Information processing system

Abstract

To simulate a biological change amount due to intervention related to a living activity by changing various items related to physical information, a lifestyle, or a health condition of a user.SOLUTION: The information processing system includes a control unit. Acquiring pre-change user information and post-change user information, the pre-change user information including a plurality of items related to physical information, a lifestyle, or a health condition of a user, the post-change user information being obtained by changing some of the items in the pre-change user information, inputting the acquired pre-change user information to a first trained model that predicts a biological change amount of the user due to a predetermined intervention related to a living activity using physical user information and user information related to the lifestyle or the health condition as explanatory variables, and acquiring a first predicted value of the biological change amount from the first trained model; Prediction information including the acquired first predicted value is transmitted to a user terminal of the user or a proxy user terminal of a proxy user who represents the user.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to an information processing system, an information processing method, and a program for predicting the amount of biological change (diet effect) of a user resulting from interventions related to lifestyle behaviors such as improving eating habits and increasing the amount of exercise. [Background technology]

[0002] 2. Description of the Related Art Conventionally, there have been systems for supporting users in improving their health. For example, Patent Document 1 below discloses an information processing method for creating and outputting advice information regarding weight loss based on an acquisition process of staying time information indicating the time during which a user's pulse rate is within a given zone, physical information indicating the user's current weight or body fat mass, a target value for the user's weight or body fat mass, and target information indicating a target period until the target value is reached, a relational expression indicating a relationship between fluctuations in the user's weight or body fat mass and the staying time information, and the information acquired in the acquisition process, the method generating fixation instruction information instructing to fix one of the three pieces of information, i.e., the staying time information, the target value, and the target period, and generating correction instruction information instructing to correct one of the remaining two pieces of information, acquiring one of the three pieces of information fixed based on the fixation instruction information, and acquiring one of the remaining two pieces of information corrected based on the correction instruction information, and then re-determining the other of the remaining two pieces of information based on one of the three pieces of information after fixing and one of the remaining two pieces of information after correcting. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-109891 Summary of the Invention [Problem to be solved by the invention]

[0004] The technology in Patent Document 1 above allows for simulation of dieting by changing the duration of stay (exercise intensity), target value, and target period, but because it uses pulse rate, the user's actions that can be simulated are limited to exercises that increase the pulse rate.

[0005] The present invention aims to provide an information processing system, an information processing method, and a program that can simulate the amount of biological change resulting from intervention in daily activities by changing various items related to a user's physical information, lifestyle habits, or health condition. [Means for solving the problem]

[0006] An information processing system according to an embodiment of the present invention includes a control unit that acquires pre-change user information consisting of a plurality of items related to a user's physical information, lifestyle habits, or health condition, and post-change user information in which some of the items of the pre-change user information have been changed, inputs the acquired post-change user information to a first trained model that predicts a biometric change amount of the user resulting from a predetermined intervention related to daily behavior using the user information related to the user's physical information, lifestyle habits, or health condition as explanatory variables, acquires a first predicted value of the biometric change amount from the first trained model, and transmits prediction information including the acquired first predicted value to a user terminal of the user or a proxy user terminal of a proxy user acting on behalf of the user.

[0007] An information processing method according to another aspect of the present invention includes: Acquire pre-change user information consisting of a plurality of items related to the user's physical information, lifestyle habits, or health condition, and post-change user information in which some of the items in the pre-change user information have been changed; inputting the acquired changed user information into a trained model that predicts the amount of biochange of the user due to a predetermined intervention related to daily behavior using user information related to physical information, lifestyle habits, or health status as explanatory variables, and obtaining a predicted value of the amount of biochange from the trained model; The method includes transmitting prediction information including the obtained prediction value to a user terminal of the user or a proxy user terminal of a proxy user acting on behalf of the user.

