Information provision method for predicting health condition of consumer and supporting health maintenance and improvement

Abstract

Provided is a technique by which a subject can be alerted to a prescribed disease or to a disability or the like associated with symptoms that do not correspond to a disease but do interfere with daily life.　This information provision method includes: acquiring personal data of a target customer; determining a segmentation to which the target customer belongs from among a plurality of segmentations, each of which indicates a classification that corresponds to at least the degree of a health condition, on the basis of the personal data and classification criteria prepared in advance; and providing health information that corresponds to the determined segmentation to the target customer. The personal data is configured from a plurality of pieces of unique data relating to the target customer. At least one of the plurality of pieces of unique data is symptom data reflecting the health condition of the target customer. The plurality of segmentations are classified according to the degree of the health symptom, and each segmentation is associated with health information prepared in advance in accordance with the health condition.

Description

A method of providing information to predict the health status of consumers and support their health maintenance and improvement

[0001] The present disclosure relates to a method for providing information to predict the health status of a person and support the maintenance and improvement of health.

[0002] In recent years, living environments around the world have been undergoing major changes, including population decline, economic recession, natural disasters, and the spread of infectious diseases. At the same time, opportunities for the use of artificial intelligence (AI) have increased, and the development and spread of fifth-generation mobile communication systems has progressed. As living environments change, the infrastructure that supports society has begun to transition to an industrial framework that utilizes technology and connects people based on data, and information on social activities and behavior has begun to be visualized.

[0003] As interest in health grows, health management needs to adapt to social trends. Until now, "passive healthcare" has been the norm, where people get sick and then receive treatment only after their symptoms start to worsen. However, in the future, healthy people will be able to collect and digitize their own data at home, which will likely lead to "proactive healthcare" that looks ahead to health management and "preventive healthcare" that prevents illness. Going further, we can also envision "predictive healthcare" that predicts future illnesses and provides health warnings.

[0004] Patent Literature 1 discloses a technology for storing personal health records (PHRs) containing each user's genomic information in a large-scale genome cohort database and performing cohort analysis on the PHR big data. Based on the analysis results, the technology in Patent Literature 1 derives the correlation between the combination of genome type and lifestyle type and health risk (i.e., disease development risk) and estimates the user's future health risk.

[0005] JP 2017-006745 A

[0006] There are various ways to provide information about health risks. As the methods of providing information become more diverse, users will be able to more easily obtain the information they need. New methods of providing information about health risks are needed.

[0007] One of the objects of the present invention is to provide a technology that can warn a subject about a specified disease or a disorder that is not a disease but is accompanied by symptoms that interfere with daily life.

[0008] The present invention provides the following: [1] An information provision method acquires personal data of a target customer, determines a segmentation to which the target customer belongs from among multiple segmentations, each of which indicates a classification according to the degree of health status, based on the personal data and predetermined classification criteria, and provides the target customer with health information according to the determined segmentation. The personal data is composed of multiple unique data related to the target customer, and at least one of the multiple unique data is health data or behavioral data reflecting the target customer's health symptoms. The multiple segmentations are classified according to the degree of health symptoms, and each segmentation is associated with health information prepared in advance according to the health status.

[0009] [2] The degree of health status is classified into a plurality of numerical ranges, each of the plurality of segmentations is pre-associated with a numerical range corresponding to the degree of health status, the degree of potential health status of the target customer is expressed by a numerical value, and the degree of potential health status of the target customer is predicted by determining the segmentation corresponding to the numerical value based on the numerical range to which the numerical value belongs. [The information provision method described in 1.

[0010] [3] The information providing method according to [2], wherein the plurality of segmentations is three or more.

[0011] [4] An information provision method described in [2] or [3], in which a table is prepared in advance that associates each of a plurality of segmentations with services and / or products that are suitable for the health status of each segmentation, and the method further provides, together with the health information, suggestions regarding services and / or products that are suitable for the determined class of health status.

[0012] [5] An information provision method described in [4], in which health information and / or suggestions are provided via email text, display on a display device, printing on a receipt, or a message presented on an app running on an electronic device.

[0013] [6] In one embodiment, the information provision method described in any one of [1] to [5], wherein the potential health condition of the target customer is one or more selected from health issues of elderly women, menopausal symptoms, and premenstrual syndrome (PMS, PMDD) symptoms.

[0014] [7] An information provision method described in any of [1] to [5], wherein the potential health condition of the target customer is one or more selected from health problems due to a predetermined unfavorable sleep state, a deterioration in sleep state, a disturbed sleep rhythm, and inappropriate sleep hours.

[0015] [8] An information provision method described in any one of [1] to [5], wherein the potential health condition of the target customer is one or more selected from lifestyle-related diseases and / or changes in physical condition due to obesity, lack of exercise, obesity, and changes in weight over a certain period of time.

[0016] [9] An information provision method described in any one of [1] to [5], wherein the potential health information of the target customer is one or more selected from health conditions due to dehydration, high serum Na, and high BUN / creatinine ratio.

