Information processing device, information processing method, and program

Abstract

The information processing device is provided with: an acquisition means for acquiring deep body temperature log information relating to a history of deep body temperatures of a user; a skin state estimation unit that estimates the skin state of the user on the basis of deep body temperature log information; and a presentation means for presenting the estimation result of the skin state to the user.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and a program. Background Art

[0002] Generally, various care actions (for example, skin massage or use of skin care products) are performed to maintain the skin condition well. In order to select the best care action, it is very important to understand the skin condition.

[0003] A technique for estimating the skin condition based on an image is known (for example, refer to Japanese Unexamined Patent Application Publication No. 2017-012337). According to the technique of Japanese Unexamined Patent Application Publication No. 2017-012337, the amount of the shiny part of the skin in the captured image and the amount of the wrinkle part of the skin in the captured image are calculated as skin evaluation indexes, and the firmness evaluation unit evaluates the firmness of the facial skin of the subject based on the skin evaluation indexes calculated by the skin index calculation unit. Summary of the Invention

[0004] Problems to be Solved by the Invention

[0005] The technique of Japanese Unexamined Patent Application Publication No. 2017-012337 only estimates the skin condition based on the image of the skin (that is, the surface of the skin).

[0006] However, the skin condition does not necessarily appear on the surface of the skin. In addition, in order to obtain an image of the skin, it is necessary to photograph the skin with a camera, so the number of samples is limited.

[0007] Therefore, when estimating the skin condition based on the image of the skin, the accuracy of the estimation is limited.

[0008] An object of the present invention is to improve the accuracy of estimating the skin condition.

[0009] Means for Solving the Problems

[0010] One aspect of the present invention is an information processing apparatus including:

[0011] a unit that acquires deep body temperature log information related to a history of the deep body temperature of a user;

[0012] a unit that estimates the skin condition of the user based on the deep body temperature log information; and

[0013] a unit that presents the estimation result of the skin condition to the user. Brief Description of the Drawings

[0014] Figure 1 is a block diagram showing the configuration of the information processing system of the present embodiment.

[0015] Figure 2 is Figure 1 a functional block diagram of an information processing system.

[0016] Figure 3 is an explanatory diagram of the outline of this embodiment.

[0017] Figure 4 is a diagram showing the data structure of the user database of this embodiment.

[0018] Figure 5 is a diagram showing the data structure of the deep body temperature log database of this embodiment.

[0019] Figure 6 is a diagram showing the data structure of the skin care log database of this embodiment.

[0020] Figure 7 is a diagram showing the data structure of the physical condition log database of this embodiment.

[0021] Figure 8 is a diagram showing the data structure of the mental condition log database of this embodiment.

[0022] Figure 9 is a diagram showing the data structure of the biological log database of this embodiment.

[0023] Figure 10 is a diagram showing the data structure of the action log database of this embodiment.

[0024] Figure 11 is a timing diagram of the information processing of this embodiment.

[0025] Figure 12 is a diagram showing an example of a screen displayed in the Figure 11 information processing.

[0026] Figure 13 is an explanatory diagram of the outline of Modification 1.

[0027] Figure 14 is an explanatory diagram of the outline of Modification 2.

[0028] Figure 15 is an explanatory diagram of the outline of Modification 3.

[0029] Figure 16 is an explanatory diagram of the outline of Modification 4.

[0030] Figure 17 is an explanatory diagram of the outline of Modification 5.

[0031] Figure 18 It is an explanatory diagram of the outline of Modification Example 6.

[0032] Figure 19 It is an explanatory diagram of the outline of Modification Example 7.

[0033] Figure 20 It is a timing diagram of the information processing of Modification Example 7.

[0034] Figure 21 It is a diagram showing Figure 20 an example of a screen displayed in the information processing.

[0035] Figure 22 It is an explanatory diagram of the outline of Modification Example 8.

[0036] Figure 23 It is a timing diagram of the information processing of Modification Example 8.

[0037] Figure 24 It is a diagram showing Figure 23 an example of a screen displayed in the information processing.

[0038] Figure 25 It is an explanatory diagram of the outline of Modification Example 9.

[0039] Figure 26 It is a timing diagram of the information processing of Modification Example 9.

[0040] Figure 27 It is a diagram showing Figure 26 an example of a screen displayed in the information processing. Detailed implementation manner

[0041] Hereinafter, an embodiment of the present invention will be described in detail based on the accompanying drawings. In addition, in the drawings for explaining the embodiment, the same reference numerals are generally assigned to the same constituent elements, and repeated explanations thereof are omitted.

[0042] (1) Configuration of the information processing system

[0043] The configuration of the information processing system will be described. Figure 1 It is a block diagram showing the configuration of the information processing system of the present embodiment. Figure 2 It is Figure 1 a functional block diagram of the information processing system.

[0044] As Figure 1 shown, the information processing system 1 includes: a client device 10 and a server 30.

[0045] The client device 10 and the server 30 are connected via a network (for example, the Internet or an intranet) NW.

[0046] The client device 10 is a computer (an example of an "information processing device") that sends a request to the server 30. The client device 10 is, for example, a smart phone, a tablet terminal, or a personal computer.

[0047] The server 30 is a computer (an example of an "information processing device") that provides a response corresponding to the request sent from the client device 10 to the client device 10. The server 30 is, for example, a Web server.

[0048] (1-1) Configuration of the client device

[0049] The configuration of the client device 10 will be described.

[0050] As Figure 2 shown, the client device 10 includes: a storage device 11, a processor 12, an input / output interface 13, and a communication interface 14.

[0051] 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).

[0052] The programs include, for example, the following programs.

[0053] · A program of an OS (Operating System)

[0054] · A program of an application program (e.g., a Web browser) that executes information processing

[0055] The data includes, for example, the following data.

[0056] · A database referred to in information processing

[0057] · Data obtained by executing information processing (i.e., the execution result of information processing)

[0058] The processor 12 is configured to implement 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.

[0059] The input / output interface 13 is configured to obtain a user's instruction from an input device connected to the client device 10 and output information to an output device connected to the client device 10.

[0060] The input device is, for example, a keyboard, a pointing device, a touch panel, or a combination thereof.

[0061] The output device is, for example, a display.

[0062] The communication interface 14 is configured to control communication between the client device 10 and the server 30.

[0063] (1-2) Configuration of the server

[0064] The configuration of the server 30 will be described.

[0065] As Figure 2 shown, the server 30 includes: a storage device 31, a processor 32, an input / output interface 33, and a communication interface 34.

[0066] 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).

[0067] The programs include, for example, the following programs.

[0068] · Program of the OS

[0069] · Program of an application program that performs information processing

[0070] The data includes, for example, the following data.

[0071] · Database referred to in information processing

[0072] · Execution result of information processing

[0073] The processor 32 is configured to implement the functions of the server 30 by starting the programs stored in the storage device 31. The processor 32 is, for example, a CPU, an ASIC, an FPGA, or a combination thereof.

[0074] 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.

[0075] The input device is, for example, a keyboard, a pointing device, a touch panel, or a combination thereof.

[0076] The output device is, for example, a display.

[0077] The communication interface 34 is configured to control the communication between the control server 30 and the client device 10.

[0078] (2) Summary of the Embodiment

[0079] The summary of this embodiment will be described. Figure 3 It is an explanatory diagram of the summary of this embodiment.

[0080] As Figure 3 shown, the server 30 stores the history of the user's deep body temperature.

[0081] The server 30 estimates the user's skin condition based on the history of the deep body temperature.

[0082] The server 30 presents the estimation result (that is, the user's skin condition) to the user via the client device 10.

[0083] (3) Database

[0084] The database of this embodiment will be described. The following databases are stored in the storage device 31.

[0085] (3-1) User Database

[0086] The user database of this embodiment will be described. Figure 4 It is a diagram showing the data structure of the user database of this embodiment.

[0087] In Figure 4 the user database, user information is stored. User information is information related to the user.

[0088] The user database includes: a "user ID" field, a "user name" field, and a "user attribute" field. Each field is associated with each other.

[0089] The "user ID" field stores user identification information. User identification information is information for identifying the user.

[0090] The "user name" field stores user name information. User name information is information related to the user name (for example, name, account name, or online name).

[0091] The "user attribute" field stores user attribute information. User attribute information is information related to the attributes of the user. The "user attribute" field includes a "gender" field, an "age" field, and a "residence" field.

[0092] The "gender" field stores gender information. Gender information is information related to the gender of the user.

[0093] The "age" field stores age information. Age information is information related to the age of the user.

[0094] The "Residence" field stores residence information. The residence information is information related to the residence of the user's place of residence.

[0095] (3-2) Deep Body Temperature Log Database

[0096] The deep body temperature log database of this embodiment will be described. Figure 5 It is a diagram showing the data structure of the deep body temperature log database of this embodiment.

[0097] Figure 5 The deep body temperature log database stores deep body temperature log information. The deep body temperature log information is information related to the history of the user's deep body temperature. The history of the deep body temperature is the history of at least one of the regularly measured deep body temperature and the irregularly measured deep body temperature.

[0098] The deep body temperature log database includes: a "Timestamp" field and a "Deep Body Temperature" field. Each field is associated with each other.

[0099] The deep body temperature log database is associated with user identification information.

[0100] The "Timestamp" field stores timestamp information. The timestamp information is information related to the date and time of the deep body temperature log.

[0101] The "Deep Body Temperature" field stores deep body temperature information. The deep body temperature information is information related to the user's deep body temperature. The deep body temperature information is obtained from at least one of the following, for example.

[0102] · Deep body temperature information input by the user

[0103] · Deep body temperature information obtained from the deep body thermometer used by the user

[0104] · Deep body temperature information obtained from the wearable device worn by the user

[0105] · An infrared sensor capable of measuring deep body temperature

[0106] (3-3) Skin Care Log Database

[0107] The skin care log database of this embodiment will be described. Figure 6 It is a diagram showing the data structure of the skin care log database of this embodiment.

[0108] Figure 6 The skin care log database stores skin care log information. The skin care log information is the history of skin care information. The skin care information is information related to the skin care performed by the user.

[0109] The skin care log database includes: a "timestamp" field and a "skin care" field. Each field is associated with each other.

[0110] The skin care log database is associated with user identification information.

[0111] The "timestamp" field stores timestamp information. The timestamp information is information related to the date and time of the skin care log.

[0112] The "skin care" field stores skin care information. The skin care information is information related to skin care. The skin care information includes, for example, at least one of the following.