[0008] According to yet another aspect of the present invention, there is provided a program for executing the program on an information processing device, acquiring pre-change user information consisting of a plurality of items related to the user's physical information, lifestyle habits, or health condition, and post-change user information in which some of the items in the pre-change user information have been changed; a step of inputting the acquired changed user information into a trained model that predicts a biological change amount of the user due to a predetermined intervention related to daily behavior using user information related to physical information, lifestyle habits, or health status as explanatory variables, and acquiring a predicted value of the biological change amount from the trained model; and transmitting prediction information including the obtained prediction value to a user terminal of the user or a proxy user terminal of a proxy user acting on behalf of the user. [Effects of the Invention]

[0009] According to an information processing system according to an embodiment of the present invention, it is possible to change various items related to a user's physical information, lifestyle habits, or health condition, and simulate the amount of biological change resulting from interventions related to daily activities. However, this effect does not limit the present invention. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a diagram showing the configuration of a diet effect prediction model generation system according to one embodiment of the present invention. [Figure 2] 1 is a diagram showing a hardware configuration of a diet information providing server according to an embodiment of the present invention. [Figure 3] 2 is a diagram showing the configuration of a database included in a diet information providing server according to one embodiment of the present invention. FIG. [Figure 4]10 is a flowchart showing the flow of a diet effect prediction information providing process by a diet information providing server according to one embodiment of the present invention. [Figure 5] 10 is a diagram conceptually illustrating a diet effect prediction information providing process performed by a diet information providing server according to one embodiment of the present invention. FIG. [Figure 6] 10 is a diagram showing an example of a selection screen for simulation conditions used in a diet effect prediction process by a diet information providing server according to one embodiment of the present invention. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0012] [System Configuration] As shown in FIG. 1, this system includes a diet information providing server 100 on the Internet 50 and a plurality of user terminals 200.

[0013] The diet information providing server 100 provides a diet method suggestion service (diet effect prediction information providing service) for users of the user terminal 200. Specifically, the diet information providing server 100 is connected to a plurality of user terminals 200 via the Internet 50, and predicts the diet effect (amount of biological change) resulting from a predetermined intervention (dietary intervention, exercise intervention, etc.) related to the user's lifestyle behavior based on user information such as the user's physical information and answers to a questionnaire, and transmits the predicted information to the user terminal 200. In this embodiment, information on the amount of weight loss is provided as a specific example of the diet effect.

[0014] Here, "dietary intervention" refers to, for example, a registered dietitian providing dietary advice every two weeks for two months to reduce the user's energy intake with the goal of losing 3% of the user's current body weight. However, dietary intervention may also be at least one of the following, and the diet information providing server 100 can set the definition of dietary intervention by appropriately combining these. Eat less (set a daily calorie intake) Reduce snacking or eat healthier snacks Reduce the amount of carbohydrates (staple foods) you consume Reduce fat intake Reduce sugar in drinks Increase your intake of fruits and vegetables Eat meals at regular times Don't eat late at night (e.g. after 8pm) Limit the time during the day when you can eat (for example, 10 hours) Stop eating when you are 80% full Replace at least one meal a day with a menu designed specifically for dieting (for example, a menu that reduces fat and increases protein, replaces fat with omega-3, and includes carbohydrates with dietary fiber).

[0015] Furthermore, "exercise intervention" refers to, for example, exercise instruction given every two weeks for two months by a fitness instructor to increase energy expenditure with the goal of losing 3% of current body weight. However, exercise intervention may also be at least one of the following, and the diet information providing server 100 can set the definition of exercise intervention by appropriately combining these. Engage in a set number of minutes (e.g., at least 150-300 minutes) of moderate-intensity (3.0-6.0 METs) physical activity per week (e.g., brisk walking, dancing, mowing the lawn). Engage in vigorous-intensity (6.0 METs or greater) physical activity (e.g., running, swimming, stair climbing) for a set period of time (e.g., at least 75-150 minutes) per week. · Perform prescribed strength training (e.g., 15 minutes of multiple strength training such as squats) a prescribed number of times per week (e.g., 3 times) Other activities that involve increasing the intensity and / or duration of daily physical activity or exercise

[0016] Other examples of "prescribed interventions" include sleep interventions, smoking cessation interventions, posture correction interventions, acupressure interventions (e.g., ear acupressure), and breathing interventions.

