the estimated request data to the skin model M11. The processor 32 estimates the skin state Y12 of the skin index "skin spots" by giving the psychosomatic data X included in the estimated request data to the skin model M12.

[0120] After step S1130, the server 30 executes advice generation (S1131). Specifically, an advice model is stored in the storage device 31. The advice model describes the correlation between the skin state and the advice. The processor 32 generates advice according to the estimation result of the skin state by inputting the estimation result of the skin state obtained in step S1130 into the advice model.

[0121] After step S1131, the server 30 executes database update (S1132). Specifically, the processor 32 adds a new record to the estimated log database (Figure 5) associated with the subject identification information used for login. The following information is stored in each field of the new record. · "Estimated log ID" field: New estimated log identification information · "Estimation date and time" field: Information regarding the execution date and time of step S1130 · "Estimation result" field: Estimation result information obtained in step S1130 · "Advice" field: Advice information obtained in step S1131

[0122] After step S1132, the server 30 executes an estimated response (S1133). Specifically, the processor 32 transmits estimated response data to the client device 10. The estimated response data includes, for example, the following information. · Estimation result of the skin condition obtained in step S1130 · Advice obtained in step S1131

[0123] After step S1133, the client device 10 executes output of the estimation result (S1111). Specifically, the processor 12 displays the screen P1111 (FIG. 8) on the display. The screen P1111 includes display objects A1111a to A1111b.

[0124] In the display object A1111a, the estimation result of the skin condition included in the estimated response data is displayed.

[0125] In the display object A1111b, the advice included in the estimated response data is displayed.

[0126] (6) Parentheses in this embodiment According to this embodiment, by giving the psychosomatic data of the subject to the model in which the correlation between the psychosomatic data and the skin condition is described, the skin condition corresponding to the psychosomatic data of the subject is estimated. Thereby, the skin condition and the factors that define the skin condition can be estimated more appropriately.

[0127] According to this embodiment, for each combination of psychosomatic data and skin index, the degree of influence between the psychosomatic data and the skin index may be defined. Thereby, the skin condition and the factors that define the skin condition can be estimated more appropriately.

[0128] According to this embodiment, advice according to the estimated skin condition may be output. Thereby, useful information for improving the skin condition can be provided to the subject.

[0129] (7) Variation A variation of this embodiment will be described.

[0130] (7-1) Variation 1 Variation 1 will be described. Variation 1 is an example using a skin model with reduced influence of human attributes.

[0131] For example, it can be said that the skin on the cheeks of men has a weaker barrier function than that of women, contains more oil than women, and has less elasticity than women. Thus, it may be affected by the difference in the secretion amount of male hormones and female hormones.

[0132] For example, with aging, the skin may form wrinkles, its elasticity may decrease, and its moisture content may decrease. This is because aging cells formed inside the skin with aging reduce the functions of surrounding cells, causing aging changes in the skin.

[0133] As described above, there is a correlation between human attributes and skin condition. If human attributes are overly considered, in cases where the skin condition is determined by factors other than attributes, the estimation result of the skin condition will be overly influenced by the attributes. Therefore, correction considering the attributes of the subject may be necessary.

[0134] (7-1-1) Outline of Variation 1 The outline of Variation 1 will be described. FIG. 9 is an explanatory diagram of the outline of Variation 1.

[0135] As shown in FIG. 9, the skin model N of Variation 1 is one in which the influence degree of the skin model M of this embodiment is adjusted in consideration of human attributes. When the physical and mental data X (including subject attributes) of the subject is given to the skin model N, the skin state Y corresponding to the physical and mental data X is estimated so that the influence of the subject attributes does not appear excessively (for example, at least one of the influence of deterioration due to aging and the influence of deterioration due to gender is excluded). The estimated skin state Y is presented to the subject.

[0136] (7-1-2) Information processing of Modification 1 The information processing of Modification 1 will be described. FIG. 10 is a sequence diagram of the information processing of Modification 1.

[0137] The trigger for the process in FIG. 10 is the same as that in FIG. 7.

[0138] As shown in FIG. 10, the client device 10 executes an estimation request (S1110) in the same manner as in FIG. 7.

[0139] After step S1110, the server 30 executes identification of subject attributes (S2130).

[0140] The first example of step S2130 is that the subject attribute is age. Specifically, the processor 32 refers to the subject database (FIG. 4) to identify the date of birth information associated with the subject identification information. The processor 32 calculates the age of the subject at the time of execution of step S2130 based on the date of birth information and the execution date and time of step S2130.