[0113] · Ingredients of skin care products (as an example, active ingredients, extracts, or fragrance ingredients)

[0114] · Usability of skin care products (as an example, moisturizing type, or refreshing type)

[0115] · Types of skin care products (as an example, lotion, emulsion, face mask, face pack, gel mask, essential oil beauty liquid, or all-in gel)

[0116] · Usage amount of skin care products

[0117] · Timing of skin care implementation (as an example, timing of using a beauty device)

[0118] · Massage type of the face during skin care implementation (as an example, massage along nasolabial folds)

[0119] · Movements of the body during skin care implementation (as an example, stretching, yoga, or taking a bath)

[0120] Based on the skin care log information, it is possible to determine the timing and frequency of skin care implementation for each content of skin care.

[0121] Based on the skin care log information, for example, it is possible to determine that EXL company's lotion is applied twice a day (as an example, after washing the face in the morning and after taking a bath in the evening).

[0122] (3 - 4) Body condition log database

[0123] The body condition log database of the present embodiment will be described. Figure 7 It is a diagram showing the data structure of the body condition log database of the present embodiment.

[0124] Figure 7The physical condition log database stores physical condition log information. The physical condition log information is a historical record of physical condition information. The physical condition information is information related to the condition of the user's body (hereinafter referred to as "physical condition").

[0125] The physical condition log database includes: a "timestamp" field and a "physical condition" field. Each field is associated with each other.

[0126] The physical condition log database is associated with user identification information.

[0127] The "timestamp" field stores timestamp information. The timestamp information is information related to the date and time of the physical condition log.

[0128] The "physical condition" field stores physical condition information. The "physical condition" field includes: a "constitution" field and a "physical condition" field.

[0129] The "constitution" field stores constitution information. The constitution information is information related to constitution disorders (especially constitutions that affect the discovery of facial symptoms). The constitution information includes, for example, at least one of the following.

[0130] · Constitution disorders caused by environmental factors (as an example, weather, season, air pressure, ultraviolet rays, pollen, PM2.5, or white night)

[0131] · Constitution disorders caused by social factors (as an example, stress, travel destination (e.g., international travel or jet lag), staying up late, or blue light (e.g., using a smartphone))

[0132] · Constitution disorders caused by physical activities (as an example, disorders of growth hormone balance, menstrual cycle disorders, or disorders caused by high-intensity exercise)

[0133] The "physical condition" field stores physical condition information. The physical condition information is information related to physical conditions (e.g., information obtained from a health check). The physical condition information includes, for example, at least one of the following.

[0134] · Height

[0135] · Weight

[0136] · Blood pressure

[0137] · Body fat mass

[0138] · Body fat percentage

[0139] · Abdominal circumference

[0140] · Blood glucose level

[0141] · BMI (Body Mass Index) value

[0142] · Results of urine test (for example, urobilinogen value, pH value, or specific gravity)

[0143] · Results of blood test (for example, AST, ALT, γ-GTP, hemoglobin value, red blood cell count, hematocrit value, white blood cell count WBC, platelet count PLT, or CRP (C-reactive protein))

[0144] · Blood glucose value (for example, FPG or NGSP)

[0145] · Results of lipid test (for example, total cholesterol, HDL, LDL, or triglyceride)

[0146] · Results of renal function test (for example, creatinine or uric acid)

[0147] (3 - 5) Mental condition log database

[0148] The mental condition log database of the present embodiment will be described. Figure 8 It is a diagram showing the data structure of the mental condition log database of the present embodiment.

[0149] Figure 8 The mental condition log database stores mental condition log information. The mental condition log information is a historical record of the user's mental condition information.

[0150] The mental condition log database includes: a "timestamp" field and a "mental condition" field. Each field is associated with each other.

[0151] The mental condition log database is associated with user identification information.

[0152] The "timestamp" field stores timestamp information. The timestamp information is information related to the date and time of the mental condition log.

[0153] The "mental condition" field stores mental condition information. The mental condition information is information related to the user's psychological condition (hereinafter referred to as "mental condition"). The mental condition information includes, for example, at least one of the following.

[0154] · Information related to the level of being prone to nervousness

[0155] · Information related to the stress state (for example, the level of stress determined based on the result of facial expression monitoring)

[0156] · Information related to the happiness state (for example, the happiness level determined based on the result of facial expression monitoring)

[0157] · Information related to the drowsy state (for example, the drowsiness level determined based on the measurement results of a known sensor)

[0158] (3 - 6) Biological log database

[0159] The biological log database of the present embodiment will be described. Figure 9 It is a diagram showing the data structure of the biological log database of the present embodiment.

[0160] Figure 9 The biological log database stores biological log information. The biological log information is a historical record of the user's biological information.

[0161] The biological log database includes: a "timestamp" field and a "biological" field. Each field is associated with each other.

[0162] The biological log database is associated with user identification information.

[0163] The "timestamp" field stores timestamp information. The timestamp information is information related to the date and time of the biological log.

[0164] The "biological" field stores biological information. The biological information is information related to the user's organism. The biological information represents, for example, at least one of the following.

[0165] · Skin temperature

[0166] · Temperature of the skin

[0167] · Temperature of the environment where the user is located

[0168] · Pulse

[0169] · Heartbeat

[0170] · Respiration rate

[0171] · Electrocardiogram

[0172] · Electromyogram

[0173] (3 - 7) Action log database

[0174] The action log database of the present embodiment will be described. Figure 10 It is a diagram showing the data structure of the action log database of the present embodiment.

[0175] Figure 10 The action log database stores action log information. The action log information is a historical record of the user's action information.

[0176] The action log database includes: a "timestamp" field and an "action" field. Each field is associated with each other.

[0177] The action log database is associated with user identification information.

[0178] The "timestamp" field stores timestamp information. The timestamp information is information related to the date and time of the action log.

[0179] The "action" field stores action information. The action information is information related to the user's actions. The "action" field includes: an "action category" field and a "duration" field.

[0180] The "action category" field stores action category information. The action category information is information related to the category of the action. The category of the action includes the content of the action and the amount of action. The category of the action includes, for example, at least one of the following.

[0181] · Lifestyle

[0182] · Information related to the sleep rhythm (from what time to what time the depth of sleep, wake-up time, or bedtime)

[0183] · Diet / nutritional supplements (from what time to what time the eating action is carried out)

[0184] · Beauty rhythm (from what time to what time the beauty action is carried out)

[0185] · Bathing and showering habits (from what time to what time the beauty action is carried out)

[0186] · Light exercise habits

[0187] · Exercise (as an example, the distance walked, the distance run, the number of steps, the number of steps up and down the stairs, the content of the activity (e.g., workout, exercise, or standing), the number of pushes, the distance moved, the distance cycled, or the distance traveled by wheelchair)

[0188] The "duration" field stores duration information. The duration information is information related to the duration of the action.

[0189] According to the action log information, for example, it is possible to determine that stretching and yoga are carried out for 30 minutes between 8:00 and 9:00 on weekdays at home.

[0190] (4) Information processing

[0191] The information processing of this embodiment will be described. Figure 11 is a timing diagram of the information processing of this embodiment. Figure 12 is shown in Figure 11A diagram of an example of a screen displayed in information processing.

[0192] Figure 8 The information processing is for estimating the skin condition.

[0193] Figure 8 The trigger for the information processing is the case where the user accesses a predetermined website using the client device 10.

[0194] As Figure 8 shown, the client device 10 executes reception of a user instruction (S1110).

[0195] Specifically, the processor 12 displays the screen P1110 ( Figure 12 ) on the display.

[0196] The screen P1110 includes: an operation object B1110 and a field object F1110.

[0197] The operation object B1110 is an object for receiving a user instruction for determining an input to the field object F1110.

[0198] The field object F1110 is an object for receiving an input of user identification information.

[0199] After step S1110, the client device 10 executes an estimation request (S1111).

[0200] Specifically, the user inputs user identification information to the field object F1110, and when operating the operation object B1110, the processor 12 sends the estimation request data to the server 30. The estimation request data includes, for example, the following information.

[0201] · User identification information input to the field object F1110

[0202] After step S1111, the server 30 executes estimation of the skin condition (S1130).

[0203] Specifically, a skin condition model is stored in the storage device 31.

[0204] In the skin condition model, the correlation between the history of deep body temperature and the skin condition is described. The skin condition model includes at least one of the current skin condition model and the future skin condition model. The skin condition includes, for example, at least one of the following.

[0205] The skin condition includes, for example, at least one of the following.

[0206] · Physical states (as an example, skin viscoelasticity, water content of the stratum corneum, barrier function of the stratum corneum, antioxidant function, sebum amount, blood flow, state of the stratum corneum, skin color, skin softness, degree of glycation, blood, urine, and sebum RNA)

[0207] · Qualitative states (as an example, skin age, skin moisture, skin laxity, skin condition, makeup adherence of the skin, and susceptibility to deterioration of skin diseases (as an example, acne or rough skin))

[0208] As an example, the following relationships exist between physical states and qualitative states.

[0209] · When viscoelasticity decreases and the water content of the stratum corneum decreases, skin age deteriorates.

[0210] · When viscoelasticity increases, the water content of the stratum corneum increases, and the sebum amount becomes above a predetermined level, skin moisture deteriorates.

[0211] · When viscoelasticity decreases, skin laxity deteriorates.

[0212] · When viscoelasticity decreases, the water content of the stratum corneum decreases, the sebum amount becomes above a predetermined level, and skin color becomes pale, skin texture, elasticity, and gloss disappear, and as a result, skin condition deteriorates.

[0213] · When the water content of the stratum corneum increases and the sebum amount becomes above a predetermined level, makeup adherence of the skin deteriorates.

[0214] · When the water content of the stratum corneum decreases and the sebum amount becomes above a predetermined level, susceptibility to deterioration of skin diseases deteriorates.

[0215] The current skin state model describes the correlation between the history of deep body temperature and the current skin state. The current skin state is the skin state at the execution time point of step S1130 (hereinafter referred to as the "current time point"). The current skin state model is configured to output the current skin state corresponding to the deep body temperature log information when the deep body temperature log information is input.

[0216] The future skin state model describes the correlation between the history of deep body temperature and the future skin state. The future skin state is the skin state at a future time point compared to the current time point. The future skin state model is configured to output the future skin state corresponding to the deep body temperature log information when the deep body temperature log information is input.