[0017] The user terminals 200 (200A, 200B, 200C...) are terminals used by users, such as smartphones, mobile phones, tablet PCs (Personal Computers), notebook PCs, desktop PCs, etc. The user terminals 200 transmit user information such as the user's physical information to the diet information providing server 100, receive information indicating the diet effect, and display it on a screen using a browser or the like. In addition, an application corresponding to a diet prediction effect information providing service may be installed in the user terminals 200, and the user terminals 200 may access the diet information providing server 100 using the application to display the diet effect prediction information.

[0018] When providing the diet effect prediction information, the diet information providing server 100 uses the first trained model 10 that predicts the diet effect of a predetermined intervention related to the lifestyle behavior using the user information as an explanatory variable.

[0019] In this case, when the diet information providing server 100 receives user information from the user terminal 200, it inputs the user information to a second trained model 20 that can estimate the value of a missing attribute (second attribute) of the user information from the value of a certain attribute (first attribute) of the user information, and obtains the user information of the missing attribute from the trained model 20.

[0020] Then, the diet information providing server 100 inputs the user information, which is a combination of the acquired user information and the user information provided by the user, into the first trained model 10 as an explanatory variable, obtains a predicted value of the diet effect, and transmits information regarding the predicted value to the user terminal 200.

[0021] The value of the first attribute and the value of the second attribute may be one attribute value or a set of multiple attribute values. The second trained model 20 may be, for example, a model capable of estimating a joint probability distribution (e.g., HI-VAE (https: / / arxiv.org / pdf / 1807.03653.pdf), Tab Transformer (https: / / arxiv.org / pdf / 2012.06678.pdf)), but is not limited thereto. Here, a "missing attribute" includes at least the following cases: "when the value of the attribute does not exist," "when the value of the attribute is missing," "when the value of the attribute is not measured," "when the value of the attribute is not obtained," "when the value of the attribute is not input and the value of the attribute is flagged as missing," "when the value of the attribute is inaccessible," or "when the value of the attribute is lost."

[0022] Here, the (first and second) attributes may be basic information such as height, weight, sex, and age, as well as information indicating the state of a person's body, such as information on general blood, liver function, lipids, metabolic system, blood pressure, etc., which are test items in a health checkup; scores, numerical values, and classification information obtained by measuring all or part of the body; and scores and numerical classification information obtained from a medical interview or questions.For example, information about your physical condition may include your physique / body composition (body fat, muscle mass, visceral fat, etc.), blood test values ​​(triglycerides, total cholesterol, HDL cholesterol, LDL cholesterol, blood sugar, HbA1c, ALT, red blood cell count, aspartate aminotransferase, alanine aminotransferase, gamma-glutamyltranspeptidase, alkaline phosphatase), various hormones (cortisol, triiodothyronine, thyroxine, dehydroepiandrosterone, testosterone, estradiol, progesterone, follicle-stimulating hormone, luteinizing hormone, prolactin, etc.), vascular function (arteriosclerosis index, lower limb arterial stenosis / occlusion index, etc.), metabolic function (blood glucose, insulin, glucagon, GLP-1, GIP), cognitive function (functional test (Cognitrax (registered trademark)), blood D or L-amino acids), motor function (grip strength, gross motor ability, walking function (speed, stride length, pitch), etc.), immune-related index (lymphoid subset analysis, NK cell activity, cytokine analysis, etc.), disease information (presence or absence of disease, medical treatment, medication, etc.), Activity level (calories burned per day, steps taken per day), sleep-related indicators (score from medical questionnaire), productivity (score from questionnaire), diet / nutritional status (calories burned, PFC balance, etc.), menstrual-related indicators (stage and disability level determined by medical interview and questions), menopausal-related indicators (stage and disability level determined by questions), hair (diameter, degree of waviness), skin condition (skin blood flow rate, skin glycation level, ceramide amount), body odor (by gas chromatography) All kinds of physical attributes are included, such as information on: quantification of odor components, Oriental medical constitution classification (clustering by questionnaire), biomicrobiota information (quantity and composition ratio of bacteria in the oral cavity, intestines, scalp, and skin), progression of hair thinning and alopecia (score by photographic assessment), DNA and RNA information, overactive bladder / urinary incontinence (score by questionnaire), lifestyle questionnaires (BDHQ (short self-administered dietary history questionnaire), international standardized physical activity questionnaire, eating behavior questionnaire, etc.), health questionnaires (35-item dietary questionnaire, WHO-5 mental health status questionnaire, brief occupational stress questionnaire, Chalder fatigue scale, Athens insomnia scale), personality questionnaires (Big-five questionnaire, etc.).

