Program, information processing method and information processing device

Abstract

A program, an information processing method, and an information processing device are provided that can provide a user with information on food intake patterns that will give a medium- to long-term realization over a certain period of time or longer. [Solution] A program provides appropriate advice to a user based on intake information about the food the user has consumed and one or more of the user's emotional information, biometric information, and behavioral information and / or actual experience score information derived from said information.The user terminal acquires the intake information and actual experience score information, outputs one or more of the acquired intake information, emotional information, biometric information, and behavioral information and / or actual experience score information derived from said information, and the user's identification information to a control unit of a server, acquires advice information from the control unit of the server including information regarding the correlation between the output intake information and actual experience score information, and displays the acquired advice information on a display unit.

Description

Technical Field

[0001] The present invention relates to a program, an information processing method, and an information processing apparatus. This application claims priority to Japanese Patent Application No. 2021-128356 filed in Japan on August 4, 2021, the content of which is incorporated herein by reference.

Background Art

[0002] With the spread of the Internet, various types of information are provided via the Internet. Regarding the technology of providing various types of information via the Internet, there is known a technology that can generate a situation in which information for user action support is more acceptable to users (for example, see Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in a conventional system as disclosed in Patent Document 1, regarding input information in which the foods eaten by ordinary users (dietary content), actions, etc. are intricately intertwined, it is difficult to accurately grasp which input information had an effect on the user, or what factors affected the user's perception of whether there was no effect. Furthermore, it is difficult to verify whether the effects given by those factors give the user momentary pleasure or an effect that gives a medium- to long-term perception over a certain period, without conducting surveys for each region and each group of those factors. Furthermore, conventional clinical trials conducted to verify the health effects of foods require subjects to be under controlled, non-everyday conditions, such as abstinence from alcohol and smoking, and to evaluate the effects of specific foods by establishing periods of consumption and non-consumption. Conventional clinical trials were not a method for selecting target foods, nor were they a method for verifying the effect of improving long-term perceived scores such as user life satisfaction.

[0005] This invention has been made in view of these circumstances, and its purpose is to provide a program or the like that can provide users with information on food consumption patterns that give a medium- to long-term sense of the effects over a certain period of time or longer. [Means for solving the problem]

[0006] A program according to one aspect of the present invention causes a computer to acquire intake information regarding food consumed by a user, to acquire one or more pieces of information from the user's emotional information, biological information, and behavioral information, and / or perceived score information derived from said information, to output the acquired intake information, one or more pieces of information from the emotional information, biological information, and behavioral information, and / or perceived score information derived from said information, and the user's identification information to an information processing unit, and to obtain advice information from the information processing unit that includes information regarding the correlation between said intake information and said perceived score information. [Effects of the Invention]

[0007] In one aspect of the present invention, information regarding food consumption patterns that provide a medium- to long-term sense of effectiveness over a certain period of time can be provided to the user. [Brief explanation of the drawing]

[0008] [Figure 1] This is a schematic diagram showing an example of the configuration of an information processing system. [Figure 2] This block diagram shows an example configuration of a server and user terminal. [Figure 3] This is a schematic diagram showing an example of the configuration of a database stored on a server. [Figure 4] It is a schematic diagram showing a configuration example of a DB stored in a server. [Figure 5] It is a schematic diagram showing a configuration example of a DB stored in a server. [Figure 6] It is a flowchart showing an example of a registration processing procedure for user information. [Figure 7] It is a flowchart showing an example of a registration processing procedure for user information. [Figure 8A] It is a schematic diagram showing an example of a screen of a user terminal. [Figure 8B] It is a schematic diagram showing an example of a screen of a user terminal. [Figure 9A] It is a schematic diagram showing an example of a screen of a user terminal. [Figure 9B] It is a schematic diagram showing an example of a screen of a user terminal. [Figure 10A] It is a schematic diagram showing an example of a screen of a user terminal. [Figure 10B] It is a schematic diagram showing an example of a screen of a user terminal. [Figure 11] It is a flowchart showing an example of an advice providing processing procedure. [Figure 12] It is a schematic diagram showing an example of a screen of a user terminal. [Figure 13] It is a schematic diagram showing a configuration example of a learning model. [Figure 14] It is a flowchart showing an example of a registration processing procedure for user information in Embodiment 2. [Figure 15A] It is a schematic diagram showing an example of a screen of a user terminal. [Figure 15B] It is a schematic diagram showing an example of a screen of a user terminal. [Figure 16] It is a schematic diagram showing a configuration example of a target amount DB. [[ID=五十]] [Figure 17A] It is a schematic diagram showing an example of a registration screen in Embodiment 3. [Figure 17B] It is a schematic diagram showing an example of an input screen for target information. [Figure 18] It is a flowchart showing an example of an advice providing processing procedure in Embodiment 3. [Figure 19A]It is a schematic diagram showing an example of a screen of a user terminal. [Figure 19B] It is a schematic diagram showing an example of a screen of a user terminal.

Embodiments for Carrying out the Invention

[0009] Hereinafter, the program, information processing method, and information processing apparatus of the present disclosure will be described in detail based on the drawings showing their embodiments.

[0010] (Embodiment 1) FIG. 1 is a schematic diagram showing a configuration example of an information processing system according to the present embodiment. The information processing system 100 of the present embodiment includes a server 10 and a plurality of user terminals 20 and the like, and the server 10 and the user terminals 20 are connected via a network N such as the Internet. The server 10 is an information processing apparatus capable of various information processing and transmission / reception of information, and is, for example, a server computer or a personal computer. A plurality of servers 10 may be provided, or may be realized by a plurality of virtual machines provided in one server device, or may be realized using a cloud server. The user terminal 20 is a terminal of a user who ingests food, and is an information processing apparatus (computer) such as a smartphone, a tablet terminal, or a personal computer, and may be configured by a dedicated terminal. Further, the information processing system 100 of the present embodiment includes wearable devices 30 used by each user, and the corresponding user terminal 20 and wearable device 30 can perform wireless communication.

[0011] The wearable device 30 is configured to measure the user's biometric information such as body temperature, blood pressure, heart rate, pulse, sweating, brain waves, emotional hormones, and stress hormones, as well as exercise information such as steps taken, distance traveled, and travel time, and sleep information such as sleep duration. The wearable device 30 has a pre-configured user terminal 20 to communicate with and transmits the various measured information to the user terminal 20. The wearable device 30 may be configured as a wristwatch as shown in Figure 1, or as glasses, a ring, etc. In this embodiment, the information processing system 100 may be configured using a blockchain (distributed ledger technology or distributed network) with multiple user terminals 20 as nodes, without having a server 10.

[0012] In the information processing system 100 of this embodiment, the user terminal 20 performs various information processing, such as receiving various types of user information entered by the user and transmitting the received information to the server 10. The server 10 performs various information processing, such as registering the user information received from the user terminal 20 and providing advice to each user based on the registered information.

[0013] Figure 2 is a block diagram showing an example configuration of a server 10 and a user terminal 20. The server 10 includes a control unit 11, a storage unit 12, a communication unit 13, an input unit 14, a display unit 15, a reading unit 16, etc., and each of these units is interconnected via a bus. The control unit 11 includes one or more processors such as a CPU (Central Processing Unit), an MPU (Micro-Processing Unit), or a GPU (Graphics Processing Unit). The control unit 11 executes various information processing and control processing that the server 10 should perform by appropriately executing a control program 12P stored in the storage unit 12.

[0014] The storage unit 12 includes RAM (Random Access Memory), flash memory, hard disk, SSD (Solid State Drive), etc. The storage unit 12 pre-stores the control program 12P executed by the control unit 11 and various data necessary for the execution of the control program 12P. The storage unit 12 also temporarily stores data generated when the control unit 11 executes the control program 12P. The storage unit 12 also stores the product information DB (database) 12a, member information DB 12b, advice DB 12c, etc., which will be described later. The product information DB 12a, member information DB 12b, and advice DB 12c may be stored in other storage devices connected to the server 10, or in other storage devices that the server 10 can communicate with.

[0015] The communication unit 13 is an interface for connecting to the network N via wired or wireless communication, and transmits and receives information with other devices via the network N. The input unit 14 includes, for example, a mouse and keyboard, and receives operation input from an administrator managing the server 10, and sends control signals corresponding to the operation content to the control unit 11. The display unit 15 is a liquid crystal display or an organic EL display, and displays various information according to instructions from the control unit 11. The input unit 14 and the display unit 15 may be configured as an integrated touch panel.

[0016] The reading unit 16 reads information stored on a portable storage medium 1a, including CD (Compact Disc)-ROM, DVD (Digital Versatile Disc)-ROM, USB (Universal Serial Bus) memory, SD (Secure Digital) card, etc. The control program 12P and various data stored in the storage unit 12 may be read by the control unit 11 from the portable storage medium 1a via the reading unit 16 and stored in the storage unit 12. Alternatively, the control unit 11 may download the control program 12P and various data stored in the storage unit 12 from another device via the communication unit 13 and store them in the storage unit 12.

[0017] The user terminal 20 includes a control unit 21, a storage unit 22, a communication unit 23, an input unit 24, a display unit 25, a reading unit 26, a camera 27, etc., and each of these units is interconnected via a bus. Since the units 21-26 of the user terminal 20 have the same configuration as the units 11-16 of the server 10, a detailed explanation of their configuration is omitted. In addition, the storage unit 22 of the user terminal 20 stores, in addition to the control program 22P executed by the control unit 21, an advice application program 22AP (hereinafter referred to as "advice app 22AP") which is the program of this disclosure and is used to obtain appropriate advice from the server 10 in accordance with the information (user intake information and one or more of the user intake information, user biometric information, and user behavior information) (hereinafter referred to as "perceived score information" or "user perceived score information"). In this invention, the term "user XX information" used for explanatory purposes does not refer to the origin of XX information, but rather means something like "XX information that is characteristic information of the user," and is synonymous with simply "XX information."

[0018] In this embodiment, the "user perception score information" represents a score derived from information among "user emotion information," "user biometric information," and "user behavior information" that has a high correlation with medium- to long-term emotions (perceptions) over a certain period of time or longer. In this embodiment, the "user perception score information" may be the data itself that scores the information among "user emotion information," "user biometric information," and "user behavior information" that has a high correlation with medium- to long-term emotions over a certain period of time or longer, or it may be a comprehensive score derived by multiplying one or more of these data by a coefficient corresponding to the correlation. Furthermore, the memory unit 22 of the user terminal 20 stores the score information for each item of "user emotion information," "user biometric information," and "user behavior information" when acquiring "user perceived score information," as well as the points used to convert the score information of each item into user perceived score information, conversion coefficients, and charts (biorhythm chart, radar chart, color chart, etc.). An example of a user perception score is derived by selecting numerical values ​​for positive or negative points based on one or more pieces of information from user emotion information, user biometric information, and user behavior information input into the input unit 24 of the user terminal 20, and keywords for each of the emotion information, biometric information, and behavior information stored in the memory unit 22. The control program 22P then performs an arithmetic mean on the selected numerical values ​​and sums the results of the arithmetic mean. Here, the selected numerical values ​​may be multiplied by a correction coefficient specific to each user. Alternatively, the perception score may be calculated by multiplying the selected numerical values ​​by a correction coefficient from any of the acquired "user emotion information," "user biometric information," and "user behavior information." Alternatively, it may be a neural network-like calculation method that calculates the perception score via multiple provisional scores obtained by multiplying the numerical values ​​by a correction coefficient. Furthermore, it may be a deep learning-like method that calculates the perception score via stages of calculating provisional scores in multiple steps. In addition, the numerical values, positive points, and negative points for each item may be plotted on a radar chart, and the area value of the radar chart obtained by plotting may be calculated. An example of a user perception score is derived by multivariate analysis with medium- to long-term emotional (perceived) scores over a certain period as the dependent variable, and then multiplying the correlation coefficients calculated for each explanatory variable (user emotional information, user biometric information, user behavioral information) by these coefficients. Here, if necessary, only explanatory variables with a strong correlation (where the absolute value of the correlation coefficient is above a certain level) may be used. Specifically, a survey was conducted on participants who consumed vinegar daily for XX days. User attribute information such as "age," "gender," "occupation," and "medical history" was collected; intake information such as "I consumed XX mL of vinegar at breakfast (lunch, snack, dinner)" and "I consumed XX at breakfast (lunch, snack, dinner)"; user emotional information such as "I feel more focused," "I feel cheerful," and "I feel energetic"; user biometric information such as "User emotional information when consuming XX mL daily," "weight," "BMI," "body fat percentage," and "blood pressure"; and user behavior information such as "I ran XX km at night," "I walked for XX minutes," and "I stretched for XX minutes in the early morning." The obtained information was then subjected to logistic regression analysis. As a result, for example, the correlation between the user emotional information "I feel more focused" and "I feel cheerful" after XX days of intake and the user behavior information "running" was stored as a negative point, and the correlation between the user emotional information "I feel energetic" after XX days of intake and the user behavior information "running" was stored as a positive point, and so on, stored in the memory. On the other hand, the correlation between the user emotional information "increased concentration" after XX days of ingestion and the user behavior information "stretching" is stored in the memory as a positive point. Furthermore, by performing analysis using decision tree models (e.g., XGBoost, LightGBM, RandomForest) on the same data and using explanatory variables that are highly important in both the logistic regression analysis and the decision tree model regression analysis, a more accurate analysis can be performed. Next, behavioral information of users who will use the advice in this embodiment, other than those who participated in this survey, is acquired as "running 10km in the morning," and emotional information of the user is acquired as "difficulty concentrating at the start of work," and these are output to the memory unit. Subsequently, from the same memory unit, based on the analysis results from the above survey, the emotional information "concentration improves" is selected, along with intake information "intake XX mL of vinegar daily," and behavioral information that is a negative point, "running," and "stretching" as a positive point. The advice DB12c then selects or generates the advice "If you reduce your morning run from 10km to 5km, stretch for 5 minutes, and drink 150mL of vinegar drink, your perceived concentration level will increase by XX points," which is acquired, output to the user terminal, and displayed on the user terminal. As described above, users who use the advice in this embodiment can continue their previous behavioral information "running" without difficulty, while also taking "XX mL of vinegar daily" and "stretching," and thus achieve the effect of "improved concentration." On the other hand, users who do not utilize the advice in this embodiment are unable to improve their concentration even if they continue running, or they are unable to improve their concentration even if they stop running. Thus, although the individual causal relationships are unclear, by cleverly combining actions and eating habits that contribute in the opposite direction to multiple emotional elements, without forcing specific actions on each user and encouraging them to continue, an unexpected effect of enhancing emotional information can be obtained. Furthermore, by calculating accuracy from data obtained from users who utilize the advice but are not included in this survey, and if the accuracy falls below a predetermined value, a logistic regression analysis is performed again using all the data obtained up to that point, the accuracy of the regression analysis can be evaluated, and analysis results with high predictive accuracy can always be adopted. In particular, by calculating accuracy from user data of users who followed the proposal and user data of users who did not follow the proposal, and if the accuracy falls below a predetermined value, a logistic regression analysis is performed again using all the data obtained up to that point, it is possible to validate the model in an arbitrarily selected group with higher homogeneity in terms of statistical analysis models (e.g., logistic regression analysis models) and learning models, and the accuracy of the regression analysis can be evaluated more precisely compared to when test samples are randomly selected. In other words, this embodiment includes a program, information processing method, or information processing device that, after outputting advice information at any time, acquires one or more pieces of information from emotional information, biometric information, and behavioral information relating to the emotions of users who followed the advice information and / or users who did not follow the advice information, and / or perceived score information derived from said information, outputs the acquired intake information, one or more pieces of information from emotional information, biometric information, and behavioral information, and / or perceived score information derived from said information and the user identification information to the information processing device, and acquires validity verification information regarding the correlation relationship at the time the advice information was output from the information processing device. Furthermore, this embodiment includes a program, information processing method, or information processing device that performs new information processing based on acquired validity verification information using one or more pieces of information from emotional information, biometric information, and behavioral information relating to the feelings of users who followed the advice information or users who did not follow the advice information, and / or perceived score information derived from said information, and further includes a program, information processing method, or information processing device that acquires new advice information from the information processing unit, including information relating to the correlation between intake information and perceived score information.

[0019] Regarding emotional information, the display unit 25 displays colors that can be selected from a color chart, or illustrations containing colors, and the color selected by the user is converted into a numerical value using a data sequence of numerical values ​​for each color stored in the memory unit 22. Alternatively, regarding emotional information, negative points, positive points, and other numerical values ​​of user emotional information, user biometric information, and user behavioral information may be calculated using numerical values ​​obtained from positions represented by a three-axis coordinate system (x, y, and z axes), or the positions represented by the three-axis coordinate system may be represented as vectors. For example, regarding emotional information, coordinates may be set to represent effects such as "standard," "model," "goal," "ideal," "prediction," "assumed," and "improvement," and the approximation rate between the user's current coordinate and the "standard" coordinate may be calculated. Alternatively, regarding emotional information, a vector obtained from the set coordinates may be obtained and calculated as the approximation rate between the user's current vector and the "standard" vector. In this embodiment, a correlation is required between the intake history of multiple users obtained in this manner and one or more pieces of information from emotional history, biometric history, and behavioral history, and / or user perception score information derived from such information (one or more pieces of information from emotional history, biometric history, and behavioral history). In other words, "information regarding the correlation between intake information and perception score information" (which may be described as "information regarding the correlation between intake information and perception score information") is sufficient if it is information regarding the correlation between intake information obtained from a hypothetical user who will use the advice (which may be simply referred to as "user") and user perception information that is to be provided to that user, and does not necessarily have to be information based on data obtained from that user. For example, if information is obtained regarding a correlation where, when a certain amount of insoluble dietary fiber is consumed from food by other users (user groups) that do not include the user in question, a specific "perceived score information increases (as a result of more users achieving high values ​​in emotional information over the long term)," then the effects of this embodiment can be achieved by providing information regarding the correlation, such as "groups with high perceived score information consume a certain amount of insoluble dietary fiber" and "emotional information increases by consuming a certain amount of insoluble dietary fiber," or advice including such information, to users whose intake information is similar (e.g., users who consume insoluble dietary fiber) or users who need improvement in their perceived score information (e.g., users with low emotional information over the long term), and to users for whom usefulness or relevance can be inferred from this correlation. In other words, in this embodiment, the "information regarding the correlation between intake information and perceived score information" may be information regarding the correlation based on data obtained from a user group that includes the assumed user who will use the advice, or it may be information regarding the correlation based on data obtained from a user group that does not include the assumed user. The memory unit 22 may also be configured to store, for example, user intake information, user emotion information, user biometric information, and user behavior information input via the input unit 24. Furthermore, the communication unit 23 of the user terminal 20 has an interface for connecting to the network N, as well as an interface for wireless communication with the wearable device 30. The communication unit 23 may also be configured to communicate with the wearable device 30 via wired communication through a cable.

[0020] The camera 27 has a lens and an image sensor, and acquires image data by converting light incident through the lens into photoelectric data using the image sensor. The camera 27 takes pictures according to instructions from the control unit 21 and sequentially sends the acquired image data (captured images) to the storage unit 22 for storage. In addition to the configuration in which the camera 27 is built into the user terminal 20, it may also be configured to have a camera connection unit that allows connection of an external camera, or a camera communication unit that performs wireless communication with an external camera. In this case, the camera connection unit or camera communication unit receives input of image data acquired by the external camera and sequentially sends the input image data to the storage unit 22 for storage.

[0021] Figures 3 to 5 are schematic diagrams showing example configurations of DB12a to 12c stored on server 10. Figure 3 shows product information DB12a, Figure 4 shows member information DB12b, and Figure 5 shows advice DB12c. Product Information DB12a stores information about products. As shown in Figure 3, Product Information DB12a includes columns such as Product ID, Category, Product Name, Raw Materials, Allergy Information, Nutritional Information, Price, and Inventory Status, and stores information about products associated with the Product ID. The Product ID column stores the identification information (Product ID) assigned to each food product sold. The Category column stores the category of products classified by food form, raw materials, manufacturing method, etc., such as vegetable sticks, vegetable paste, noodles, fruit sticks, fruit paste, etc. The Product Name column stores the name given to the product, the Raw Materials column stores the names of the ingredients contained in the product, and the Allergy Information column stores the names of allergenic ingredients (ingredients that may cause allergies) contained in the product. The Nutritional Information column stores the names and amounts of energy and nutrients obtained by consuming the product, associated with each component.

[0022] Furthermore, the intake amounts for energy and nutrients should be based on the intake per predetermined quantity of the product; for example, the intake per stick for stick-shaped foods and the intake per 100g for paste-like foods. The price column stores the price of the product, and the inventory status column stores the inventory quantity of the product, etc. The product ID stored in the product information DB12a is issued and stored by the control unit 11 when information on a new product to be sold is registered. The type, product name, raw materials, allergy information, nutritional information, and price information stored in the product information DB12a are stored by the control unit 11 when the control unit 11 acquires information on a new product to be sold, for example, via the communication unit 13 or the input unit 14. The inventory status stored in the product information DB 12a is updated as follows: for example, when the control unit 11 receives the number of products manufactured via the communication unit 13 or input unit 14, the number of products manufactured is added by the control unit 11; and for example, when the control unit 11 receives the number of products sold via the communication unit 13 or input unit 14, the number of products sold is subtracted by the control unit 11. The contents stored in the product information DB 12a are not limited to the example shown in Figure 3, and various types of information related to the food products to be sold can be stored. For example, information such as the food storage method, expiration date, manufacturer, and manufacturing process may be stored in the product information DB 12a.

[0023] The Member Information DB12b stores information about users who have registered as members in order to receive various advice from the Server 10. As shown in Figure 4, the Member Information DB12b includes columns such as Member ID, Password, Name, Email Address, Address, Age, Gender, Place of Origin, Beliefs, Vegetarianism, Preferences, Thinking Tendencies, Biological Information, Exercise Information, Sleep Information, Purchase History, Intake History, Emotional History, Biological History, and Behavioral History, and stores information about members in association with the Member ID. The Member ID column stores the identification information (Member ID) assigned to each registered user, and the Password column stores the password set by the user during or after registration. The Name, Email Address, Address, Age, Gender, Place of Origin, and Beliefs columns store attribute information entered (specified) by the user during or after registration, such as name, email address, address, age, gender, place of origin (country, region, or home country), and beliefs (e.g., religion). In addition, the date of birth may be stored instead of age, the gender as perceived by the user may be stored, the place of origin can be the country (or region or home country or its PDI (Power Distance Index) value), the country (or region or nationality or its PDI value) where the user was born, or where they grew up, and beliefs can be, for example, the religion they practice.