[0217] The future time point is a predetermined time point. The future time point is preferably a time point 2 weeks after the current time point considering the skin turnover cycle (for example, 2 weeks).

[0218] Prediction of future skin conditions, for example, predicting the tendency of changes in skin conditions that will occur in the future on the skin (as an example, improvement or deterioration of skin conditions).

[0219] The processor 32 refers to the deep body temperature log database associated with the user identification information included in the estimation request data ( Figure 5 ) and determines the deep body temperature log information for a predetermined period (for example, one month back from the execution date and time of step S1130).

[0220] The processor 32 inputs the determined deep body temperature log information into the current skin condition model and outputs the current skin condition corresponding to the deep body temperature log information.

[0221] The processor 32 inputs the determined deep body temperature log information into the future skin condition model and outputs the future skin condition corresponding to the deep body temperature log information.

[0222] After step S1130, the server 30 executes the generation of suggestions (S1131).

[0223] Specifically, a suggestion model is stored in the storage device 31. The correlation between skin conditions and suggestions is described in the suggestion model.

[0224] A first example of step S1131 will be described.

[0225] The correlation between the current skin condition and suggestion information is described in the suggestion model. The suggestion information is information related to suggestions corresponding to the current skin condition.

[0226] The processor 32 inputs the current skin condition obtained in step S1130 into the suggestion model and outputs the suggestion information corresponding to the current skin condition.

[0227] The suggestion information represents, for example, at least one of the following.

[0228] · Suggestions related to skin care methods

[0229] · Suggestions related to recommended skin care products or cosmetics

[0230] Suggestions corresponding to the current skin condition preferably represent at least one of the following.

[0231] · Warning messages about risks to the skin (as an example, a message such as "The skin is exposed to an excessive environment")

[0232] · Messages urging immediate action (as an example, a message such as "Due to the skin being exposed to an excessive environment, urgent skin care is required")

[0233] Describe the second example of step S1131.

[0234] In the recommendation model, the correlation between the future skin condition and the recommendation information is described. The recommendation information is information related to the recommendation corresponding to the future skin condition.

[0235] The processor 32 inputs the future skin condition obtained in step S1130 into the recommendation model and outputs the recommendation information corresponding to the future skin condition.

[0236] The recommendation information represents, for example, at least one of the following.

[0237] · Recommendations related to skin care methods

[0238] · Recommendations related to skin care products or cosmetics recommended to be used

[0239] The recommendation corresponding to the future skin condition preferably represents at least one of the following.

[0240] · A message warning of skin discomfort (abnormality) tendency (as an example, a message such as "Skin discomfort may be caused by the female-specific cycle" or "The sleep rhythm was not good last night, and skin roughness may occur")

[0241] · A message urging an action to improve the skin discomfort tendency (as an example, a message such as "Since skin roughness may occur, please ensure sufficient sleep")

[0242] The first example to the second example of step S1131 can be combined.

[0243] After step S1131, the server 30 executes the estimation response (S1132).

[0244] Specifically, the processor 32 sends the estimation response data to the client device 10. The estimation response data includes, for example, the following information.

[0245] · Information related to the current skin condition obtained in step S1130 (hereinafter referred to as "current skin condition information")

[0246] · Information related to the future skin condition obtained in step S1130 (hereinafter referred to as "future skin condition information")

[0247] · The recommendation information obtained in step S1131

[0248] · The deep body temperature log information used in step S1130

[0249] After step S1132, the client device 10 performs display of the estimation result (S1112).

[0250] Specifically, the processor 12 displays the screen P1111 ( Figure 12 ) on the display.

[0251] The screen P1111 includes: a display object A1111 and a graph object G1111.

[0252] The display object A1111 displays the current skin state information, future skin state information, and advice information included in the estimation response data.

[0253] The graph object G1111 is a graph representing the deep body temperature log information (that is, the temporal change of the deep body temperature) included in the estimation response data.

[0254] (5) Summary of this embodiment

[0255] According to this embodiment, the skin state is estimated based on the history of the deep body temperature. Thus, the accuracy of the skin state estimation can be improved compared with the prior art.

[0256] For example, in this embodiment, the skin state is estimated based on the deep body temperature (that is, the sensing data of a higher dimension for the skin) obtained by a device directly attached to the skin (that is, a wearable device).

[0257] This deep body temperature includes factors that have not yet appeared on the skin surface. Thus, it is possible to perform the skin state estimation considering factors (as an example, prediction of cell renewal, movement of macrophages generated in the dermis, or immune pathways) that were not considered in the prior skin state estimation.

[0258] According to this embodiment, the future skin state of the user can also be estimated based on the history of the deep body temperature. Thus, the user can accurately know their future skin state.

[0259] According to this embodiment, the future skin state can also be the tendency of the change in the skin state that will occur in the future. Thus, the user can know the change in the skin state that will occur in their future skin.

[0260] According to this embodiment, the current skin state of the user can also be estimated based on the history of the deep body temperature. Thus, the user can accurately know their current skin state.

[0261] According to the present embodiment, it is also possible to present a suggestion corresponding to the estimation result of the skin condition to the user. Thereby, the user can take an action (for example, skin care action) based on the suggestion corresponding to the correct skin condition.

[0262] (6) Variation

[0263] A variation of the present embodiment will be described.

[0264] (6-1) Variation 1

[0265] Variation 1 will be described. Variation 1 is an example of estimating at least one of the previous skin condition and the future skin condition based on the history of deep body temperature and the history of skin care.

[0266] (6-1-1) Outline of Variation 1

[0267] The outline of Variation 1 will be described. Figure 13 It is an explanatory diagram of the outline of Variation 1.

[0268] As Figure 13 shown, the history of the user's deep body temperature and the history of skin care are stored in the server 30.

[0269] The server 30 estimates the skin condition of the user based on the history of deep body temperature and the history of skin care.

[0270] The server 30 presents the estimation result (that is, the skin condition of the user) to the user via the client device 10.

[0271] (6-1-1) Information Processing of Variation 1

[0272] The information processing of Variation 1 will be described.

[0273] As Figure 11 shown, the client device 10, in the same manner as the present embodiment, executes reception of user instructions (S1110) to estimation request (S1111).

[0274] After step S1111, the server 30 executes estimation of the skin condition (S1130).

[0275] Specifically, a skin condition model is stored in the storage device 31. In the skin condition model, the correlation between the history of deep body temperature and the history of skin care and the skin condition is described. The skin condition model includes at least one of the current skin condition model and the future skin condition model.

[0276] The current skin condition model records the history of deep body temperature, skin care, and the correlation with the current skin condition.

[0277] The future skin condition model records the history of deep body temperature, skin care, and the correlation with the future skin condition.

[0278] The processor 32 refers to the deep body temperature log database associated with the user identification information included in the estimation request data ( Figure 5 ) to determine the deep body temperature log information.

[0279] The processor 32 refers to the skin care log database associated with the user identification information included in the estimation request data ( Figure 6 ) to determine the history of skin care. The history of skin care is determined by skin care information within a predetermined period (e.g., one month back from the current time point) or the most recent skin care information.

[0280] The processor 32 inputs the determined deep body temperature log information and skin care information into the current skin condition model, and outputs the current skin condition corresponding to the deep body temperature log information and skin care information.

[0281] The processor 32 inputs the determined deep body temperature log information and skin care information into the future skin condition model, and outputs the future skin condition corresponding to the deep body temperature log information and skin care information.

[0282] After step S1130, the server 30, in the same manner as in this embodiment, executes generation of a suggestion (S1131) to estimation response (S1132).

[0283] After step S1132, the client device 10, in the same manner as in this embodiment, executes display of the estimation result (S1112).

[0284] (6 - 1 - 3) Summary of Modification 1

[0285] According to Modification 1, it is also possible to estimate the skin condition based on the history of deep body temperature and the history of skin care. Thus, it is possible to further improve the accuracy of estimating the skin condition and to provide suggestions more suitable for improving the skin condition.

[0286] (6 - 2) Modification 2

[0287] Modification 2 will be described. Modification 2 is an example of estimating the skin condition based on the history of deep body temperature and the history of physical condition.

[0288] (6 - 2 - 1) Outline of Modification 2

[0289] Describe the outline of Modification 2. Figure 14 It is an explanatory diagram of the outline of Modification 2.

[0290] As Figure 15 shown, the server 30 stores the historical record of the user's deep body temperature and the historical record of the physical condition.

[0291] Based on the historical record of the deep body temperature and the historical record of the physical condition, the server 30 estimates the user's skin condition.

[0292] The server 30 presents the estimation result (that is, the user's skin condition) to the user via the client device 10.

[0293] (6 - 2 - 2) Information processing of Modification 2

[0294] Describe the information processing of Modification 2.

[0295] As Figure 11 shown, the client device 10, in the same manner as in this embodiment, executes acceptance of user instructions (S1110) to estimation request (S1111).

[0296] After step S1111, the server 30 executes estimation of the skin condition (S1130).

[0297] Specifically, a skin condition model is stored in the storage device 31. In the skin condition model, the correlation between the historical record of the deep body temperature and the historical record of the physical condition and the skin condition is described. The skin condition model includes at least one of the current skin condition model and the future skin condition model.

[0298] In the current skin condition model, the correlation between the historical record of the deep body temperature and the physical condition and the current skin condition is described.

[0299] In the future skin condition model, the correlation between the historical record of the deep body temperature and the physical condition and the future skin condition is described.

[0300] The processor 32 refers to the deep body temperature log database associated with the user identification information included in the estimation request data ( Figure 5 ) to determine the deep body temperature log information.

[0301] The processor 32 refers to the physical condition log database associated with the user identification information included in the estimation request data ( Figure 7 ) to determine the historical record of the physical condition. The historical record of the physical condition is determined by the physical condition information within a predetermined period (for example, one month back from the current time point) or the most recent physical condition information.

[0302] The processor 32 inputs the determined deep body temperature log information and the physical condition information into the current skin state model, and outputs the current skin state corresponding to the deep body temperature log information and the physical condition information.

[0303] The processor 32 inputs the determined deep body temperature log information and the physical condition information into the future skin state model, and outputs the future skin state corresponding to the deep body temperature log information and the physical condition information.

[0304] After step S1130, the server 30, in the same manner as in this embodiment, executes generation of a suggestion (S1131) to estimation of a response (S1132).

[0305] After step S1132, the client device 10, in the same manner as in this embodiment, executes display of an estimation result (S1112).