[0017]

[10] An information provision method described in any one of [1] to [5], wherein the potential health information of the target customer is one or more selected from health problems due to weakened immunity, susceptibility to catching colds, SIgA concentration, allergic symptoms, and oral health status.

[0018]

[11] An information provision method described in any one of [1] to [5], wherein the potential health information of the target customer is one or more selected from nutritional health problems, nutritional deficiencies, nutritional imbalances, and eating habits.

[0019]

[12] The information provision method described in [1], wherein the target customer is a woman, and the multiple unique data regarding the target customer include the target customer's age, and a value indicating the target customer's equol production ability as health data.

[0020]

[13] The information provision method described in

[12] , wherein the multiple segmentations are classified according to a combination of multiple life stages determined according to age and the presence or absence of equol production ability, and each segmentation is associated with pre-prepared health information.

[0021]

[14] The information providing method described in

[13] , wherein the presence or absence of equol production ability is determined based on the relationship between a value indicating equol production ability and a threshold value.

[0022]

[15] The information provision method described in [1], wherein the plurality of unique data regarding the target customer includes the target customer's age, sex, height, weight, weight change over a certain period of time, and health data such as behavioral data regarding the target customer's smoking habit and number of steps taken.

[0023]

[16] The information provision method described in

[15] , wherein the multiple segmentations are classified according to a combination of age, sex, body mass index, weight change over a certain period, behavioral data related to the number of steps, and whether or not the person has a smoking habit, and each segmentation is associated with pre-prepared health information.

[0024]

[17] The information provision method described in [1], wherein the plurality of unique data regarding the target customer includes the target customer's insomnia score, a time value indicating sleep time, and a time value indicating the target customer's social jet lag as symptom data.

[0025]

[18] The information provision method described in

[17] , wherein the multiple segmentations are classified according to a combination of the degree of insomnia and the presence or absence of social jet lag, and each segmentation is associated with pre-prepared health information according to the degree of sleep problems as a health condition.

[0026]

[19] The information provision method described in [1], wherein the multiple unique data regarding the target customer include the target customer's age, and health data such as the target customer's serum Na value and BUN / creatinine ratio, or the target customer's serum Na value and urine specific gravity and / or urine color.

[0027]

[20] The information provision method described in

[19] , wherein the multiple segmentations are classified according to a combination of serum Na value and whether the person is above or below a predetermined age, and each segmentation is associated with pre-prepared health information according to the degree of problem related to deficiency in water content as a health condition.

[0028]

[21] The information provision method described in [1], wherein the plurality of unique data regarding the target customer includes the target customer's ideal weight, and health data such as the target customer's age, height, and weight.

[0029]

[22] The information provision method described in

[21] , wherein the multiple segmentations are classified according to a combination of age-based age, current body mass index, and ideal body mass index calculated from height and ideal weight, and each segmentation is associated with pre-prepared health information according to the degree of nutritional problems as a health condition.

[0030]

[23] The information provision method described in [1], wherein the plurality of unique data relating to the target customer includes salivary IgA.

[0031]

[24] The information provision method described in

[23] , wherein the multiple segmentations are classified according to salivary IgA values, and each segmentation is associated with pre-prepared health information according to the degree of immune problems as a health condition.

[0032]

[25] An information provision method described in any one of [1] to

[24] , wherein the multiple segmentations further include classification according to daily behavior, and the multiple unique data constituting the personal data further include health data reflecting the health status of the target customer.

[0033]

[26] An information provision method comprising: preparing in advance a plurality of contents to be presented to a customer and provision order information that specifies the order in which the plurality of contents are to be provided according to the degree of health risk of the customer; acquiring risk data indicating the degree of health risk of the target customer; and providing the plurality of contents in an order according to the degree of health risk of the target customer based on the risk data and provision order information.

[0034]

[27] The information provision method described in

[26] , wherein the risk data is data indicating a segmentation selected from a plurality of segmentations, each of which indicates a classification according to at least the degree of health status, based on the personal data of the target customer and pre-prepared classification criteria, the personal data is composed of a plurality of unique data regarding the target customer, at least one of the plurality of unique data is health data reflecting the target customer's health status, the plurality of segmentations are classified according to the degree of health status, and each segmentation is associated with health information prepared in advance according to the health status.

[0035] According to an exemplary embodiment of the present invention, a technology can be provided that can warn a subject about a specified disease or a disorder that is not a disease but has symptoms that interfere with daily life.