[0023] For example, by inputting the attribute values ​​of age, gender, height, and weight into the trained model 10 as a set of user information for the first attribute, predicted values ​​for the information of the attributes of visceral fat cross-sectional area, triglyceride, blood glucose level, and stress level can be obtained as a set of user information for the second attribute that is missing from the set of user information for the first attribute.

[0024] Furthermore, in this embodiment, the diet information providing server 100 acquires, from the user terminal 200, changed user information in which some items of the user information provided from the user terminal 200 have been changed. Specifically, the diet information providing server 100 receives, from the user terminal 200, selection information for selecting items to be changed for a user simulation (what-if analysis) from the pre-change user information, changes the selected items from the pre-change user information according to the selection information, and inputs the changed user information to the second trained model 20. Then, the changed user information in which the missing information has been complemented by the second trained model 20 is input to the first trained model 10, a predicted value of the diet effect is obtained, and information regarding the predicted value is transmitted to the user terminal 200.

[0025] [Hardware configuration of diet information server] As shown in FIG. 2, the diet information providing server 100 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, an input / output interface 15, and a bus 14 connecting these components to one another.

[0026] The CPU 11 accesses the RAM 13 etc. as needed, and performs various arithmetic processing while controlling all blocks of the diet information providing server 100 in an integrated manner. Multiple CPUs 11 may be provided depending on the processing. The ROM 12 is a non-volatile memory in which firmware such as the OS, programs, and various parameters to be executed by the CPU 11 are permanently stored. The RAM 13 is used as a working area for the CPU 11, and temporarily stores the OS, various applications currently being executed, and various data currently being processed.

[0027] The input / output interface 15 is connected to a display unit 16, an operation reception unit 17, a storage unit 18, a communication unit 19, and the like.

[0028] The display unit 16 is a display device that uses, for example, an LCD (Liquid Crystal Display), an OLED (Organic ElectroLuminescence Display), a CRT (Cathode Ray Tube), or the like.

[0029] The operation reception unit 17 is, for example, a pointing device such as a mouse, a keyboard, a touch panel, or other input device. When the operation reception unit 17 is a touch panel, the touch panel can be integrated with the display unit 16.

[0030] The storage unit 18 is a non-volatile memory such as a hard disk drive (HDD), a flash memory (solid state drive (SSD)), or other solid-state memory. The storage unit 18 stores the OS, various applications, and various data.

[0031] As will be described later, particularly in this embodiment, the memory unit 18 has programs such as applications necessary for the diet effect prediction information provision process described later, as well as a user physical information database, a questionnaire information database, a complementary physical information database, and an effect prediction information database.

[0032] The communication unit 19 is, for example, a NIC (Network Interface Card) for Ethernet or various modules for wireless communication such as wireless LAN, and is responsible for communication processing with the user terminal 200.

[0033] Although not shown, the basic hardware configuration of the user terminal 200 is also substantially the same as the hardware configuration of the diet information providing server 100 described above.

[0034] [Database configuration of diet information server]

[0035] 3, the diet information providing server 100 has, in the storage unit 18, a user physical information database 31, a questionnaire information database 32, a supplementary physical information database 33, and an effect prediction information database 34. Note that each of these databases may be stored in a storage device or server externally connected to the diet information providing server 100, rather than in the storage unit 18.

[0036] The user physical information database 31 stores physical information received from the user terminal 200, such as the user's age, sex, height, weight, body composition, visceral fat area, waist size, blood pressure, heart rate, and body temperature, in association with general information such as the user's name, a user ID for identifying the user, occupation, address, and email address.