[0306] (6-2-3) Summary of Modification 2

[0307] According to Modification 2, it is also possible to estimate the skin state based on the history of the deep body temperature and the history of the physical condition. Thereby, it is possible to further improve the accuracy of the estimation of the skin state and to present a suggestion more suitable for the improvement of the skin state.

[0308] (6-3) Modification 3

[0309] Modification 3 will be described. Modification 3 is an example of estimating the skin state based on the history of the deep body temperature and the history of the mental state.

[0310] (6-3-1) Outline of Modification 3

[0311] The outline of Modification 3 will be described. Figure 15 It is an explanatory diagram of the outline of Modification 3.

[0312] As Figure 15 shown, the server 30 stores the history of the deep body temperature of the user and the history of the mental state.

[0313] The server 30 estimates the skin state of the user based on the history of the deep body temperature and the history of the mental state.

[0314] The server 30 presents the estimation result (that is, the skin state of the user) to the user via the client device 10.

[0315] (6-3-2) Information Processing of Modification 3

[0316] The information processing of Modification 3 will be described.

[0317] AsFigure 11 As shown, the client device 10, similar to this embodiment, executes reception of user instructions (S1110) to estimation requests (S1111).

[0318] After step S1111, the server 30 executes estimation of skin condition (S1130).

[0319] Specifically, the storage device 31 stores a skin condition model. The skin condition model describes the correlation between the history of deep body temperature and mental state and the skin condition. The skin condition model includes at least one of the current skin condition model and the future skin condition model.

[0320] The current skin condition model describes the correlation between the history of deep body temperature and mental state and the current skin condition.

[0321] The future skin condition model describes the correlation between the history of deep body temperature and mental state and the future skin condition.

[0322] The processor 32 refers to the deep body temperature log database associated with the user identification information included in the estimation request data ( Figure 5 ) and determines the deep body temperature log information.

[0323] The processor 32 refers to the mental state log database associated with the user identification information included in the estimation request data ( Figure 8 ) and determines the history of mental state. The history of mental state is determined by the mental state information within a predetermined period (e.g., one month back from the current time point) or the most recent mental state information.

[0324] The processor 32 inputs the determined deep body temperature log information and mental state information into the current skin condition model and outputs the current skin condition corresponding to the deep body temperature log information and mental state information.

[0325] The processor 32 inputs the determined deep body temperature log information and mental state information into the future skin condition model and outputs the future skin condition corresponding to the deep body temperature log information and mental state information.

[0326] After step S1130, the server 30, similar to this embodiment, executes generation of suggestions (S1131) to estimation response (S1132).

[0327] After step S1132, the client device 10, similar to this embodiment, executes display of the estimation result (S1112).

[0328] (6-3-3) Summary of Modification Example 3

[0329] According to Modification Example 3, the skin condition can also be estimated based on the history of deep body temperature and the history of mental state. Thereby, the accuracy of the estimation of the skin condition can be further improved, and advice more suitable for improving the skin condition can be presented.

[0330] (6-4) Modification Example 4

[0331] Modification Example 4 will be described. Modification Example 4 is an example of estimating the skin condition based on the history of deep body temperature and the history of biological information.

[0332] (6-4-1) Outline of Modification Example 4

[0333] The outline of Modification Example 4 will be described. Figure 16 It is an explanatory diagram of the outline of Modification Example 4.

[0334] As Figure 16 shown, the history of the user's deep body temperature and the history of the organism are stored in the server 30.

[0335] The server 30 estimates the skin condition of the user based on the history of deep body temperature and the history of the organism.

[0336] The server 30 presents the estimation result (that is, the skin condition of the user) to the user via the client device 10.

[0337] (6-4-2) Information Processing of Modification Example 4

[0338] The information processing of Modification Example 4 will be described.

[0339] As Figure 11 shown, the client device 10, in the same manner as in the present embodiment, executes acceptance of user instructions (S1110) to estimation request (S1111).

[0340] After step S1111, the server 30 executes estimation of the skin condition (S1130).

[0341] Specifically, a skin condition model is stored in the storage device 31. In the skin condition model, the correlation between the history of deep body temperature and the history of the organism and the skin condition is described. The skin condition model includes at least one of the current skin condition model and the future skin condition model.

[0342] In the current skin condition model, the correlation between the history of deep body temperature and the organism and the current skin condition is described.

[0343] The future skin condition model describes the historical record of deep body temperature, the organism, and the correlation with the future skin condition.

[0344] The processor 32 refers to the deep body temperature log database associated with the user identification information included in the estimation request data ( Figure 5 ) and determines the deep body temperature log information.

[0345] The processor 32 refers to the organism log database associated with the user identification information included in the estimation request data ( Figure 9 ) and determines the historical record of the organism. The historical record of the organism is determined by the organism information within a predetermined period (e.g., one month back from the current time point) or the most recent organism information.

[0346] The processor 32 inputs the determined deep body temperature log information and organism information into the current skin condition model and outputs the current skin condition corresponding to the deep body temperature log information and organism information.

[0347] The processor 32 inputs the determined deep body temperature log information and organism information into the future skin condition model and outputs the future skin condition corresponding to the deep body temperature log information and organism information.

[0348] After step S1130, the server 30, in the same manner as in this embodiment, executes the generation of suggestions (S1131) to the estimation response (S1132).

[0349] After step S1132, the client device 10, in the same manner as in this embodiment, executes the display of the estimation result (S1112).

[0350] (6-4-3) Summary of Variation 4

[0351] According to Variation 4, it is also possible to estimate the skin condition based on the historical record of deep body temperature and the historical record of the organism. Thereby, the accuracy of estimating the skin condition can be further improved, and suggestions more suitable for improving the skin condition can be presented.

[0352] (6-5) Variation 5

[0353] Variation 5 will be described. Variation 5 is an example of estimating the skin condition based on the historical record of deep body temperature and the historical record of actions.

[0354] (6-5-1) Outline of Variation 5

[0355] The outline of Variation 5 will be described. Figure 17 It is an explanatory diagram of the outline of Variation 5.

[0356] AsFigure 17 As shown, the history of the user's deep body temperature and the history of actions are stored in the server 30.

[0357] Based on the history of deep body temperature and the history of actions, the server 30 estimates the user's skin condition.

[0358] The server 30 presents the estimation result (i.e., the user's skin condition) to the user via the client device 10.

[0359] (6-5-2) Information processing of Modification 5

[0360] The information processing of Modification 5 will be described.

[0361] As Figure 11 shown, the client device 10, in the same manner as in this embodiment, executes acceptance of user instructions (S1110) to estimation request (S1111).

[0362] After step S1111, the server 30 executes estimation of skin condition (S1130).

[0363] Specifically, a skin condition model is stored in the storage device 31. The relationship between the history of deep body temperature and the history of actions and the skin condition is described in the skin condition model. The skin condition model includes at least one of the current skin condition model and the future skin condition model.

[0364] The relationship between the history of deep body temperature and actions and the current skin condition is described in the current skin condition model.

[0365] The relationship between the history of deep body temperature and actions and the future skin condition is described in the future skin condition model.

[0366] The processor 32 refers to the deep body temperature log database associated with the user identification information included in the estimation request data ( Figure 5 ) to determine the deep body temperature log information.

[0367] The processor 32 refers to the action log database associated with the user identification information included in the estimation request data ( Figure 10 ) to determine the history of actions. The history of actions is determined by the action information within a predetermined period (e.g., one month back from the current time point) or the most recent action information.

[0368] The processor 32 inputs the determined deep body temperature log information and action information into the current skin condition model and outputs the current skin condition corresponding to the deep body temperature log information and action information.

[0369] The processor 32 inputs the determined deep body temperature log information and action information into the future skin state model, and outputs the future skin state corresponding to the deep body temperature log information and action information.

[0370] After step S1130, the server 30, similarly to this embodiment, executes generation of a suggestion (S1131) - estimation of a response (S1132).

[0371] After step S1132, the client device 10, similarly to this embodiment, executes display of an estimation result (S1112).

[0372] (6 - 5 - 3) Summary of Modification 5

[0373] According to Modification 5, it is also possible to estimate the skin state based on the history of deep body temperature and the history of actions. Thereby, it is possible to further improve the accuracy of estimating the skin state, and it is possible to present a suggestion more suitable for improving the skin state.

[0374] Modification 5 can also be applied to an example of promoting the skin state based on the history of deep body temperature and the user's future scheduled actions.

[0375] For example, scheduled action information is stored in the storage device 31. The scheduled action information is information related to the user's future scheduled actions. The scheduled action information is associated with user identification information.

[0376] A skin state model is stored in the storage device 31. In the skin state model, the correlation between the history of deep body temperature and scheduled actions and the future skin state is described.

[0377] In the estimation of the skin state (S1130), the server 30 refers to the deep body temperature log database associated with the user identification information included in the estimation request data ( Figure 5 ) and obtains the deep body temperature log information.

[0378] The processor 32 refers to the scheduled action information associated with the user identification information included in the estimation request data to determine the user's scheduled actions. The scheduled actions are determined by the scheduled action information for a scheduled period (for example, the next one month from the current time point) or the scheduled action information immediately following.

[0379] The processor 32 inputs the determined deep body temperature log information and scheduled action information into the skin state model, and outputs the future skin state corresponding to the deep body temperature log information and scheduled action information.

[0380] According to this example, the skin condition can also be inferred based on the historical record of the deep body temperature and the predetermined actions. Thereby, the accuracy of the inference of the skin condition can be further improved, and suggestions more suitable for the improvement of the skin condition can be presented.

[0381] (6-6) Variant Example 6

[0382] Variant Example 6 will be described. Variant Example 6 is an example of presenting suggestions corresponding to the user's preferences.

[0383] (6-6-1) Outline of Variant Example 6

[0384] The outline of Variant Example 6 will be described. Figure 18 It is an explanatory diagram of the outline of Variant Example 6.

[0385] As Figure 18 shown, the server 30 stores the historical record of the user's deep body temperature and the historical record of actions.

[0386] The server 30 infers the skin condition of the user based on the historical record of the deep body temperature.

[0387] The server 30 infers the user's preferences based on at least one of the historical record of actions, the historical record of the organism, the result of the medical interview, and the historical record of skin care.

[0388] The server 30 generates suggestions based on the user's skin condition and preferences.

[0389] The server 30 presents the inference result (that is, the skin condition of the user) and the suggestions to the user via the client device 10.