[0141] The second example of step S2130 is that the subject attribute is gender. Specifically, the processor 32 refers to the subject database (FIG. 4) to identify the gender information associated with the subject identification information.

[0142] After step S2130, the server 30 executes estimation of the skin state (S2131). Specifically, the processor 32 provides the mental and physical data X included in the estimation request data and the subject attributes (at least one of age and gender) obtained in step S2130 to the skin model N, and estimates the skin condition Y according to the combination of the mental and physical data X and the subject attributes.

[0143] After step S2131, the server 30 generates advice (S1131), updates the database (S1132), and estimates a response (S1133), similar to FIG.

[0144] After step S1133, the client device 10 outputs the estimation result (S1111) in the same manner as in FIG.

[0145] (7-1-3) Summary of Variation 1 According to the first modification, the skin condition can be estimated without excessively being affected by the subject's attributes (for example, at least one of age and gender). This makes it possible to present an estimation result of the skin condition according to the subject's age. As a result, the skin condition and the factors that determine the skin condition can be more appropriately estimated.

[0146] (7-2) Variation 2 A description will be given of Modification 2. Modification 2 is an example in which a skin model in which the influence of the subject's body composition is reduced is used.

[0147] For example, obesity is known to increase water loss, delay collagen production, increase sebum secretion, and restrict lymphatic flow. Thus, there is a correlation between body composition and skin condition. Therefore, it is necessary to consider the influence of body composition, such as physique, fat mass, and BMI.

[0148] (7-2-1) Overview of Modification 2 An outline of Modification 2 will be described below. FIG. 11 is an explanatory diagram of an outline of Modification 2.

[0149] As shown in FIG. 11, in the skin model N of the second modification, the influence of the skin model M of the present embodiment is adjusted in consideration of the body composition. When the psychosomatic data X (including the body composition of the subject) of the subject is given to the skin model N, a skin state Y corresponding to the psychosomatic data X is estimated so that the influence of the subject's body composition does not appear excessively (for example, the influence of deterioration due to obesity is excluded).

[0150] The body composition is, for example, at least one of the following. · Muscle mass per unit body weight · Body fat mass · Muscle mass · BMI · Body fat mass by part (as an example, arm, leg, and trunk) · Muscle mass by part (as an example, arm and leg) · Water content by part (arm, leg, and trunk) · Skeletal muscle mass · Basal metabolic rate · Fat-free mass

[0151] When the psychosomatic data X (including the body composition of the subject) of the subject is given to the skin model N, a skin state Y corresponding to the psychosomatic data X is estimated. The estimated skin state Y is presented to the subject.

[0152] (7-2-2) Information processing of Modification 2[[ID=3…]] The information processing of Modification 2 will be described. FIG. 12 is a sequence diagram of the information processing of Modification 2.

[0153] The trigger for the process in FIG. 12 is the same as that in FIG. 7.

[0154] As shown in FIG. 12, the client device 10 executes an estimation request (S1110) in the same manner as in FIG. 7.

[0155] After step S1110, the server 30 executes a body composition calculation (S3130). Specifically, the processor 32 calculates the body composition based on the psychosomatic data included in the estimation request data.

[0156] After step S3130, the server 30 executes a skin state estimation (S3131). Specifically, the processor 32 estimates the skin state Y corresponding to the psychosomatic data X by providing the skin model N with the psychosomatic data X included in the estimated request data.

[0157] After step S3131, the server 30 executes advice generation (S1131), database update (S1132), and estimated response (S1133) in the same manner as in FIG. 7.

[0158] After step S1133, the client device 10 executes output of the estimation result (S1111) in the same manner as in FIG. 7.

[0159] (7-2-3) Parentheses in Modification Example 2 According to Modification Example 2, the skin state can be estimated so that the influence on the body composition of the subject does not appear excessively. Thereby, the skin state and the factors defining the skin state can be made more appropriate.

[0160] (7-3) Modification Example 3 Modification Example 3 will be described. Modification Example 3 is an example in which two types of skin models are selectively used according to the invasiveness of the psychosomatic data.

[0161] (7-3-1) Outline of Modification Example 3 The outline of Modification Example 3 will be described. FIG. 13 is an explanatory diagram of the outline of Modification Example 3.