[0037] In addition to the current physical information of the user, the physical information may also include physical information after changing any of the user information items based on the user's selection. Examples of changes to the physical information include, but are not limited to, increasing the user's age to simulate the effect of a diet several years from now, or increasing the skeletal muscle mass to simulate the effect of a diet if the user's muscle mass is increased from the current level.

[0038] The diet information providing server 100 may receive the physical information not directly from the user terminal 200 but via another organization such as a health checkup institution or a sports gym.

[0039] The questionnaire information database 32 stores questionnaire information for users and information on responses to the questionnaire information received from the user terminal 200. Specifically, the questionnaires are about the user's lifestyle, health status, or personality. Questionnaires about lifestyle include the BDHQ (Brief Self-Administered Diet History Questionnaire), the International Standardized Physical Activity Questionnaire, and the Eating Behavior Questionnaire. Questionnaires about health status include the 35-item Diet Questionnaire, the WHO-5 Mental Health Questionnaire, the Occupational Stress Brief Questionnaire, the Chalder Fatigue Scale, the Athens Insomnia Scale, and the Self-Efficacy Questionnaire. Questionnaires about personality include the Big-five questionnaire.

[0040] In addition to the current user's answer information, the answer information also stores changed answer information obtained by changing any of the items in the answer information based on the user's selection. Examples of changes to the answer information include, but are not limited to, changing the answer "yes" to "no" in response to a question about whether the user has the habit of snacking or eating at night in order to simulate the diet effect if the user stops snacking or eating at night, which is the user's current habit, or changing the current walking time from 30 minutes to 60 minutes in order to simulate the diet effect if the user increases the time spent walking, which is the user's current habit.

[0041] The supplemented physical information database 33 inputs the pre-change user information and the post-change user information received from the user terminal 200 into the second trained model 20, respectively, and stores the user information in which the information of the second attribute missing from the user information of the first attribute is supplemented in association with the user ID. There may be multiple types of user information of the first attribute and user information of the second attribute.

[0042] The effect prediction information database 34 stores diet effect prediction information (weight loss information) obtained by inputting the pre-change user information and the post-change user information, in which information on the second attribute is stored by the second trained model 20, into the first trained model 10, in association with the user ID.

[0043] These databases are used by mutual reference as needed in the diet effect prediction information providing process by the diet information providing server 100, which will be described later.

[0044] [Operation of diet information server] Next, the operation of the diet information providing server 100 configured as above will be described. The operation is performed by the cooperation of hardware such as the CPU 11 and communication unit 19 of the diet information providing server 100 and software stored in the storage unit 18. For convenience, in the following description, the CPU 11 is the subject of the operation.

[0045] Fig. 4 is a flowchart showing the flow of the process for providing the diet effect prediction information, and Fig. 5 is a diagram conceptually showing the process for providing the diet effect prediction information.

[0046] 4, first, the CPU 11 determines whether or not a diet effect (weight loss rate) prediction request has been received from the user terminal 200 (step 41). The diet effect prediction request includes user information (physical information and answer information to a questionnaire), and is transmitted, for example, via an application installed on the user terminal 200. When transmitting the diet effect prediction request on the application, the user inputs their own physical information (age, sex, height, weight, etc.) and further inputs answer information to a questionnaire regarding lifestyle habits and / or health condition.

[0047] If it is determined that the prediction request has been received (Yes in step 41), the CPU 11 receives selection information of items to be changed from the user terminal 200 among the user information (step 42).

[0048] That is, when the CPU 11 receives the prediction request, it transmits to the user terminal 200 a simulation condition (change item) selection screen that accepts input for changing some items of the user information for a simulation (what-if analysis).

[0049] FIG. 6 is a diagram showing an example of the simulation condition selection screen.

[0050] As shown in the figure, the simulation condition selection screen allows the user to select items in categories such as eating habits, exercise habits, sleeping habits, smoking habits, and health (stress) status, for which the user would like to simulate the diet effects if they were to change (improve) their current lifestyle behavior. In other words, the user can simulate the hypothetical diet effects of their own hypothetical life by performing a "what-if" analysis, asking, "What if I were like this?" The items such as eating habits, exercise habits, sleeping habits, smoking habits, and health (stress) status correspond to the answer information of the questionnaire received as the user information.