[0024] The Vegetarianism level column, Preference Information column, Thinking Tendency column, Emotional History column, Biometric Information column, Exercise Information column, and Sleep Information column each store the Vegetarianism level, Preference Information, Thinking Tendency, Emotional Information, Biometric Information, and Behavioral Information (Exercise Information, Sleep Information) specified by the user during or after registration. The Vegetarianism level column indicates the degree to which the user is vegetarian, for example, vegan (a complete vegetarian who does not eat animal products), lacto-vegetarian (who eats dairy products), ovo-vegetarian (who eats eggs), etc. Preference Information indicates the user's preferences (likes and dislikes) regarding food and ingredients, for example, gluten-free, plant-based only (who does not eat animal products), additive-free, etc. Thinking Tendency indicates the user's tendencies in their thinking in their daily life or lifestyle, for example, eco-conscious (who is conscious of reducing food waste), wants to buy high-quality products even if they are expensive, price is a priority, etc. Emotional information includes information that identifies the emotions a user felt when they ate the product. Examples of information that identifies emotions include user feedback (including free-form written responses and methods where emotions are selected using checkboxes, radio buttons, icons, etc.), user facial expressions, and other information that directly allows for the estimation of emotions, as well as information that indirectly allows for the estimation of emotions, such as written text, statements, and tone of voice.

[0025] Furthermore, specific types of emotional information include not only joy, anger, sadness, and pleasure, but also explicit emotions such as "satisfaction," "anxiety," "happiness," and "sense of accomplishment," demands such as "appetite" and "desire to purchase," and implicit desires such as "wanting to lose weight," "wanting to look younger," and "wanting to recommend to others." In addition, this emotional information can be recorded over time as an emotional history column to calculate medium- to long-term emotional information. Biometric information includes various information about the user's physical condition, such as height, weight, body fat percentage, body temperature, blood pressure, heart rate, pulse, sweating, brain waves, emotional hormones, stress hormones, etc. It may also include information about the user's physical condition (sometimes simply referred to as "physical information"), such as the type and amount of medication being taken, medical history, frequency and amount of smoking and drinking, results of various tests including blood and urine tests performed at medical institutions, results of various tests obtained from health checkups or medical examinations, lifespan (especially healthy lifespan), number of teeth, joint pain, medical history, results of medical examinations, hospitalization history, period of being bedridden, ability to walk independently, and whether daily activities (eating, dressing, excretion, bathing, etc.) can be performed without assistance. Furthermore, biometric information can be classified into positive and negative categories based on its relationship to emotional information over time. Each category can then be weighted accordingly, and those that show a high correlation with medium- to long-term emotional information over a certain period of time or longer can be used to derive user perception score information as information that can potentially estimate emotions from unconscious expressions of the user. For example, it is preferable to use electroencephalograms, emotional hormones, stress hormones, and information related to the body that show a high correlation with medium- to long-term emotions (perceived emotions) over a certain period of time or longer.

[0026] Exercise information is about the exercise performed by the user, including, for example, the type (content) of exercise and the duration of exercise. Sleep information is about the user's sleep, including, for example, sleep duration, bedtime (sleep start time), wake-up time (sleep end time), and pulse rate during sleep. Behavioral information may include not only information such as the amount and frequency of exercise and the duration and quality of sleep, but also information on various user behaviors in daily life, such as the amount and frequency of smoking, the amount and frequency of drinking alcohol in the evening, marital status, the frequency and intensity of marital arguments, the frequency of masturbation and sexual intercourse, the presence or absence of menopausal symptoms or dementia in the elderly, the timing and frequency of menstruation and ovulation in women, the presence or absence of pregnancy and gestational age, and the presence or absence of menopause. Furthermore, behavioral information may be classified into positive and negative categories based on its relationship with emotional information over time, weighted for each time-based item, and those with a high overall correlation to medium- to long-term emotional information over a certain period of time or longer may be used to derive user perception score information as information that can potentially estimate emotions from the user's unconscious expressive phenomena. For example, it is preferable to use the amount and frequency of exercise, the duration and quality of sleep, the amount and frequency of smoking (high negative correlation), and the amount and frequency of drinking alcohol in the evening, which have a high correlation to medium- to long-term emotions (perceived) over a certain period of time or longer. The Purchase History column stores purchase information, associating the product purchased by the user with the purchase date. The Intake History column stores intake information, associating the product consumed by the user with the date and time of consumption. The biometric history column stores information that identifies the user's biometric data at the time they consumed the product. The behavioral history column stores information that identifies the user's actions at the time they consumed the product. Furthermore, the intake information may include information on the energy and nutrients consumed by the user as a result of eating the product, instead of information on the product itself. Examples include nutritional information stored in product information DB12a, information obtained from websites provided by companies selling the product, information based on the "Standard Tables of Food Composition in Japan 2015 (Seventh Revised Edition)," or information based on the "Dietary Reference Intakes for Japanese (2015 Edition)."

[0027] The member ID stored in the member information DB 12b is issued and stored by the control unit 11 when the information of a newly registered user is registered. Other information stored in the member information DB 12b is stored by the control unit 11 when it acquires user information, for example, via the communication unit 13 or the input unit 14, and is modified by the control unit 11 when it receives a change instruction via the communication unit 13 or the input unit 14. In addition, attribute information, biometric information, exercise information, sleep information, purchase history, intake history, emotional history (emotional information), biometric history, behavioral history, and user perception score information stored in the member information DB 12b are each accumulated (added and stored) in the member information DB 12b by the control unit 11 each time it acquires the respective information from the user terminal 20 via the communication unit 13. The contents of the member information DB12b are not limited to the example shown in Figure 4, and various types of information about registered users may be stored for each user. For example, the user's family structure, place of residence (country name or PDI value), occupation, information on foods or ingredients that cause the user allergic reactions, hobbies, and goals in daily life may be stored in the member information DB12b. In addition, biometric information, exercise information, sleep information, intake history, emotional history (emotional information), biometric history, behavioral history, user perception score information, etc. may be stored in a predetermined area of ​​the storage unit 12 or in other storage devices, in addition to being stored in the member information DB12b. Furthermore, user perception score information (or user emotion information, user biometric information, and user behavior information used for its derivation) may be registered as data processed as member information, or data including user emotion information, user biometric information, and user behavior information used for its derivation may be registered and calculated each time. In this case, each of the biometric information column, exercise information column, sleep information column, intake history column, emotion history column, biometric history column, and behavior history column stores information for reading each piece of information (for example, a file name indicating the data storage location). Member information DB12b is not limited to a single DB configuration, but may also be configured to store each piece of information in multiple DBs.

[0028] The advice database 12c stores information about advice to be provided to the user (user terminal 20). The advice database 12c shown in Figure 5 includes columns such as advice ID, provision conditions, and advice content, and stores the provision conditions and advice content in association with the advice ID. The advice ID column stores identification information (advice ID) assigned to each piece of advice. The provision conditions column stores the conditions under which advice should be provided, such as conditions related to the correlation between intake information and the perceived score information, conditions related to nutritional components, conditions related to emotions, conditions related to biological systems, conditions related to behavior, and conditions related to the timing of providing advice (season, time of day, etc.). This allows different advice content to be registered in the advice database 12c according to the user's dietary habits and perceived behavior, as well as the timing of advice provision. Furthermore, the information regarding the correlation between intake information and perceived score information stored in the advice DB12c may be information regarding correlation based on expert knowledge, information regarding correlation obtained by a learning model based on current or past data of the assumed user who will use the advice, information regarding correlation obtained by a learning model based on current or past data obtained from a user group that arbitrarily includes the assumed user, or information regarding correlation obtained by a model that combines these. In particular, it is preferable to generate advice information using "information regarding the correlation between intake information and perceived score information" obtained by a learning model based on data obtained from a user group that does not include the assumed user in the past (preferably a group with similar attributes to the user), because this allows for prompt advice to be given to the user using data for which correlation analysis has already been completed. It is also preferable to generate advice information using "information regarding the correlation between intake information and perceived score information" obtained by a learning model based on current or past data obtained from a user group that includes the assumed user, because this results in advice that exhibits a high self-fulfilling effect. For example, based on the correlation between arbitrary intake information (e.g., intake of the food "vinegar") and the aforementioned perceived score information (e.g., a high perceived score due to the large amount of emotional information indicating "good physical movement" in a certain group), it is possible to output advice including information about the correlation between intake information and perceived score information to users judged to have a high relevance on the intake information side (e.g., "low / high intake of vinegar", "prefer ramen, a dish in which vinegar can be used", "have experience eating sweet and sour pork, a dish highly associated with vinegar"), and to users judged to have a high relevance on the perceived score information side (e.g., "it is said that consuming vinegar improves physical movement", "it is said that vinegar added to ramen improves physical movement", "it is said that vinegar in sweet and sour pork improves physical movement"). It is also possible to output advice including information about the correlation between intake information and perceived score information to users judged to have a high relevance on the perceived score information side (e.g., "recently, my physical movement has been poor"), and to users judged to have a high relevance on the perceived score information side, it is possible to output advice including information about the correlation between intake information and perceived score information ("it is said that consuming vinegar improves physical movement"). Furthermore, by using member information and attribute information when determining the conditions for provision, more effective advice can be given. For example, users in countries with relatively low PDI values ​​place importance on word-of-mouth information, so by outputting advice to these users that includes information on the correlation between intake information and perceived score information, such as "In country XX, it is said that consuming vinegar improves physical function," the self-fulfilling effect of the present invention can be made more pronounced. Also, users in countries with relatively high PDI values ​​place importance on expert information, so by outputting advice to these users that includes information on the correlation between expert intake information and perceived score information, such as "Experts say that consuming vinegar improves physical function," the self-fulfilling effect of the present invention can be made more pronounced.

[0029] In this embodiment, "advice information including information on the correlation between intake information and perceived score information" refers to identifying user intake information that has a positive or negative correlation with the "perceived score," and representing either the information itself regarding the correlation between the identified user intake information and the "perceived score," or advice information using that correlation. In other words, by identifying intake information (type of food, amount, timing of intake, season, food combinations, cooking method, ingredients, etc.) that has a positive or negative correlation with long-term feelings (perceived) over a certain period of time, and providing either the information itself regarding the correlation between the identified intake information and the "perceived score," or advice information using that correlation, it is possible to provide users with information on food intake patterns that give them long-term feelings over a certain period of time, rather than just fleeting pleasure. Here, "information regarding the correlation between intake information and perceived score information" may be information regarding correlations based on expert knowledge, information regarding correlations based on data obtained from a user group that includes assumed users who will utilize the advice, or information regarding correlations based on data obtained from a user group that does not include assumed users. In other words, if a user group that optionally includes such users exhibits high (or low) perceived score information, the effects of this embodiment can be obtained by identifying one or more intake information factors (type of food, quantity, timing of intake, season, food combinations, cooking method, ingredients, etc.) that contribute to that perceived score information, and providing information regarding the correlation to users whose intake information is similar or to users for whom improvement of their perceived score information is needed, or other users for whom usefulness or relevance can be inferred from the correlation. Users who consume food along with this information can expect a higher perceived score than those who consume food without this information. This is because the information itself—that "perceived scores are high in a certain region" or "perceived scores are high in a certain group"—is valuable to the user, and even when consuming the same food in the same manner, the perceived score increases due to the so-called placebo effect and the user's self-fulfilling prophecy that they are "taking a healthy action." In other words, this embodiment identifies food intake information that increases user perception scores in specific regions and by specific means, and not only provides that food but also provides it along with information indicating its correlation with the perception score. This allows the efficacy of the food itself to be enhanced by the provided information, and it is expected to be used to realize food intake that improves the user's quality of life. Here, in conventional clinical trials that verify effects in a comparative manner over a short period of time, it is possible to verify foods (or active ingredients in foods) that affect the body, but it is not possible to identify food intake patterns that lead to user perception, and certainly no information is provided that synergistically enhances the user perception of food.

[0030] Furthermore, information such as "high perceived score in a certain region" or "high perceived score in a certain group" can enhance the perceived value of food and is expected to increase the perceived score if it pertains to a group with similar attributes to the user. For example, it is preferable that the information be based on data obtained in a country with a PDI value relatively close to or the same as the country to which the user receiving the information belongs, and more specifically, it is preferable that the information be based on data obtained in a country with a PDI value within plus or minus 20. This is because information from such groups with similar attributes is more likely to trigger the so-called placebo effect and the user's self-awareness that they are "taking healthy actions," thus more effectively achieving the self-fulfilling prophecy effect of the present invention that increases the perceived score. It is particularly preferable to provide this information from such groups with similar attributes to countries with relatively low PDI values ​​that tend to highly value word-of-mouth information. Furthermore, when countries are categorized into those with relatively low PDI values ​​(0 to less than 30), those with moderate values ​​(30 to less than 60), those with relatively high values ​​(60 to less than 90), and those with high values ​​(90 to less than 120), it is preferable that the food intake information that increases the user's perceived score is obtained from a country belonging to the same category as the country to which the user receiving the information belongs, and it is particularly preferable that the information is obtained from the same country. Information from these groups with similar attributes is more likely to trigger the so-called placebo effect and the user's self-awareness that they are "taking healthy actions," and the self-fulfilling prophecy effect of the present invention that increases the perceived score is more pronounced, thus yielding a higher effect. Furthermore, while it is preferable that the food intake information be obtained from countries with relatively low PDI values ​​(0 to less than 30) or moderate values ​​(30 to less than 60), from the viewpoint of ease of data acquisition, it is preferable that the food intake information that increases the user's perceived score is obtained from countries with moderate PDI values ​​(30 to less than 60). Moreover, providing this information to users in countries with relatively low PDI values ​​(0 to less than 30) or moderate values ​​(30 to less than 60) is preferable because it more strongly enhances the so-called placebo effect and the self-fulfilling prophecy effect of increasing the perceived score due to the user's awareness that they are "taking healthy behaviors." Even more surprisingly, the effect of increasing the perceived score is greater when the food subject to the dietary information is ordinary food or its prepared dishes compared to when the food subject to the dietary information is high-functional food with enhanced active ingredients. The principle behind this is not clear, but it is thought that users who encounter this information highly value the information that "the perceived score is high in a certain region" or "the perceived score is high in a certain group" for ordinary food or its prepared dishes whose mechanisms of action are difficult to understand, and a strong self-fulfilling prophecy effect is manifested.

[0031] Furthermore, it is preferable that the users of this embodiment are users in countries with low power inequality. In this embodiment, "power inequality" refers to the degree to which citizens in a country accept power inequality, and the higher the degree to which citizens accept power inequality, the higher the percentage. Users in countries with high power inequality tend to place more importance on trust in corporate experts, while users in countries with low power inequality (such as Japan, the United States, the United Kingdom, and Germany) tend to place more importance on a sense of closeness with other users. Therefore, it is thought that they highly value information originating from users with similar attributes, such as "high perceived score in a certain region" or "high perceived score in a certain group," and a strong self-fulfilling prophecy effect is manifested. In this embodiment, a "country with a low power gap" can be defined as a country where the Power distance index (sometimes simply called PDI or PDI value) in the "Hofstede Index," which Geert Hofstede used to quantitatively measure the culture (national character) of various countries and express the culture and national character of each country numerically, is below a certain value (for example, see the data in "6-dimensions-for-website-2015-08-16.xls" at https: / / geerthofstede.com / research-and-vsm / dimension-data-matrix / or https: / / web.archive.org / web / 20180222070021 / http: / / geerthofstede.com / research-and-vsm / dimension-data-matrix / ). Specifically, it is preferable that the PDI value in a "country with a low power gap" is 80 or less, more preferably 75 or less, particularly 70 or less, or 65 or less. Alternatively, the PDI value may be defined as being less than or equal to the arithmetic mean of all countries for which data is available (in the above file, the average for 78 countries is 59). There is no particular lower limit, but it must be greater than or equal to 0. Specifically, countries with low power inequality include Japan (54), the United States (40), Germany, the United Kingdom (35), Finland (33), Norway, Sweden (31), Switzerland (German-speaking region) (26), New Zealand (22), and Austria (11).

[0032] On the other hand, for users in countries with high power disparities, providing feedback that includes expert comments enhances the effectiveness of this embodiment, which is preferable. In this embodiment, "countries with high power disparities" refers to countries other than those described above as "countries with low power disparities." Specifically, countries with high power disparities include Malaysia, Slovakia (104), Guatemala, Panama (95), the Philippines (94), and Russia (93). The advice content column stores advice messages that should be suggested to the user regarding lifestyle habits such as diet, messages that should be notified to the user regarding contributions to the global environment through eating food, messages that include virtual user intake information, which is intake information to increase the user perception score per unit time, messages that include foods that have a relatively low user perception score but are strongly requested by the user's preferences and / or lifestyle habits, and that include virtual user intake information, which is intake information to increase the user perception score per unit time without changing behavioral information, and messages that include virtual user behavioral information, which is behavioral information to increase the user perception score per unit time. Dietary advice includes, for example, recommendations on the intake of various nutrients such as dietary fiber and protein, the effects of consuming them, and menu suggestions to achieve certain effects. Exercise advice includes suggestions on the type and duration of exercise, as well as messages that motivate users to exercise.

[0033] Furthermore, messages regarding contributions to the global environment include messages about the global environment that can be protected in proportion to the amount of food consumed, such as messages notifying the amount of CO2 emissions that can be reduced in proportion to the amount of food consumed. The advice messages include various advice on lifestyle habits, including diet and exercise, from experts such as doctors, nurses, pharmacists, nutritionists, sports trainers, and researchers. The advice ID stored in the advice DB12c is issued and stored by the control unit 11 when new advice is registered. Other information stored in the advice DB12c is stored by the control unit 11 when it acquires information about new advice, for example, via the communication unit 13 or the input unit 14. The contents of the advice DB12c are not limited to the example shown in Figure 5, and various types of advice messages may be stored, as well as various types of information related to the advice.

[0034] The following describes the processes performed by each device when a user registers user information (user information) with the server 10 using the user terminal 20 in the information processing system 100 of this embodiment. Figures 6 and 7 are flowcharts showing an example of the user information registration process, and Figures 8A to 10B are schematic diagrams showing example screens of the user terminal 20. In Figures 6 and 7, the processes performed by the user terminal 20 are shown on the left, and the processes performed by the server 10 are shown on the right. The following processes are executed by the control unit 21 according to the control program 22P and advice application 22AP stored in the storage unit 22 of the user terminal 20, and are executed by the control unit 11 according to the control program 12P stored in the storage unit 12 of the server 10. Some of the following processes may be implemented by dedicated hardware circuits. In the following processes, the user is assumed to be a user who has registered in advance to obtain advice from the server 10, and is performing the login process to the server 10 using their member ID and password. Furthermore, when a user registers as a member, the control unit 11 (registration unit) of the server 10 obtains personal information necessary for member registration, such as the user's name, password, email address, and address, from the user terminal 20, and registers each of the obtained pieces of information in the member information DB 12b. At that time, the control unit 11 issues a member ID and stores each piece of information in association with the member ID.

[0035] In the information processing system 100 of this embodiment, if a user wishes to register or change information about themselves (user information), they input user information using the user terminal 20 and send it to the server 10 for registration by the server 10. Alternatively, the user can launch the advice application 22AP on the user terminal 20 to display a user information input screen (registration screen), and input user information through the input screen. If the user terminal 20 has a web browser stored in the storage unit 22 for viewing websites via the network N, the user may access the server 10 by launching the browser on the user terminal 20 and obtain the user information input screen (registration screen) from the server 10.

[0036] When the control unit 21 of the user terminal 20 receives a command from the user to start the advice application 22AP via the input unit 24, it starts the advice application 22AP. Subsequently, when the control unit 21 receives a command to register or change user information via the input unit 24, it displays a registration screen as shown in Figure 8A on the display unit 25 (S11). Figure 8A shows an example of the first input screen (registration screen) for entering user information, and the screen shown in Figure 8A is a selection screen that accepts the selection of the type of user information to be entered (registered). The screen shown in Figure 8A is configured so that the user can select from one of the following as the user information to be entered (registered): user profile, physical information, exercise / sleep information, diet information, or subjective information. In the screen shown in Figure 8A, when one of the following is selected via the input unit 24, the control unit 21 accepts a command to enter the selected user information.

[0037] First, the control unit 21 determines whether it has received an input instruction for any of the user information, such as profile, physical information, or exercise / sleep information, on the screen shown in Figure 8A (S12). If it determines that it has received an input instruction for any of the user information (S12: YES), the control unit 21 displays the input screen for the selected user information on the display unit 25 (S13). If profile is selected on the screen shown in Figure 8A, the control unit 21 displays the profile input screen shown in Figure 8B. The input screen shown in Figure 8B has input fields for inputting attribute information including the user's age and gender, profile information including the user's place of origin, beliefs, degree of vegetarianism, preferences (tastes) regarding food (foods and ingredients), principles and way of thinking, etc. The age input field includes a dropdown menu that allows users to select any age or age group from a set of options. The gender input field includes radio buttons that allow users to select either male or female. The input field for place of origin includes a dropdown menu that allows you to select any one of the designated places of origin (country or region), and you can enter any place of origin using the dropdown menu. The input field for beliefs and ideologies includes a dropdown menu that allows you to select any one of the designated beliefs and ideologies (e.g., religion), and you can enter any beliefs and ideologies using the dropdown menu. The input field for vegetarianism includes a pull-down menu that allows the user to select any one of the predetermined options (vegan, lacto-vegetarian, etc.) indicating their vegetarianism level. The input field for dietary preferences and attitudes allows the user to enter any comment via the input unit 24. The control unit 21 may also extract the user's dietary preferences and attitudes from the browsing history of websites (e.g., cooking recipe sites) viewed by the user via the network N using the user terminal 20. In this case, the input field for dietary preferences and attitudes will be populated with the information extracted by the control unit 21 from the browsing history. The profile input screen is not limited to the configuration shown in Figure 8B. Each input field may be configured to allow arbitrary information to be entered via the input unit 24, and may also be provided with a pull-down menu that allows the user to select any one of the predetermined options.