[0036] 1 is a hardware configuration diagram of an information processing device according to an exemplary embodiment of the present invention;

[0023] FIG. 1 shows an example of a display when providing health information related to women's health;

[0024] FIG. 2 shows an example of classification criteria for classifying the presence or absence of equol production ability;

[0025] FIG. 3 shows a table associating each segmentation related to women's health with each file name;

[0026] FIG. 4 shows an example of a display when providing health information related to lifestyle habits;

[0027] FIG. 5 shows an example of a display when providing health information related to sleep;

[0028] FIG. 6 shows a table associating each segmentation related to sleep with each file name;

[0029] FIG. 7 shows an example of a display when providing health information related to body water content;

[0029] FIG. 8 shows the relationship between segmentation according to each input, prediction results of whether or not there is a deficiency in water, and predicted items of health risk caused by a deficiency in water when serum Na value, urine specific gravity, and urine color are input;

[0029] FIG. 9 shows a first display example of health information related to health risks;

[0029] FIG. 10 shows a second display example of health information related to health risks;

[0029] FIG. 11 shows a third display example of health information related to health risks;

[0029] FIG. 12 shows an example of a display when providing health information related to nutrition;

[0029] FIG. 13 shows an example of health information provided to a customer of segmentation 1;

[0029] FIG. 1 is a diagram showing an example of health information provided to a customer. FIG. 1 is a diagram showing an example of health information provided to a customer in segmentation 3. FIG. 1 is a diagram showing an example of health information provided to a customer in segmentation 4. FIG. 1 is a diagram showing an example of health information provided to a customer in segmentation 5. FIG. 2 is a diagram showing an example of health information provided to a customer in segmentation 6. FIG. 2 is a diagram showing segmentation according to salivary IgA values ​​when salivary IgA is input. FIG. 3 is a diagram showing an example of health information provided to a customer in level 1 segmentation. FIG. 4 is a diagram showing an example of health information provided to a customer in level 2 segmentation. FIG. 5 is a diagram showing an example of health information provided to a customer in level 3 segmentation. FIG. 6 is a diagram showing details for providing health feedback and proposing products to improve health conditions for various health conditions. FIG. 7 is a diagram showing other examples of health conditions and examples of unique data entered by a customer for each health condition. FIG. 8 is a diagram showing other examples of health conditions and examples of unique data entered by a customer for each health condition.A diagram showing a schematic diagram of an opportunity. A diagram showing an example of a service provided to a customer. A block diagram showing the configuration of an information provision system according to embodiment 2 of the present disclosure. A block diagram showing the configuration of an information terminal. A diagram showing an example in which the CPU of a server device changes the information sent to an information terminal depending on the level of sleep risk. A diagram showing a table summarizing the results of a questionnaire. A transition diagram of display content showing that the order of information provided changes depending on whether the sleep risk is "light," "medium," or "heavy." A diagram illustrating the relationship between the reasons why each of users A to E wants to continue purchasing a product and the factors (presented information) that influence those reasons. A flowchart showing the processing procedure according to embodiment 2.

[0037] Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, unnecessary detailed description may be omitted. For example, detailed description of well-known matters or redundant description of substantially identical configurations may be omitted. This is to avoid unnecessary redundancy in the following description and to facilitate understanding by those skilled in the art. Note that the inventor provides the accompanying drawings and the following description to enable those skilled in the art to fully understand the present disclosure, and they do not intend to limit the subject matter described in the claims.

[0038] Note that the configurations and operations of the embodiments described below are examples. The present disclosure is not limited to the configurations and operations of the embodiments described below. Furthermore, each figure is a schematic diagram and is not necessarily an exact illustration. Note that in each figure, substantially the same configuration is assigned the same reference numeral, and duplicate explanations are omitted or simplified.

[0039] The present disclosure relates to a method for providing health information. Hereinafter, an aspect of providing health information using an information processing device will be described as an example.

[0040] (Embodiment 1) FIG. 1 is a hardware configuration diagram of an information processing device 100 used in connection with this embodiment. The information processing device 100 receives personal data of a target customer and uses the personal data to provide health information corresponding to the target customer's health condition. The target customer may be any person who has not previously been aware of their own health risks. Alternatively, the target customer may be a person who has been assessed as having potential health risks, for example, based on the results of a questionnaire survey, a health check, or information provided by a testing institution. This allows for more accurate and / or more detailed categorization of health information corresponding to the health condition of target customers identified as being at risk by various methods.

[0041] The information processing device 100 may be, for example, a PC or a server computer. Specifically, the information processing device 100 includes a CPU (Central Processing Unit) 121, an input interface (I / F) 122, a storage device 123, and an output interface (I / F) 124.

[0042] The CPU 121 is an example of an arithmetic circuit of the information processing device in this embodiment. The CPU 121 realizes the operation described below by executing a control program 126 stored in the storage device 123. In other words, the CPU 121 realizes the functions of the information processing device 100 in this embodiment by executing the control program 126. The arithmetic circuit configured as the CPU 121 in this embodiment may be realized by various processors such as an MPU or a GPU, or may be configured by one or more processors.

[0043] The input I / F 122 is an input device for receiving input of personal data of the target customer. For example, the input I / F 122 may be a communication circuit, a touch panel, a keyboard, or a mouse.

[0044] The storage device 123 is a storage medium that stores a computer program 126 and health information 127 required to operate the information processing device 100. The storage device 123 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD), which is a semiconductor storage device. The storage device 123 may include a temporary storage element configured, for example, by RAM such as DRAM or SRAM, and may function as a work area for the CPU 121. The control program 126 is an example of a computer program in this embodiment. Details of the health information 127 will be described later.