[0390] (6-6-2) Information Processing of Variant Example 6

[0391] The information processing of Variant Example 6 will be described.

[0392] As Figure 11 shown, the client device 10, in the same manner as in this embodiment, executes the acceptance of the user's instruction (S1110) to the inference request (S1111).

[0393] After step S1111, the server 30, in the same manner as in this embodiment, executes the inference of the skin condition (S1130).

[0394] After step S1130, the server 30 executes the generation of suggestions (S1131).

[0395] Specifically, a suggestion model is stored in the storage device 31. The correlation between the skin condition and preferences and the suggestions is described in the suggestion model.

[0396] Describe the first example of step S1131.

[0397] The processor 32 refers to the action log database associated with the user identification information included in the estimation request data ( Figure 10 ), and estimates the actions that the user is good at (as an example, frequently performed actions) as the preferences of the user's actions.

[0398] The processor 32 inputs the skin condition and the actions that the user is good at into the recommendation model, and outputs recommendation information urging the user to perform the actions that the user is good at according to the skin condition.

[0399] Describe the second example of step S1131.

[0400] The processor 32 refers to the action log database associated with the user identification information included in the estimation request data ( Figure 10 ), and estimates the actions that the user is not good at (as an example, actions with a duration shorter than the standard duration or a frequency less than the standard frequency) as the preferences of the user's actions.

[0401] The processor 32 inputs the skin condition and the actions that the user is not good at into the recommendation model, and outputs recommendation information urging the user to perform actions other than the actions that the user is not good at according to the skin condition.

[0402] Describe the third example of step S1131.

[0403] The processor 32 refers to the biological log database associated with the user identification information included in the estimation request data ( Figure 9 ) and the action log database ( Figure 10 ), and estimates the preferences for actions with significant biological responses (as an example, actions with a high level of excitement of the user) as the preferences of the user's actions.

[0404] The processor 32 inputs the skin condition and the estimation result into the recommendation model, and outputs recommendation information urging the user to perform the actions with significant biological responses according to the skin condition.

[0405] Describe the fourth example of step S1131.

[0406] The inquiry information is stored in the storage device 31. The inquiry information is information related to the results of the questionnaire survey conducted on the user. The inquiry information is associated with the user identification information.

[0407] The processor 32 refers to the inquiry information associated with the user identification information included in the estimation request data, and estimates the preferences of the user (for example, likes and dislikes).

[0408] The processor 32 inputs the skin condition and the estimation result into the recommendation model, and outputs recommendation information suitable for the user's preferences according to the skin condition.

[0409] The fifth case of step S1131 will be described.

[0410] The processor 32 refers to the skin care log database associated with the user identification information included in the estimation request data ( Figure 6 ), and estimates the user's preferences for cosmetics (for example, cosmetics with a high usage frequency or cosmetics preferred by the user).

[0411] The processor 32 inputs the skin condition and the estimation result into the recommendation model, and outputs recommendation information suitable for the user's preferences according to the skin condition.

[0412] The first to fifth cases of step S1131 can be combined.

[0413] After step S1131, the server 30 performs an estimation response (S1132) in the same manner as in this embodiment.

[0414] After step S1132, the client device 10 displays the estimation result (S1112) in the same manner as in this embodiment.

[0415] (6-6-3) Summary of Modification Example 6

[0416] According to Modification Example 6, it is also possible to generate a recommendation by referring to the estimation result of the skin condition and the user's action log information. Thus, it is possible to provide a recommendation that takes into account not only the user's skin condition but also the user's action preferences.

[0417] According to Modification Example 6, it is also possible to generate a recommendation that encourages the user's proficient actions. Thus, it is possible to encourage actions that are easy for the user to perform.

[0418] According to Modification Example 6, it is also possible to generate a recommendation that encourages actions other than the user's unskilled actions. Thus, it is possible to encourage actions that are easy for the user to perform.

[0419] According to Modification Example 6, it is also possible to generate a recommendation that takes into account actions with significant biological responses based on the combination of the biological history and the action history. Thus, it is possible to provide a recommendation that takes into account not only the user's skin condition but also the user's action preferences.

[0420] According to Modification Example 6, it is also possible to estimate the user's preferences based on the result of the consultation and generate a recommendation corresponding to the estimated preferences. Thus, it is possible to provide a recommendation that takes into account not only the user's skin condition but also the user's preferences.

[0421] According to Modification Example 6, it is also possible to presume the user's preference for cosmetics based on the history of skin care, and generate suggestions corresponding to the presumed preference. Thus, it is possible to provide suggestions that take into account not only the user's skin condition but also the user's preference for cosmetics.

[0422] (6-7) Modification Example 7

[0423] Modification Example 7 will be described. Modification Example 7 is an example of presuming the skin condition based on the history of DPG (Distal Proximal-temperature Gradient) parameters.

[0424] (6-7-1) Outline of Modification Example 7

[0425] The outline of Modification Example 7 will be described. Figure 19 It is an explanatory diagram of the outline of Modification Example 7.

[0426] As Figure 19 shown, the history of the user's deep body temperature and the history of the skin temperature are stored in the server 30.

[0427] The server 30 calculates the history of the DPG parameters based on the history of the deep body temperature and the history of the skin temperature.

[0428] The server 30 presumes the user's skin condition based on the history of the DPG parameters.

[0429] The server 30 presents the presumption result (that is, the user's skin condition) and the history of the DPG parameters to the user via the client device 10.

[0430] The DPG parameter can also be referred to as the distal proximal temperature gradient. The DPG parameter is any one of the following.

[0431] · The difference between the deep body temperature at the center of the body and the peripheral body temperature at the peripheral part (for example, the tips of hands and feet, etc.)

[0432] · The difference between the peripheral skin temperature and the deep body temperature

[0433] · The difference between the distal body temperature and the proximal body temperature

[0434] Generally speaking, it is known that when the DPG decreases, it promotes falling asleep, and the DPG parameter has the same characteristic.

[0435] (6-7-2) Information Processing of Modification Example 7

[0436] The information processing of Modification Example 7 will be described. Figure 20 It is a timing diagram of the information processing of Modification Example 7. Figure 21 It is shown inFigure 20 A diagram showing an example of a screen displayed during information processing.

[0437] As Figure 20 shown, the client device 10 acquires the deep body temperature (S8110).

[0438] Specifically, the processor 12 acquires the deep body temperature information of the user.

[0439] The deep body temperature information is acquired from at least one of the following, for example.

[0440] · Deep body temperature information obtained from a deep thermometer used by the user

[0441] · Deep body temperature information obtained from a wearable device worn by the user

[0442] · An infrared sensor capable of measuring deep body temperature

[0443] After step S8110, the client device 10, in the same manner as in this embodiment ( Figure 11 ), accepts a user instruction (S1110).

[0444] After step S1110, the client device 10 issues a presumption request (S8111).

[0445] Specifically, when the user inputs user identification information to the field object F1110 and operates the operation object B1110, the processor 12 sends presumption request data to the server 30. The presumption request data includes the following information, for example.

[0446] · User identification information input to the field object F1110

[0447] · Deep body temperature information obtained in step S8110

[0448] After step S1111, the server 30 calculates DPG parameters (S8130).

[0449] Specifically, the processor 32 refers to the deep body temperature log database associated with the user identification information included in the presumption request data ( Figure 5 ), and determines the history of deep body temperature for a predetermined period (for example, 24 hours back from the execution date and time of step S1310).

[0450] The processor 32 refers to the biological log database associated with the user identification information included in the presumption request data ( Figure 9 ), and determines the history of skin temperature for the same predetermined period (that is, the same period as the deep body temperature log information).

[0451] The processor 32 calculates the value of at least one of the following as the DPG based on the determined history of the deep body temperature and the history of the skin temperature.

[0452] · The difference between the deep temperature and the skin temperature included in the same time window (for example, the time difference between the timestamp information of the deep body temperature log database ( Figure 5 ) and the timestamp information of the organism log database ( Figure 9 ) is within a predetermined time)

[0453] · The value obtained by applying a predetermined filter (as an example, a smoothing filter configured to reduce noise (for example, Savitzky-Golay filter)) to the difference between the deep temperature and the skin temperature included in the same time window

[0454] In this way, the DPG parameter for each time window can be obtained.

[0455] As a result, a history of the DPG parameters can be obtained (that is, information arranging the DPG parameters for each time window in chronological order).

[0456] After step S8130, the server 30 estimates the skin condition (S8131).

[0457] Specifically, a skin condition model is stored in the storage device 31. The relationship between the history of the DPG parameters and the skin condition is described in the skin condition model. The skin condition model includes at least one of the current skin condition model and the future skin condition model.

[0458] The relationship between the history of the DPG parameters and the current skin condition is described in the current skin condition model.

[0459] The relationship between the history of the DPG parameters and the future skin condition is described in the future skin condition model.

[0460] The processor 32 inputs the DPG parameter into the current skin condition model and outputs the current skin condition corresponding to the history of the DPG parameter.

[0461] The processor 32 inputs the determined DPG parameter into the future skin condition model and outputs the future skin condition corresponding to the DPG parameter.

[0462] After step S8131, the server 30 performs generation of a suggestion (S1131) in the same manner as in this embodiment ( Figure 11 ).

[0463] After step S8131, the server 30 estimates the internal rhythm (S8132).

[0464] The internal rhythm means that, when centered on a person, there are rhythmically occurring changes in a cycle (e.g., 24 hours) on the time axis of the person.

[0465] In the first example of step S8132, an internal rhythm estimation model is stored in the storage device 31. The relationship between the history of deep body temperature and the internal rhythm is described in the internal rhythm estimation model.

[0466] The processor 32 inputs the history of deep body temperature determined in step S8130 into the internal rhythm estimation model and outputs the internal rhythm corresponding to the history of deep body temperature.

[0467] In the second example of step S8132, an internal rhythm estimation model is stored in the storage device 31. The relationship between the actions during sleep and the internal rhythm is described in the internal rhythm estimation model.

[0468] The processor 32 obtains sleep action information related to the actions of the user during sleep from a device equipped with an acceleration sensor (e.g., a wearable device, a smartphone, a pillow, a mattress, or a bed) or an image sensor.

[0469] The processor 32 inputs the sleep action information into the internal rhythm estimation model and estimates the internal rhythm corresponding to the actions during sleep.

[0470] After step S8131, the server 30 estimates the internal clock (biological clock) (S8133).