[0162] As shown in FIG. 13, the skin model of Modification Example 3 includes a first skin model MA and a second skin model MB. The first skin model MA describes the correlation between the non-invasive data XA that can be obtained by non-invasive examination and the first skin state YA. The second skin model MB describes the correlation between the invasive data XB and the non-invasive data XA that can be obtained by invasive examination and the second skin state YB. That is, the second skin model MB is the same as the skin model M of the present embodiment. When the psychosomatic data X of the subject is given to the first skin model MA, the first skin state YA corresponding to the non-invasive data XA among the psychosomatic data X is estimated. The estimated first skin state YA is presented to the subject. When the psychosomatic data X of the subject is given to the second skin model MB, the second skin state YB corresponding to the psychosomatic data X (that is, the non-invasive data XA and the invasive data XB) is estimated. The estimated second skin state YB is presented to the subject.

[0163] The parameters given to the second skin model MB to estimate the second skin state YB are more than the parameters given to the first skin model MA to estimate the first skin state YA. Therefore, the estimation accuracy of the second skin model MB is higher than that of the first skin model MA.

[0164] (7-3-2) Skin model of Modification 3 The skin model of Modification 3 will be described.

[0165] (7-3-2- 1) Configuration of the first skin model of Modification 3 The configuration of the first skin model of Modification 3 will be described. Fig. 14 is a schematic diagram showing the configuration of the first skin model of Modification 3.

[0166] As shown in Fig. 14, in the first skin model MAi, an explanatory variable Xiit, an influence degree Kiit, and an objective variable YAi are defined. The argument t (t is a natural number) is an identifier of psychosomatic data (hereinafter referred to as "non-invasive data") collected by an examination without invasion (hereinafter referred to as "non-invasive examination").

[0167] The explanatory variable Xiit is a variable related to non-invasive data.

[0168] The influence degree Kiit indicates the influence degree of non-invasive data on the skin state.

[0169] The objective variable YAi is the skin state.

[0170] (7-3-2-2) Configuration of the second skin model of Modification 3 The configuration of the second muscle model in Modification 3 will be described. FIG. 15 is a schematic diagram showing the configuration of the second muscle model in Modification 3.

[0171] As shown in FIG. 15, in the second muscle model MBi, an explanatory variable Xiit, an influence degree Kiit, an objective variable YBi, an explanatory variable Xinu (u is a natural number), an influence degree Kiiu, and an objective variable YBi are defined.

[0172] As shown in FIG. 15, in the second muscle model MBi, an explanatory variable Xinu, an influence degree Kinu, and an objective variable YBi are defined. The argument u (u is a natural number) is an identifier of psychosomatic data (hereinafter referred to as "invasive data") collected by an examination involving an invasion (hereinafter referred to as an "invasive examination").

[0173] The explanatory variable Xinu is a variable related to the invasive data.

[0174] The influence degree Kinu indicates the influence degree that the invasive data has on the muscle state.

[0175] The objective variable YBi is the muscle state.

[0176] (7-3-3) Information Processing in Modification 3 The information processing in Modification 3 will be described. FIG. 16 is a sequence diagram of the information processing in Modification 3. FIG. 17 is a diagram showing an example of a screen displayed in the information processing of FIG. 16.

[0177] The trigger for the process in FIG. 16 is the same as that in FIG. 7.

[0178] As shown in FIG. 16, the client device 10 executes an estimation request (S6110). Specifically, the processor 12 displays the screen P6110 (FIG. 17) on the display. The screen P6110 includes operation objects B6110a to B6110b and a field object F1110. The operation object B6110a is an object that determines the input to the field object F1110 and receives a user instruction to request estimation in the first estimation mode. The first estimation mode is a mode that performs estimation using the first muscle model. The operation object B6110a is assigned the mode identification information "MODE1" that identifies the first mode. The operation object B6110b is an object that determines the input to the field object F1110 and receives a user instruction to request estimation in the second estimation mode. The second estimation mode is a mode that performs estimation using the second muscle model. The operation object B6110b is assigned the mode identification information "MODE2" that identifies the second mode. The field object F1110 is the same as that in FIG. 8.