[0038] Furthermore, if body information is selected on the screen shown in Figure 8A, the control unit 21 displays a body information input screen as shown in Figure 9A. The input screen shown in Figure 9A has input fields for inputting biometric information (information about the body's condition), including the user's height, weight, blood pressure (systolic blood pressure, diastolic blood pressure), heart rate, body fat percentage, pulse rate, body temperature, bowel movement frequency, blood glucose level, sweating, electroencephalogram, emotional hormones, stress hormones, etc. Any numerical value can be entered into each biometric information input field via the input unit 24, but each input field may also be provided with a pull-down menu that allows the user to select any one of predetermined options. In addition, the user terminal 20 can acquire measured values ​​of biometric information that can be measured by the wearable device 30 from the wearable device 30, and in the input screen shown in Figure 9A, the biometric information measurement data acquired from the wearable device 30 is entered into each biometric information input field. Specifically, the measured values ​​such as body temperature, blood pressure, heart rate, pulse, and oral sensor values ​​that can be measured by the wearable device 30 are entered into the respective input fields and displayed. The user terminal 20 may also be configured to acquire measured values ​​from measuring instruments other than the wearable device 30, in which case the measured values ​​acquired from the measuring instruments will be displayed in the corresponding input fields. The measuring instruments that can be used include a height meter for measuring height, a scale for measuring weight, visceral fat and body fat percentage, a pulse meter for measuring pulse rate, a blood glucose meter for measuring blood glucose levels, and a skin sensor. The input screen for physical information is not limited to the configuration shown in Figure 9A, and may include input fields for the type and amount of medication being taken, medical history, frequency and amount of smoking and drinking, results of various tests including blood and urine tests performed at medical institutions, and results of various tests obtained from health checkups or medical examinations. Furthermore, the system may be configured to acquire test result data by photographing the paper containing the test results with the camera 27. In this case, the user terminal 20 reads the test results by taking a picture of the paper containing the test results with the camera 27 and generating text data from the captured image using OCR (Optical Character Recognition). The user terminal 20 can then obtain the user's biometric information by extracting various data from the read test results. Note that the process of generating text data from the captured image of the test results paper using OCR may be performed by a device other than the user terminal 20 (for example, the server 10).

[0039] Furthermore, if exercise and sleep information is selected on the screen shown in Figure 8A, the control unit 21 displays an input screen for exercise and sleep information as shown in Figure 9B. The input screen shown in Figure 9B has input fields for entering exercise details (exercise information), including the type of exercise and the amount or duration of exercise, and input fields for entering sleep information, including sleep duration, bedtime, and wake-up time. The input field for the type of exercise is provided with a pull-down menu that allows the user to select any one of a predetermined type, and any type of exercise can be entered using the pull-down menu. The input fields for exercise amount or exercise time allow for the input of any numerical value via the input unit 24. The user terminal 20 can also acquire measured values ​​related to exercise information that can be measured by the wearable device 30 from the wearable device 30, and in the input screen shown in Figure 9B, the measured values ​​related to exercise information acquired from the wearable device 30 are entered into each input field. Furthermore, the screen shown in Figure 9B includes an add button for adding an input field for exercise information. When the add button is pressed, a new input field for exercise details is displayed. Each input field for sleep information allows for the input of any numerical value via the input unit 24, but a pull-down menu may be provided to allow the user to select any one value from a predetermined set. In addition, each input field for sleep information may also contain measured values ​​related to sleep information acquired by the user terminal 20 from the wearable device 30. The input screen for exercise and sleep information is not limited to the configuration shown in Figure 9B. For example, input fields may be provided for exercise information and sleep information that allow for the input of any comments via the input unit 24. Furthermore, the input screen for exercise and sleep information may also be configured to input information about various activities in the user's daily life (activity information) in addition to information about exercise and sleep.

[0040] The user enters each available information into the respective input fields on the input screen shown in Figures 8B to 9B. The control unit 21 of the user terminal 20 receives each piece of information (user information) entered by the user via the input unit 24 (S14) and displays each piece of received information in the corresponding input field. The user terminal 20 may also store previously entered information in the storage unit 22. In this case, when the control unit 21 displays the input screen shown in Figures 8B to 9B on the display unit 25, it displays the entered information in the corresponding input fields. On the input screen where the entered information is displayed, each piece of information displayed in the input fields can be modified via the input unit 24, and the user can not only enter information into each input field but also modify the entered information as appropriate. Furthermore, regarding information that can be obtained from measuring instruments on the input screen shown in Figures 8B to 9B, the control unit 21 periodically acquires the information from the measuring instruments or when the input screen is displayed on the display unit 25, and displays each piece of acquired information in the corresponding input field.

[0041] The input screens shown in Figures 8B to 9B have a registration button for instructing the server 10 to execute a process to register each piece of entered information (user information), and a cancel button for instructing the server 10 to terminate (cancel) the registration process. The control unit 21 determines whether or not it has received an instruction to execute the user information registration process (registration instruction) based on whether or not the registration button has been operated via the input unit 24 (S15). If it determines that it has not received a registration instruction (S15: NO), it repeats the process in step S14. If it determines that it has received a registration instruction (S15: YES), the control unit 21 associates each piece of information (user information) entered via the input screen with the user's member ID and sends it to the server 10 (S16), instructing the server 10 to register the user information. Specifically, when profile information (user attribute information, preference information, thinking tendency) is entered via the input screen shown in Figure 8B, the profile information and the member ID are sent to the server 10. Furthermore, when physical information is entered via the input screen shown in Figure 9A, the physical information (biometric information) and the member ID are sent to the server 10. Similarly, when exercise and sleep information is entered via the input screen shown in Figure 9B, the exercise and sleep information and the member ID are sent to the server 10. The user's member ID is set, for example, in the advice app 22AP.

[0042] The control unit 11 of the server 10 acquires user information transmitted by the user terminal 20 and registers the acquired user information in the member information DB 12b (S17). Specifically, the control unit 11 acquires the member ID and user information from the user terminal 20, associates the acquired member ID with the acquired user information, and stores each piece of information in the member information DB 12b. Note that information already stored in the member information DB 12b may be added to the newly acquired information from the user terminal 20, or it may be overwritten. Furthermore, each piece of information included in the user information may be stored in association with the current date and time (update date and time). If each piece of information obtained from the user terminal 20 is added and stored sequentially, the server 10 can obtain chronological user information from the user terminal 20 and store chronological user information. Also, if each piece of information is stored by overwriting, the server 10 can retain the latest user information.

[0043] The control unit 11 stores the user information obtained from the user terminal 20 in the member information DB 12b, and then notifies the user terminal 20 that the registration of user information is complete (S18). When the control unit 21 of the user terminal 20 is notified by the server 10 that the registration is complete, it displays a screen indicating the completion of user information registration on the display unit 25 (S19). Alternatively, instead of displaying the user information registration completion screen, the control unit 21 may return to the initial screen of the input screen (registration screen) by displaying the screen shown in Figure 8A on the display unit 25. In step S12, if the control unit 21 determines that it has not received any input instructions for any user information on the screen shown in Figure 8A (S12: NO), it skips the processing in steps S13 to S19.

[0044] Next, the control unit 21 determines whether or not it has received an instruction to input meal information (user information) on the screen shown in Figure 8A (S20). If it determines that it has received an instruction to input meal information (S20: YES), the control unit 21 displays the meal information input screen shown in Figure 10A on the display unit 25 (S21). The meal information input screen shown in Figure 10A has input fields for inputting information about the food the user has eaten (ingested) (user intake information). Specifically, the meal information input screen has input fields for the user to input the product name, the amount eaten (ingestion), the day eaten (date), and the time of day eaten. The input field for the product name of the eaten food is provided with a pull-down menu that allows the user to select any one product from the products for sale, and any product name can be entered using the pull-down menu. For example, if a list of food product names is stored in the storage unit 22, the control unit 21 can read the product name list from the storage unit 22 and provide a pull-down menu displaying the read product name list in the product name input field. For example, the control unit 21 stores in the storage unit 22 a list of product names of food owned by the user and stored in a food storage device such as a refrigerator. The control unit 21 updates the list of product names stored in the storage unit 22 with information about food owned by the user that the user did not actually consume by deleting the information about food that the user actually consumed from the list of product names stored in the storage unit 22.

[0045] Furthermore, if the product name list described above is not stored in the storage unit 22, the control unit 21 may obtain the product name list from the server 10 and provide a pull-down menu displaying the obtained product name list in the product name input field. Furthermore, as shown in Figure 10A, a pull-down menu provided in the input field for the product name may display a list of product names of foods that the user has purchased but has not yet eaten. In this case, if the user's food purchase history and consumption history are stored in the storage unit 22, the control unit 21 may identify the unconsumed (not yet eaten) foods based on the purchase history and consumption history, generate a list of the names of the unconsumed foods, and display it in the pull-down menu. At this time, the control unit 21 may also identify the remaining amount of the unconsumed foods based on the purchase history and consumption history, and display the remaining amount of each food along with the list of the names of the unconsumed foods in the pull-down menu. Furthermore, if the user's food purchase and consumption history is not stored in the storage unit 22, the control unit 21 may obtain the purchase and consumption history from the member information DB 12b of the server 10, and based on the obtained purchase and consumption history, generate a list of the names of unconsumed food items and display it in a pull-down menu. Alternatively, the control unit 11 of the server 10 may generate a list of the names of unconsumed food items and determine the remaining quantity of each food item based on the purchase and consumption history stored in the member information DB 12b. In this case, the control unit 21 should display the list of product names obtained from the server 10 and the remaining quantity of each food item in a pull-down menu. Note that product names of foods the user has already eaten or has not purchased may be displayed in the pull-down menu in a way that prevents selection, or they may not be displayed at all. This configuration allows the user to easily select the products they have eaten, simplifying the input process. Furthermore, as shown in Figure 10A, if the remaining quantity of each food item is displayed, the user can easily ascertain the remaining amount of food.

[0046] The input field for the amount eaten is provided with a pull-down menu that allows the user to select any value from several options. The pull-down menu is configured such that, for example, if a paste-like food item is entered, the user can select the weight in grams; if a stick-shaped food item is entered, the user can select the number of pieces. The input fields for the date and time of consumption (timing of intake) are also provided with pull-down menus that allow the user to select any date and time from several options. The input screen for meal information is not limited to the configuration shown in Figure 10A; each input field may be configured to allow arbitrary information to be entered via the input unit 24.

[0047] On an input screen as shown in Figure 10A, the user enters the product name, quantity, and date and time (date and time) of the food they ate into the respective input fields. The control unit 21 of the user terminal 20 receives each piece of information (meal information) entered by the user via the input unit 24 (S22) and displays each piece of received information in the corresponding input field. The input screen shown in Figure 10A has a registration button to instruct the execution of a process to register each piece of entered information (meal information) with the server 10, and a cancel button to instruct the termination (cancellation) of the registration process. Depending on whether the registration button has been operated via the input unit 24, the control unit 21 determines whether or not it has received an instruction to execute the meal information registration process (registration instruction) (S23). If it determines that it has not received a registration instruction (S23: NO), it repeats the process in step S22. If the control unit determines that it has received a registration instruction (S23: YES), it associates each piece of information entered via the input screen (meal information, user intake information) with the user's member ID and sends it to the server 10 (S24), instructing the server 10 to register the meal information (user information).

[0048] The control unit 11 (acquisition unit) of the server 10 acquires meal information (user intake information) transmitted by the user terminal 20 and registers the acquired meal information in the member information DB 12b (S25). Specifically, the control unit 11 acquires the member ID and meal information from the user terminal 20 and stores the acquired meal information in the intake history stored in the member information DB 12b, associated with the acquired member ID. Here, the control unit 11 adds the meal information acquired from the user terminal 20 to the intake history already stored in the member information DB 12b and stores it. After the control unit 11 stores the meal information acquired from the user terminal 20 in the member information DB 12b, it notifies the user terminal 20 that the registration of the meal information is complete (S26). When the control unit 21 of the user terminal 20 is notified by the server 10 that the registration is complete, it displays a screen indicating the completion of meal information registration on the display unit 25 (S27). In this case, the control unit 21 may return to the initial registration screen by displaying the screen shown in Figure 8A on the display unit 25, instead of displaying the registration completion screen for the meal information. In step S20, if the control unit 21 determines that it has not received an input instruction for meal information on the screen shown in Figure 8A (S20: NO), it skips the processing in steps S21 to S27.

[0049] Furthermore, the control unit 21 determines whether or not it has received an input instruction for subjective information on the screen shown in Figure 8A (S28). If it determines that it has received an input instruction for subjective information (S28: YES), the control unit 21 displays the subjective information input screen shown in Figure 10B on the display unit 25 (S29). The subjective information input screen shown in Figure 10B has input fields for inputting information that the user has experienced (user subjective score information). Specifically, the subjective information input screen has input fields for inputting information about the user's emotions, information about the user's biological information, and information about the user's actions. The input field for user emotion information is provided with a pull-down menu that allows the user to select any one emotion, enabling them to input any emotion using the pull-down menu. For example, if a list of information identifying emotions is stored in the memory unit 22, the control unit 21 can read the list of information identifying emotions from the memory unit 22 and provide a pull-down menu displaying the read list of information identifying emotions in the user emotion information input field.

[0050] Furthermore, if the list of information identifying emotions as described above is not stored in the memory unit 22, the control unit 21 may obtain the list of information identifying emotions from the server 10 and provide a pull-down menu displaying the obtained list of information identifying emotions in the emotion information input field. Alternatively, as shown in Figure 10B, a list of methods used to obtain the user's emotions may be displayed in a pull-down menu provided in the user's emotion information input field. The input field for the method used to obtain the user's emotions is provided with a pull-down menu that allows the user to select any acquisition method from multiple acquisition methods, and any acquisition method can be entered using the pull-down menu. Furthermore, user emotional information may be obtained from user biometric information relating to the user's biological characteristics, or from user behavioral information relating to the user's actions. Alternatively, user emotional information may be time-series data of the user's emotions. In this case, the user emotional information may include time-series data of the user's emotions before and after the user consumes food, or both. Additionally, the user emotional information may include at least one of user emotional information per unit time and information identifying emotional fluctuations. Here, a unit time may be one day or more, or one week or more, or one month or more, or three months or more, or it may include two or more unit times in the same season of different years. It is particularly preferable that the user emotional information is an average value per unit time of one month or more, as this allows for the provision of information about food consumption patterns that provide the user with a medium- to long-term sense of satisfaction over a certain period rather than providing fleeting pleasure. Furthermore, it is preferable to include food consumption patterns where the fluctuations in emotional information over shorter unit times (e.g., one hour) are below a certain level (i.e., emotional fluctuations are small) and the average value of user emotional information is high. For example, foods that increase emotional information over a relatively short period (e.g., 1 hour), such as sugary candies, but then decrease in average emotional information over a relatively long period (e.g., 1 year or more) due to factors such as weight gain, are less likely to increase emotional information over a relatively short period (e.g., 1 hour) compared to sweet vegetables, but then increase in average emotional information over a relatively long period (e.g., 1 year or more) due to factors such as the physical condition-regulating effect of dietary fiber. These foods can be selected as preferred embodiments for increasing perceived score information.

[0051] The input field for the user's biological information is provided with a pull-down menu that allows the user to select any one of the user's biological information, enabling input of any biological information using the pull-down menu. For example, if a list of information identifying a biological organism is stored in the storage unit 22, the control unit 21 can read the list of information identifying a biological organism from the storage unit 22 and provide a pull-down menu displaying the read list of information identifying a biological organism in the input field for the user's biological information. Alternatively, if such a list of information identifying a biological organism is not stored in the storage unit 22, the control unit 21 may obtain the list of information identifying a biological organism from the server 10 and provide a pull-down menu displaying the obtained list of information identifying a biological organism in the input field for the biological information. Furthermore, as shown in Figure 10B, the pull-down menu provided in the input field for the user's biological information may display a list of methods used to acquire the user's biological information. The input field for the method of acquiring the user's biological information is provided with a pull-down menu that allows the user to select any acquisition method from multiple acquisition methods, enabling input of any acquisition method using the pull-down menu. In addition, the user's biological information may be time-series data of the user's biological organism. The input field for user action information is provided with a pull-down menu that allows the user to select any one action, enabling the input of any action using the pull-down menu. For example, if a list of action-specific information is stored in the storage unit 22, the control unit 21 can read the list of action-specific information from the storage unit 22 and provide a pull-down menu displaying the read list of action-specific information in the user action information input field.

[0052] Furthermore, if the list of information identifying the aforementioned behaviors is not stored in the storage unit 22, the control unit 21 may obtain the list of information identifying the aforementioned behaviors from the server 10 and provide a pull-down menu displaying the obtained list of information identifying the behaviors in the behavior information input field. Alternatively, as shown in Figure 10B, a list of methods used to obtain the user's behaviors may be displayed in a pull-down menu provided in the user behavior information input field. The input field for methods used to obtain the user's behaviors is provided with a pull-down menu that allows the user to select any acquisition method from multiple acquisition methods, and any acquisition method can be entered using the pull-down menu. The user behavior information may include information that identifies behaviors that have a relatively low user perception score but are strongly requested by the user's preferences and / or lifestyle habits, based on information regarding the correlation between user intake information and user perception score information. The perception information input screen is not limited to the configuration shown in Figure 10B, and each input field may be configured to allow arbitrary information to be entered via the input unit 24.

[0053] The user enters one or more pieces of information from emotional information, biological information, and behavioral information into the respective input fields on the input screen shown in Figure 10B. The control unit 21 of the user terminal 20 receives each piece of information (sensory information) entered by the user via the input unit 24 (S30) and displays each piece of received information in the corresponding input field. The input screen shown in Figure 10B has a registration button to instruct the execution of a process to register each piece of entered information (sensory information) with the server 10, and a cancel button to instruct the termination (cancellation) of the registration process. The control unit 21 determines whether or not it has received an instruction to execute the registration process for meal information (registration instruction) depending on whether or not the registration button has been operated via the input unit 24 (S31). If it determines that it has not received a registration instruction (S31: NO), it repeats the process in step S30. If the control unit determines that it has received a registration instruction (S31: YES), it associates each piece of information entered via the input screen (emotional information, biological information, behavioral information) with the user's member ID and sends it to the server 10 (S32), instructing the server 10 to register the meal information (user information).

[0054] The control unit 11 (acquisition unit) of the server 10 acquires emotional information, biological information, and behavioral information (information about perceived feelings) transmitted by the user terminal 20, and registers the acquired emotional information, biological information, and behavioral information in the member information DB 12b (S33). Specifically, the control unit 11 acquires the member ID and emotional information from the user terminal 20, and stores the acquired emotional information in the emotional history stored in the member information DB 12b, associated with the acquired member ID. Here, the control unit 11 adds the emotional information acquired from the user terminal 20 to the emotional history already stored in the member information DB 12b and stores it. The control unit 11 acquires the member ID and biometric information from the user terminal 20, and stores the acquired biometric information in the biometric history stored in the member information DB 12b, associating it with the acquired member ID. Here, the control unit 11 adds the biometric information acquired from the user terminal 20 to the biometric history already stored in the member information DB 12b and stores it. The control unit 11 acquires the member ID and behavior information from the user terminal 20, and stores the acquired behavior information in the behavior history stored in the member information DB 12b, associating it with the acquired member ID. Here, the control unit 11 adds the behavior information acquired from the user terminal 20 to the behavior history already stored in the member information DB 12b and stores it.

[0055] The control unit 11 stores the emotion information, biometric information, and behavioral information acquired from the user terminal 20 in the member information DB 12b, and then notifies the user terminal 20 that the registration of the feeling information is complete (S34). When the control unit 21 of the user terminal 20 is notified by the server 10 that the registration is complete, it displays a screen indicating the completion of the registration of the feeling information on the display unit 25 (S35). However, here as well, instead of displaying the screen indicating the completion of the registration of the feeling information, the control unit 21 may return to the initial registration screen by displaying the screen shown in Figure 8A on the display unit 25. In step S28, if the control unit 21 determines that it has not received an input instruction for the feeling information on the screen shown in Figure 10B (S28: NO), it skips the processing in steps S29 to S35.

[0056] Through the process described above, in the information processing system 100 of this embodiment, user information such as user attribute information, profile information, information about physical condition (biometric information), information about exercise, information about sleep, the type, amount (intake) and timing of food eaten by the user, and information about perceived effects are input using the user terminal 20 and registered in the server 10. In the server 10, the control unit 11 may create predetermined information, such as information to be used for product development, based on the registered user intake information, user perceived score information, and user identification information, and output the created predetermined information. For example, the control unit 11 may input the type, amount (intake) and timing of food related to the user's perceived score, or the nutritional components, active ingredients, or useful ingredients containing nutritional components or active ingredients of the above food, based on the relationship between relatively high user perceived score information and user intake information, and register the input type, amount (intake) and timing of food intake, or the nutritional components, active ingredients, or useful ingredients containing nutritional components or active ingredients of the above food. The control unit 11 may create predetermined information, such as information to be used for product development, including the registered information, and output the created predetermined information.

[0057] In another example, the control unit 11 may, based on the relationship between relatively low user perceived score information and user intake information, input the type, quantity (amount of intake), and timing of intake of food necessary for the user, or the nutritional components, active ingredients, or useful ingredients containing the nutritional components or active ingredients of the food, and register the input type, quantity (amount of intake), and timing of intake of food, or the nutritional components, active ingredients, or useful ingredients containing the nutritional components or active ingredients of the food. The control unit 11 may also create predetermined information, such as information to be used in product development, including the registered information, and output the created predetermined information. User information includes information on the user's vegetarianism level, food preferences or principles (preference information), and thinking tendencies in daily life (things they are conscious of). User information may also include information on daily lifestyle habits (habitual behaviors), including exercise and sleep (behavioral information). User attribute information may be time-series data of user attribute information. The foods the user has eaten (foods consumed) may include foods that have a relatively low user perception score but are strongly desired by the user's preferences and / or lifestyle habits, based on information on the correlation between user intake information and user perception score information.

[0058] Furthermore, the information may include comments from users regarding changes in their physical and emotional state after consuming food, as well as information (shared information) such as food recommendations or recipes using food that users would like to share with other users. Furthermore, through the processing described above, the information processing system 100 of this embodiment can provide more appropriate advice to the user (such as advice based on current or future disease risks or scores based on various criteria described later) by combining the aforementioned input information (information based on actual experience) with information regarding the amount and timing of food intake consumed by the user. Moreover, if the food is a food that contains an effective amount or more of any nutrient described later, or a food that contains both nutrient and beneficial components, it is preferable to provide advice based on the health functions that can be expected from the nutrient, the beneficial component, or both the nutrient and beneficial component.