[0045] The output I / F 124 is a communication circuit and / or a communication terminal connected to various output devices provided outside the information processing device 100. For example, the output I / F 124 is a video output terminal connected to the display 52. ​​The CPU 121 of the information processing device 100 transmits information to be provided to the customer to the display 52 for display. The pharmacist 36 checks the content displayed on the display 52 and, for example, suggests to a customer with menopausal symptoms one or more products that many subjects with menopausal symptoms have purchased. Alternatively, the customer checks the content displayed on the display 52 and considers the proposed products and services.

[0046] Alternatively, the output I / F 124 transmits health information to the customer's smartphone 38 via, for example, a mobile phone line. When the input I / F 122 and the output I / F 124 are communication terminals or communication circuits connected to a communication network 54 or the like and capable of data communication, the output I / F 124 and the input I / F 122 may be made of the same hardware. In this case, the input I / F 122 and the output I / F 124 may be collectively referred to as a communication interface (I / F). The communication I / F may be, for example, an Ethernet (registered trademark) communication terminal or a USB (registered trademark) terminal. Alternatively, the communication I / F may be a communication circuit that communicates in accordance with standards such as IEEE 802.11, 4G, or 5G. The communication I / F may be connectable to a communication network such as an intranet or the Internet. Furthermore, the information processing device 100 may communicate directly with other devices via the communication I / F or via an access point.

[0047] The operation of the information processing device 100 will be described below.

[0048] The input I / F 122 acquires the target customer's personal data. "Personal data" is data unique to the target customer (hereinafter referred to as "unique data"), such as age, gender, height, and weight. In this embodiment, the unique data also includes health data and behavioral data that reflect the target customer's health status. Examples of health data include equol production capacity, smoking habits, serum Na level, and BUN / creatine ratio, while behavioral data is the time and / or number of steps indicating "social jet lag," a disruption of the body's internal rhythm. In this embodiment, the personal data includes multiple unique data, and at least one of the multiple unique data is health data or behavioral data.

[0049] The CPU 121 determines a segmentation to which the target customer belongs from among a plurality of segmentations based on the personal data and pre-prepared classification criteria. Each of the "plural segmentations" is a classification according to the level of health status. Two or more than three segmentations may be provided. Specific examples of segmentation will be described later.

[0050] Health information 127 pre-stored in storage device 123 is a collection of explanations for various health conditions expected of the user. Health information 127 includes, for example, multiple explanations related to medical knowledge, explanations of expected symptoms, and explanations showing advice for alleviating symptoms, depending on the level of the health condition.

[0051] Each segmentation and the health information 127 are associated in advance according to the health condition. When the CPU 121 determines a certain segmentation for the target customer, it reads the corresponding explanation from the health information 127 associated with that segmentation and provides it to the target customer via, for example, the output I / F 124. The explanation may be provided, for example, by displaying it on the display 52, by sending it to the smartphone 38 via an email, or by printing it on a receipt received by the customer. The explanation regarding the target customer's health information may be provided by displaying a message in an app running on the smartphone 38.

[0052] Various aspects will be described below with specific examples. In the following examples, it is assumed that the information processing device 100 is a PC, and health information 127 is displayed on a display 52 connected to the PC.

[0053] 2 shows an example of a display when providing health information 127 related to women's health. A female customer inputs her age and a numerical value indicating her equol production capacity in input fields 130a and 130b, respectively. The numerical value indicating equol production capacity can be obtained in advance, for example, by a urine test.

[0054] The inventors set six segmentations 132 according to combinations of age and equol production ability. Specifically, age was divided into three types (life stages): sexual maturity, menopause, and old age, and each was further divided into two types: presence or absence of equol production ability, setting a total of six segmentations 132. Sexual maturity, menopause, and old age each correspond to three ranges previously divided for age values.

[0055] The CPU 121 determines whether the input age value corresponds to sexual maturity, menopause, or old age, and further determines whether the input age has equol production ability and determines which segmentation the person belongs to. For example, if the age is 50 and the person does not have equol production ability, in the example of Figure 2, segmentation 132a labeled "menopausal non-producer" is selected. Note that age ranges corresponding to each of sexual maturity, menopause, and old age can be prepared in advance as classification criteria. For example, sexual maturity is between 20 and 45 years old, menopause is between 45 and 55 years old, and old age is 55 years old or older.

[0056] The presence or absence of equol production ability can also be determined based on the relationship between the value indicating equol production ability and a certain threshold (classification criterion). That is, if the numerical value of equol production ability is equal to or greater than the threshold, equol production ability is determined to be "present," and if it is less than the threshold, equol production ability is determined to be "absent." For example, Figure 3 shows an example of classification criteria for classifying the presence or absence of equol production ability. If urinary equol is 1.0 μM or greater, equol production ability is determined to be "present," and if it is 0.9 μM or less, equol production ability is determined to be "absent." For example, a threshold value of 0.95 μM of urinary equol can be set.