[0179] When the user inputs psychosomatic data to the field object F1110 and operates the operation object B6110a or B6110b, the processor 12 transmits estimation request data to the server 30. The estimation request data includes, for example, the following information. · The psychosomatic data input to the field object F1110 · The mode identification information "MODE1" or "MODE2" corresponding to the operated operation object B6110a or B6110b

[0180] After step S6110, the server 30 executes the selection of the muscle model (S6130). Specifically, the storage device 31 stores the first muscle model MAi and the second muscle model MBi. The first muscle model MAi corresponds to the mode identification information "MODE1". The second muscle model MBi corresponds to the mode identification information "MODE2". The processor 32 selects a muscle model (the first muscle model MAi or the second muscle model MBi) corresponding to the mode identification information included in the estimation request data.

[0181] After step S6130, the server 30 executes an estimation of the skin condition (S6131). Specifically, when the first skin model MAi is selected in step S6130, the processor 32 gives the skin and mental data X included in the estimation request data and assigned the label "non-invasive" (hereinafter referred to as "non-invasive data") to the first skin model MAi selected in step S6130, thereby estimating the first skin condition YAi corresponding to the non-invasive data. When the second skin model MBi is selected in step S6130, the processor 32 gives the skin and mental data X (that is, the non-invasive data assigned the label "non-invasive" and the invasive data assigned the label "invasive") included in the estimation request data to the second skin model MBi selected in step S6130, thereby estimating the second skin condition YBi corresponding to the skin and mental data X.

[0182] After step S6131, the server 30 executes generation of advice (S1131), update of the database (S1132), and an estimation response (S1133) in the same manner as in FIG. 7.

[0183] After step S1133, the client device 10 executes output of the estimation result (S1111) in the same manner as in FIG. 7.

[0184] (7-3-4) Parentheses in Modification Example 3 According to Modification Example 3, the determination by the first skin model MAi and the determination by the second skin model MBi are switched according to the instruction of the subject. Since the first skin model MAi is configured to estimate the skin condition using the non-invasive data of the subject, the burden on the subject for estimating the skin condition is smaller than that of the second skin model MBi. Since the second skin model MBi is configured to estimate the skin condition using the non-invasive data and the invasive data of the subject, the estimation accuracy is higher than that of the first skin model MAi. Thereby, it is possible to provide an estimation result of the skin condition using an appropriate skin model according to the needs of the subject.

[0185] (7-4) Modification Example 4 Describe Modification Example 4. Modification Example 4 is an example using a skin model with a reduced influence of the subject's habits.

[0186] For example, people with morning or night activity time zones have differences in the peak of body temperature rise, cortisol in the endocrine system, and the secretion of melatonin, respectively. Also, people with morning or night time zones have differences in the rhythm of the daily variation of blood factors, respectively.

[0187] For example, due to ultraviolet irradiation, decomposition of collagen and secretion of melanin pigment occur, and elasticity and skin color change.

[0188] Thus, there is a correlation between a person's habits and skin condition. If a person's habits are overly considered, in cases where the skin condition is determined by factors other than habits, the estimated result of the skin condition will be overly influenced by habits.

[0189] (7-4-1) Outline of Modification Example 4 Describe the outline of Modification Example 4. FIG. 18 is an explanatory diagram of the outline of Modification Example 4.

[0190] As shown in FIG. 18, the skin model N of Modification Example 4 is obtained by adjusting the influence degree of the skin model M of the present embodiment in consideration of a person's habits. When the psychosomatic data X of the subject and the subject's habits are given to the skin model N, a skin condition Y corresponding to the psychosomatic data X is estimated so that the influence of the subject's habits does not appear overly. The estimated skin condition Y is presented to the subject.

[0191] For example, the subject's habits include at least one of the following. · Sleep habit (as an example, morning type or night type) · Lifestyle habit (as an example, tendency of ultraviolet exposure)

[0192] (7-4-2) Information Processing of Modification Example 4 Describe the information processing of Modification Example 4. FIG. 19 is a sequence diagram of the information processing of Modification Example 4.

[0193] The trigger for the process in FIG. 19 is the same as that in FIG. 7.

[0194] As shown in FIG. 19, the client device 10 executes an estimated request (S1110) in the same manner as in FIG. 7.

[0195] After step S1110, the server 30 executes identification of the subject's habits (S7130). Specifically, in the storage device 31, subject habit information (not shown) is stored in association with subject identification information. The subject habit information is information regarding the subject's habits. The subject habit information is obtained, for example, by at least one of the following methods. · Questionnaire to the subject · Wearable device worn by the subject · Mobile phone (e.g., smartphone) carried by the subject

[0196] The processor 32 identifies the subject habit information associated with the subject identification information. The processor 32 identifies the subject's habits at the time of execution of step S7130 based on the subject habit information and the execution date and time of step S2130.