[0059] As described above, the server 10 sequentially registers various information about the user and provides appropriate advice to the user according to the registered information. Next, the processing performed by each device in the information processing system 100 when the server 10 provides advice according to each user's user information will be explained. Figure 11 is a flowchart showing an example of the advice provision processing procedure, and Figure 12 is a schematic diagram showing an example of the screen of the user terminal 20. In Figure 11, the processing performed by the user terminal 20 is shown on the left, and the processing performed by the server 10 is shown on the right. The following processing is executed by the control unit 21 according to the control program 22P and advice application 22AP stored in the storage unit 22 of the user terminal 20, and is executed by the control unit 11 according to the control program 12P stored in the storage unit 12 of the server 10. Some of the following processing may be implemented with dedicated hardware circuits.

[0060] In the information processing system 100, if a user wishes to receive advice from the server 10, they launch the advice application 22AP on the user terminal 20 and request advice from the server 10 via the advice application 22AP. Furthermore, by launching the advice application 22AP on the user terminal 20, the user can display the home screen (startup screen) on the user terminal 20 and request advice via the home screen. When the control unit 21 of the user terminal 20 receives a command from the user to start the advice application 22AP via the input unit 24, it starts the advice application 22AP and displays the home screen on the display unit 25. Subsequently, when the control unit 21 receives a command to request advice via the input unit 24, it requests advice from the server 10. The control unit 21 may also request advice from the server 10 at the time the advice application 22AP is started without receiving a request command from the user. In this case, the user only needs to perform the operation to start the advice application 22AP and does not need to perform any operation related to the command to request advice. Furthermore, after the advice application 22AP is started (i.e., while the advice application 22AP is running), the control unit 21 may periodically or at predetermined times request advice from the server 10.

[0061] When the user terminal 20 launches the advice application 22AP, the control unit 21 determines whether or not it is time to request advice from the server 10 (S41). For example, the control unit 21 determines that the time to request advice has arrived when it receives an instruction to request advice via the home screen. The control unit 21 may also determine that the time to request advice has arrived when it launches the advice application 22AP, or when a predetermined time has elapsed, or when a pre-set time has arrived. If the control unit 21 determines that the time to request advice has not arrived (S41: NO), it waits while performing other processes.

[0062] When the control unit 21 determines that the timing for requesting advice has arrived (S41: YES), it requests advice from the server 10 (S42). Specifically, the control unit 21 sends the user's member ID and a request signal for advice to the server 10. When the control unit 11 of the server 10 receives an advice request from the user terminal 20, it reads the user information stored in the member information DB 12b in association with the member ID received from the user terminal 20 (S43). The control unit 11 (information processing unit) then generates appropriate advice information for the read user information based on the contents stored in the advice DB 12c (S44). The control unit 11 generates the advice information to be provided by considering at least a portion of the user information stored in the member information DB 12b, as well as the time period (time) when the advice was requested. For example, the control unit 11 calculates the correlation between the amount of nutrients consumed by the user and one or more of the emotional history, biological history, and behavioral history, based on the user's intake history read from the member information DB 12b and one or more of the emotional history, biological history, and behavioral history. That is, the information obtained based on one or more of the emotional history, biological history, and behavioral history may be one or more of the user emotional information, user biological information, and user behavioral information that have been registered in advance and / or user perception score information derived from said information, or user perception score information calculated each time may be used. Specifically, the system selects the user's intake history, emotional history, biometric history, and behavioral history over a certain period, and assigns a pre-set score based on the magnitude of the fluctuation to determine the perceived score. Furthermore, it can select other histories that have a high correlation with the selected history from the learning model stored in the memory unit, and add or accumulate the score predicted from the learning model of the other histories to the selected score to arrive at the perceived score.

[0063] As a method for calculating the user perception score, the control unit 21 converts the keywords and numerical data of the emotional information, biometric information, and behavioral information stored in the memory unit 22 into positive points or negative points, takes an arithmetic mean of the converted values, and derives the sum of the arithmetic mean. Alternatively, the control unit 21 may use a value calculated by multiplying the positive points or negative points by a correction coefficient specific to each user, take an arithmetic mean of that value, and derive the sum of the arithmetic mean. Here, it is not limited to an arithmetic mean; any statistical value may be derived and used. Alternatively, the control unit 21 may use data such as the maximum and minimum peak values ​​and chart area obtained from the biorhythm chart, or the control unit 21 may plot the numerical values ​​and points of each item on a radar chart and represent them by the area value of the radar chart. Furthermore, the perceived score may be calculated by multiplying a selected numerical value from any of the acquired "user emotion information," "user biometric information," and "user behavior information" by a correction coefficient, or it may be a neural network-like calculation method that calculates the perceived score via multiple provisional scores obtained by multiplying the numerical value by a correction coefficient, or it may be a deep learning-like method that calculates the perceived score via a multi-stage process of calculating provisional scores.

[0064] Regarding emotional information, the control unit 11 may use a color chart to have the user select a color from the color chart to represent their emotion, and then convert the selected color into a numerical value. Alternatively, the control unit 11 may convert the selected color into positive and negative points, quantify them, perform an arithmetic mean on the resulting numerical values, and use the sum of the arithmetic mean. Here, it is not limited to the arithmetic mean; any statistical value may be derived and used. Alternatively, the control unit 21 may calculate the negative points, positive points, and other numerical values ​​of user emotional information, user biometric information, and user behavioral information using numerical values ​​obtained from positions represented by a three-axis coordinate system (x, y, and z axes), or it may represent the positions represented by the three-axis coordinate system as vectors. For example, with respect to emotional information, the control unit 11 may set coordinates to represent effects such as "standard," "model," "goal," "ideal," "prediction," "assumment," and "improvement," and calculate the approximation rate between the user's current coordinate and the "standard" coordinate. Alternatively, the control unit 11 may determine a vector from the set coordinates with respect to emotional information and calculate it as an approximation rate between the user's current vector and a vector such as a "reference."

[0065] The control unit 11 determines the correlation between the user's intake history and one or more pieces of information from emotional history, biological history, and behavioral history, and / or user perception score information derived from such information (one or more pieces of information from emotional history, biological history, and behavioral history). The control unit 11 may use data for one or more pieces of information history from emotional history, biological history, and behavioral history, such as data for a specific date and time, morning, evening, night, day of the week, etc., or it may use data obtained by summing, averaging, or integrating, in the case of continuous data, numerical values ​​for a specific period, such as a specific time, a specific number of days, every other day, a week, a month, a year, etc. Alternatively, the control unit 11 may use numerical values ​​calculated by multiplying numerical values ​​for a specific period, such as a specific time, a specific number of days, every other day, a week, a month, a year, etc., by a correction coefficient specified for each user, for data for one or more pieces of information history from emotional history, biological history, and behavioral history. The control unit 21 may identify one or more of the foods consumed by the user and / or the nutrients contained in those foods and / or their intake amounts, based on the correlation between the user's intake history and one or more of the emotional history, biological history, behavioral history, or user perception score information derived from these (emotional history, biological history, behavioral history). For example, an example of a change in emotional history is a decrease in anxiety about one's own health and an increase in "happiness" (1). An example of a change in biological history is a decrease in blood pressure, leading to the feeling that elevated blood pressure has become closer to normal (2). An example of a change in behavioral history is an increase in motivation to exercise (3).

[0066] The control unit 21 determines positive and negative factors based on changes in one or more pieces of information from emotional history, biometric history, behavioral history, or user perception score information derived from these (emotional history, biometric history, behavioral history). For example, the control unit 21 determines each of (1) to (3) as a positive factor. The control unit 21 extracts one or more pieces of information from the emotional history, biometric history, behavioral history, or user perception score information derived from these (emotional history, biometric history, behavioral history) that it determined to be a positive factor. Based on the extracted information and the amount of nutrients consumed by the user, the control unit 21 performs analytical processing such as multivariate analysis to derive a causal relationship between the food consumed by the user in a day and / or one or more nutrients contained in the food and / or their intake. Based on the derived causal relationship, the control unit 21 outputs advice information based on one or more of the causally related foods and / or nutrients contained in the food and / or their intake. For example, the control unit 21 performs analysis processing such as multivariate analysis based on the extracted information and the amount of nutrients consumed by the user. If it derives a causal relationship between the user consuming 10 mL more vinegar than usual in their daily diet and the results, it outputs advice information based on the vinegar. Alternatively, the control unit 21 may identify at least one of the nutrients consumed by a user whose user perception score information has changed, and the amount of that nutrient consumed, based on the correlation between the user's intake history and user perception score information derived from one or more pieces of information from emotional history, biological history, and behavioral history. Here, an example of user perception score information is at least one of the following: a decrease in anxiety about one's own health and an increase in "happiness" from the emotional history; a decrease in blood pressure from the biological history, leading to the feeling that elevated blood pressure has approached normal levels; and an increase in motivation to exercise from the behavioral history. Specifically, the distinction between the positive and negative factors mentioned above can be achieved using information generated from, for example, a user's intake history of vinegar, etc., over a specific period (e.g., 30 days or more), along with questionnaire responses regarding emotional history, biometric history, and behavioral history, using logistic regression analysis or decision tree model analysis (e.g., XGBoost, LightGBM, RandomForest). This information can be generated using machine learning or deep learning. In particular, by performing decision tree model analysis (e.g., XGBoost, LightGBM, RandomForest) on the same data and using explanatory variables that are highly important in both the logistic regression analysis and the decision tree model regression analysis, more accurate analysis can be achieved. Furthermore, when distinguishing between positive and negative factors from intake history, emotional history, biometric history, and behavioral history obtained from a specific user, algorithms such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), Random Forest, SVM (Support Vector Machine), and neural networks, as shown below, can be used. Furthermore, the control unit 21 can output advice information based on attribute information, biological information, behavioral information, or intake information (for example, one or more of food and / or nutritional components contained in food and / or their intake amounts) that have an inverse causal relationship with multiple emotional information items among the derived causal relationships. For example, the behavioral information "running history" has a positive correlation with the first emotional information "daily vitality," but a negative correlation with the second emotional information "able to concentrate" or "cheerful mood." In this case, in order to enhance both the first and second emotional information, the control unit 21 can advise, for example, to enhance the behavioral information "running history" while increasing the dietary intake information "frequency of vinegar intake," which enhances the second emotional information "able to concentrate," or to suppress the behavioral information "running history" while performing "about 10 minutes of exercise a day" to enhance the first emotional information "daily vitality." In addition, the control unit 21 can also provide the user with predicted emotional information calculated from the user's attribute information etc. before the advice is implemented as advice information.

[0067] The control unit 21 distinguishes between positive and negative factors based on changes in the user's perceived score information. For example, the control unit 21 identifies the following as positive factors: (1) a decrease in anxiety about one's own health in the emotional history and an increase in "happiness"; (2) a decrease in blood pressure in the biological history and a feeling that elevated blood pressure was approaching normal levels; and (3) an increase in motivation to exercise in the behavioral history. The control unit 21 derives user perceived score information from one or more pieces of information from the emotional history, biological history, and behavioral history that have been identified as positive factors. Based on the derived user perceived score information and the amount of nutrients consumed by the user, the control unit 21 performs analytical processing such as multivariate analysis to derive a causal relationship between at least one of the nutrients consumed by the user in their daily meals and the amount of those nutrients consumed. Based on the derived causal relationship, the control unit 21 outputs advice information based on at least one of the causally related nutrients and the amount of those nutrients consumed. For example, the control unit 21 performs analysis processing such as multivariate analysis based on the extracted information and the amount of nutrients consumed by the user. If it derives a causal relationship between the user consuming 10 mL more vinegar than usual in their daily diet and the results, it outputs advice information based on the vinegar.

[0068] As a concrete example, let's consider a case where a user has a history of consuming 10 mL more vinegar than usual in their daily diet. Suppose that, in terms of biometric information, the user feels that their elevated blood pressure has approached normal levels, increasing positive factors, and in terms of emotional information, the decrease in blood pressure reduces anxiety about their health and increases "happiness." In other words, when a user feels the effects of drinking vinegar or becomes more health-conscious, the former corresponds to a decrease in negative factors, and the latter to an increase in positive factors. Also, in terms of behavioral information, an increase in motivation to exercise corresponds to an increase in positive factors. In such cases, the control unit 21 identifies at least one of the nutrients consumed by a user whose emotional history, biological history, or behavioral history has changed, based on the correlation between the user's intake history and one or more pieces of information from among emotional history, biological history, and behavioral history. The control unit 21 distinguishes between positive and negative factors based on the changes in one or more pieces of information from among emotional history, biological history, and behavioral history. The control unit 21 extracts one or more pieces of information from emotional history, biological history, and behavioral history that have been identified as positive factors. Based on the extracted information and the amount of nutrients consumed by the user, the control unit 21 performs analytical processing such as multivariate analysis to derive a causal relationship between at least one of the nutrients consumed by the user in their daily meals and the amount of those nutrients consumed.

[0069] Alternatively, the control unit 21 derives a correlation between the user's intake history and user perception score information derived from one or more pieces of information from emotional history, biometric history, and behavioral history. For example, the control unit 21 converts the keywords and numerical data in the emotional information, biometric information, and behavioral information stored in the memory unit 22 into numerical values ​​of positive or negative points, takes an arithmetic mean of the converted values, and derives the sum of the arithmetic mean. Based on the derived sum, the control unit 21 calculates user perception score information. The control unit 21 obtains the correlation between the user's intake history, such as consuming vinegar, and the calculated user perception score information. For example, the correlation coefficient between the dependent variable and each explanatory variable can be calculated by performing a multivariate analysis with the medium- to long-term emotional (perceived) score over a certain period of time as the dependent variable, and determining the correlation coefficient for each explanatory variable (user emotional information, user biometric information, user behavior information). The control unit 21 classifies the explanatory variables based on the calculated correlation coefficients. For example, the control unit 21 classifies the correlation coefficients by classifying explanatory variables with a correlation coefficient above a certain level as positive factors and explanatory variables below a certain level as negative factors. Based on the results of classifying the correlation coefficients, the control unit 11 may read the advice content registered in the advice DB 12c, which contains information on the correlation relationships, and generate the advice information to be provided using the read advice content.

[0070] Furthermore, for example, the control unit 11 may create virtual user intake information to increase the user perceived score per unit time based on the information obtained regarding the correlation, and generate advice information including the created virtual user intake information. Alternatively, for example, the control unit 11 may create virtual user intake information to increase the user perceived score per unit time, including foods that have a relatively low user perceived score but are strongly requested by the user's preferences and / or lifestyle, based on the information obtained regarding the correlation, and generate advice information including the created virtual user intake information. Furthermore, for example, the control unit 11 may create virtual user intake information to increase the user perception score per unit time without changing the behavioral information, based on the information obtained regarding the correlation, and generate advice information including the created virtual user intake information. Furthermore, for example, the control unit 11 may create virtual user behavior information to increase the user perception score per unit time, based on the information obtained regarding the correlation, and generate advice information including the created virtual user behavior information. Furthermore, for example, the control unit 11 may create information that identifies a method to improve the user perception score without changing either or both of the information related to preferences and lifestyle habits, based on the information obtained regarding the correlation, and generate advice information including the information that identifies a method to improve the created user perception score.

[0071] The control unit 11 may also generate advice information using advice registered in the advice DB 12c, associated with the user's age, gender, place of origin, beliefs, etc., or it may generate advice information using advice registered in the advice DB 12c, associated with the user's degree of vegetarianism, preference information, thinking tendencies, etc. In this case, advice information can be generated that takes into account the user's attributes, preferences regarding ingredients and food, principles, way of thinking, etc. The control unit 11 may also generate advice information using advice content registered in the advice DB 12c, corresponding to the user's physical condition indicated by biometric information, exercise amount indicated by exercise information, sleep amount indicated by sleep information, etc. In this case, advice information that takes into account the user's physical condition, exercise amount, sleep amount, etc. can be generated. Furthermore, when using time-series information (time-series data) such as the user's intake history, biometric information, exercise information, sleep information, etc., the control unit 11 may generate advice information corresponding to the user's physical condition, exercise amount, sleep amount, etc., identified from information spanning any point in time or any period. Furthermore, the control unit 11 can also generate advice information tailored to the user's food consumption trends for users who continue to consume food, based on the user's food intake history.

[0072] The control unit 11 may also freely select a user population and acquire user intake information, user emotional information, user biometric information, user behavior information, user attribute information (such as place of origin or PDI value in place of origin), etc., of users included in the selected user population. The control unit 11 may also change the selected users. For users who have received advice information, the control unit 11 may acquire various user information of the user after receiving the advice information. The control unit 11 may identify foods and behaviors that are assumed to have a high correlation with the perceived score and output advice recommending the identified foods and behaviors. More specifically, based on information regarding the correlation between intake information stored in the advice DB 12c and the perceived score information (for example, information regarding correlations based on expert knowledge, information regarding correlations obtained by a learning model based on current or past data of assumed users who will use the advice, and information regarding correlations obtained by a learning model based on current or past data obtained from a user group that arbitrarily includes the assumed users), appropriate advice can be given to users who have a high correlation on the intake information side or the perceived score information side of the correlation, or to randomly selected users. For example, based on the correlation between arbitrary intake information (e.g., intake of the food "vinegar") and the aforementioned perceived score information (e.g., a high perceived score due to the large amount of emotional information indicating "good physical movement" in a certain group), advice including information about the correlation between intake information and perceived score information can be output to users judged to have a high relevance on the intake information side (e.g., "low / high intake of vinegar", "prefer ramen, a dish in which vinegar can be used", "have experience eating sweet and sour pork, a dish highly associated with vinegar") (e.g., "It is said that consuming vinegar improves physical movement", "It is said that vinegar added to ramen improves physical movement", "It is said that vinegar in sweet and sour pork improves physical movement"). Advice including information about the correlation between intake information and perceived score information can be output to users judged to have a high relevance on the perceived score information side (e.g., "Recently, my physical movement has been poor") (e.g., "It is said that consuming vinegar improves physical movement"). In particular, it is preferable to generate advice information using "information on the correlation between intake information and perceived score information" obtained by a learning model based on data obtained from a past user group that does not include the hypothetical user in question (preferably a group with similar attributes to the user), because this allows for prompt advice to be given to the user using data for which correlation analysis has already been completed. Furthermore, it is preferable to generate advice information using "information on the correlation between intake information and perceived score information" obtained by a learning model based on current or past data obtained from a user group that includes the hypothetical user in question, because this results in advice that is highly effective in achieving self-fulfilling promises. Furthermore, by using user attribute information when determining the conditions for provision, more effective advice can be given. For example, users in countries with relatively low PDI values ​​place importance on word-of-mouth information, so by outputting advice to these users that includes information on the correlation between intake information and perceived score information, such as "In country XX, it is said that consuming vinegar improves physical function," the self-fulfilling effect of the present invention can be made stronger. Also, users in countries with relatively high PDI values ​​place importance on expert information, so by outputting advice to these users that includes information on the correlation between expert intake information and perceived score information, such as "Experts say that consuming vinegar improves physical function," the self-fulfilling effect of the present invention can be made stronger. In this way, the control unit 11 generates advice information regarding the user's daily activities (lifestyle habits) such as diet, exercise, and sleep, based on advice information including information about the correlation between intake information and the perceived score information, such as user information. The control unit 11 may also generate advice information for users consuming food, based on user environment information, which is information that identifies the user's environment when the user consumes food. Here, user environment information may be temperature, humidity, etc., and may be time-series data. An example of advice information may include advice to alleviate either or both anxiety and stress information. Another example of advice information may include advice for improving lifestyle habits and advice to alleviate either or both anxiety and stress information regarding the user's behavioral change. In this way, the control unit 11 generates advice information regarding the user's daily activities (lifestyle habits), such as eating, exercise, and sleep, based on the user environment information.

[0073] The foods in this embodiment are mainly general foods and include foods manufactured using not only the edible parts but also the non-edible parts of the ingredients. In this disclosure, "non-edible parts" of ingredients refer to parts of ingredients that are not normally suitable for consumption and parts that are discarded in normal eating habits. Specifically, these are the discarded parts listed in, for example, the "Standard Tables of Food Composition in Japan 2015 (Seventh Revised Edition)" (a food composition table established by the Ministry of Health, Labour and Welfare). Furthermore, the ingredients used in the food of this embodiment can be any food intended for human consumption (foods listed in the "Standard Tables of Food Composition in Japan 2015 (Seventh Revised Edition)"), but it is preferable that they be plants, i.e., edible plants. As plants, legumes, nuts and seeds, vegetables, grains, fruits, potatoes, mushrooms, algae, etc., from the classification listed in the "Standard Tables of Food Composition in Japan 2015 (Seventh Revised Edition)" can be used. Note that the "edible portion" of an ingredient refers to the part of the ingredient excluding the discarded parts (inedible parts). The inedible parts of an ingredient are generally discarded because they are not palatable or do not mix well with other ingredients. However, if the food of this embodiment contains inedible parts of the ingredients, the amount of food waste can be reduced. Therefore, by consuming the food of this embodiment, the burden on the global environment due to the disposal of ingredients (inedible parts) can be reduced, and the protection of the global environment can be contributed to. Accordingly, the control unit 11 may generate advice information not only on diet and nutritional components based on the user's intake history, but also on contributions to the protection of the global environment. In this case as well, the control unit 11 can generate advice information on contributions to the protection of the global environment using the advice content registered in the advice DB 12d, which is associated with each food and the amount of food consumed. Furthermore, it is preferable that the food of this embodiment contains both edible and inedible parts of the ingredients, it is even more preferable that it contains both edible and inedible parts derived from the same type of ingredient, and it is most preferable that it contains both edible and inedible parts derived from the same type and the same individual ingredient.

[0074] As described above, in this embodiment, advice information to be provided to the user is generated based on at least a portion of the user information. If multiple pieces of advice information are generated based on the contents of the advice DB 12c, the control unit 11 may choose all of the generated pieces of advice information to be provided, or it may choose one randomly selected piece of advice information to be provided. Furthermore, by setting a priority for each piece of advice stored in the advice DB 12c, if multiple pieces of advice information are generated, the advice information with the highest priority may be chosen as the advice information to be provided.