[0057] FIG. 4 shows a table 140 associating each segmentation related to women's health with each filename. The filenames point to files containing explanations for the health status corresponding to each segmentation. FIG. 4 shows that when the "menopausal non-producer" segmentation 132a (FIG. 2) is selected, a file named "Comment5.dat" is associated with the "menopausal non-producer" segmentation 132a. The file Comment5.dat exists in the storage device 123, and health information 142 related to women who are menopausal and who do not have the ability to produce equol is described in the file. Table 140 associates similar filenames with each of the other five segmentations, and these filenames point to files containing explanations for the women's health status corresponding to each segmentation. The explanation may include, for example, that women who are unable to produce equol tend to have higher triglyceride levels, a higher risk of experiencing allergic rhinitis, and a tendency for menopausal symptoms to be more severe than women who are able to produce equol. Products and health suggestions for increasing equol production may also be described. Segmentation other than "menopausal non-producers" is outlined below.

[0058] The "pubertal producers" segmentation targets women who are pubertal and have the ability to produce equol. The current differences between those in this segment and women who are pubertal and do not have the ability to produce equol can be used as an explanation for their health status.

[0059] The "menopausal producers" segment targets women who are menopausal and have equol-producing ability. A possible explanation for their health status is the current difference between those in this segment and women who are menopausal and do not have equol-producing ability.

[0060] The "elderly producers" segmentation targets women who are elderly and capable of producing equol. A possible explanation for their health status is the current difference between those in this segment and elderly women who are not capable of producing equol.

[0061] The "non-equol-producing" segmentation targets women who are sexually mature and do not have the ability to produce equol. The current differences between those in this segment and women who are sexually mature and have the ability to produce equol can be used to explain their health status.

[0062] The "elderly non-producers" segmentation targets elderly women who are not equol-producing. The current differences between those in this segment and elderly women who are equol-producing can be used to explain their health status.

[0063] Further explanations of health status could include future differences between individuals in each segmentation and women who are menopausal / elderly and have the ability to produce equol, or future differences between individuals in each segmentation and women who are menopausal / elderly and do not have the ability to produce equol.

[0064] Depending on the symptoms, services such as using a sports gym or getting a massage may be suggested. That is, the table 140 associates products and / or services to be suggested with the level of health condition. As a result, when using the information processing device 100, for example, the information processing device 100 can suggest products and services corresponding to the prediction results along with the prediction results. This suggestion can also be made in the various examples described below.

[0065] According to the above-described process, even if the target customer does not have any obvious changes in menopausal symptoms, in other words, it is possible to propose products and services for potential menopausal symptoms. In addition to potential menopausal symptoms, it is also possible to propose products and services for health issues of elderly women and premenstrual syndrome (PMS, PMDD).

[0066] 5 shows an example of a display when providing health information related to lifestyle habits. In this example, multiple unique data 140 related to the customer are displayed, including age, sex, height, weight, smoking status, number of steps taken, and weight change over a certain period. Of these, for example, information on smoking status is health data that reflects the customer's health condition, and number of steps taken is behavioral data.

[0067] The inventors set a large number of segmentations according to combinations of multiple unique data 140. First, segmentation groups 142a and 142b were set according to gender. Then, each of the segmentation groups 142a and 142b was classified into three age groups based on age. Furthermore, each age group was classified into three categories based on body mass index (BMI) calculated from height and weight. Each of these categories was further divided into two categories: smoking and non-smoking. As a result, a total of 18 segmentations were set for each of the gender segmentation groups 142a and 142b. In the example of FIG. 5, as in the examples of FIGS. 2 and 4, each segmentation is associated with pre-prepared health information.

[0068] When a customer inputs a plurality of unique data 140 relating to the customer, the CPU 121 determines a segmentation to which the customer belongs based on the plurality of unique data 140, and provides the customer with health information according to the determined segmentation. The health information may include, for example, a recommended number of steps per day. The customer can use the health information to manage their own health.

[0069] According to the above-described process, it is possible to propose products and services that improve or maintain the health of target customers, even for potential health conditions that may lead to lifestyle-related diseases. The potential health conditions may be one or more selected from lifestyle-related diseases and / or changes in physical condition associated with obesity, lack of exercise, obesity, and changes in weight over a certain period of time.

[0070] 6 is a display example for providing health information related to sleep. In this example, a plurality of unique data 150 related to the customer are shown, including an insomnia (AIS) score, a time value indicating sleep time, and a time value indicating social jet lag (SJL). Of these, for example, the time value indicating social jet lag is symptom data that reflects the customer's health condition. Numerous studies have pointed out that "social jet lag" indicates a disruption in the body's rhythm, and the greater the disruption, i.e., the longer the social jet lag, the greater the impact on physical and mental health.

[0071] The inventors established eight segmentations 152 based on combinations of multiple unique data 150. First, the data was divided into an upper segmentation group 152a and a lower segmentation group 152b based on whether the social jet lag was less than one hour or more than one hour. In FIG. 6 , if the social jet lag was one hour or less, it was described as "no SJL problem," and if the social jet lag was longer than one hour, it was described as "SJL problem." Then, for each of the segmentation groups 152a and 152b, the severity of insomnia was classified into four levels based on an insomnia score calculated using a predetermined method. Specifically, in this embodiment, the insomnia scale known as the Athens Insomnia Scale was used to classify the insomnia into four levels: no problem, mild problem, moderate problem, and severe problem. An insomnia score of less than 1, i.e., 0, indicates no problem, an insomnia score of 1 or greater but less than 4 indicates mild insomnia, a score of 4 or greater but less than 6 indicates moderate insomnia, and a score of 6 or greater indicates severe insomnia. As a result, a total of eight segmentations were set. In the example of Figure 6, as in the examples of Figures 2 and 4, each segmentation is associated with health information prepared in advance.