[0197] After step S7130, the server 30 executes estimation of skin condition (S7131). Specifically, the processor 32 gives the mental and physical data X included in the estimated request data and the subject habit information obtained in step S7130 to the skin model N, thereby estimating the skin condition Y corresponding to the combination of the mental and physical data X and the subject habit information.

[0198] After step S7131, the server 30 executes generation of advice (S1131), update of the database (S1132), and estimated response (S1133) in the same manner as in FIG. 7.

[0199] After step S1133, the client device 10 executes output of the estimated result (S1111) in the same manner as in FIG. 7.

[0200] (7-4-3) Variation Example 4's parenthesis According to Variation Example 4, the skin condition can be estimated so that the influence of the subject's habits does not appear excessively. As a result, the skin condition and the factors that define the skin condition can be estimated more appropriately.

[0201] (7-5) Variation Example 5 Variation Example 5 will be described. Variation Example 5 is an example using a skin model with a reduced influence degree at the estimation time.

[0202] For example, the skin is greatly affected by ultraviolet rays, humidity, and temperature, and the moisture content, skin color, and sebum secretion amount change with the seasons. Also, since the body's metabolism and vitamin production are also affected by temperature and ultraviolet rays, the skin condition varies with the seasons.

[0203] Thus, there is a correlation between the time and the skin condition. If the time is excessively considered, in the case where the skin condition is determined by factors other than the time originally, the estimation result of the skin condition will be excessively affected by the time.

[0204] (7-5-1) Outline of Variation Example 5 The outline of Variation Example 5 will be described. FIG. 20 is an explanatory diagram of the outline of Variation Example 5.

[0205] As shown in FIG. 20, the skin model N of Variation Example 5 is obtained by adjusting the influence degree of the skin model M of the present embodiment in consideration of the estimation time. The estimation time is the time specified from the execution date of the skin condition estimation. When the psychosomatic data X of the subject is given to the skin model N, the skin condition Y corresponding to the psychosomatic data X is estimated so that the influence of the estimation time does not appear excessively. The estimated skin condition Y is presented to the subject.

[0206] For example, the estimation time includes at least one of the following. · The season estimated from the execution date of the skin condition estimation (for example, spring, summer, autumn, winter) · The year, month, day, or day on which the skin condition estimation is executed

[0207] (7-5-2) Information processing of Modification Example 5 The information processing of Modification Example 5 will be described. FIG. 21 is a sequence diagram of the information processing of Modification Example 5.

[0208] The trigger for the process in FIG. 21 is the same as that in FIG. 7.

[0209] As shown in FIG. 21, the client device 10 executes an estimation request (S1110) in the same manner as in FIG. 7.

[0210] After step S1110, the server 30 executes identification of the estimated time (S8130). Specifically, in the storage device 31, a rule for identifying the estimated time (hereinafter referred to as the "estimated time identification rule") is stored. The estimated time identification rule describes the correspondence between the execution date and time of step S8130 and the estimated time. The processor 32 gives the execution date and time of step S8130 (that is, the timing for estimating the skin condition) to the estimated time identification rule, thereby identifying the estimated time corresponding to the execution date and time of step S8130.

[0211] After step S8130, the server 30 executes estimation of the skin condition (S8131). Specifically, the processor 32 gives the psychosomatic data X included in the estimation request data and the estimated time obtained in step S8130 to the skin model N, thereby estimating the skin condition Y corresponding to the combination of the psychosomatic data X and the estimated time.

[0212] After step S8131, the server 30 executes generation of advice (S1131), update of the database (S1132), and estimation response (S1133) in the same manner as in FIG. 7.

[0213] After step S1133, the client device 10 executes output of the estimation result (S1111) in the same manner as in FIG. 7.

[0214] (7-5-3) Variation Example 5's parentheses According to Variation Example 5, the skin condition can be estimated so that the influence of the estimation timing does not appear excessively. Thereby, the skin condition and the factors defining the skin condition can be estimated more appropriately.

[0215] (8) Other Variation Examples Other variation examples will be described.

[0216] The storage device 11 may be connected to the client device 10 via the network NW. The storage device 31 may be connected to the server 30 via the network NW.