[0075] In addition, it is preferable that the food of this embodiment contains an effective amount or more of any nutrient (for example, above the standard value that allows for nutritional claims under the Food Labeling Act). Furthermore, it is preferable that the food contains one or more useful ingredients. In the above, useful ingredients are, for example, ingredients other than the nutrients specified in Appendix 9 of the Food Labeling Standards (Cabinet Office Ordinance No. 10 of 2015) that are useful for maintaining or improving health, athletic function, beauty, etc. Specifically, examples include polyphenols, carotenoids, chlorophyll, vitamin U, etc. Regarding the content of nutrients, it is preferable that the amount is sufficient to allow for claims such as "supplementable," "appropriate intake," "no additives," absolute claims (high, contains), absolute claims (does not contain, low), relative claims (enhanced, reduced), and additive-free claims (sugars, sodium salts, etc.). More specifically, it is preferable that the dietary fiber content in the food (especially the insoluble dietary fiber content) is 3% by mass or more, more preferably 4% by mass or more, more preferably 5% by mass or more, more preferably 6% by mass or more, more preferably 7% by mass or more, more preferably 8% by mass or more, and even more preferably 9% by mass or more, and that the amount of dietary fiber (especially the insoluble dietary fiber content) contained per serving size (corresponding to the amount of food that is normally eaten in one serving, for example, one piece in the case of a single-serving product) is 1g or more, more preferably 2g or more, and more preferably 3g or more. Having such nutritional components in the food makes it possible to collect the amount of nutrients consumed by the user within a specific period, along with the date and time of consumption, and to find out the effective timing and method of intake of the nutrients that could not be known in the past, and consequently it is preferable that we can propose to the user a more effective timing and method of intake of nutrients than in the past.

[0076] Furthermore, the food of this embodiment is preferably a useful food ingredient containing the above-mentioned nutritional components or active ingredients, and more preferably a food made using ingredients containing dietary fiber (particularly preferably derived from dietary fiber localization sites, and even more preferably derived from the inedible parts of the ingredients) (a food containing dietary fiber), and even more preferably a food made using ingredients containing insoluble dietary fiber (particularly preferably derived from insoluble dietary fiber localization sites, and even more preferably derived from the inedible parts of the ingredients) (a food containing insoluble dietary fiber). Insoluble dietary fiber localized sites refer to parts of the food ingredient where insoluble dietary fiber is localized, and specifically, parts that have a higher insoluble dietary fiber content than the insoluble dietary fiber content of the edible portion. In this disclosure, insoluble dietary fiber localized sites refer to parts of the food ingredient where insoluble dietary fiber is localized, and specifically, parts that have a higher insoluble dietary fiber content than the edible portion of the food ingredient, and more preferably, in a dry state, parts that have an insoluble dietary fiber content of 1.1 times or more, even more preferably 1.2 times or more, even more preferably 1.3 times or more, even more preferably 1.4 times or more, even more preferably 1.5 times or more, even more preferably 1.6 times or more, even more preferably 1.7 times or more, even more preferably 1.8 times or more, even more preferably 1.9 times or more, and most preferably 2.0 times or more than the edible portion. Furthermore, it is preferable that the insoluble dietary fiber content in the localized insoluble dietary fiber area (especially the non-edible portion) is more than 10% by mass, more preferably more than 11% by mass, more preferably more than 12% by mass, more preferably more than 13% by mass, more preferably more than 14% by mass, more preferably more than 15% by mass, more preferably more than 16% by mass, more preferably more than 17% by mass, more preferably more than 18% by mass, more preferably more than 19% by mass, and more preferably more than 20% by mass, on a dry mass basis. The same applies to the localized sites of dietary fiber. In this disclosure, "dry mass equivalent" refers to the mass equivalent value when the moisture content is 0% by mass. The moisture content in the sample can be measured in accordance with the Standard Tables of Food Composition in Japan 2015 (7th Revised Edition). In this disclosure, the localized sites of dietary fiber or insoluble dietary fiber may be a part of the "edible portion" of the aforementioned food ingredients (for example, the seed coat portion of vegetables, grains, legumes, or fruits, especially the seed coat portion of legumes) or the "non-edible portion," but it is preferable that the localized site of insoluble dietary fiber is the "non-edible portion."

[0077] Furthermore, the content of dietary fiber localized parts or insoluble dietary fiber localized parts (particularly inedible parts) relative to the total mass of the food of this disclosure is preferably 1% by mass or more, more preferably 3% by mass or more, and even more preferably 5% by mass or more. On the other hand, there is usually no upper limit, but it may be preferably 70% by mass or less, more preferably 60% by mass or less, and even more preferably 50% by mass or less. Furthermore, the total content of ingredients including dietary fiber localized parts or insoluble dietary fiber localized parts (particularly inedible parts) relative to the total mass of the food of this disclosure is preferably 3% by mass or more, more preferably 5% by mass or more, and even more preferably 9% by mass or more. On the other hand, while there is usually no upper limit, it is preferably 70% by mass or less, more preferably 60% by mass or less, and even more preferably 50% by mass or less. Generally, the inedible parts of ingredients contain a lot of dietary fiber or insoluble dietary fiber, so it is preferable to manufacture food by including not only the edible parts but also the inedible parts of ingredients, as in the food of this embodiment, because it is possible to provide food in which the nutritional components of the ingredients can be consumed without waste. Therefore, the food of this embodiment is a food in which nutritional components such as dietary fiber can be consumed efficiently and the amount of waste can be reduced.

[0078] The control unit 11 of the server 10 may generate advice information tailored to each user's user information using a pre-trained model trained by machine learning or deep learning. In this case, for example, a pre-trained model can be used that takes at least a portion of the user information stored in the member information DB 12b and the time period (time) when advice is requested as input, calculates the optimal advice information based on the input information, and outputs the calculated result (advice information). Such a pre-trained model can be constructed using, for example, a CNN (Convolutional Neural Network), an RNN (Recurrent Neural Network), or an LSTM (Long Short-Term Memory).

[0079] Figure 13 is a schematic diagram showing an example of a learning model configuration. The example learning model shown in Figure 13 takes food intake information from user information and perceived score information that includes one or more of the following: emotional information, biometric information, and behavioral information, as inputs. The learning model is trained to identify the advice to be provided when food intake information and perceived score information are input. The learning model shown in Figure 13 consists of an input layer, an intermediate layer, and an output layer. The input layer has three input nodes, and each input node receives food intake information such as the amount of vegetable sticks consumed and the amount of vegetable paste consumed, as well as the user's perceived score information. Although Figure 13 illustrates a configuration in which perceived score information is input to one input node, if the configuration is set up to accept multiple types of information such as emotional information, biometric information, and behavioral information, separate input nodes will be provided for each type of information. In addition, each piece of information (each numerical value) input to the input node may be information for one day, or it may be time-series information spanning a predetermined period such as one week. For example, the system may be configured to input a history of food intake over a week, changes in perceived score information over a week, etc. In this case, advice can be identified that takes into account various histories and changes over a predetermined period.

[0080] Each data point input to an input node is input to a hidden layer. The hidden layer has multiple (three layers in Figure 13) fully connected layers, and the nodes in each layer use weighting coefficients and functions between layers to calculate an output value based on the input data, and input the calculated output value to the nodes of the next layer. The hidden layer sequentially inputs the output values ​​of the nodes in each layer to the nodes of the next layer, ultimately providing the respective output values ​​to each output node of the output layer. The output layer has multiple output nodes, and each output node outputs the discrimination probability for each of the advice stored in the advice DB12c. For example, the first output node outputs the discrimination probability for the advice with advice ID A001 stored in the advice DB12c, and the second output node outputs the discrimination probability for the advice with advice ID A002. The discrimination probability output by each output node indicates the likelihood that the advice with the advice ID associated with that output node is appropriate for the data input to the input layer. The output value of each output node in the output layer is, for example, a value between 0 and 1.0, and the sum of the discrimination probabilities output from all output nodes is 1.0. The number of input nodes in the input layer, the number of layers in the hidden layer, and the number of output nodes in the output layer are not limited to the example shown in Figure 13. The learning model is not limited to a neural network (deep learning) with multiple hidden layers as shown in Figure 13, but may be a learning model constructed using various machine learning algorithms. Furthermore, the learning model may be based on current or past data of the assumed user who will use the advice, or it may be based on data obtained from a user group that arbitrarily includes the assumed user in the present or past, or it may be a model that combines these. In particular, it is preferable to generate advice information using "information on the correlation between intake information and perceived score information" obtained by a learning model based on data obtained from a user group that does not include the assumed user in the past (preferably a group with similar attributes to the user), because this allows for prompt advice to be given to the user using data for which correlation analysis has already been completed.Furthermore, it is preferable to generate advice information using "information regarding the correlation between intake information and perceived score information" obtained from a learning model based on data obtained from a user group including the user (preferably a group with similar attributes to the user), which is based on data obtained from the user group, either currently or in the past, as this enhances the self-fulfilling effect of the present invention. The control unit 11 may acquire the output value of the intermediate layer (information regarding the correlation between user intake information and user perceived score information, or advice information including information regarding said correlation), and may include the acquired output value in the advice information, or it may store the acquired output value in the advice DB 12c.

[0081] Furthermore, when generating advice information tailored to user information such as age, gender, place of origin, and beliefs, a learning model trained to identify the appropriate advice based on the input of this information can be used. Similarly, when generating advice information tailored to user information such as vegetarianism, preferences, and thinking tendencies, a learning model trained to identify the appropriate advice based on the input of this information can be used. Furthermore, when generating advice information tailored to user information such as exercise and sleep data, a learning model can be used that takes the user's exercise and sleep data as input and identifies the advice to be provided when this information is received. In this way, by using user information to consider when identifying advice information as input and training the model to identify advice content appropriate to the input user information, it is possible to identify the optimal advice for any given user information. By identifying advice using such a learning model, it is possible to generate advice that takes into account the user's physical condition (health), etc. Also, when using a learning model, different learning models may be used depending on the timing of advice provision. For example, different learning models may be used for each season, or different learning models may be used for each time of day to identify advice tailored to user information. In this case, it is possible to generate optimal advice that also takes into account the timing of advice provision (season, time of day, etc.).

[0082] The learning model described above learns using training data in which a set consists of user information to be input and an advice ID corresponding to that user information. In the learning model shown in Figure 13, training is performed using training data in which a set consists of food intake information, perceived score information, and an advice ID corresponding to this intake information and perceived score information. The advice used in the training data can be advice recommended by a specialist such as a doctor for the corresponding user information, or advice that the user related to the user information felt was effective. The learning model learns so that when user information (information to be input) included in the training data is input to each input node, an output value of 1.0 is output from the output node corresponding to the advice ID included in the training data, and an output value of 0.0 is output from the other output nodes. The learning model is trained to optimize the weighting coefficients and functions that connect the nodes of each layer in the hidden layer. The learning process of the learning model may be performed on server 10, on another device, or a pre-trained model may be used. If the learning process of the learning model is performed on another device, the control unit 11 on server 10 uses the learning model obtained by the learning process on the other device to perform the aforementioned processing. In addition, the output values ​​of the hidden layer of the learning model (information on the correlation between user intake information and user perceived score information) may be obtained on the other device. If the output values ​​of the hidden layer of the learning model are obtained on the other device, the control unit 11 on server 10 uses the output values ​​of the hidden layer obtained on the other device to perform the aforementioned processing.

[0083] When the control unit 11 (output unit) generates advice information according to user information, it sends (outputs) an advice screen displaying the advice information to the user terminal 20, as shown in Figure 12 (S45). When the control unit 21 of the user terminal 20 receives (acquires) the advice screen from the server 10, it displays the received advice screen on the display unit 25 (S46). The advice screen shown in Figure 12 displays advice messages related to meals, and the advice messages shown in Figure 12 are generated based on the correlation between, for example, the user's meal content (intake information) and the user's perceived score information. Furthermore, the advice messages shown in Figure 12 may be generated considering not only the correlation between the user's dietary content and the user's perceived score information, but also the user's blood glucose level information (biometric information). The advice screen has a save button for instructing the user to save the displayed advice information and a cancel button for instructing the user to end the display without saving. When the save button is operated via the input unit 24 on the advice screen, the control unit 21 stores the displayed advice information in the storage unit 22, for example, associating it with the date and time information at that time. This allows the user terminal 20 to store the advice provided by the server 10, associating it with the date and time it was provided.

[0084] Furthermore, the control unit 21 may use a pre-trained model, which has been trained on various sensor information using machine learning or deep learning, to determine a user perception score based on sensor information acquired by the wearable device worn by the user, when the sensors on the wearable device sense the user. In this case, a pre-trained model can be used that takes sensor information as input and outputs a user perception score that includes one or more pieces of information from user emotion information, user biometric information, and user behavior information. Alternatively, a pre-trained model can be used that takes sensor information and user intake information as input and outputs information regarding the correlation between user intake information and user perception score information. Such a pre-trained model can be constructed, for example, using a CNN. Through the processing described above, in the information processing system 100 of this embodiment, the server 10 can generate and provide appropriate advice to the user based on the user information registered in the member information DB 12b. The advice provided by the server 10 is generated based on at least a portion of the user information registered in the member information DB 12b, and may differ depending on the timing (time or time of day) at which the advice is provided. As a result, the server 10 can provide various advice on lifestyle habits in the user's behavior, including diet, exercise, and sleep, as well as advice on contributing to the protection of the global environment, based on the correlation between user information such as user attribute information, profile information, physical information, exercise information, sleep information, and information on food eaten, and user perception score information. By appropriately setting the conditions for providing advice according to the content of the advice, it is possible to generate appropriate advice to provide to the user according to various information about the user. In the information processing system 100 of this embodiment, the user terminal 20 can also be configured to generate advice information corresponding to user information. For example, if the user terminal 20 stores user information and advice DB 12c in the storage unit 22, the control unit 21 can identify the advice content corresponding to the user information based on the contents of the advice DB 12c. Also, if the user terminal 20 stores a learning model in addition to user information and advice DB 12c in the storage unit 22, the control unit 21 can identify the advice content corresponding to the user information using the learning model. For example, based on information regarding the correlation between intake information and perceived score information stored in the advice DB12c, and / or information regarding the correlation obtained by analyzing current or past intake information and perceived score information in a group that arbitrarily includes assumed users who will use the advice, if a group exhibits high (or low) perceived score information, one or more intake information factors (such as type of food, quantity, timing of intake, season, food combinations, cooking method, and ingredients) that are influencing that perceived score information can be identified. Then, advice including information about the correlation can be generated or identified and provided to users whose intake information is similar to that of users or users who need improvement in their perceived score information, or other users for whom usefulness or relevance can be inferred from the correlation.

[0085] In the information processing system 100 of this embodiment, the server 10 is not limited to a configuration in which it generates and provides advice to the user in response to an advice request from the user terminal 20. For example, the server 10 may generate and provide advice to the user periodically or when a predetermined time has arrived. In such a configuration, for example, the control unit 11 of the server 10 generates advice information to be provided to each user based on the user information of each user registered in the member information DB 12b when a predetermined time has elapsed or when a set time has arrived, and stores the advice information in the storage unit 12 in association with each user's member ID. In addition to the member ID and advice information, the control unit 11 may also store the date and time when the advice information was generated in the storage unit 12. On the other hand, the user terminal 20 may be configured to access the server 10 to obtain advice information when the advice application 22AP is launched, and to display the obtained advice information on the home screen (startup screen). If the advice information is displayed on the home screen, the user can check the advice on the home screen simply by launching the advice application 22AP on the user terminal 20. In addition, when the server 10 generates advice information to be provided to each user, instead of storing it in the storage unit 12, the server 10 may push notification of the generated advice information to the user terminal 20. In this case, the user terminal 20 can display on the display unit 25 that new advice information has been received, thus notifying the user that new advice information is available.

[0086] In the information processing system 100 of this embodiment, the user terminal 20 may be configured to accept user evaluations of the advice content obtained from the server 10. With such a configuration, user evaluations can be fed back into the advice content, and by updating the advice content with low evaluations, effective advice can be prepared for the user. For example, in the screen shown in Figure 12, an evaluation button (like button) may be provided for inputting whether the displayed advice was helpful, and the system may select the advice to be updated according to the evaluation content from the evaluation button. Alternatively, the system may accept evaluations of the advice on a scale of, for example, five levels, and update the content of the advice with low evaluations. The content of the evaluations made by the user on the advice may be sent to the server 10 and stored in the member information DB 12b.

[0087] In the information processing system 100 of this embodiment, the advice provided to the user of the user terminal 20 is not limited to being generated by the server 10, but may be generated by the user terminal 20 and displayed on the display unit 25. In this case, the user terminal 20 stores various user information and the advice DB 12d input via the input unit 24 in the storage unit 22, and the control unit 21 (information processing unit) can be configured to generate advice information corresponding to at least a part of the user information stored in the storage unit 22 based on the contents of the advice DB 12d.

[0088] (Embodiment 2) In the information processing system 100 of this embodiment, the user terminal 20 not only processes to receive information on the food the user has eaten based on input from the input unit 24, but also processes to receive information on the food the user has eaten by taking a picture of it with the camera 27. Furthermore, when the server 10 obtains a picture of the food the user has eaten from the user terminal 20, it processes the pictured image to identify information about the food the user has eaten or information about the nutrients the user has consumed from that food. Note that the process of identifying information about the food the user has eaten or the nutrients the user has consumed from the pictured image may be performed by the user terminal 20, in addition to the configuration in which the server 10 performs this process. In this case, the user terminal 20 identifies information about the food the user has eaten or the nutrients the user has consumed by analyzing the image taken with the camera 27, and transmits the identified intake information to the server 10.

[0089] Figure 14 is a flowchart showing an example of the user information registration process procedure in Embodiment 2, and Figures 15A and 15B are schematic diagrams showing example screens of the user terminal 20. The process shown in Figure 14 is the same as the process shown in Figures 6 and 7, but with steps S51 to S53 added between steps S22 and S23, and steps S54 to S55 added before step S25. The same steps as in Figures 6 and 7 are omitted from the explanation. Also, in Figure 14, the illustration of each step in Figure 6 and steps S28 to S35 in Figure 7 is omitted.

[0090] In the information processing system 100 of this embodiment, the user terminal 20 and the server 10 perform the same processing as in steps S11 to S22 in Figures 6 and 7. As a result, when the user terminal 20 receives user information (profile information, physical information, exercise information, and sleep information) entered via the input screen in Figures 8B to 9B, it sends the received information to the server 10, and the server 10 registers the user information received from the user terminal 20 in the member information DB 12b.

[0091] In this embodiment, the user terminal 20 displays a meal information input screen, as shown in Figure 15A, on the display unit 25 in step S21. The meal information input screen shown in Figure 15A has the same configuration as the input screen shown in Figure 10A, and further includes a photo-taking button for the user to take pictures of the meal and food items they have eaten with the camera 27. The input screen shown in Figure 15A may also have an input field for the user to input the contents of the meal they ate, or the contents of the nutrients they ingested from the meal, for example, as text data. In this case, the control unit 21 can receive the contents of the meal the user ate or the contents of the nutrients they ingested via the input field.

[0092] On the input screen shown in Figure 15A, the user enters information about the food they ate, the date and time they ate it, into the respective input fields. The control unit 21 of the user terminal 20 then receives the food intake information (meal information) entered by the user (S22). The control unit 21 displays the received information in the corresponding input field. If the user wishes to photograph the general food (meal) they ate with the camera 27, they select the photo-taking button via the input unit 24. The control unit 21 determines whether it has received a request (S51) to take a picture of the meal the user ate with the camera 27, based on whether the photo-taking button was operated via the input unit 24. If it determines that no photo-taking request has been received (S51: NO), the control unit 21 proceeds to step S23. If it determines that a photo-taking request has been received (S51: YES), the control unit 21 activates the camera 27 (S52) and takes a picture in accordance with the photo-taking request via the input unit 24, acquiring the image (S53). When the control unit 21 acquires a photograph of a meal, it displays the photograph on the meal information input screen, for example, as shown in Figure 15B. The input screen shown in Figure 15B displays a "Retake" button for retaking the photograph of the meal. When the "Retake" button is pressed, the control unit 21 restarts the camera 27, acquires another photograph in response to the shooting instruction via the input unit 24, and displays the acquired photograph on the meal information input screen. When the registration button is pressed on the input screen shown in Figure 15B, that is, when the control unit 21 receives a registration instruction (S23:YES), the control unit 21 associates the meal information entered via the input screen (intake information entered via the input fields and the photograph) with the user's member ID and sends it to the server 10 (S24).

[0093] In this embodiment, when the control unit 11 of the server 10 acquires meal information from the user terminal 20, it determines whether or not a captured image is included (S54). If it determines that a captured image is included (S54: YES), the control unit 11 analyzes the captured image to identify the contents of the meal eaten by the user (S55). The control unit 11 identifies the types of food, ingredients, and dishes shown in the captured image, for example, by template matching. In this case, templates showing image features extracted from captured images of food, ingredients, and dishes are stored in the storage unit 12 in advance, and when the control unit 11 detects a region that matches the template from the captured image, it identifies the detected region as the region of food, ingredients, or dishes corresponding to the template. The control unit 11 identifies all food, ingredients, and dishes included in the captured image. Alternatively, instead of identifying the food, ingredients, and dishes included in the captured image, the control unit 11 may be configured to identify the types and amounts of nutrients ingested by eating the food, ingredients, and dishes included in the captured image. The control unit 11 may also use a pre-trained model, which has been trained in advance on various foods, ingredients, and dishes using machine learning or deep learning, to identify the types of foods, ingredients, and dishes in the captured image. In this case, a pre-trained model that takes the captured image as input and outputs the types and quantities of foods, ingredients, or dishes in the input image may be used. The pre-trained model may also be one that, when an image is input, outputs the types and quantities of nutrients ingested by eating the foods, ingredients, or dishes shown in the image. Such a pre-trained model can be constructed, for example, using a CNN.

[0094] If the control unit 11 determines that the meal information acquired from the user terminal 20 does not include a captured image (S54: NO), it skips the process in step S55. Subsequently, the control unit 11 registers the meal information acquired from the user terminal 20 and the meal information identified from the captured image in the member information DB 12b (S25). As a result, not only the intake information entered via the input fields on the input screen of the user terminal 20, but also the contents of the meal (intake information) captured by the camera 27 of the user terminal 20 are registered in the member information DB 12b. After that, the control unit 11 of the server 10 and the control unit 21 of the user terminal 20 execute the processes from step S26 onwards.