[0471] Specifically, an internal clock estimation model is stored in the storage device 31. The relationship between the history of deep body temperature and the internal clock (biological clock) is described in the internal clock estimation model.

[0472] The processor 32 inputs the history of deep body temperature determined in step S8130 into the internal clock estimation model and outputs information related to the internal clock corresponding to the history of deep body temperature (hereinafter referred to as "internal clock information (biological clock information)").

[0473] The processor 32 associates the internal clock information with the combination of the user identification information and the execution date and time of step S8133 and stores it in the storage device 31.

[0474] After step S8133, the server 30 generates DPG suggestions (S8134).

[0475] Specifically, a DPG suggestion model is stored in the storage device 31.

[0476] The DPG recommendation model records the history of DPG parameters and their correlation with DPG recommendations. A DPG recommendation refers to a recommendation for improving the variation of biological rhythms (as an example, increasing the frequency of variation of DPG parameters (i.e., the increase or decrease of DPG parameters)). Biological rhythms refer to the rhythms possessed by living organisms (including organisms other than humans). There are more than 300 kinds of biological rhythms.

[0477] The DPG recommendations include, for example, at least one of the following.

[0478] · Recommendations related to the content of exercise

[0479] · Recommendations related to bathing (for example, at least one of the recommended bathing time, recommended bathing temperature, recommended bath gel (as an example, the active ingredient contained in the bath gel and effectively acting on deep body temperature (such as ginger extract)), and bathing method (as an example, the usage method of the recommended bath gel))

[0480] · Recommendations related to bedtime

[0481] · Recommendations related to wake-up time

[0482] · Beauty methods (such as medical techniques like facial massage (for example, massage of facial expression muscles)), medical techniques using beauty products or beauty equipment (as an example, the usage method of a medicament with a warming effect)

[0483] · Recommendations related to relaxation (as an example, recommendations related to the warming of the face and body achieved by a warming device or a steam warming device, and the usage method of a medicament with a warming effect)

[0484] The processor 32 inputs the DPG parameters obtained in step S8130 into the DPG recommendation model and outputs information related to DPG recommendations (hereinafter referred to as "DPG recommendation information").

[0485] After step S8134, the server 30 performs an update of the database (S8135).

[0486] Specifically, the processor 32 adds a new record to the deep body temperature log database associated with the user identification information included in the estimation request data ( Figure 5 ).

[0487] The following information is stored in each field of the new record.

[0488] · "Timestamp" field: Information related to the execution date and time of step S8110

[0489] · "Deep body temperature" field: Deep body temperature information included in the estimation request data

[0490] After step S8135, the server 30 executes a presumption response (S8136).

[0491] Specifically, the processor 32 sends the presumption response data to the client device 10. The presumption response data includes, for example, the following information.

[0492] · Deep body temperature information obtained in step S8130

[0493] · Current skin state information obtained in step S1130

[0494] · Future skin state information obtained in step S1130

[0495] · History of DPG parameters obtained in step S8130

[0496] · Advice information obtained in step S1131

[0497] · Presumption result of the body rhythm obtained in step S8132

[0498] · Body clock information obtained in step S8133

[0499] · DPG advice information obtained in step S8134

[0500] After step S8136, the client device 10 executes display of the presumption result (S8112).

[0501] Specifically, the processor 12 displays the screen P8110 ( Figure 21 ) on the display.

[0502] The screen P8110 includes: display objects A1111 and A8110a to A8110c, and an image object IMG8110.

[0503] The display object A1111 is the same as Figure 12 The same.

[0504] The display object A8110a is an object for displaying the deep body temperature information obtained in step S8130 (that is, the deep body temperature information at the current time point (the execution time point of the display of the presumption result (S1112))).

[0505] The display object A8110b is an object for displaying the image object IMG8110, which represents the current time and the history of DPG parameters.

[0506] The image object IMG8110 has a circular ring shape (that is, the same shape as an analog clock).

[0507] The image object IMG8110 has the following regions.

[0508] · Inner region of the circular ring IMG8110a

[0509] · Outer region of the circular ring IMG8110b

[0510] The inner region of the circular ring IMG8110a is the region forming the inner side of the circular ring shape.

[0511] In the inner region of the circular ring IMG8110a, numbers representing the time (e.g., 0 to 23), the current time line L8110c, and the internal time line L8110d are displayed in the same way as an analog clock.

[0512] The current time line L8110c represents the current time (the time at the execution point of the display of the estimated result (S1112)).

[0513] The internal time line L8110d represents the time of the internal clock information.

[0514] The outer region of the circular ring IMG8110b is the region forming the outer side of the circular ring shape.

[0515] In the outer region of the circular ring IMG8110b, a line representing the history of DPG parameters (hereinafter referred to as the "DPG parameter line") L8110a and a line representing the internal rhythm (hereinafter referred to as the "internal rhythm line") L8110b are displayed.

[0516] The DPG parameter line L8110a is drawn at a position corresponding to the value of the DPG parameter at each time shown in the outer region of the circular ring IMG8110b. This means that the farther the drawing position of the DPG parameter line L8110a is from the center of the inner region of the circular ring IMG8110a, the higher the DPG parameter (i.e., the greater the difference between the deep body temperature and the skin temperature).

[0517] The internal rhythm line L8110b represents the daily rhythm at each time shown in the outer region of the circular ring IMG8110b. The internal rhythm line L8110b is drawn at a position corresponding to the value of the level of the internal rhythm (e.g., the sleep rhythm stored in the Figure 10 ) action log database). This means that the farther the drawing position of the internal rhythm line L8110b is from the center of the inner region of the circular ring IMG8110a, the better the internal rhythm (e.g., the higher the sleep level (i.e., in a deep sleep state)).

[0518] The display object A8110c is an object that displays DPG advice information.

[0519] For example, the DPG advice information is information related to the ideal bath time.

[0520] (6-7-3) Generalization of Variant Example 7

[0521] According to Variant Example 7, the skin condition can also be estimated based on DPG parameters. Thus, compared with the case where DPG parameters are not used, since more parameters are referred to, the accuracy of estimating the skin condition can be further improved, and suggestions more suitable for improving the skin condition can be presented.

[0522] According to Variant Example 7, the history of DPG parameters can also be displayed in a circular ring form. Thus, the user can clearly know the rhythm of the DPG parameters.

[0523] In Variant Example 7, the internal rhythm can also be obtained from the wearable device worn by the user. In this case, the estimation of the internal rhythm (S8132) can be omitted.

[0524] In Variant Example 7, in the internal clock estimation model, at least one of the following correlations with the internal clock (biological clock) can be described instead of the history of deep body temperature.

[0525] · Rhythmicity of gene expression

[0526] · Sleep-wake cycle that can be determined by electroencephalogram measurement

[0527] · Urinary steroid hormones that can be determined by blood sampling

[0528] (6-8) Variant Example 8

[0529] Variant Example 8 will be described. Variant Example 8 is an example of estimating at least one of the current skin condition and the future skin condition, and the internal rhythm based on the history of deep body temperature.

[0530] (6-8-1) Outline of Variant Example 8

[0531] The outline of Variant Example 8 will be described. Figure 22 It is an explanatory diagram of the outline of Variant Example 8.

[0532] As Figure 22 shown, the history of the user's deep body temperature is stored in the server 30.

[0533] The server 30 estimates the user's skin condition and internal rhythm based on the history of deep body temperature.

[0534] The server 30 presents the estimation results (that is, the estimation results of the user's skin condition and the internal rhythm) to the user via the client device 10.

[0535] (6-8-2) Information Processing of Variant Example 8

[0536] The information processing of Modification 8 will be described. Figure 23 This is a timing chart of the information processing of Modification 8. Figure 24 This is a diagram showing Figure 23 an example of a screen displayed in the information processing.

[0537] As Figure 24 shown, the client device 10, similar to this embodiment ( Figure 11 ), executes reception of a user instruction (S1110) to estimation request (S1111).

[0538] After step S1111, the server 30, similar to this embodiment ( Figure 11 ), executes estimation of skin condition (S1130) to generation of a suggestion (S1131).

[0539] After step S1131, the server 30 executes estimation of the body rhythm (S9130).

[0540] Specifically, a body rhythm estimation model is stored in the storage device 31. The correlation between the history of deep body temperature and the body rhythm is described in the body rhythm estimation model. The body rhythm includes, for example, at least one of the following.

[0541] · Circadian rhythm

[0542] · Circaseptan rhythm (that is, 1-week rhythm)

[0543] · Circamenstrual rhythm (1-month rhythm)

[0544] · Circannual rhythm (1-year rhythm)

[0545] · Sleep rhythm

[0546] · Temperature rhythm

[0547] · Psychogenic stress rhythm

[0548] · Heatstroke risk rhythm (for example, the time course of the level of the risk of heatstroke)

[0549] · Depression rhythm (for example, the time course of the level of the depressive state)

[0550] · Menstrual rhythm

[0551] · Seasonal rhythm

[0552] The body rhythm estimation model includes: a real rhythm estimation model and an ideal rhythm estimation model.

[0553] The correlation between the history of deep body temperature and the real body rhythm is described in the real rhythm estimation model.

[0554] The ideal rhythm estimation model describes the correlation between at least one of the user's place of residence and the action history and the ideal internal rhythm.

[0555] The processor 32 refers to the deep body temperature log database associated with the user identification information included in the estimation request data ( Figure 5 ), and determines the deep body temperature log information for a predetermined period (for example, one month back from the execution date and time of step S1130).

[0556] The processor 32 inputs the determined deep body temperature log information into the actual rhythm model, and outputs information related to the actual internal rhythm corresponding to the deep body temperature log information (hereinafter referred to as "actual internal rhythm information").

[0557] The actual internal rhythm information is at least one of the following.

[0558] · Information related to the actual internal rhythm for one day (for example, 24 hours back from the execution date and time of step S1130, or 24 hours of the previous day of the execution date and time of step S1130)

[0559] · Information related to the average of the actual internal rhythms for n (n is an integer of 2 or more) days (for example, n days back from the execution date and time of step S1130, or n days back from the previous day of the execution date and time of step S1130)

[0560] The processor 32 refers to the user database associated with the user identification information included in the estimation request data ( Figure 4 ), and determines the user's residence information.

[0561] The processor 32 refers to the action log database associated with the user identification information included in the estimation request data ( Figure 10 ), and determines the action log information for a predetermined period (for example, one month back from the execution date and time of step S1130).