[0072] FIG. 7 shows a table 140A that associates each sleep-related segmentation with a file name. The file name indicates a file containing an explanation corresponding to the health condition 127A corresponding to each segmentation. FIG. 7 shows that a file named "AIS-SJL-Comment7.dat" is associated with the segmentation "Sleep Refugee 1." The file AIS-SJL-Comment7.dat exists in the storage device 123, and contains health information 142A for a patient with a moderate AIS problem related to insomnia and a social jet lag SJL problem. The health information 142A includes an illustration of a doctor and an explanatory speech bubble, as if the doctor were explaining something.

[0073] When a customer inputs multiple pieces of unique data 150 related to the customer, the CPU 121 determines the segmentation to which the customer belongs based on the multiple pieces of unique data 150, and provides the customer with health information corresponding to the determined segmentation. The health information can include, for example, whether or not to consult a doctor and the names of medicines that are recommended to be taken. The customer can use the health information to manage their own health.

[0074] The following describes eight segmentations 152 established by the inventor based on the combination of the degree of insomnia (AIS) problem and the presence or absence of social jet lag (SJL) problem. The degree of AIS problem and the presence or absence of social jet lag (SJL) problem will be expressed below as (AIS problem, SJL problem) = (none, none).

[0075] 1: If (AIS question, SJL question) = (none, none), it is classified as the "Sleep Master 1" segment. For example, this indicates that you are getting good sleep because you lead a regular daily life. Maintaining this state is considered effective in preventing colds and improving work performance, and is therefore a desirable state.

[0076] 2: If (AIS questions, SJL questions) = (mild, none), the person is classified as "Sleep Master 2." For example, this indicates that the person generally sleeps well because they lead a regular daily life. It has been reported that even a slight deterioration in sleep quality can increase the risk of developing depression, so it is advisable to warn the person.

[0077] 3: If (AIS questions, SJL questions) = (absent, present), the person is classified into the "irregular sleep rhythm type 1" segment. It is assumed that the person does not have sleep problems, but disturbances in sleep rhythm are observed between weekdays and weekends. Disturbances in sleep rhythm have been reported to lead to mental and physical disorders such as depression, metabolic syndrome, and overweight, so it is best to warn the person to start making an effort to regulate their lifestyle rhythm now.

[0078] 4: If (AIS problems, SJL problems) = (mild, present), the individual is classified into the "irregular sleep rhythm type 2" segment. It is assumed that the individual is not troubled by sleep, but since even a slight deterioration in sleep status has been reported to increase the risk of developing depression, it is advisable to warn the individual. Furthermore, sleep rhythm disturbances are observed between weekdays and weekends. Therefore, the same approach as in 3 above is advisable.

[0079] 5: If (AIS questions, SJL questions) = (moderate, none), the person is classified as "Sleep status caution type 1" and should pay attention to their sleep status. There is no disruption in the sleep rhythm between weekdays and weekends, but their sleep status is deteriorating. It has been reported that a worsening sleep status increases the risk of developing depression. If you are not feeling well physically or mentally, you cannot get a good night's sleep. Therefore, it is best to warn the person in question to avoid accumulating stress and to find their own way to relax and refresh themselves.

[0080] 6: If (AIS problems, SJL problems) = (severe, none), the person is classified as "Sleep status caution type 2" and should pay attention to their sleep status. There is no disruption in the sleep rhythm between weekdays and weekends, but the quality of sleep is deteriorating. Poor sleep status increases the risk of developing depression and has been reported to cause diabetes and metabolic syndrome. If you are not in good physical or mental condition, you cannot get a good night's sleep. Therefore, it is best to warn the person in question to avoid accumulating stress and to find their own way to relax and refresh themselves.

[0081] 7: If (AIS problems, SJL problems) = (moderate, present), the individual is classified into the "Sleep Refugee 1" segment. This individual is expected to have disrupted sleep rhythms on weekdays and weekends, and to be troubled by their sleep status. Disrupted sleep rhythms and poor sleep status have been reported to be factors in the development of depression, overweight, diabetes, and metabolic syndrome. While various issues, such as physical and mental disorders, can be considered as causes of poor sleep, disrupted sleep rhythms may be one of the contributing factors. This individual should be warned to consciously try to minimize the discrepancy between bedtime and wake-up times on weekdays and weekends.