[0095] Through the process described above, the user terminal 20 sends an image of the meal the user has eaten to the server 10, and the server 10 can obtain the details of the meal (food, ingredients, dish) shown in the image received from the user terminal 20. Therefore, in this embodiment as well, the user's attribute information, profile information, information about the user's physical condition (biometric information), information about exercise, information about sleep, and information such as the type of food the user ate, the amount consumed, and the timing of consumption (user information) can be input using the user terminal 20 and registered with the server 10. Furthermore, if the user takes a picture of the meal immediately before eating, the date and time of taking the picture can be set as the date and time of meal consumption, so the timing of meal consumption (date and time of consumption) can be automatically obtained from the date and time of taking the picture.

[0096] The user terminal 20 and server 10 in this embodiment can perform the same processing as shown in Figure 11. Therefore, by performing the same processing, the user terminal 20 and server 10 provide the user terminal 20 with advice information corresponding to the user information registered in the server 10.

[0097] In this embodiment, the same effects as in Embodiment 1 described above can be obtained. In this embodiment, in the user terminal 20, the user can input information about the meals (general foods) they have eaten not only through the input unit 24, but also by taking pictures with the camera 27. In this embodiment, it is also possible to provide advice according to the intake information of general foods eaten by the user. In the information processing system 100 of this embodiment, the modifications described as appropriate in Embodiment 1 described above can also be applied.

[0098] (Embodiment 3) This invention describes an information processing system that suggests food intake to a user based on the user's target intake of a predetermined nutrient or the user's target contribution to the global environment. In this embodiment, the system identifies foods to suggest to the user based on the target intake or target contribution over a predetermined period, such as one month, but the predetermined period is not limited to one month. In particular, when the target value in this invention is a difficult target to achieve, this invention is preferable because it adopts foods and combinations suitable for achieving that target, sets multiple intermediate targets, and enables the user to achieve their goal. In particular, because meals are consumed continuously every day, it is difficult to continue eating them without ensuring variety, as users will get bored. However, known foods (e.g., supplements) that contribute significantly to difficult-to-achieve targets generally lack variety in taste, and menus using these foods tend to be similar. However, the present invention makes it possible to adopt foods that are highly relevant to the foods consumers are interested in and that contribute significantly to the goal, resulting in an achievement method that consumers are less likely to get bored with and can be continued. In particular, it is preferable to focus on adopting foods that contribute significantly to the goal, such as noodles, bread, and artificial rice used as staple foods, specifically processed legumes, and output achievement methods including cooking methods and consumption methods, as this ensures diversity while presenting a sustainable achievement method. The information processing system of this embodiment can be implemented with the same equipment as the information processing system 100 of Embodiment 1, so a description of the configuration will be omitted. The storage unit 12 of the server 10 of this embodiment stores the target amount DB12d in addition to DB12a to 12d shown in Figures 3A to 5.

[0099] Figure 16 is a schematic diagram showing an example of the configuration of Target Amount DB12d. Target Amount DB12d stores the user's target intake and intake for a given nutrient over a given period, as well as the user's target contribution and contribution to the global environment over a given period. The Target Amount DB12d shown in Figure 16 includes columns for Member ID, Target Intake and Intake for Nutrients, Target Contribution and Contribution for the Global Environment, etc., and stores the target amount (target intake and target contribution) set by each user, and the amount achieved relative to the target amount (intake and contribution), associated with the Member ID. The Member ID column stores the Member ID of the registered user. The Target Intake Column for Nutrients stores the target intake and target calorie intake set by the user for a given nutrient. The given nutrient can be, for example, a nutrient whose deficiency affects the maintenance and promotion of health, or a nutrient whose excessive intake affects the maintenance and promotion of health. As an example of a "nutrient whose deficiency affects the maintenance and promotion of health" (or "nutrient whose excessive intake affects the maintenance and promotion of health") in this embodiment, we can list nutrients that are specified in the Nutrition Labeling Handbook (published in August 2019 by the Food Monitoring Division, Health and Safety Department, Bureau of Social Welfare and Public Health, Tokyo Metropolitan Government) as "nutrients whose deficiency or excessive intake affects the maintenance and promotion of the health of the public," and that fall under the category of "indications indicating that supplementation is possible (emphasizing a large amount of the nutrient)" (or "indications indicating that appropriate intake is possible (emphasizing a small amount of the nutrient or calories)"). Specifically, "nutrients whose deficiencies affect the maintenance and promotion of health" include protein, dietary fiber, zinc, potassium, calcium, iron, copper, magnesium, niacin, pantothenic acid, biotin, vitamin A, vitamin B1, vitamin B2, vitamin B6, vitamin B12, vitamin C, vitamin D, vitamin E, vitamin K, and folic acid. Furthermore, "nutrients whose excessive intake affects the maintenance and promotion of health" include calories, lipids, saturated fatty acids, trans fatty acids, cholesterol, sugars, and sodium. Therefore, the target intake column for nutrients stores the target intake set by the user for at least one of the nutrients mentioned above, and the amount of nutrients consumed by the user stores the amount of each of the nutrients mentioned above.

[0100] The target contribution column for the global environment stores the target contribution amount set by the user for their contribution to the global environment. In this invention, the global environment refers to the totality of external factors surrounding people and living beings on Earth, such as families, societies, and nature. It is a broad environmental concept that includes not only natural environments such as the marine environment and atmospheric environment, but also the social environment, and this same meaning applies to this embodiment. More specifically, a typical example of a target for the global environment in this invention is one or more of the 17 goals, 169 targets, and 232 indicators of the Sustainable Development Goals (SDGs), which are international goals aimed at creating a sustainable and better world by 2030, as described in the "2030 Agenda for Sustainable Development" adopted at the UN Summit in September 2015. More specifically, these include items such as reductions in greenhouse gas emissions (especially reductions in carbon dioxide and carbon footprint), reductions in industrial waste (improvements in zero-emission rates, recycling rates, and resource recovery rates), reductions in food loss (especially reductions in waste and reductions in the amount of non-edible food waste), and effective use of water resources (especially reductions in water usage and wastewater). In this embodiment, these targets can also be used as targets for contributions to the global environment, and the target values ​​for each target item can be the same as the target values ​​for that item. Therefore, the target contribution amount column for the global environment stores the target contribution amount set by the user for at least one of the items described above, and the contributed amount column for the global environment stores the contribution amount (reduction amount) that the user has contributed for each of the items described above. The member ID stored in the target amount DB12d is stored by the control unit 11 when a new member is registered in the member information DB12b, for example. The target amounts (target intake amount and target contribution amount) stored in the target amount DB12d are stored by the control unit 11 when the control unit 11 acquires each target amount via the communication unit 13. The achieved amount (intake amount and contributed amount) stored in the target amount DB12d is stored by the control unit 11 when the control unit 11 acquires information on the food (general food) consumed by the user via the communication unit 13, and the amount achieved by the food consumed by the user is identified and stored by the control unit 11. The contents of the target amount DB12d are not limited to the example shown in Figure 16, and the items related to nutritional components and contributions to the global environment stored in the target amount DB12d are not limited to the example described above.

[0101] In the information processing system 100 of this embodiment, the user terminal 20 and the server 10 perform user information registration processing similar to the processing shown in Figures 6 and 7. As a result, the user terminal 20 can receive user information such as user attribute information, profile information, biometric information, exercise information, sleep information, intake information (meal information), and subjective experience information, and the server 10 can register the user information entered via the user terminal 20 in the member information DB 12b. In this embodiment, in addition to the user information described above, the user terminal 20 also receives the user's target intake amount for nutrients and the user's target contribution amount to the global environment and registers them in the server 10 (target amount DB 12d). Here, the user's target intake amount for nutrients may be information shared with other users (shared information). Therefore, in this embodiment, the user information input screen (registration screen) is slightly different from the configuration of Embodiment 1 shown in Figure 8A, and the user information registration processing is slightly different from the processing shown in Figures 6 and 7.

[0102] Figure 17A is a schematic diagram showing an example of the registration screen of Embodiment 3, and Figure 17B is a schematic diagram showing an example of the target information input screen. In addition to the configuration shown in Figure 8A, the registration screen of this embodiment is configured to allow the user to select target information as user information to be input (registered). When target information is selected on the screen shown in Figure 17A, the control unit 21 of the user terminal 20 displays the target information input screen shown in Figure 17B on the display unit 25. The target information input screen shown in Figure 17B has an input field for inputting a target intake amount for a pre-set nutrient and an input field for inputting a pre-set target contribution amount to the global environment. The input fields for target intake and target contribution amount are each provided with a pull-down menu that allows the user to select one of the numerical values ​​(amounts) that have been pre-set for each nutrient or contribution item to the global environment. The selectable numerical values ​​may be determined, for example, from the user's eating habits or lifestyle. Alternatively, a food can be selected, and the values ​​can be determined from the nutrients contained in that food.

[0103] Furthermore, the input fields for target intake and target contribution may be configured to allow the input of arbitrary values. In addition, the target information input screen may display the registered target amounts for targets already registered by the user. In this configuration, the user can check already registered target amounts (or check their registration history chronologically) and change them if desired. Alternatively, they can select a period for achieving the target amount, and multiple target amounts and periods can be selected. Specifically, it is possible to set a daily intake target amount, specifying settings for breakfast, lunch, dinner, snacks, etc., or by time of day. Alternatively, weekly intake targets can be set for each day of the week, and the daily settings can also be repeated. Furthermore, individual settings such as setting special days can be made in conjunction with the user's schedule. In the target information input screen shown in Figure 17B, "greater than or equal to" or "less than or equal to" is displayed corresponding to the input field for the target intake of each nutrient. Alternatively, it is possible to set a period for achieving the target intake and set multiple periods, for example, by selecting them in stages. Specifically, "less than or equal to" is displayed for calories, lipids, and sugars, and "greater than or equal to" is displayed for dietary fiber and protein. As a result, for nutrients whose deficiency affects the maintenance and promotion of health, such as dietary fiber and protein, a lower limit value is entered in the input field, and a value greater than or equal to the entered value is accepted as the target intake. On the other hand, for nutrients whose excessive intake affects the maintenance and promotion of health, such as calories, lipids, and sugars, an upper limit value is entered in the input field, and a value less than or equal to the entered value is accepted as the target intake. Note that "greater than or equal to" or "less than or equal to" for each nutrient may be changed. Furthermore, for items that contribute to the global environment, a lower limit value is entered in the input field, and a value greater than or equal to the entered value is accepted as the target contribution.

[0104] On the input screen shown in Figure 17B, the user selects the nutrient or environmental contribution item for which they wish to set a target and enters the target amount in the corresponding input field. When the registration button is pressed on the target information input screen, the control unit 21 of the user terminal 20 associates the target information entered via the input screen (target intake amount for nutrient or target contribution amount for environmental contribution item) with the user's member ID and sends it to the server 10, instructing the server 10 to register the target information. The control unit 11 of the server 10 (target intake amount acquisition unit, target contribution amount acquisition unit) acquires the target information from the user terminal 20 and stores the acquired target information in the target amount DB 12d (target intake amount storage unit, target contribution amount storage unit) in association with the member ID. As a result, the target intake amount for nutrient or target contribution amount for environmental contribution item entered by the user via the user terminal 20 is registered in the server 10 (target amount DB 12d).

[0105] In this embodiment, when the server 10 receives intake information (meal information) from the user terminal 20, it identifies the amount of each nutrient consumed (amount consumed) and the amount contributed to the global environment (amount contributed) from the food consumed by the user, and registers the identified intake and contribution amounts in the target amount DB 12d. Specifically, after the processing in step S25 in Figure 7, the control unit 11 of the server 10 identifies the amount of nutrients consumed by the user and the amount contributed to the global environment (amount achieved) from this meal, based on the meal information obtained from the user terminal 20. For example, the amount of nutrients and the amount contributed to the global environment (amount achieved) that can be achieved when eating food (general food) are registered in advance in the storage unit 12 or another server. The control unit 11 then extracts the foods included in the user's meal and obtains the amount of nutrients and the amount contributed to the global environment corresponding to each food from the storage unit 12 or another server. The control unit 11 stores the obtained amount achieved in the target amount DB 12d, associating it with the member ID. The control unit 11 adds the acquired achievement amount to the intake amount and contribution amount already stored in the target amount DB12f and stores it in the target amount DB12f. This allows the user to continuously update and store the amount of nutrients and contributions to the global environment achieved over a predetermined period (for example, one month).

[0106] As described above, in this embodiment, a target intake amount for nutrients and a target contribution amount to the global environment are set in the server 10 for each user. In addition, each time that user intake information (meal information) is transmitted from the user terminal 20 to the server 10, the amount of nutrients the user has consumed and the amount of contribution to the global environment achieved by the meal are accumulated in the server 10. In the above configuration, for example, when a user selects a food item displayed on a website provided by the server 10, the server 10 performs a process to suggest (advise) a more suitable food item to the user based on the selected food item and the target amount set by the user.

[0107] Figure 18 is a flowchart showing an example of the advice provision process procedure in Embodiment 3, and Figures 19A and 19B are schematic diagrams showing example screens of the user terminal 20. In Figure 18, the processing performed by the user terminal 20 is shown on the left, and the processing performed by the server 10 is shown on the right. The following processing is executed by the control unit 21 according to the control program 22P and advice application 22AP stored in the storage unit 22 of the user terminal 20, and by the control unit 11 according to the control program 12P stored in the storage unit 12 of the server 10. Some of the following processing may be implemented by dedicated hardware circuits.

[0108] In the information processing system 100 of this embodiment, a user, for example, while browsing a website published by the server 10 via the network N using the user terminal 20, selects a food item of interest from among the foods displayed on the website using the input unit 24. For example, as shown in Figure 19A, on a website displaying multiple types of food items, the user selects any food item. The control unit 21 of the user terminal 20 determines whether or not any food item has been selected via the displayed website (S111). If it determines that no food item has been selected (S111: NO), it waits while performing other processing. If it determines that any food item has been selected (S111: YES), the control unit 21 sends the product ID of the selected food item and the user's member ID to the server 10 (S112). The product ID is, for example, listed on the website, and the member ID is, for example, set in the advice application 22AP.

[0109] When the control unit 11 of the server 10 receives the product ID and member ID from the user terminal 20, it reads the target information (target intake of nutrients and target contribution to the global environment) and the achieved amount (amount of nutrients consumed and amount of contribution to the global environment) stored in the target amount DB 12f, associated with the member ID (S113). The control unit 11 only needs to read the target amount and achieved amount for nutrients and contribution items to the global environment for which the target amount (target intake or target contribution) is registered. Next, the control unit 11 obtains the amount of nutrients and the amount of contribution to the global environment (achievable amount) that can be achieved by eating the food selected by the user (S114). The amount of nutrients and the amount of contribution to the global environment that can be achieved by eating each food are stored in advance in, for example, the product information DB 12a, and the control unit 11 reads the achievable amount corresponding to the product ID from the product information DB 12a. The achievable quantities for each food item may be stored on another server, in which case the control unit 11 may obtain the achievable quantities for each food item from the other server.

[0110] Next, the control unit 11 identifies foods to be suggested to the user (S115) based on the target information and achieved amount read in step S113 and the achievable amount obtained in step S114. For example, the control unit 11 (achieved intake amount acquisition unit, achieved contribution amount acquisition unit) acquires the amount of nutrients that can be achieved by eating each of the foods to be sold and the amount of contribution to the global environment (achievable amount). Here again, the control unit 11 acquires the achievable amount corresponding to each food from the product information DB 12a or another server. The control unit 11 then calculates the remaining target intake amount by subtracting the amount already consumed by the user from the user's target intake amount for nutrients, and identifies foods that should be consumed in order to achieve (realize) the remaining target intake amount.

[0111] For example, for nutrients for which a target intake value is set to be above a predetermined amount, the control unit 11 extracts foods from the range of products that can achieve a higher amount of the nutrient than the food selected by the user, and identifies them as foods to be suggested. Here, the control unit 11 may also identify foods to be suggested if the remaining target intake is greater than the amount of the nutrient that can be achieved by the food selected by the user. Furthermore, for nutrients for which a target intake value is set to be below a predetermined amount, the control unit 11 extracts foods from the range of products that can achieve a lower amount of the nutrient than the food selected by the user, and identifies them as foods to be suggested. Here, the control unit 11 may also identify foods to be suggested if the remaining target intake is less than the amount of the nutrient that can be achieved by the food selected by the user. Similarly, the control unit 11 calculates the remaining target contribution amount by subtracting the amount already contributed by the user from the user's target contribution amount for the global environment, and identifies foods that should be consumed to achieve (realize) the remaining target contribution amount. For example, the control unit 11 extracts foods from the range of foods for sale that can achieve a greater contribution amount than the foods selected by the user, and identifies foods to be suggested. Here again, the control unit 11 may identify foods to be suggested if the remaining target intake amount is greater than the contribution amount that can be achieved by the foods selected by the user. If target amounts are set for multiple nutritional components or multiple items of contribution to the global environment, the control unit 11 identifies foods to be suggested that bring the user closer to each target amount. The control unit 11 may also take into account the user's preference information (food preferences) and identify foods that are suitable for the user's preferences as foods to be suggested.

[0112] Furthermore, if the food in this embodiment contains a quantity of "nutrients whose deficiency affects the maintenance and promotion of health" to the extent that it can be emphasized as "high," "contains," or "fortified," and / or contains a quantity of "nutrients whose excessive intake affects the maintenance and promotion of health" to the extent that it can be emphasized as "low," "does not contain," or "reduced," then it is possible to identify a food that allows users to consume an appropriate amount of nutrients that are highly acceptable to them. For example, if a high target intake is set for protein and / or dietary fiber and a low target intake is set for calories, it is preferable to identify foods that contain enough protein and / or dietary fiber to be able to emphasize that they are "high," "contain," or "fortified" (more specifically, foods that contain 8% by mass or more of dietary fiber, and more preferably foods in which the ratio of dietary fiber to carbohydrates is 0.15 or more, and foods that use processed legumes as the main ingredient (especially noodles, bread, and artificial rice used as staple foods)), and that contain enough calories to be able to emphasize that they are "low," "do not contain," or "reduced."

[0113] The control unit 11 (generation unit) generates advice information as shown in Figure 19B (S116) based on the food identified in step S115. Figure 19B shows an example of an advice screen displayed when vegetable sticks [beets] are selected by the user on the screen shown in Figure 19A. The advice screen shown in Figure 19B displays the target intake of dietary fiber and the target reduction in water usage as target information set by the user, and the amount of dietary fiber consumed and the reduction in water usage as achieved information. In addition, the screen shown in Figure 19B displays a message suggesting a vegetable paste [beets] that allows for more efficient intake of dietary fiber than the selected vegetable sticks [beets], and displays information comparing the amount of dietary fiber from the vegetable sticks [beets] selected by the user with the amount of dietary fiber from the suggested vegetable paste [beets]. If the suggested food contributes more to the global environment than the food selected by the user, the screen shown in Figure 19B displays a message suggesting other foods that contribute more to the global environment than the selected food. In this case, information comparing the contribution of the food selected by the user to the global environment with the contribution of the suggested food to the global environment may be displayed. Furthermore, the screen shown in Figure 19B includes a link to display menus (methods of consumption and preparation) using the suggested food, and menus using the suggested food are provided via this link. Note that menus using the suggested food may also be displayed on the screen shown in Figure 19B.

[0114] The control unit 11 sends an advice screen displaying the generated advice information to the user terminal 20 (S117). The control unit 21 of the user terminal 20 receives the advice screen from the server 10 and displays the advice screen shown in Figure 19B on the display unit 25 (S118). As shown in Figure 19B, the user terminal 20 displays the advice screen received from the server 10 overlaid on the display screen shown in Figure 19A, but the configuration is not limited to this. Through the above process, when a user selects any food item while browsing a website using the user terminal 20, the server 10 can identify the food item that will best help the user achieve their goals and provide advice to the user. As a result, the user can receive advice to achieve the goals they have set.

[0115] In this embodiment, the same effects as those of the embodiments described above can be obtained. Furthermore, in this embodiment, when a user selects any food item via a browsing image, they can receive advice to achieve their target information. At that time, the achievable amount of the user's target can be provided for both the food item selected by the user and the suggested food item. Therefore, when considering purchasing and consuming food, the user can consider the degree to which they can achieve their target. Moreover, they can check the degree of achievement in their achievement plan for any period set by the user and receive and consider advice to realize their achievement plan. The configuration of this embodiment can also be applied to the information processing system 100 of Embodiment 2, and the same effects can be obtained even when applied to the information processing system 100 of Embodiment 2. Furthermore, in the information processing system 100 of this embodiment, the modified examples described as appropriate in the embodiments described above can be applied.

[0116] In this embodiment, the configuration is not limited to the user setting the target information themselves. For example, the target intake amount for each nutrient and the target contribution amount for each item contributing to the global environment may be registered in advance in the storage unit 12 or another server according to information such as age, gender, place of origin (country or region), religion, degree of vegetarianism, and dietary preferences (preference information). In this case, the control unit 11 of the server 10 may obtain the target amounts corresponding to the user's information from the storage unit 12 or another server and set them in the user's target information. Alternatively, the control unit 11 may obtain and set target information according to the user from a web server that publishes such information. For example, the target intake for dietary fiber can be the median intake listed in "Table 2: Values ​​referenced to calculate the target amount of dietary fiber (g / day)" under "II. Specifics, 1. Energy and Nutrients," "1-4. Carbohydrates, 4. Dietary Fiber" in the "Dietary Reference Intakes for Japanese (2020 Edition)." Alternatively, target amounts corresponding to the user's place of origin or residence can be obtained from the intake standards for each nutrient published in each country and used as target information. With such a configuration, target amounts can be automatically set according to the user's situation. Furthermore, the system can automatically set target amounts based on the user's eating habits or lifestyle, such as whether the user is on a diet. Specifically, the system can select intake standards based on the user's country of origin or place of residence associated with the user ID, and use the intake amounts listed in those standards. For example, for the target intake of dietary fiber in the United States, the user can use the intake amount listed under "Dietary Fiber, g" corresponding to the user's age and gender from the list in "Table A7-1. Daily Nutritional Goals for Age-Sex Groups Based on Dietary Reference Intakes & Dietary Guidelines Recommendations" in "Appendix 7. Nutritional Goals for Age-Sex Groups Based on Dietary Reference Intakes & Dietary Guidelines Recommendations" of the "2015-2020 Dietary Guidelines for Americans". Alternatively, if the user's country of origin or residence is the United Kingdom, the target intake for dietary fiber can be selected from the "Dietary Fibre" list in "Nutrition Requirements_Revised August 2019," specifically the intake listed under the "Age group" corresponding to the user's age. The intake standards for each nutrient published in each country are not limited to those mentioned above and can be obtained from data stored in the memory unit or from information such as websites of government agencies and research institutions in each country that are publicly available via Network N.