[0562] The processor 32 inputs at least one of the determined residence information and the determined action log information into the ideal rhythm model, and outputs information related to the ideal internal rhythm corresponding to at least one of the residence information and the action log information (hereinafter referred to as "ideal rhythm information"). The ideal internal rhythm refers to the internal rhythm that has a beneficial effect on the skin condition.

[0563] The temperature of the place where the user is located affects the DPG parameter due to the change in the peripheral skin temperature caused by vascular heat release.

[0564] For example, in the case where the user's residence information indicates a high-temperature area, an ideal body rhythm is that the variation in the body rhythm increases during a cool period and decreases during a hot period.

[0565] For example, in the case where the user's residence information indicates a low-temperature area, an ideal body rhythm is that the variation in the body rhythm decreases during a cool period and increases during a hot period.

[0566] For example, in the case where the user's action log information indicates active morning actions, an ideal body rhythm is that the variation in the body rhythm increases in the morning and decreases at night.

[0567] For example, in the case where the user's action log information indicates active night actions, an ideal body rhythm is that the variation in the body rhythm increases at night and decreases in the morning.

[0568] After step S9130, the server 30 generates a body rhythm recommendation (S9131).

[0569] Specifically, a body rhythm recommendation model is stored in the storage device 31. The relationship between the body rhythm and the body rhythm recommendation is described in the body rhythm recommendation model. The body rhythm recommendation refers to a recommendation for improving the body rhythm to have a positive cycle or positive influence on at least one of the physical state (e.g., at least one of the skin state and the internal state) and the mental state. The body rhythm recommendation includes, for example, at least one of the following.

[0570] · Recommendations related to exercise

[0571] · Recommendations related to bath time

[0572] · Recommendations related to sleep time (e.g., at least one of the wake-up time and the bedtime)

[0573] · Recommendations related to rest time

[0574] · Recommendations related to beauty actions (e.g., the type of cosmetics, skin care products, or beauty equipment (hereinafter referred to as "beauty products"), the usage method of beauty products, the recommended time for beauty actions, and beauty methods (e.g., massage methods))

[0575] · Recommendations related to rest or napping

[0576] · Recommendations related to beauty supplements (e.g., containing ingredients with a sweating or heat-absorbing effect)

[0577] The processor 32 inputs the circadian rhythm information obtained in step S9130 into the circadian rhythm recommendation model and outputs information related to the circadian rhythm recommendation corresponding to the circadian rhythm information (hereinafter referred to as "circadian rhythm recommendation information").

[0578] After step S9131, the server 30 performs a presumption response (S9132).

[0579] Specifically, the processor 32 sends the presumption response data to the client device 10. The presumption response data includes, for example, the following information.

[0580] · The current skin condition information obtained in step S1130

[0581] · The future skin condition information obtained in step S1130

[0582] · The recommendation information obtained in step S1131

[0583] · The actual circadian rhythm information obtained in step S9130

[0584] · The ideal circadian rhythm information obtained in step S9130

[0585] · The circadian rhythm recommendation information obtained in step S9131

[0586] After step S9132, the client device 10 displays the presumption result (S9110).

[0587] Specifically, the processor 12 displays the screen P9110 ( Figure 24 ) on the display.

[0588] The screen P9110 includes display objects A1111 and A9110, and an image object IMG9110. The display object A1111 is the same as Figure 12 Same.

[0589] The display object A9110 is an object for displaying the circadian rhythm recommendation information.

[0590] For example, the circadian rhythm recommendation information includes information related to the following.

[0591] · Ideal exercise

[0592] · Ideal input time

[0593] · Ideal bedtime

[0594] The image object IMG9110 has a circular ring shape (that is, the same shape as an analog clock).

[0595] In the image object IMG9110, the following are displayed in the same way as an analog clock: numbers representing time (e.g., 0 to 23), an ideal line L9110a, and an actual line L9110b.

[0596] The ideal line L9110a represents ideal internal rhythm information. The ideal internal rhythm information is, for example, an ideal sleep rhythm (as an example, sleep time and wake-up time). Figure 24 This is an example showing that the ideal sleep time is from 0:00 to 8:00 and the ideal wake-up time is after 8:00.

[0597] The actual line L9110b represents actual internal rhythm information. The actual internal rhythm information is, for example, an actual sleep rhythm (as an example, sleep time and wake-up time). For example, Figure 24 This shows that the actual sleep time is from 0:00 to 8:00 and the actual wake-up time is after 8:00.

[0598] That is to say, Figure 24 This indicates that the ideal internal rhythm is consistent with the actual internal rhythm.

[0599] (6 - 8 - 3) Summary of Variation Example 8

[0600] According to Variation Example 8, it is also possible to estimate the internal rhythm based on the historical record of deep body temperature. Thus, it is possible to further improve the accuracy of estimating the internal rhythm that affects the skin condition, and it is possible to give suggestions that are more suitable for improving the skin condition.

[0601] (6 - 9) Variation Example 9

[0602] Variation Example 9 will be described. Variation Example 9 is an example in which, in addition to the historical record of deep body temperature, information on the user's time displacement (hereinafter referred to as "time displacement information") is presented to the user.

[0603] (6 - 9 - 1) Outline of Variation Example 9

[0604] The outline of Variation Example 9 will be described. Figure 25 This is an explanatory diagram of the outline of Variation Example 9.

[0605] As Figure 25 shown, the historical record of the user's deep body temperature is stored in the server 30.

[0606] The server 30 estimates the user's skin condition based on the historical record of deep body temperature.

[0607] The server 30 generates time displacement information by the method described later.

[0608] The server 30 presents the estimation result (i.e., the estimation result of the user's skin condition) and the time displacement information to the user via the client device 10.

[0609] The time displacement information includes, for example, at least one of the following.

[0610] · Skin grade information related to the history of the grade of the user's skin condition

[0611] · Location information related to the history of the user's location at each time

[0612] · Environment information related to the history of the user's environment at each time (i.e., the environment where the user is located)

[0613] · Menstrual cycle information related to the history of the user's menstrual cycle

[0614] · Skin age information related to the history of the user's skin age

[0615] · The user's biological log information

[0616] (6-9-2) Information processing of Modification Example 9

[0617] The information processing of Modification Example 9 will be described. Figure 26 is a timing chart of the information processing of Modification Example 9. Figure 27 is shown in Figure 26 a diagram showing an example of a screen displayed in the information processing.

[0618] As Figure 26 shown, the client device 10, similar to this embodiment ( Figure 11 ), executes the reception of the user's instruction (S1110) to the estimation request (S1111).

[0619] After step S1111, the server 30, similar to this embodiment ( Figure 11 ), executes the estimation of the skin condition (S1130) to the generation of the advice (S1131).

[0620] After step S1131, the server 30 executes the generation of the time displacement information (S10130).

[0621] In the first example of step S10130, the server 30 generates skin grade information as the time displacement information.

[0622] Specifically, the skin grade information includes at least the following information related to the history of the grade of the skin condition.

[0623] · Physical states (as an example, skin viscoelasticity, water content of the stratum corneum, barrier function of the stratum corneum, antioxidant function, sebum amount, blood flow, state of the stratum corneum, skin color, skin softness, degree of glycation, blood, urine, and sebum RNA)

[0624] · Qualitative states (as an example, skin age, skin moisture, skin laxity, skin condition, makeup adherence of the skin, and susceptibility to deterioration of skin diseases (as an example, acne or rough skin))

[0625] The skin state level determination model is stored in the storage device 31. The correlation between the current skin state and the skin state level is described in the skin state level determination model.

[0626] The processor 32 inputs the current skin state obtained in step S1130 into the skin state level determination model, and outputs the skin level information corresponding to the current skin state.

[0627] The processor 32 associates the combination of the skin level information, the user identification information, and the information related to the execution date and time of step S10130, and stores it in the storage device 31 as the time displacement information.

[0628] In the second example of step S10130, the server 30 generates location information as the time displacement information.

[0629] Specifically, the processor 32 obtains the location information at each time from the device carried by the user, and associates the combination of the location information, the user identification information, and the information related to the execution date and time of step S10130, and stores it in the storage device 31.

[0630] The device includes, for example, at least one of the following.

[0631] · A smartphone equipped with GPS (Global Positioning System)

[0632] · A wearable device equipped with GPS (as an example, a ring-shaped device)

[0633] In the third example of step S10130, the server 30 generates environment information as the time displacement information.

[0634] Specifically, the processor 32 obtains the location information at each time from the device carried by the user, and associates the combination of the location information, the user identification information, and the information related to the execution date and time of step S10130, and stores it in the storage device 31 as the time displacement information.

[0635] The processor 32 obtains the environmental information corresponding to the location information from an external server (for example, a server that provides environmental information for each combination of time and location), associates the combination of the environmental information with the user identification information and the information related to the execution date and time of step S10130, and stores it in the storage device 31 as time displacement information.

[0636] The environmental information includes, for example, at least one of the following related information.

[0637] · Weather

[0638] · Temperature

[0639] · Humidity

[0640] · Ultraviolet exposure

[0641] · The amount of light the user is exposed to during bathing (hereinafter referred to as "illuminance")

[0642] In the fourth example of step S10130, the server 30 generates menstrual cycle information as time displacement information.

[0643] Specifically, a menstrual cycle determination model is stored in the storage device 31. The relationship between the historical record of deep body temperature and the menstrual cycle is described in the menstrual cycle determination model.

[0644] The processor 32 refers to the deep body temperature log database associated with the user identification information included in the estimation request data ( Figure 5 ) to determine the deep body temperature log information for a predetermined period (for example, one month back from the execution date and time of step S1130).

[0645] The processor 32 inputs the determined deep body temperature log information into the menstrual cycle determination model and outputs the menstrual cycle corresponding to the deep body temperature log information.

[0646] The processor 32 associates the menstrual cycle with the combination of the user identification information and the information related to the execution date and time of step S10130, and stores it in the storage device 31 as time displacement information.

[0647] In the fifth example of step S10130, the server 30 generates skin age information corresponding to the historical record of deep body temperature as time displacement information.

[0648] Specifically, a skin age determination model is stored in the storage device 31. The relationship between the historical record of deep body temperature and the skin age is described in the skin age determination model.

[0649] The processor 32 refers to the deep body temperature log database associated with the user identification information included in the estimation request data ( Figure 5 ) and determines the deep body temperature log information for a predetermined period (e.g., one month back from the execution date and time of step S1130).

[0650] The processor 32 inputs the determined deep body temperature log information into the skin age determination model and outputs the skin age corresponding to the deep body temperature log information.