[0217] Each step of the above information processing can be executed by either the client device 10 or the server 30. For example, when the client device 10 can execute all the steps of the above information processing, the client device 10 functions as a stand-alone information processing device without sending a request to the server 30.

[0218] In this embodiment, as an example of output, an example of displaying a screen on a display is shown, but the scope of this embodiment is not limited to this. This embodiment is also applicable to the following examples. · Audio output of the estimation result · Print output of the estimation result · Optical output of the estimation result

[0219] Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited to the above embodiments. Also, the above embodiments can be variously improved and modified without departing from the gist of the present invention. Further, the above embodiments and variation examples can be combined.

Explanation of Reference Numerals

[0220] 1: Information Processing System 10: Client Device 11: Storage Device 12: Processor 13: Input / Output Interface 14: Communication Interface 30: Server 31: Memory Device 32: Processor 33: Input / Output Interface 34: Communication Interface

Claims

**Claim 1** Comprising the step of obtaining psychosomatic data related to the body and mind of the subject for estimating skin condition, Comprising the step of estimating the skin condition of the subject using a skin model in which the correlation between the psychosomatic data and the skin condition is described, The step of estimating includes estimating the skin condition by providing psychosomatic data including the attributes of the subject to a skin model with reduced influence of any one or more of human attributes, human body composition, human habits, and time, The psychosomatic data includes at least one of liver function-related variables, muscle and bone-related variables, heart rate / blood pressure-related variables, blood oxidation-related variables, blood count-related variables, lipid metabolism-related variables, autonomic nerve function-related variables, personal attribute-related variables, kidney function-related variables, body composition-related variables, glucose tolerance-related variables, electrolyte-related variables, cognitive function-related variables, immune metabolism-related variables, and depression / fatigue / sleepiness-related variables, A method for estimating skin condition. **Claim 2** The attribute of the subject is age, The method for estimating skin condition according to Claim 1. **Claim 3** The attribute of the subject is gender, The method for estimating skin condition according to Claim 1. **Claim 4** Comprising the step of obtaining psychosomatic data related to the body and mind of the subject for estimating skin condition, Comprising the step of estimating the skin condition of the subject using a skin model in which the correlation between the psychosomatic data and the skin condition is described, The skin model includes a first skin model generated using only non-invasive data measured by a non-invasive test without invasion, and a second skin model generated using invasive data measured by an invasive test with invasion and the non-invasive data, The psychosomatic data includes at least one of liver function-related variables, muscle and bone-related variables, heart rate / blood pressure-related variables, blood oxidation-related variables, blood count-related variables, lipid metabolism-related variables, autonomic nerve function-related variables, personal attribute-related variables, kidney function-related variables, body composition-related variables, glucose tolerance-related variables, electrolyte-related variables, cognitive function-related variables, immune metabolism-related variables, and depression / fatigue / sleepiness-related variables, A method for estimating skin condition. **Claim 5** The liver function-related variables include at least one of ALT (GPT) in blood, AST (GOT) in blood, ALP in blood, γ-GTP in blood, indirect bilirubin in blood, total bilirubin in blood, and direct bilirubin in blood, The method for estimating skin condition according to any one of Claims 1 to 4. **Claim 6** The muscle and bone-related variables include at least one of grip strength and bone density, The method for estimating skin condition according to any one of Claims 1 to 4. **Claim 7** The heartbeat / blood pressure-related variable includes at least one of diastolic blood pressure, systolic blood pressure, and heart rate. The method for estimating skin condition according to any one of claims 1 to 4.

8. The blood oxidation-related variable includes at least one of the antioxidant capacity value in blood, the oxidative stress value in blood, and the degree of oxidative stress. The method for estimating skin condition according to any one of claims 1 to 4.

9. The blood count-related variable includes at least one of hematocrit in blood, hemoglobin in blood, platelets in blood, red blood cells in blood, white blood cells in blood, mean corpuscular hemoglobin concentration in blood, mean corpuscular hemoglobin amount in blood, and mean corpuscular volume in blood. The method for estimating skin condition according to any one of claims 1 to 4.

10. The lipid metabolism-related variable includes at least one of HDL cholesterol in blood, LDL cholesterol in blood, total cholesterol in blood, the ratio of LDL cholesterol in blood to HDL cholesterol in blood, and triglycerides in blood. The method for estimating skin condition according to any one of claims 1 to 4.