[0051] For example, regarding eating habits, for each simulation item such as "avoid snacking," "avoid late-night snacking," and "reduce the frequency of drinking alcohol," three options for effort level are set: "I want to know the results of maximum effort," "I want to know the results of little effort," and "No simulation."

[0052] Regarding exercise habits, for each simulation item such as "walking," "moderate-intensity exercise," and "high-intensity exercise," three effort level options are set: "add about 60 minutes each day," "add about 30 minutes each day," and "don't simulate."

[0053] The user selects the level of effort for each simulation item using, for example, radio buttons, and when he or she has completed selection for all options in each category, he or she sends the selected information to the diet information providing server 100 using, for example, a send button (not shown).

[0054] Next, the CPU 11 changes the value of the item selected in the received selection information from the user information received from the user terminal 200 in accordance with the selection information (step 43, (1) in FIG. 5).

[0055] That is, for example, if "I want to know the results of my best efforts" is selected for the simulation item "refrain from snacking" in the selection information, and if the answer "I snack" is included in the answer information to the questionnaire regarding eating habits in the received user information, the CPU 11 changes the answer information "I snack" to answer information "I do not snack."

[0056] Furthermore, if "Add about 60 minutes every day" is selected for the simulation item "moderate intensity exercise" in the selection information, and if the answer to the questionnaire regarding exercise habits in the received user information is "perform about 30 minutes of moderate intensity exercise," the answer information "perform about 30 minutes of moderate intensity exercise" is changed to the answer information "perform about 90 minutes of moderate intensity exercise."

[0057] 5, the user's physical information (height, weight, age, etc.) may be changeable on the simulation condition selection screen. For example, when "weight" is selected on the simulation condition selection screen as an item of the user's physical information for which a change is desired to simulate the effect of dieting, and "-5 kg" is input (selected) as the change value, CPU 11 changes the weight value in the user information received from user terminal 200 to a value that is reduced by 5 kg.

[0058] Next, the CPU 11 inputs the user information changed based on the selected information to the second trained model 20 (step 44).

[0059] Next, the CPU 11 acquires, from the second trained model 20, a predicted value of the user information of the predetermined attribute that is missing from the changed user information (step 45, FIG. 5(2)).

[0060] Next, the CPU 11 inputs the supplemented and changed user information, which is obtained by supplementing the user information received from the second trained model 20 with the changed user information, into the first trained model 10 (step 46, Figure 5 (3)).

[0061] Next, the CPU 11 acquires a predicted value of the weight loss amount from the first trained model 10 (step 47).

[0062] The CPU 11 then transmits the acquired predicted value information of the weight loss to the user terminal (step 48, FIG. 5(4)). The predicted value information is a predicted value of the weight loss amount when the user performs a predetermined intervention (the above-mentioned two-month diet / exercise intervention) assuming that the user has made lifestyle behavior changes according to the above-mentioned selection information, and is displayed on the application of the user terminal 200 in the form of "-0.0 kg."

[0063] In this case, the CPU 11 not only obtains a predicted value of the weight loss amount by complementing the user information changed by the above selection information with the second trained model 20 and inputting the information into the first trained model 10, but may also obtain a predicted value of the weight loss amount by complementing the user information before the change received by the user terminal 200 with the second trained model 20 and inputting the information into the first trained model 10. The CPU 11 may then transmit the predicted value of the weight loss amount based on the changed user information (first predicted value) and the predicted value of the weight loss amount based on the user information before the change (second predicted value) to the user terminal 200 in a comparable state and display them.

[0064] For example, the CPU 11 displays the above two predicted values ​​on the same screen so that they can be compared, as shown below. "If I continue my current diet, I will lose XX kg." "If you stop snacking and go on a diet, you'll lose XX kg."

[0065] Furthermore, when the user changes multiple different simulation items on the simulation condition selection screen, the CPU 11 may input the user information after all of those multiple items have been changed into the first trained model 10 and display the predicted value of the weight loss amount, or may input user information in which only one of the multiple different changed items has been changed into the first trained model 10, obtain a predicted value of the weight loss amount for each different changed item, and send these predicted values ​​to the user terminal 200 in a comparable state and display them.