[0117] Furthermore, as described above, the server 10 may provide target information obtained from other servers, etc., based on user information to the user terminal 20, and when the user terminal 20 displays the target information input screen shown in Figure 17B, it may display the target information (each target quantity) provided by the server 10. In this case, the user can understand general target information for their own situation (age, gender, place of origin, religion, etc.) and set their own target information appropriately. Therefore, the user can set target information within an achievable range.

[0118] In the information processing system 100 of each embodiment described above, when the control unit 11 of the server 10 generates advice information to be provided to the user, it may consider not only the user information stored in the member information DB 12b, but also foods that the user has not purchased or consumed. This makes it possible to generate advice information that recommends the purchase of foods that the user has never purchased or consumed. The user may input information about foods that they have not purchased or consumed via the user terminal 20 and register it with the server 10. Alternatively, the server 10 may identify foods other than those already purchased by the user from the foods for sale, and consider these identified foods as foods that the user has not purchased or consumed.

[0119] In the information processing system 100 of each embodiment described above, the recipient of advice provided by the server 10 is not limited to the user terminal 20, but may also be the terminal (user terminal 20) of, for example, the user's guardian, family, friend, caregiver, etc. In this case, by registering the recipient terminal of advice with the server 10 in advance, when the server 10 generates advice information for the user, it can provide the advice information to the user's guardian, etc. Therefore, by providing advice information to others in addition to the user themselves, various information regarding the user's diet, exercise, sleep, etc. can be shared between the user and the guardian, etc., and it is expected that this will lead to an improvement in the user's lifestyle habits.

[0120] In the information processing system 100 of each embodiment described above, the processing realized by the user terminal 20 executing the advice application 22AP may be divided into multiple application programs. For example, the process of the user terminal 20 acquiring information about the user (user information) via the input unit 24, etc., and storing it in the server 10, and the process of acquiring advice from the server 10 according to the user information stored in the server 10 may be realized by separate application programs. In this case, the control unit 21 of the user terminal 20 executes a program for accumulating user information, and performs processing to receive user information such as user attribute information, profile information, biometric information, exercise information, sleep information, type of food eaten by the user, intake amount and timing of intake (user intake information), and subjective information via the input unit 24, etc., and transmit it to the server 10. Furthermore, the control unit 21 executes a program to obtain advice, sends the user's member ID (identification information) to the server 10 to request advice, and retrieves advice from the server 10 according to the user's user information and displays it on the display unit 25. Even with this configuration, the same processing as in the embodiments described above is possible and the same effects can be obtained. Note that the user information stored in the server 10 may be input via an application program stored in the user terminal 20, or via a predetermined website that is publicly available via the network N. In addition, if the user information is stored in another storage device, the server 10 may be configured to retrieve it from that other storage device.

[0121] Furthermore, in the information processing system 100 of each embodiment described above, the processing performed by server 10 may be distributed to multiple servers. For example, the processing of storing user information acquired by server 10 from each user terminal 20 and the processing of providing advice to each user terminal 20 according to the user information stored by server 10 may be performed by separate servers. In this case, the storage server that stores user information acquires various information about the user (user information) entered using the user terminal 20 and stores it in the member information DB 12b as shown in Figure 4. On the other hand, the advice server, in response to a request from the user terminal 20, for example, retrieves user information of the user corresponding to the user terminal 20 from the storage server, generates advice based on the contents of the advice DB 12d, as shown in Figure 5, and outputs it to the user terminal 20. At this time, the advice server accepts the advice request by obtaining the user's member ID (identification information) from the user terminal 20, retrieves user information corresponding to the received member ID from the storage server, and generates advice based on the retrieved user information. The user terminal 20, having received the advice generated in this way, can display the retrieved advice on the display unit 25, thereby providing the user of the user terminal 20 with advice tailored to their user information. For example, a sales company that sells food can store user information using a storage server, and the advice provider can use the stored user information to provide advice appropriate to each user via the advice server. In the embodiments 1 to 3 described above, user intake information is an example of intake information, user emotional information is an example of emotional information, user biometric information is an example of biometric information, user behavior information is an example of behavior information, user perceived score information is an example of perceived score information, user attribute information is an example of behavior information, user preferences are an example of preferences, user environment information is an example of environment information, and virtual user behavior information is an example of virtual behavior information.

[0122] Although embodiments have been described above, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, modifications, and combinations are possible without departing from the spirit of the invention. These embodiments are included in the scope and spirit of the invention, as well as in the claims and their equivalents. Furthermore, the server 10, user terminal 20, and wearable device 30 included in the above-described information processing system 100 may be implemented using a computer. In that case, the programs for implementing the functions of each functional block are recorded on a computer-readable recording medium. The programs recorded on this recording medium may be loaded into a computer system and executed by the CPU. The term "computer system" here includes hardware such as an OS (Operating System) and peripheral devices. Furthermore, "computer-readable recording media" refers to portable media such as flexible disks, magneto-optical disks, ROMs, and CD-ROMs. It also includes storage devices such as hard disks built into computer systems. Furthermore, "computer-readable recording media" may include those that dynamically hold programs for a short period of time. Examples of those that dynamically hold programs for a short period of time include communication lines used when transmitting programs via networks such as the Internet or communication lines such as telephone lines. Furthermore, "computer-readable recording media" may include volatile memory within a server or client computer system that retains programs for a certain period of time. The program itself may also be intended to implement some of the functions described above. Additionally, the program may be capable of implementing the aforementioned functions in combination with programs already recorded in the computer system. Furthermore, the program may be implemented using a programmable logic device. An example of a programmable logic device is an FPGA (Field Programmable Gate Array).

[0123] Furthermore, the above embodiments include the following aspects. [1a] A program and a recording medium on which the program is recorded, which causes a computer to acquire intake information regarding food consumed by a user, acquire one or more pieces of information from the user's emotional information, biometric information and behavioral information and / or perceived score information derived from said information, output the acquired intake information, one or more pieces of information from the emotional information, biometric information and behavioral information and / or perceived score information derived from said information and the user's identification information to an information processing unit, and obtain advice information from the information processing unit that includes information regarding the correlation between said intake information and said perceived score information. [2a] A program described in [1a] and a recording medium on which the program is recorded, which causes the program to acquire attribute information relating to the user's attributes and to output the acquired attribute information and the user's identification information to the information processing unit. [3a] The user is a user in a country or region with a Power distance index (PDI value) of 80 or less. The program described in [1a] or [2a] and a recording medium on which the program is recorded. [4a] A program and a recording medium on which the program is recorded, wherein the information relating to the correlation between the intake information and the perceived score information is information relating to a group with the same attributes as the user. [5a] The program described in [3a] or [4a] and a recording medium on which the program is recorded, wherein the information relating to the correlation between the intake information and the perceived score information is based on data obtained in a country where the PDI value is within plus or minus 20 for the country to which the user belongs. [6a] The information relating to the correlation between the intake information and the perceived score information is based on data obtained in countries belonging to the same category when countries are categorized by PDI values ​​of 0 or more and less than 30, 30 or more and less than 60, 60 or more and less than 90, and 90 or more and less than 120, as described in any one of [3a] to [5a], and a recording medium on which the program is recorded. [7a] A program according to any one of [2a] to [6a] that outputs time-series data of attribute information relating to the user's attributes and the user's identification information to the information processing unit, and a recording medium on which the program is recorded. [8a] The attribute information relating to the user's attributes is one or more selected from nationality, place of residence, address, gender recognized by the user, and occupation, and the program and recording medium on which the program is recorded, as described in any one of [2a] to [7a]. [9a] The intake information includes information about foods that the user has actually consumed from among the foods owned by the user, and the program described in any one of [1a] to [8a] and the recording medium on which the program is recorded. [10a] The intake information includes information about foods that the user has possessed but that the user did not actually consume, as described in any one of [1a] to [9a], and a recording medium on which the program is recorded. [11a] The food is mainly a general food, and the program described in any one of [1a] to [10a] and the recording medium on which the program is recorded. [12a] A program according to any one of [1a] to [11a], and a recording medium on which the program is recorded, which causes the program to acquire preference information relating to the user's food preferences or principles, and to output the acquired preference information and the user's identification information to the information processing unit. [13a] A program according to any one of [1a] to [12a], and a recording medium on which the program is recorded, wherein the food consumed by the user includes, based on information relating to the correlation between the intake information and the perceived score information, foods that have a relatively low perceived score but are strongly desired due to preference and / or lifestyle. [14a] The perceived score information includes the perceived score information obtained from biometric information relating to the user's body, and the program described in any one of [1a] to [13a] and the recording medium on which the program is recorded. [15a] The perceived score information includes the perceived score information obtained from behavioral information relating to the user's actions, and the program described in any one of [1a] to [14a] and the recording medium on which the program is recorded. [16a] The perceived score information includes time-series data of the emotional information, and the program described in any one of [1a] to [15a] and the recording medium on which the program is recorded. [17a] The program described in [16a] and a recording medium on which the program is recorded, wherein the time-series data includes data before and after the user consumes food. [18a] The perceived score information includes one or more of the following: emotional information, biological information, and behavioral information, and a program described in any one of [1a] to [17a] and a recording medium on which the program is recorded. [19a] A program according to any one of [1a] to [18a], and a recording medium on which the program is recorded, wherein the advice information includes virtual intake information for increasing the perceived score per unit time, based on information relating to the correlation between the intake information and the perceived score information. [20a] The program described in [19a] and a recording medium on which the program is recorded, wherein the advice information includes, based on information relating to the correlation between the intake information and the perceived score information, hypothetical intake information for increasing the perceived score per unit time, while including foods that have a relatively low perceived score but are strongly desired due to taste and / or lifestyle. [21a] The program described in [19a] or [20a] and a recording medium on which the program is recorded, wherein the advice information includes virtual intake information for increasing the perceived score per unit time without changing the behavioral information, based on information relating to the correlation between the intake information and the perceived score information. [22a] The program described in any one of [1a] to [21a], and a recording medium on which the program is recorded, wherein the advice information includes virtual behavioral information for increasing the perceived score per unit time. [23a] The program according to any one of [1a] to [22a] and a recording medium on which the program is recorded, wherein the emotional information includes at least one of emotional information per unit time and information that identifies fluctuations in emotion. [24a] A program according to any one of [19a] to [23a], wherein the unit time is one day or more, or one week or more, or one month or more, or three months or more, or one year or more, or three years or more. A recording medium on which the program is recorded. [25a] A program according to any one of [19a] to [24a], and a recording medium on which the program is recorded, comprising two or more unit times in the same season of different years. [26a] A program according to any one of [1a] to [25a] and a recording medium on which the program is recorded, which causes a learning model obtained by machine learning the relationship between sensor information and information that identifies the perceived score to be obtained from sensor information acquired by the sensor sensing the user, and the learning model to obtain information that identifies the perceived score output by the learning model. [27a] The intake information includes information that identifies the amount and timing of the intake of food consumed by the user, and the program described in any one of [1a] to [26a] and the recording medium on which the program is recorded. [28a] The advice information includes a program according to any one of [1a] to [27a], and a recording medium on which the program is recorded, which includes a method for improving the perceived score without changing either or both of the information relating to preferences and lifestyle. [29a] A program according to any one of [1a] to [28a], and a recording medium on which the program is recorded, which causes the user to obtain environmental information when the user consumes the food, and outputs the obtained environmental information and the user's identification information to the information processing unit. [30a] A program described in [29a] that outputs time-series data of the environmental information and the user's identification information to an information processing unit, and a recording medium on which the program is recorded. [31a] The advice information includes advice to alleviate either or both anxiety and stress information, and the program described in any one of [1a] to [30a] and the recording medium on which the program is recorded. [32a] A program according to any one of [1a] to [31a], and a recording medium on which the program is recorded, which causes the program to acquire behavioral information relating to the user's actions and to output the acquired behavioral information and the user's identification information to the information processing unit. [33a] The behavioral information includes, based on information relating to the correlation between the intake information and the perceived score information, behaviors that have a relatively low perceived score but are strongly requested by preferences and / or lifestyles, as described in any one of [1a] to [32a], and a recording medium on which the program is recorded. [34a] The program described in any one of [1a] to [33a] and a recording medium on which the program is recorded, wherein the advice information includes either advice for improving lifestyle habits and advice to alleviate either or both of the user's anxiety and stress information regarding behavioral change. [35a] A program according to any one of [1a] to [34a] that causes the user to acquire biological information relating to the user's biological body, and outputs the acquired biological information and the user's identification information to the information processing unit, and a recording medium on which the program is recorded. [36a] A program described in [35a] that outputs time-series data of the biological information and the user's identification information to an information processing unit, and a recording medium on which the program is recorded. [37a] A program described in any one of [1a] to [36a] that causes the program to obtain shared information with other users regarding the food product and to output the obtained shared information to the information processing unit, and a recording medium on which the program is recorded. [38a] The shared information includes information that identifies a target intake for a predetermined nutrient, and the advice information includes information on how to consume and prepare food in order to achieve the target intake for the predetermined nutrient, as described in [37a], and a recording medium on which the program is recorded. [39a] The specified nutrient is a nutrient whose deficiency affects the maintenance and promotion of health or a nutrient whose excessive intake affects the maintenance and promotion of health, as described in [38a], and a recording medium on which the program is recorded. [40a] A program according to any one of [1a] to [39a], and a recording medium on which the program is recorded, which causes the user to obtain the target amount of contribution to the global environment and outputs the obtained target amount of contribution and the user's identification information to the information processing unit. [41a] The target amount of contribution to the global environment includes the target amount in the Sustainable Development Goals (SDGs), as described in [40a], and a recording medium on which the program is recorded. [42a] The program described in [40a] or [41a] and a recording medium on which the program is recorded, wherein the target contribution to the global environment includes at least one of the following: a reduction in greenhouse gas emissions, a reduction in industrial waste, a reduction in food loss, a reduction in water usage, or a reduction in emissions. [43a] The advice information includes information for achieving the target amount of contribution to the global environment, and the program described in any one of [40a] to [42a] and the recording medium on which the program is recorded. [44a] The biological information includes electroencephalograms and stress hormones; a program as described in any one of [1a] to [43a] and a recording medium on which the program is recorded. [45a] A program described in any one of [1a] to [44a] and a recording medium on which the program is recorded, wherein the information processing unit creates predetermined information from intake information, perceived score information and user identification information, and outputs the created predetermined information. [46a] A program and recording medium on which the program is recorded, wherein, after outputting advice information at any time, the program obtains one or more pieces of information from emotional information, biometric information and behavioral information relating to the emotions of users who followed the advice information or users who did not follow the advice information, and / or perceived score information derived from said information, outputs the obtained intake information, one or more pieces of information from emotional information, biometric information and behavioral information and / or perceived score information derived from said information and the identification information of the user to the information processing unit, and obtains validity verification information regarding the correlation at the time of the advice information output from the information processing unit. [47a] A program as described in [46a] and a recording medium on which the program is recorded, which, based on the validity verification information obtained, causes the system to perform new information processing using one or more pieces of information from emotional information, biometric information, and behavioral information relating to the emotions of users who followed the advice information or users who did not follow the advice information, and / or perceived score information derived from said information. [48a] A program described in [47a] that causes the information processing unit to acquire new advice information, including information relating to the correlation between intake information and the perceived score information, and a recording medium on which the program is recorded.

[0124] [1b] A computer-based information processing method comprising: acquiring intake information relating to food consumed by a user; acquiring one or more pieces of information from emotional information, biometric information, and behavioral information relating to the user's emotions and / or perceived score information derived from said information; outputting the acquired intake information, one or more pieces of information from emotional information, biometric information, and behavioral information and / or perceived score information derived from said information and user identification information to an information processing unit; and acquiring advice information from the information processing unit that includes information relating to the correlation between said intake information and said perceived score information. [2b] The information processing method according to [1b], which obtains attribute information relating to the user's attributes and outputs the obtained attribute information and the user's identification information to the information processing unit. [3b] The information processing method according to [1b] or [2b], wherein the user is a user in a country or region with a Power distance index (PDI value) of 80 or less. [4b] The information processing method according to any one of [1b] to [3b], wherein the information relating to the correlation between the intake information and the perceived score information is information relating to a group with the same attributes as the user. [5b] The information processing method according to [3b] or [4b], wherein the information relating to the correlation between the intake information and the perceived score information is based on data obtained in a country where the PDI value is within plus or minus 20 for the country to which the user belongs. [6b] The information processing method according to any one of [3b] to [5b], wherein the information relating to the correlation between the intake information and the perceived score information is based on data obtained in countries belonging to the same category when countries are categorized by PDI values ​​of 0 or more and less than 30, 30 or more and less than 60, 60 or more and less than 90, and 90 or more and less than 120. [7b] An information processing method according to any one of [2b] to [6b], which outputs time-series data of attribute information relating to the user's attributes and the user's identification information to the information processing unit. [8b] The attribute information relating to the user's attributes is one or more selected from nationality, place of residence, address, gender recognized by the user, and occupation, as described in any one of the information processing methods described in any one of [2b] to [7b]. [9b] The information processing method according to any one of [1b] to [8b], wherein the intake information includes information about food that the user has actually consumed from among the food owned by the user. [10b] The information processing method according to any one of [1b] to [9b], wherein the intake information includes information about foods that the user has owned but did not actually consume. [11b] The information processing method according to any one of [1b] to [10b], wherein the food is mainly a general food. [12b] The information processing method according to any one of [1b] to [11b], which acquires preference information relating to the user's food preferences or principles, and outputs the acquired preference information and the user's identification information to the information processing unit. [13b] The information processing method according to any one of [1b] to [12b], wherein the food consumed by the user includes, based on information relating to the correlation between the intake information and the perceived score information, foods that have a relatively low perceived score but are strongly desired due to preference and / or lifestyle. [14b] The information processing method according to any one of [1b] to [13b], wherein the perceived score information includes perceived score information obtained from biometric information relating to the user's body. [15b] The information processing method according to any one of [1b] to [14b], wherein the perceived score information includes perceived score information obtained from behavioral information relating to the user's actions. [16b] The information processing method according to any one of [1b] to [15b], wherein the perceived score information includes time-series data of the emotional information. [17b] The information processing method according to [16b], wherein the time-series data includes data before and after the user consumes the food. [18b] The information processing method described in any one of [1b] to [17b], wherein the perceived score information includes one or more of emotional information, biological information, and behavioral information. [19b] The information processing method according to any one of [1b] to [18b], wherein the advice information includes virtual intake information for increasing the perceived score per unit time, based on information relating to the correlation between the intake information and the perceived score information. [20b] The information processing method according to [19b], wherein the advice information includes, based on information relating to the correlation between the intake information and the perceived score information, hypothetical intake information for increasing the perceived score per unit time, while including foods that have a relatively low perceived score but are strongly desired due to taste and / or lifestyle. [21b] The information processing method according to [19b] or [20b], wherein the advice information includes virtual intake information for increasing the perceived score per unit time without changing the behavioral information, based on information relating to the correlation between the intake information and the perceived score information. [22b] The information processing method according to any one of [1b] to [21b], wherein the advice information includes virtual behavioral information for increasing the perceived score per unit time. [23b] The information processing method according to any one of [1b] to [22b], wherein the emotional information includes at least one of emotional information per unit time and information that identifies fluctuations in emotion. [24b] The information processing method according to any one of [19b] to [23b], wherein the unit time is one day or more, or one week or more, or one month or more, or three months or more, or one year or more, or three years or more. [25b] The information processing method according to any one of [19b] to [24b], which includes two or more unit times in the same season of different years. [26b] An information processing method according to any one of [1b] to [25b], which acquires information that identifies the perceived score output by the learning model for sensor information acquired by the sensor sensing the user, based on a learning model obtained by machine learning the relationship between sensor information and information that identifies the perceived score. [27b] The information processing method according to any one of [1b] to [26b], wherein the intake information includes information that identifies the amount and timing of intake of food consumed by the user. [28b] The information processing method according to any one of [1b] to [27b], which includes a method for improving the perceived score without changing either or both of the advice information and information relating to preferences and lifestyle. [29b] An information processing method according to any one of [1b] to [28b], which acquires environmental information when the user consumes the food, and outputs the acquired environmental information and the user's identification information to the information processing unit. [30b] The information processing method according to [29b], which outputs time-series data of the environmental information and the user's identification information to the information processing unit. [31b] ​​The information processing method according to any one of [1b] to [30b], wherein the advice information includes advice to alleviate either or both of the anxiety and stress information. [32b] An information processing method according to any one of [1b] to [31b], which acquires behavioral information relating to the user's actions and outputs the acquired behavioral information and the user's identification information to the information processing unit. [33b] The information processing method according to any one of [1b] to [32b], wherein the behavioral information includes behaviors that have a relatively low perceived score but are strongly desired due to preferences and / or lifestyles, based on information relating to the correlation between the intake information and the perceived score information. [34b] The information processing method according to any one of [1b] to [33b], wherein the advice information includes either advice for improving lifestyle habits or advice to alleviate either or both of the user's anxiety and stress information regarding behavioral change. [35b] The information processing method according to any one of [1b] to [34b], which acquires biometric information relating to the user's biological body and outputs the acquired biometric information and the user's identification information to the information processing unit. [36b] The information processing method according to [35b], which outputs time-series data of the biological information and the user's identification information to an information processing unit. [37b] An information processing method according to any one of [1b] to [36b], which obtains shared information with other users regarding the food and outputs the obtained shared information to the information processing unit. [38b] The information processing method according to [37b], wherein the shared information includes information that identifies a target intake for a predetermined nutrient, and the advice information includes information on how to consume and prepare food in order to achieve the target intake for the predetermined nutrient. [39b] The information processing method according to [38b], wherein the specified nutrient is a nutrient whose deficiency affects the maintenance and promotion of health or a nutrient whose excessive intake affects the maintenance and promotion of health. [40b] An information processing method according to any one of [1b] to [39b], wherein the user's target contribution amount to the global environment is obtained, and the obtained target contribution amount and the user's identification information are output to the information processing unit. [41b] The information processing method according to [40b], wherein the target amount of contribution to the global environment includes the target amount in the Sustainable Development Goals (SDGs). [42b] The information processing method according to [40b] or [41b], wherein the target contribution to the global environment includes at least one of the following: a reduction in greenhouse gas emissions, a reduction in industrial waste, a reduction in food loss, a reduction in water usage, or a reduction in emissions. [43b] The advice information includes information for achieving the target contribution to the global environment, as described in any one of the paragraphs [40b] to [42b]. [44b] The biological information includes electroencephalograms and stress hormones. The information processing method according to any one of [1b] to [43b]. [45b] The information processing method according to any one of [1b] to [44b], wherein the information processing unit creates predetermined information from intake information, perceived score information and user identification information, and outputs the created predetermined information. [46b] An information processing method according to any one of paragraphs [1b] to [45b], wherein after outputting advice information at any time, one or more pieces of information from emotional information, biometric information and behavioral information relating to the emotions of users who followed the advice information or users who did not follow the advice information, and / or perceived score information derived from said information, outputs the acquired intake information, one or more pieces of information from emotional information, biometric information and behavioral information and / or perceived score information derived from said information and the identification information of the user to the information processing unit, and obtains validity verification information regarding the correlation at the time of outputting the advice information from the information processing unit. [47b] The information processing method according to [46b], which performs new information processing based on the acquired validation information using one or more pieces of information from emotional information, biometric information and behavioral information relating to the emotions of users who followed the advice information or users who did not follow the advice information, and / or perceived score information derived from said information. [48b] The information processing method according to [47b], wherein the information processing unit obtains new advice information, including information regarding the correlation between intake information and the perceived score information.