11. The autonomic nerve function-related variable includes at least one of the autonomic nerve index HF, the autonomic nerve index LF, and the ratio of the autonomic nerve index HF to the autonomic nerve index LF. The method for estimating skin condition according to any one of claims 1 to 4.

12. The personal attribute-related variable includes at least one of exercise habits, sleep duration value, fatigue duration, average working hours, medical history, smoking experience, regular medications, and marital history. The method for estimating skin condition according to any one of claims 1 to 4.

13. The renal function-related variable includes at least one of the glomerular filtration value and blood urea nitrogen. The method for estimating skin condition according to any one of claims 1 to 4.

14. The body composition-related variable includes at least one of BMI (Body Mass Index), muscle mass, height, body fat percentage, skeletal muscle mass, and body weight. The method for estimating skin condition according to any one of claims 1 to 4.

15. The glucose tolerance-related variable includes at least one of hemoglobin A1c (HbA1c) in blood and blood glucose. The method for estimating skin condition according to any one of claims 1 to 4.

16. The electrolyte-related variable includes at least one of potassium in blood, chloride in blood, the ratio of sodium in blood to potassium in blood, and sodium in blood. The method for estimating skin condition according to any one of claims 1 to 4.

17. The cognitive function-related variables include at least one of the reaction time of a cognitive task, the correct answer rate of a cognitive task, the incorrect answer rate of a cognitive task, and the number of achieved tasks of a cognitive task. The method for estimating skin condition according to any one of claims 1 to 4.

18. The immune metabolism-related variables include at least one of highly sensitive CRP in blood, amylase in blood, albumin in blood, creatine kinase in blood, cortisol in blood, total protein in blood, lactate dehydrogenase in blood, IgE in blood, IgG in blood, and uric acid in blood. The method for estimating skin condition according to any one of claims 1 to 4.

19. The depression / fatigue / sleepiness-related variables include at least one of a fatigue evaluation scale, a depression / anxiety evaluation scale, a motivation evaluation scale, a sleepiness evaluation scale, and a life Performance Status evaluation scale. The method for estimating skin condition according to any one of claims 1 to 4.

20. The step of estimating the skin condition is to estimate the moisture of the skin with reference to the combination of the body composition-related variables, the blood oxidation-related variables, the blood count-related variables, the depression / fatigue / sleepiness-related variables, the electrolyte-related variables, the glucose tolerance-related variables, and the immune metabolism-related variables. The method for estimating skin condition according to any one of claims 1 to 4.

21. The step of estimating the skin condition is to estimate the barrier strength of the skin with reference to the combination of the electrolyte-related variables, the liver function-related variables, the glucose tolerance-related variables, the blood oxidation-related variables, the blood count-related variables, the glucose tolerance-related variables, and the depression / fatigue / sleepiness-related variables. The method for estimating skin condition according to any one of claims 1 to 4.

22. The step of estimating the skin condition is to estimate the texture of the skin with reference to the combination of the immune metabolism-related variables, the electrolyte-related variables, the autonomic nerve function-related variables, and the liver function-related variables. The method for estimating skin condition according to any one of claims 1 to 4.

23. The step of estimating the skin condition is to estimate the firmness of the skin with reference to the combination of the blood oxidation-related variables, the liver function-related variables, the immune metabolism-related variables, the blood oxidation-related variables, the body composition-related variables, the glucose tolerance-related variables, the blood count-related variables, the muscle-skeleton-related variables, and the autonomic nerve function-related variables. The method for estimating skin condition according to any one of claims 1 to 4.

24. The step of estimating the skin condition estimates the melanin of the skin with reference to a combination of the autonomic nerve function-related variable, the depression / fatigue / sleepiness-related variable, the immune metabolism-related variable, the glucose tolerance-related variable, the liver function-related variable, the blood count-related variable, and the blood oxidation-related variable. The skin condition estimation method according to any one of claims 1 to 4.

25. The step of estimating the skin condition estimates the brightness of the skin with reference to a combination of the body composition-related variable, the blood count-related variable, the kidney function-related variable, the immune metabolism-related variable, the heart rate / blood pressure-related variable, the lipid metabolism-related variable, the electrolyte-related variable, and the depression / fatigue / sleepiness-related variable. The skin condition estimation method according to any one of claims 1 to 4.