[0066] For example, the CPU 11 may display the two predicted values ​​on the same screen so that they can be compared, as shown below. Then, the simulation with the highest diet effect (largest weight loss amount) among the multiple predicted values ​​may be clearly indicated to the user. "If you stop snacking and go on a diet, you'll lose XX kg." "If you add about 60 minutes of moderate-intensity exercise every day to your diet, you will lose XX kg." "You will see great results if you stop snacking and go on a diet."

[0067] In this case, the CPU 11 may compare the simulation results of the user with those of other users, and indicate to the user among multiple simulation items which simulation items have a higher diet effect than other users (for example, items that rank highly in terms of weight loss in a group of people of the same gender and similar ages, heights, and weights), as follows: "Stopping snacking and dieting will work better for you than it will for others."

[0068] Furthermore, the CPU 11 may allow the user to select two or more options with different amounts of change for the same item on the simulation condition selection screen, input the multiple pieces of changed user information with different amounts of change into the first trained model 10, obtain predicted values ​​for weight loss for each different amount of change for the same item, and transmit these predicted values ​​to the user terminal 200 in a comparable state for display.

[0069] For example, the CPU 11 displays the above two predicted values ​​on the same screen so that they can be compared, as shown below. "If you add about 30 minutes of moderate-intensity exercise every day to your diet, you will lose XX kg." "If you add about 60 minutes of moderate-intensity exercise every day to your diet, you will lose XX kg."

[0070] As described above, according to this embodiment, the diet information providing server 100 can change various items related to the user's physical information, lifestyle habits, or health condition to simulate the amount of biological change caused by a predetermined intervention related to lifestyle behavior (diet, exercise, sleep, etc.).

[0071] [Variations] Although the embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments, and it goes without saying that various modifications can be made within the scope of the gist of the present invention.

[0072] In the above-described embodiment, the diet information providing server 100 predicts the amount of weight loss as the amount of biochange of the user due to a predetermined intervention using the first trained model 10. However, the amount of biochange to be predicted is not limited to the amount of weight loss. For example, the first trained model 10 can be used to predict the amount of biochange, such as the weight loss rate, the visceral fat area reduction rate, the change (quantity and quality) of skeletal muscle, the change in blood pressure, the change in blood components (cholesterol and fasting blood glucose level or HbA1c), the change in stress level, and the change in sleep quality (the ratio of non-REM sleep to REM sleep), and these predicted values ​​can be provided to the user.

[0073] In the above-described embodiment, the diet information providing server 100 received the user's physical information and the answer information to the questionnaire as user information from the user terminal 200 and input them into the second trained model 20 and the first trained model 10. However, either one of the two does not need to be used, and only the user's physical information or only the answer information to the questionnaire may be input into the second trained model 20 and the first trained model 10.

[0074] In the above-described embodiment, the diet information providing server 100 receives a diet effect prediction request from the user terminal 200 and transmits information about the predicted value of the diet effect by the first trained model 10 to the user terminal 200. However, the diet information providing server 100 may also receive a diet effect prediction request from a proxy user terminal of a proxy user who acts as a proxy for the user of the user terminal 200 and transmit information about the predicted value of the diet effect to the proxy user terminal. Here, the proxy user terminal is, for example, a terminal of a business operator (such as a health check business operator representative or a gym trainer) who is between the user and this diet method proposal system, but is not limited to these.

[0075] In the above embodiment, only one diet information providing server 100 is shown, but the processes executed by the diet information providing server 100 may be distributed and executed by a plurality of servers.