[0651] The processor 32 associates the skin age with the combination of the user identification information and the information related to the execution date and time of step S10130 and stores it as time displacement information in the storage device 31.

[0652] In the sixth example of step S10130, the server 30 generates biological log information as time displacement information.

[0653] Specifically, the processor 32 refers to the biological log database associated with the user identification information included in the estimation request data ( Figure 9 ) and determines the biological log information for a predetermined period (e.g., one month back from the execution date and time of step S1130).

[0654] After step S10130, the server 30 executes an estimation response (S10131).

[0655] Specifically, the processor 32 sends the estimation response data to the client device 10. The estimation response data includes, for example, the following information.

[0656] · The current skin condition information obtained in step S1130

[0657] · The future skin condition information obtained in step S1130

[0658] · The advice information obtained in step S1131

[0659] · The time displacement information obtained in step S10130

[0660] After step S10131, the client device 10 executes the display of the estimation result (S10110).

[0661] Specifically, the processor 12 displays the screen P10110 ( Figure 27 ) on the display.

[0662] The screen P10110 includes: a display object A1111, an operation object B10110, and an image object IMG10110. The display object A1111 is the same as Figure 12 Same.

[0663] The operation object B10110 is an object (e.g., a slider object) that accepts a user instruction to change the scale of the time for the image object IMG10110.

[0664] The image object IMG10110 is a line graph.

[0665] The horizontal axis of the line graph is time T.

[0666] The vertical axis of the line graph is the deep body temperature and the values of the time displacement information.

[0667] The image object IMG10110 includes: a deep body temperature log line L10110a and a time displacement line L10110b.

[0668] The deep body temperature log line L10110a represents the history of the deep body temperature corresponding to the time scale on the horizontal axis.

[0669] The time displacement line L10110b represents the history of the time displacement information corresponding to the time scale on the horizontal axis.

[0670] When the user operates the operation object B10110, the processor 12 selects the scale of the horizontal axis of the line graph corresponding to the time scale corresponding to the position of the slider, and displays the deep body temperature log line L10110a and the time displacement line L10110b corresponding to the changed scale.

[0671] The options for the time scale include the following.

[0672] · Second

[0673] · Minute

[0674] · Hour

[0675] · Day

[0676] · Month

[0677] · Year

[0678] The time displacement information in the case where the time scale is the first time scale (e.g., second, minute, or hour) is preferably the following.

[0679] · Skin grade information

[0680] · Location information

[0681] · Environment information

[0682] The time displacement information in the case where the time scale is the second time scale (e.g., day, month, or year) larger than the first time scale is preferably the following.

[0683] · Menstrual cycle

[0684] In the case where the time ratio is the largest time ratio (for example, year) in the second time ratio, the time displacement information is preferably as follows.

[0685] · Skin age

[0686] (6-9-3) Summary of Modification Example 9

[0687] According to Modification Example 9, in addition to the history of the deep body temperature, time displacement information is also presented. Thus, it is possible to let the user know the factors (the history of the deep body temperature and the time displacement information) that affect the skin condition.

[0688] (7) Other modification examples

[0689] Other modification examples will be described.

[0690] The storage device 11 may also be connected to the client device 10 via the network NW. The storage device 31 may also be connected to the server 30 via the network NW.

[0691] Each step of the above information processing can be executed in the client device 10 and the server 30.

[0692] For example, when the client device 10 can execute all steps of the above information processing, the client device 10 functions as an information processing device that works independently (standalone) without sending a request to the server 30.

[0693] In the present embodiment, Figure 8 the trigger of the information processing shows an example in which the user uses the client device 10 to access a predetermined website, but the present embodiment is not limited thereto.

[0694] The present embodiment can also be applied to an example in which the display of the estimation result (S1112) is executed without the user's instruction.

[0695] For example, the client device 10 acquires deep body temperature information from the wearable sensor and sends it to the server 30.

[0696] The server 30 uses the deep body temperature information sent from the client device 10 to execute the estimation of the internal rhythm (S1130) to the estimation response (S1132).

[0697] The client device 10 uses the estimation response data sent from the server 30 to execute the display of the estimation result (S1112).

[0698] According to this example, corresponding to the acquisition of deep body temperature information by the wearable device, the estimated skin condition is presented to the user. Thus, the user can obtain the estimated result of the skin condition corresponding to the deep body temperature without the burden of receiving instructions from the user.

[0699] In the present embodiment, an example of the future skin condition is shown by estimating the skin condition at a time point two weeks after the execution time point of the estimation of the skin condition (S1131), but the scope of the present embodiment is not limited thereto.

[0700] The present embodiment can also be applied to an example of estimating the skin condition at an arbitrarily specified time point by the user as the future skin condition.

[0701] In this case, the relationship between the circadian rhythm, the future time point, and the future skin condition is described in the future skin condition model.

[0702] When the user specifies an arbitrary future time point, the processor 32 inputs the circadian rhythm corresponding to the history of the deep body temperature and the future time point specified by the user into the future skin condition model, and outputs the future skin condition corresponding to the combination of the circadian rhythm and the future time point specified by the user.

[0703] As described above, the embodiments of the present invention have been described in detail, but the scope of the present invention is not limited to the above embodiments. In addition, various improvements and changes can be made to the above embodiments without departing from the gist of the present invention. In addition, the above embodiments and modifications can be combined.

[0704] Reference Numeral Explanation

[0705] 1 Information processing system

[0706] 10 Client device

[0707] 11 Storage device

[0708] 12 Processor

[0709] 13 Input / output interface

[0710] 14 Communication interface

[0711] 30 Server

[0712] 31 Storage device

[0713] 32 Processor

[0714] 33 Input / output interface

[0715] 34 Communication interface.

Claims

1. An information processing device, comprising: An acquisition unit that acquires deep body temperature log information related to the history of the user's deep body temperature; A skin condition estimation unit that estimates the user's skin condition based on the deep body temperature log information; and A presentation unit that presents the estimation result of the skin condition to the user.

2. The information processing device according to claim 1, wherein the skin condition estimation unit estimates the user's future skin condition.

3. The information processing device according to claim 2, wherein the future skin condition is the tendency of the change in the skin condition that will occur on the skin in the future.

4. The information processing device according to claim 1, wherein the skin condition estimation unit estimates the user's current skin condition.

5. The information processing device according to any one of claims 1 to 4, further comprising: a recommendation generation unit that generates a recommendation corresponding to the estimation result of the skin condition, wherein the presentation unit presents the recommendation.

6. The information processing device according to claim 5, wherein the recommendation generation unit generates a recommendation corresponding to the combination of the estimation result of the skin condition and the user's preferences.

7. The information processing device according to claim 6, further comprising: a preference estimation unit that refers to the user's action log information to estimate the user's preference for actions.

8. The information processing device according to claim 7, wherein the preference estimation unit refers to the user's action log information to estimate the actions that the user is good at, and the recommendation generation unit generates a recommendation that urges the actions that the user is good at.

9. The information processing device according to claim 7, wherein the preference estimation unit refers to the user's action log information to estimate the actions that the user is not good at, and the recommendation generation unit generates a recommendation that urges actions other than the actions that the user is not good at.

10. The information processing device according to claim 6, wherein the preference estimation unit refers to the user's medical consultation information to estimate the user's preferences.

11. The information processing device according to claim 6, wherein the preference estimation unit refers to the user's skin care log information to estimate the user's preference for cosmetics.

12. The information processing device according to claim 6, wherein the preference estimation unit refers to the user's action log information and biological log information to estimate the user's preference for actions with significant biological reactions.

13. The information processing device according to any one of claims 1 to 4, wherein the skin condition estimation unit estimates the skin condition based on the deep body temperature log information and the user's skin care log information.

14. The information processing device according to any one of claims 1 to 4, wherein the skin condition estimation unit estimates the skin condition based on the deep body temperature log information and the user's physical condition log information.

15. The information processing device according to any one of claims 1 to 4, wherein the skin condition estimation unit estimates the skin condition based on the deep body temperature log information and the user's mental condition log information.

16. The information processing apparatus according to any one of claims 1 to 4, wherein the skin condition estimation unit estimates the skin condition based on the deep body temperature log information and the biological log information of the user.

17. The information processing apparatus according to any one of claims 1 to 4, wherein the skin condition estimation unit estimates the skin condition based on the deep body temperature log information and the action log information of the user.

18. The information processing apparatus according to any one of claims 1 to 4, wherein the skin condition estimation unit estimates the skin condition based on the history of DPG parameters, i.e., distal-proximal temperature gradient parameters.

19. The information processing apparatus according to claim 18, wherein the prompting unit prompts the history of the DPG parameters in a circular form.

20. The information processing apparatus according to claim 18, further comprising: a DPG advice generation unit that generates a DPG advice for improving the rhythm of the change in the DPG parameters based on the history of the DPG parameters.

21. The information processing apparatus according to any one of claims 1 to 4, further comprising: an in-body rhythm estimation unit that estimates the actual in-body rhythm based on the history of the deep body temperature, wherein the prompting unit prompts the actual in-body rhythm.

22. The information processing apparatus according to claim 21, wherein the in-body rhythm estimation unit estimates an ideal in-body rhythm based on at least one of the place of residence of the user and the history of the actions of the user.

23. The information processing apparatus according to claim 22, wherein the prompting unit prompts the actual in-body rhythm and the ideal in-body rhythm in a circular form.

24. The information processing apparatus according to claim 21, further comprising: an in-body rhythm advice generation unit that generates an in-body rhythm advice based on the in-body rhythm, wherein the in-body rhythm advice is advice for improving the in-body rhythm to give a positive cycle or positive influence to at least one of the physical state and the mental state.

25. The information processing apparatus according to any one of claims 1 to 4, wherein the prompting unit prompts the deep body temperature log information and the time displacement information of the user.

26. An information processing method, which is an information processing method using a computer, comprising: an acquisition step of acquiring deep body temperature log information related to the history of the deep body temperature of a user; a skin condition estimation step of estimating the skin condition of the user based on the deep body temperature log information; a prompting step of prompting the estimation result of the skin condition to the user.

27. A program for causing a computer to function as the following units: an acquisition unit that acquires deep body temperature log information related to the history of the deep body temperature of a user; a skin condition estimation unit that estimates the skin condition of the user based on the deep body temperature log information; and a prompting unit that prompts the estimation result of the skin condition to the user.

Images