[0082] 8: If (AIS Problems, SJL Problems) = (Severe, Present), the individual is classified as "Sleep Refugee 2." This individual has disrupted sleep rhythms on weekdays and weekends, and is likely to be troubled by their sleep status. It has been reported that disrupted sleep rhythms and significant deterioration in sleep status increase the risk of developing depression, overweight, diabetes, and metabolic syndrome. While various issues, such as physical and mental disorders, can be considered as causes of poor sleep, disrupted sleep rhythms may be one of the contributing factors. Awareness should be given to those individuals to minimize discrepancies between weekday and weekend bedtimes and wake-up times.

[0083] In the above description, a disruption in a customer's biological rhythm, more specifically, sleep rhythm, is considered to be a potential health condition of the customer, and health information related to the potential health condition is proposed to the customer. In addition to sleep rhythm disruption, for example, a deterioration in sleep status and / or inadequate sleep duration may also be considered to be a potential health condition of the customer. That is, in this embodiment, one or more selected from a health problem due to a predetermined unfavorable sleep status, a deterioration in sleep status, a sleep rhythm disruption, and inadequate sleep duration may be considered to be a potential sleep-related health condition of the customer.

[0084] 8 shows an example of a display that provides health information related to body water content. In this example, age, serum Na level, and / or BUN / creatinine ratio are displayed as multiple unique data 160 related to the customer. Of these, for example, serum Na level and / or BUN / creatinine ratio are symptom data that reflect the customer's health condition.

[0085] The inventors established ten segmentations 162 based on the combination of multiple unique data 160. First, the subjects were divided into an upper segmentation group 162a and a lower segmentation group 162b based on whether they were over or under 60 years old. Then, for each of the segmentation groups 162a and 162b, the range of serum Na values ​​was classified into five levels in the example of FIG. 8 . Specifically, the ranges are serum Na value < 142, serum Na value ≥ 142, serum Na value ≥ 144, serum Na value ≥ 146, and serum Na value ≥ 148. As a result, a total of ten segmentations were established. In the example of FIG. 8 , as in the examples of FIGS. 2 and 4 , each segmentation is associated with pre-prepared health information. The segmentation groups may be classified based on the range of the BUN / creatinine ratio, or may be classified based on both the serum Na value and the BUN / creatinine ratio.

[0086] When a customer inputs a plurality of unique data 160 related to the customer, the CPU 121 determines the segmentation to which the customer belongs based on the plurality of unique data 160, and provides the customer with health information according to the determined segmentation. The health information can include, for example, the degree of the problem related to deficiency in water intake, whether or not to consult a doctor, and the names of products that are recommended to be taken. The customer can use the health information to manage their own health.

[0087] In the above example, the serum Na level and the BUN / creatinine ratio were used to assess fluid insufficiency. However, other indicators, such as urine specific gravity and / or urine color, can also be used to assess fluid insufficiency. The serum Na level is a useful indicator for assessing whether or not a patient has chronic fluid insufficiency, while both urine specific gravity and urine color are useful indicators for assessing whether or not a patient has transient fluid insufficiency. The susceptibility to diseases differs depending on whether the patient has chronic or transient fluid insufficiency.

[0088] Figure 9 shows the relationship between segmentation 172 based on each input, prediction result 174 of whether or not there is a deficiency in hydration, and predicted items 176 of health risks caused by deficiency in hydration, when a customer inputs serum Na value, urine specific gravity, and urine color as multiple unique data 170 about themselves.

[0089] 9, a customer with a serum Na value of 135 or greater but less than 142 is evaluated as belonging to low-risk segmentation 172a, and a customer with a serum Na value of 142 or greater but less than 146 is evaluated as belonging to high-risk segmentation 172b. If a customer is evaluated as belonging to segmentation 172b based on their serum Na value, the customer is indicated as possibly suffering from chronic hydration deficiency. "Risk" here refers to health risks resulting from chronic hydration deficiency, specifically dementia, heart failure, chronic lung disease, chronic kidney disease, coronary artery disease, high blood pressure, stroke, asthma, and intermittent claudication.

[0090] Similarly, if the urine specific gravity is less than 1.013, the customer is evaluated as belonging to the low-risk segmentation 172a, and if the urine specific gravity is 1.013 or more, the customer is evaluated as belonging to the high-risk segmentation 172b. The urine color indicates the degree of dehydration. The urine color is determined by a doctor or a user with reference to a known color chart divided into eight levels, ranging from no color or light (value: 1) to dark (value: 8). For example, if the urine color is less than 4 (any of 1 to 3), the customer is evaluated as belonging to the low-risk segmentation 172a, and if the urine color is 4 or more (any of 4 to 8), the customer is evaluated as belonging to the high-risk segmentation 172b. If the customer is evaluated as belonging to the segmentation 172b based on the urine specific gravity or urine color, the customer is indicated as possibly suffering from transient dehydration. The "risks" associated with urine specific gravity and urine color refer to health risks resulting from transient hydration deficits, specifically increased prevalence of obesity, high waist circumference, insulin resistance, diabetes, low HDL, high blood pressure, and metabolic syndrome.