[0076] Among the inventions described in the claims of this application, the invention described as an "information processing method" is one in which each step is automatically performed by at least one device such as a computer through software-based information processing, and is not performed by a human using a device such as a computer. In other words, the "information processing method" is an information processing method using computer software, and is not a method in which a human operates a computing tool called a computer. [Explanation of symbols]

[0077] 10…First trained model 20…Second trained model 11...CPU 18...Storage section 19…Communications Department 31...User physical information database 32...Questionnaire information database 33...Complementary Body Information Database 34...Effectiveness prediction information database 100...Diet information server 200...User terminal

Claims

1. Acquire pre-change user information consisting of a plurality of items related to the user's physical information, lifestyle habits, or health condition, and post-change user information in which some of the items in the pre-change user information have been changed; inputting the acquired changed user information into a first trained model that predicts a biochange amount of the user due to a predetermined intervention related to daily behavior using user information related to the user's physical information, lifestyle habits, or health condition as explanatory variables, and acquiring a first predicted value of the biochange amount from the first trained model; Transmitting prediction information including the acquired first predicted value to a user terminal of the user or a proxy user terminal of a proxy user acting on behalf of the user. Control unit An information processing system comprising:

2. The control unit receives selection information for selecting an item to be changed in the pre-change user information from the user terminal, and acquires the post-change user information by changing the selected item in the pre-change user information in accordance with the selection information. The information processing system according to claim 1 .

3. The control unit Inputting the pre-change user information into the first trained model; obtaining, from the first trained model, a second predicted value of the biological change amount estimated based on the pre-change user information; The acquired first predicted value and second predicted value are transmitted as the prediction information in a state in which they can be compared.

3. The information processing system according to claim 1.

4. The control unit As the changed user information, first changed user information obtained by changing a first item in the pre-change user information and second changed user information obtained by changing a second item in the pre-change user information that is different from the first item are acquired; inputting the first changed user information into the first trained model and obtaining the first predicted value from the first trained model; inputting the second changed user information into the first trained model to obtain a second predicted value from the first trained model; The acquired first predicted value and second predicted value are transmitted as the prediction information in a state in which they can be compared.

3. The information processing system according to claim 1.

5. The control unit As the changed user information, first changed user information in which a first item of the pre-change user information is changed by a first amount, and second changed user information in which the first item is changed by a second amount that is greater than the first amount are acquired; inputting the first changed user information into the first trained model and obtaining the first predicted value from the first trained model; inputting the second changed user information into the first trained model to obtain a second predicted value from the first trained model; The acquired first predicted value and second predicted value are transmitted as the prediction information in a state in which they can be compared.

3. The information processing system according to claim 1.

6. The control unit inputs a value of the user information of a first attribute of the user into a second trained model that has been trained to learn user information of a plurality of attributes so as to estimate a value of a missing attribute from a value of a certain attribute of the user information, and acquires, from the second trained model, a predicted value of the user information of a second attribute that is missing from the user information of the first attribute of the user, as the pre-change user information to be input to the first trained model.

3. The information processing system according to claim 1.

7. The control unit As the pre-change user information, at least the values ​​of age, sex, height, and weight of the user are acquired; The first predicted value of the weight loss rate or the visceral fat area loss rate of the user is obtained from the first trained model as the biological change amount.

3. The information processing system according to claim 1.

8. The control unit As the pre-change user information, information on the user's answers to a questionnaire regarding the user's lifestyle habits or health condition is acquired.

3. The information processing system according to claim 1.

9. Acquire pre-change user information consisting of a plurality of items related to the user's physical information, lifestyle habits, or health condition, and post-change user information in which some of the items in the pre-change user information have been changed; inputting the acquired changed user information into a trained model that predicts the amount of biochange of the user due to a predetermined intervention related to daily behavior using user information related to physical information, lifestyle habits, or health status as explanatory variables, and obtaining a predicted value of the amount of biochange from the trained model; Transmitting prediction information including the obtained prediction value to a user terminal of the user or a proxy user terminal of a proxy user acting on behalf of the user. Information processing methods.

10. In the information processing device, acquiring pre-change user information consisting of a plurality of items related to the user's physical information, lifestyle habits, or health condition, and post-change user information in which some of the items in the pre-change user information have been changed; a step of inputting the acquired changed user information into a trained model that predicts a biological change amount of the user due to a predetermined intervention related to daily behavior using user information related to physical information, lifestyle habits, or health status as explanatory variables, and acquiring a predicted value of the biological change amount from the trained model; transmitting prediction information including the obtained prediction value to a user terminal of the user or a proxy user terminal of a proxy user acting on behalf of the user; A program that executes the following.

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