[0125] [1c] Information processing device comprising: a registration unit for registering a user; an acquisition unit for acquiring from the terminal of the registered user intake information regarding the food consumed by the user, one or more pieces of information from emotional information, biometric information and behavioral information regarding the user's emotions and / or perceived score information derived from said information; a storage unit for storing the acquired intake information, one or more pieces of information from emotional information, biometric information and behavioral information and / or perceived score information derived from said information and the user's identification information in association with each other; and an output unit for outputting advice information including information regarding the correlation between the intake information and the perceived score information to the user's terminal. [2c] The information processing apparatus according to [1c], wherein the acquisition unit acquires attribute information relating to the user's attributes. [3c] The information processing device according to [1c] or [2c], wherein the user is a user in a country or region with a Power distance index (PDI value) of 80 or less. [4c] The information processing device according to any one of [1c] to [3c], wherein the information relating to the correlation between the intake information and the perceived score information is information relating to a group of people with the same attributes as the user. [5c] The information processing device according to [3c] or [4c], wherein the information relating to the correlation between the intake information and the perceived score information is based on data obtained in a country where the PDI value is within plus or minus 20 for the country to which the user belongs. [6c] The information processing device according to any one of [3c] to [5c], wherein the information relating to the correlation between the intake information and the perceived score information is based on data obtained in countries belonging to the same category when countries are categorized by PDI values ​​of 0 or more and less than 30, 30 or more and less than 60, 60 or more and less than 90, and 90 or more and less than 120. [7c] The information processing apparatus according to any one of [2c] to [6c], wherein the acquisition unit acquires time-series data of attribute information relating to the user's attributes and the user's identification information. [8c] The information processing device according to any one of [2c] to [7c], wherein the attribute information relating to the user's attributes is one or more selected from nationality, place of residence, address, gender recognized by the user, and occupation. [9c] The information processing device according to any one of [1c] to [8c], wherein the intake information includes information about food that the user has actually consumed from among the food owned by the user. [10c] The information processing device according to any one of [1c] to [9c], wherein the intake information includes information about foods that the user has owned but did not actually consume. [11c] The information processing apparatus according to any one of [1c] to [10c], wherein the food is mainly a general food. [12c] The information processing apparatus according to any one of [1c] to [11c], wherein the acquisition unit acquires preference information relating to the user's food preferences or principles. [13c] The information processing device according to any one of [1c] to [12c], wherein the food consumed by the user includes, based on information relating to the correlation between the intake information and the perceived score information, a food with a relatively low perceived score but a strong demand from preference and / or lifestyle. [14c] The information processing device according to any one of [1c] to [13c], wherein the perceived score information includes perceived score information obtained from biological information relating to the user's body. [15c] The information processing device according to any one of [1c] to [14c], wherein the perceived score information includes perceived score information obtained from behavioral information relating to the user's actions. [16c] The information processing device according to any one of [1c] to [15c], wherein the perceived score information includes time-series data of the emotional information. [17c] The information processing device according to [16c], wherein the time-series data includes data before and after the user consumes food. [18c] The information processing device according to any one of [1c] to [17c], wherein the perceived score information includes one or more of emotional information, biological information, and behavioral information. [19c] The information processing device according to any one of [1c] to [18c], wherein the advice information includes virtual intake information for increasing the perceived score per unit time, based on information relating to the correlation between the intake information and the perceived score information. [20c] The information processing device according to [19c], wherein the advice information includes, based on information relating to the correlation between the intake information and the perceived score information, hypothetical intake information for increasing the perceived score per unit time, while including foods that have a relatively low perceived score but are strongly desired due to taste and / or lifestyle. [21c] The information processing device according to [19c] or [20c], wherein the advice information includes virtual intake information for increasing the perceived score per unit time without changing the behavioral information, based on information relating to the correlation between the intake information and the perceived score information. [22c] The information processing device according to any one of [1c] to [21c], wherein the advice information includes virtual behavior information for increasing the perceived score per unit time. [23c] The information processing apparatus according to any one of [1c] to [22c], wherein the emotional information includes at least one of emotional information per unit time and information that identifies fluctuations in emotion. [24c] The information processing apparatus according to any one of [19c] to [23c], wherein the unit time is one day or more, or one week or more, or one month or more, or three months or more, or one year or more, or three years or more. [25c] The information processing apparatus according to any one of [19c] to [24c], which includes two or more unit times in the same season of different years. [26c] The information processing device according to any one of [1c] to [25c], wherein the acquisition unit acquires information that identifies the perceived score output by the learning model for sensor information acquired by the sensor sensing the user, based on a learning model obtained by machine learning the relationship between sensor information and information that identifies the perceived score. [27c] The information processing device according to any one of [1c] to [26c], wherein the intake information includes information that identifies the amount and timing of intake of food consumed by the user. [28c] The information processing device according to any one of [1c] to [27c], wherein the advice information includes a method for improving the perceived score without changing either or both of the information relating to preferences and lifestyle. [29c] The information processing device according to any one of [1c] to [28c], wherein the acquisition unit acquires environmental information when the user consumes the food. [30c] The information processing apparatus according to [29c], which outputs time-series data of the environmental information and the user's identification information to the information processing apparatus. [31c] The information processing device according to any one of [1c] to [30c], wherein the advice information includes advice to alleviate either or both of anxiety and stress information. [32c] The information processing apparatus according to any one of [1c] to [31c], wherein the acquisition unit acquires behavioral information relating to the user's actions. [33c] The information processing device according to any one of [1c] to [32c], wherein the behavioral information includes behaviors that have a relatively low perceived score but are strongly desired due to preferences and / or lifestyle habits, based on information relating to the correlation between the intake information and the perceived score information. [34c] The information processing device according to any one of [1c] to [33c], wherein the advice information includes either advice for improving lifestyle habits or advice to alleviate either or both of the user's anxiety and stress information regarding behavioral change. [35c] The information processing apparatus according to any one of [1c] to [34c], wherein the acquisition unit acquires biological information relating to the user's biological body. [36c] The information processing apparatus according to [35c], which outputs time-series data of the biological information and the user's identification information to an information processing unit. [37c] An information processing device according to any one of [1c] to [36c], which acquires shared information with other users regarding the food and outputs the acquired shared information to the information processing device. [38c] The information processing apparatus according to [37c], wherein the shared information includes information that identifies a target intake for a predetermined nutrient, and the advice information includes information on how to consume and prepare food in order to achieve the target intake for the predetermined nutrient. [39c] The information processing apparatus according to [38c], wherein the specified nutrient is a nutrient whose deficiency affects the maintenance and promotion of health or a nutrient whose excessive intake affects the maintenance and promotion of health. [40c] An information processing device according to any one of [1c] to [39c], which obtains the user's target contribution amount to the global environment and outputs the obtained target contribution amount and the user's identification information to the information processing device. [41c] The information processing device according to [40c], wherein the target amount of contribution to the global environment includes the target amount in the Sustainable Development Goals (SDGs). [42c] The information processing apparatus according to [40c] or [41c], wherein the target contribution to the global environment includes at least one of the following: a reduction in greenhouse gas emissions, a reduction in industrial waste, a reduction in food loss, a reduction in water usage, or a reduction in emissions. [43c] The information processing device according to any one of [40c] to [42c], wherein the advice information includes information for achieving the target amount of contribution to the global environment. [44c] The biological information includes electroencephalograms and stress hormones. An information processing device according to any one of [1c] to [43c]. [45c] The information processing device according to any one of [1c] to [44c], wherein the output unit creates predetermined information from intake information, perceived score information and user identification information, and outputs the created predetermined information. [46c] The information processing device according to any one of [1c] to [45c], wherein the acquisition unit acquires, after the output unit has output advice information at any time, one or more pieces of information from emotional information, biometric information, and behavioral information relating to the emotions of a user who followed the advice information or a user who did not follow the advice information, and / or perceived score information derived from said information, and based on the acquired intake information, one or more pieces of information from emotional information, biometric information, and behavioral information, and / or perceived score information derived from said information and the user identification information, it acquires validity verification information regarding the correlation at the time the advice information was output. [47c] The acquisition unit performs new information processing based on the acquired validity verification information using one or more pieces of information from emotional information, biometric information, and behavioral information relating to the emotions of users who followed the advice information or users who did not follow the advice information, and / or perceived score information derived from said information, as described in [46c]. [48c] The information processing device according to [47c], wherein the acquisition unit acquires new advice information including information relating to the correlation between intake information and the perceived score information.

[0126] The embodiments disclosed herein should be considered in all respects to be illustrative and not restrictive. The scope of the invention is indicated by the claims, not in the sense described above, and all modifications within the meaning and scope equivalent to the claims are intended to be included. [Explanation of Symbols]

[0127] 100 Information Processing Systems 10 servers 11 Control Unit 12 Storage section 13 Communications Department 20 User Terminals 21 Control Unit 22 Memory section 23 Communications Department 25 Display section 27 Cameras 30 Wearable Devices 12a Product information DB 12b Member Information Database 12c Advice DB

Claims

1. On the computer, Calculating nutritional component intake information of the user from nutritional component information associated with the product information, and acquiring the nutritional component intake information of the user; acquiring one or more pieces of emotional information, biological information, and behavioral information of the user and / or actual feeling score information derived from the information; acquiring attribute information relating to the attributes of the user; outputting, to an information processing unit, one or more of the acquired intake information, the emotion information, the biological information, and the behavioral information, and / or actual feeling score information and attribute information derived from the acquired information; obtaining, from the information processing unit, advice information including information regarding a correlation between the intake information and one or more pieces of information selected from the emotion information, the biological information, and the behavior information and / or the actual feeling score information derived from the information; outputting the advice information to one or more users selected from the user, other users included in a group with the same attribute as the user, and related persons of the user; program.

2. The nutritional component of the nutritional component information is dietary fiber. The program according to claim 1.

3. The user is a user in a country or region where the power distance index (PDI value) is 80 or less. The program according to claim 1.

4. The information regarding the correlation between the intake information and the actual feeling score information is information regarding a group with the same attributes as the user. The program according to claim 1.

5. The information regarding the correlation between the intake information and the actual feeling score information is information based on data obtained in a country whose PDI value is within ±20 of the country to which the user belongs. The program according to claim 3.

6. the information regarding the correlation between the intake information and the actual feeling score information is information based on data obtained from countries in the same category when countries are categorized according to PDI values of 0 or more and less than 30, 30 or more and less than 60, 60 or more and less than 90, and 90 or more and less than 120; The program according to claim 3.

7. outputting time-series data of attribute information relating to the attributes of the user and identification information of the user to the information processing unit; The program according to claim 1.

8. The attribute information regarding the user's attributes is one or more selected from nationality, residence, address, gender recognized by the user, and occupation. The program according to claim 1.

9. The intake information includes information about foods that the user actually ingested among the foods owned by the user. The program according to claim 1.

10. The intake information includes information about foods owned by the user that the user did not actually consume. The program according to claim 1.

11. The food is mainly a general food. The program according to claim 10.

12. acquiring preference information regarding the user's food preferences or principles; outputting the acquired preference information and the user identification information to the information processing unit; The program according to claim 1.

13. The food ingested by the user includes a food having a relatively low sensation score based on information relating to the correlation between the intake information and the sensation score information but having a strong demand based on preferences and / or lifestyle habits. The program according to claim 9.

14. The actual feeling score information includes actual feeling score information acquired from biometric information related to a biometric information of the user. The program according to claim 1.

15. The actual feeling score information includes actual feeling score information acquired from behavioral information regarding the behavior of the user. The program according to claim 1.

16. The actual feeling score information includes time-series data of the emotion information. The program according to claim 1.

17. The time-series data includes data before and after a user eats food. The program according to claim 16.

18. The actual feeling score information includes one or more of emotional information, biological information, and behavioral information. The program according to claim 1.

19. the advice information includes virtual intake information for increasing the actual experience score per unit time based on information regarding a correlation between the intake information and the actual experience score information; The program according to claim 1.

20. the advice information includes hypothetical intake information for increasing the actual score per unit time, based on information relating to the correlation between the intake information and the actual score information, while including foods that have relatively low actual scores but are highly desired due to preferences and / or lifestyle habits; 20. The program of claim 19.

21. the advice information includes virtual intake information for increasing the actual feeling score per unit time without changing the behavioral information, based on information regarding a correlation between the intake information and the actual feeling score information; 20. The program of claim 19.

22. the advice information includes virtual behavior information for increasing an actual feeling score per unit time; The program according to claim 1.

23. the emotion information includes at least one of emotion information per unit time and information specifying emotional fluctuations; The program according to claim 1.

24. The unit time is one day or more, or one week or more, or one month or more, or three months or more, or one year or more, or three years or more, 20. The program of claim 19.

25. the unit time includes two or more unit times in the same season of different years, 20. The program of claim 19.

26. acquiring information specifying an actual score output by the learning model for sensor information acquired by a sensor sensing the user, based on a learning model obtained by machine learning the relationship between sensor information and information specifying an actual score; The program according to claim 1.

27. The intake information includes information specifying the intake amount and intake timing of the food ingested by the user. The program according to claim 1.

28. The advice information includes a method for improving the perceived score without changing either or both of the information related to the preference and the lifestyle habit. The program according to claim 1.

29. acquiring environmental information when the user ingested food; outputting the acquired environmental information and the user identification information to the information processing unit; The program according to claim 1.

30. outputting the time-series data of the environmental information and the identification information of the user to an information processing unit; 30. The program of claim 29.

31. The advice information includes advice to relieve either or both of anxiety and stress information. The program according to claim 1.

32. acquiring behavioral information regarding the user's behavior; outputting the acquired behavioral information and the user identification information to the information processing unit; The program according to claim 1.

33. The behavioral information includes behaviors that have relatively low perception scores but are strongly required by preferences and / or lifestyle habits, based on information regarding the correlation between the intake information and the perception score information. The program according to claim 1.

34. The advice information includes either or both of advice for improving lifestyle habits and advice for resolving either or both of anxiety about behavioral changes and stress information of the user. The program according to claim 1.

35. Acquiring biometric information relating to the user's body, outputting the acquired biometric information and the user's identification information to the information processing unit; The program according to claim 1.

36. outputting the time-series data of the biometric information and the identification information of the user to an information processing unit; 36. The program of claim 35.

37. Allows users to share information about food with other users, outputting the acquired shared information to the information processing unit; The program according to claim 1.

38. the shared information includes information identifying a target intake amount for a predetermined nutrient component; The advice information includes information on how to consume and cook food to achieve the target intake amount of the predetermined nutritional component.

38. The program of claim 37.

39. The predetermined nutritional component is a nutritional component whose deficiency affects the maintenance and promotion of health or a nutritional component whose excessive intake affects the maintenance and promotion of health.

39. The program of claim 38.

40. obtaining a target contribution amount of the user to the global environment; outputting the acquired target contribution amount and the user's identification information to the information processing unit; The program according to claim 1.

41. The target contribution to the global environment includes a target amount in the Sustainable Development Goals (SDGs), 41. The program of claim 40.

42. The target contribution to the global environment includes at least one of a reduction in greenhouse gas emissions, a reduction in industrial waste disposal, a reduction in food loss, a reduction in water usage, or a reduction in emissions.

41. The program of claim 40.

43. the advice information includes information for achieving the target contribution to the global environment; 41. The program of claim 40.

44. The biological information includes brain waves and stress hormones. The program according to claim 1.

45. the information processing unit creates predetermined information from the intake information, the actual feeling score information, and the user's identification information, and outputs the created predetermined information. The program according to claim 1.

46. outputting advice information at an arbitrary timing, and then acquiring one or more pieces of emotional information, biological information, and behavioral information relating to the emotions of a user who followed the advice information or a user who did not follow the advice information, and / or actual feeling score information derived from the information; outputting to the information processing unit one or more of the acquired intake information, the emotion information, the biological information, and the behavior information, and / or actual feeling score information derived from the acquired information and the user's identification information; obtains from the information processing unit validity verification information relating to the correlation at the time of outputting the advice information; The program according to claim 1.

47. Based on the acquired validity verification information, new information processing is performed using one or more pieces of emotional information, biological information, and behavioral information relating to the emotions of the user who followed the advice information or the user who did not follow the advice information, and / or actual feeling score information derived from the information.

47. The program of claim 46.

48. acquires new advice information from the information processing unit, the advice information including information relating to the correlation between the intake information and the actual feeling score information; 48. The program of claim 47.

49. The program described in claim 1, which includes acquiring a target intake of nutritional components for a specified period of time, outputting the acquired target intake to the information processing unit, and acquiring advice information from the information processing unit, which includes identifying foods to be suggested to the user based on the target intake.

50. The program described in claim 49, wherein the target intake of the nutritional component is a target intake based on the intake standards of a public institution.

51. The program described in claim 1, wherein the intake information, one or more of the emotional information, the biometric information and the behavioral information and / or the actual feeling score information derived from said information, and the attribute information are obtained and / or output by word-of-mouth information.

52. 1. A computer-implemented information processing method, comprising: Calculating nutritional component intake information of the user from nutritional component information associated with the product information, and acquiring the nutritional component intake information of the user; acquiring one or more pieces of emotional information, biological information, and behavioral information relating to the user's emotions and / or actual feeling score information derived from the information; Acquire attribute information regarding the attributes of the user; outputting, to an information processing unit, one or more pieces of information among the acquired intake information, the emotion information, the biological information, and the behavioral information, and / or actual feeling score information and attribute information derived from the acquired information; acquires advice information from the information processing unit, the advice information including information regarding a correlation between the intake information and one or more pieces of information among the emotion information, the biological information, and the behavioral information and / or the actual feeling score information derived from the information, and outputs the advice information to one or more users selected from the user, other users included in a group with the same attributes as the user, and related persons of the user; Information processing methods.

53. outputting advice information at an arbitrary timing, and then acquiring one or more pieces of emotional information, biological information, and behavioral information relating to the emotions of a user who followed the advice information or a user who did not follow the advice information, and / or actual feeling score information derived from the information; outputting, to an information processing unit, one or more pieces of information among the acquired intake information, the emotion information, the biological information, and the behavior information, and / or actual feeling score information derived from the acquired information and the user's identification information; obtaining from the information processing unit validity verification information relating to the correlation at the time of outputting the advice information; 53. The information processing method according to claim 52.

54. Based on the obtained validity verification information, new information processing is performed using one or more pieces of emotional information, biological information, and behavioral information relating to the emotions of a user who followed the advice information or a user who did not follow the advice information, and / or actual feeling score information derived from the information.

54. The information processing method according to claim 53.

55. newly acquiring advice information from the information processing unit, the advice information including information relating to the correlation between the intake information and the actual feeling score information; 55. The information processing method according to claim 54.

56. a registration unit for registering users; an acquisition unit that acquires, from the registered user's terminal, user nutritional component intake information calculated from nutritional component information associated with product information, one or more pieces of emotional information, biological information, and behavioral information related to the user's emotions and / or actual feeling score information derived from the information, and attribute information related to the user's attributes; a storage unit that stores, in association with one or more of the acquired intake information, the emotion information, the biological information, and the behavior information, and / or actual feeling score information, attribute information, and identification information of the user derived from the acquired information; an output unit that outputs advice information including information regarding a correlation between the intake information and one or more pieces of information among the emotion information, the biological information, and the behavior information and / or the actual feeling score information derived from the information to the terminal of any one or more users selected from the user, other users included in a group with the same attribute as the user, and related persons of the user; An information processing device comprising:

57. the acquisition unit, after the output unit outputs advice information at any timing, acquires one or more pieces of emotional information, biological information, and behavioral information relating to emotions of a user who followed the advice information or a user who did not follow the advice information, and / or actual feeling score information derived from the information, outputs the acquired one or more pieces of information among the intake information, emotional information, biological information, and behavioral information, and / or actual feeling score information derived from the information, and identification information of the user, to an information processing unit, and acquires validity verification information regarding information relating to correlation at the time of output of the advice information from the information processing unit.

57. The information processing device according to claim 56.

58. The acquisition unit performs new information processing based on the acquired validity verification information using one or more of emotion information, biological information, and behavior information relating to emotions of a user who followed the advice information or a user who did not follow the advice information, and / or actual feeling score information derived from the information.

58. The information processing device according to claim 57.

59. newly acquiring advice information from the information processing unit, the advice information including information relating to the correlation between the intake information and the actual feeling score information; 59. The information processing device according to claim 58.

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