26. The step of estimating the skin condition estimates the redness of the skin with reference to a combination of the body composition-related variable, the depression / fatigue / sleepiness-related variable, the blood count-related variable, the liver function-related variable, the blood count-related variable, the blood oxidation-related variable, and the immune metabolism-related variable. The skin condition estimation method according to any one of claims 1 to 4.

27. In the skin model, the influence degree between the psychosomatic data and the skin index is defined for each combination of the psychosomatic data and the skin index. The skin condition estimation method according to any one of claims 1 to 4.

28. Comprising the step of generating advice according to the skin condition, The step of outputting outputs the skin condition and the advice. The skin condition estimation method according to any one of claims 1 to 4.

29. Comprising means for acquiring psychosomatic data related to the body and mind of the subject of skin condition estimation, Comprising means for estimating the skin condition of the subject using a skin model in which the correlation between the psychosomatic data and the skin condition is described, The means for estimating estimates the skin condition by providing the psychosomatic data including the attributes of the subject to a skin model in which the influence degree of any one or more of the attributes of a person, the body composition of a person, the habits of a person, and the time is reduced. The psychosomatic data includes at least one of a liver function-related variable, a muscle-skeleton-related variable, a heart rate / blood pressure-related variable, a blood oxidation-related variable, a blood count-related variable, a lipid metabolism-related variable, an autonomic nerve function-related variable, a personal attribute-related variable, a kidney function-related variable, a body composition-related variable, a glucose tolerance-related variable, an electrolyte-related variable, a cognitive function-related variable, an immune metabolism-related variable, and a depression / fatigue / sleepiness-related variable. Information processing apparatus.

30. Comprises means for acquiring psychosomatic data regarding the body and mind of the subject for estimating skin condition; Comprises means for estimating the skin condition of the subject using a skin model in which the correlation between the psychosomatic data and the skin condition is described; The skin model includes a first skin model generated using only non-invasive data measured by a non-invasive test without invasion, and a second skin model generated using invasive data measured by an invasive test with invasion and the non-invasive data; The psychosomatic data includes at least one of a liver function-related variable, a muscle-skeleton-related variable, a heart rate / blood pressure-related variable, a blood oxidation-related variable, a blood count-related variable, a lipid metabolism-related variable, an autonomic nerve function-related variable, a personal attribute-related variable, a kidney function-related variable, a body composition-related variable, a glucose tolerance-related variable, an electrolyte-related variable, a cognitive function-related variable, an immune metabolism-related variable, and a depression / fatigue / sleepiness-related variable; An information processing apparatus.

31. A program for causing a computer to function as means for acquiring psychosomatic data regarding the body and mind of the subject for estimating skin condition, and function as means for estimating the skin condition of the subject using a skin model in which the correlation between the psychosomatic data and the skin condition is described, wherein the means for estimating estimates the skin condition by providing psychosomatic data including the attributes of the subject to a skin model in which the influence degree of any one or more of a person's attributes, a person's body composition, a person's habits, and time is reduced; the psychosomatic data includes at least one of a liver function-related variable, a muscle-skeleton-related variable, a heart rate / blood pressure-related variable, a blood oxidation-related variable, a blood count-related variable, a lipid metabolism-related variable, an autonomic nerve function-related variable, a personal attribute-related variable, a kidney function-related variable, a body composition-related variable, a glucose tolerance-related variable, an electrolyte-related variable, a cognitive function-related variable, an immune metabolism-related variable, and a depression / fatigue / sleepiness-related variable; A program.

32. A program for causing a computer to function as means for acquiring psychosomatic data regarding the body and mind of the subject for estimating skin condition, and function as means for estimating the skin condition of the subject using a skin model in which the correlation between the psychosomatic data and the skin condition is described, wherein the skin model includes a first skin model generated using only non-invasive data measured by a non-invasive test without invasion, and a second skin model generated using invasive data measured by an invasive test with invasion and the non-invasive data; The psychosomatic data includes at least one of a liver function-related variable, a muscle and bone-related variable, a heart rate and blood pressure-related variable, a blood oxidation-related variable, a blood count-related variable, a lipid metabolism-related variable, an autonomic nerve function-related variable, a personal attribute-related variable, a kidney function-related variable, a body composition-related variable, a glucose tolerance-related variable, an electrolyte-related variable, a cognitive function-related variable, an immune metabolism-related variable, and a depression, fatigue, and sleepiness-related variable. Program.

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