[0091] It is known that serum Na level, urine specific gravity, and urine color change independently. The serum Na level is generally constant for each individual. If a person is not chronically dehydrated, the serum Na level is less than 142, whereas if a person is chronically dehydrated, the serum Na level is 142 or greater. If a person without chronic dehydration experiences transient dehydration, the serum Na level will remain below 142 (low risk), but the urine specific gravity may be 1.013 or greater (high risk), and the urine color may be 4 or greater. Therefore, by combining serum Na level with urine specific gravity and / or urine color, it is possible to assess whether the dehydration is chronic or transient, and to predict health risks according to the assessment results.

[0092] FIG. 10 is a first display example of health information regarding health risks. This health information is presented to customers who have been assessed to belong to the high-risk segmentation based on their serum Na levels. FIG. 11 is a second display example of health information regarding health risks. This health information is presented to customers who have been assessed to belong to the high-risk segmentation based on their urine specific gravity. FIG. 12 is a third display example of health information regarding health risks. This health information is presented to customers who have been assessed to belong to the high-risk segmentation based on their urine color.

[0093] In the above description, the serum Na level alone, the serum Na level and the BUN / creatinine ratio, or the serum Na level and the urine specific gravity and / or urine color are considered to be potential health conditions of a customer, and health information related to these potential health conditions is proposed to the customer. In addition to the serum Na level, for example, the BUN / creatinine ratio may also be considered to be a potential health condition of a customer. That is, in this embodiment, one or more of the following may be used as potential health conditions related to the customer's body water content: a predicted health risk due to chronic / transient deficiency of water, a high serum Na level, and / or a high BUN / creatinine ratio.

[0094] 13 shows an example of a display for providing health information related to nutrition. In this example, multiple unique data 180 related to a customer are displayed, including age, height, weight, and the customer's ideal weight (ideal weight). Of these, age, height, and weight, for example, are symptom data that reflect the customer's health condition.

[0095] The inventors set six segmentations 182 according to combinations of multiple unique data 180. First, the data was divided into an upper segmentation group 182a and a lower segmentation group 182b according to whether the individual's ideal BMI was less than or equal to 19. Then, in the example of FIG. 13 , each of the segmentation groups 182a and 182b was classified into three levels determined based on the current BMI. Specifically, these are "thin," "normal," and "obese." As a result, a total of six segmentations 1 to 6 are set. In the example of FIG. 13 , as in the examples of FIGS. 2 and 4 , each segmentation is associated with pre-prepared health information.

[0096] When a customer inputs multiple pieces of unique data 160 about himself / herself, the CPU 121 determines the segmentation to which the customer belongs from segmentations 1 to 6 based on the multiple pieces of unique data 160, and provides the customer with health information according to the determined segmentation.

[0097] 14 to 19 show examples of health information provided to customers according to segmentations 1 to 6. In each of FIGS. 14 to 19, the left column provides explanations about BMI and health, and the right column provides explanations about BMI and pregnancy / childbirth. The explanations in the right column may be provided only to those who wish to view them. The explanations in each of FIGS. 14 to 19 are self-explanatory, so specific details will not be provided. The explanations in FIGS. 14 to 19 are merely examples, and different explanations may be used.

[0098] FIG. 20 shows segmentations 182 based on salivary IgA values ​​when a customer inputs salivary IgA as one of the multiple unique data 190 related to the customer. In the example of FIG. 20 , the segmentation group 182 is classified into three segmentations based on the salivary IgA value. Specifically, there is a level 1 segmentation where salivary IgA < 40, a level 2 segmentation where 40 ≦ salivary IgA ≦ 180, and a level 3 segmentation where 180 < salivary IgA. In the example of FIG. 20 , each segmentation is also associated with pre-prepared health information. Note that the number of segmentations may be two or four or more. FIGS. 21 to 23 show examples of health information provided to a customer based on level 1 to level 3 segmentation. An explanation for each level 1 to level 3 segmentation is presented in each of FIGS. 21 to 23 . 21 to 23 are also self-explanatory and will not be specifically mentioned. Note that the contents of the descriptions in Figures 21 to 23 are merely examples, and different descriptions may be used.

[0099] In this embodiment, women's health, sleep, exercise, hydration, nutrition, and immunity were described in detail as examples of health conditions. Regarding exercise, the subject's physique can also be taken into consideration. The inventor believes that it is possible to not only explain each health condition but also suggest products to further improve that health condition. FIG. 24 shows details for providing health feedback and suggesting products to improve the health condition for each health condition. As shown in FIG. 24 , solutions can be presented, such as suggesting product E to a customer with a weakened immune system and product F to a customer with inadequate nutrition. For customers with weakened immune systems, in addition to salivary IgA, one or more of the following may be selected as potential health information: health problems due to weakened immune system, susceptibility to catching colds, allergic symptoms, and oral health status. For customers with inadequate nutrition, one or more of the following may be selected as potential health information: nutritional health problems due to nutritional balance or inadequate nutrition, nutritional deficiency, nutritional imbalance, and diet.

[0100] The health conditions mentioned above are merely examples. The inventors have further investigated various health conditions, setting segmentations for each health condition, explaining each segmentation, and extracting products to further improve each health condition. Other health conditions that have been investigated include: dementia, oxygen utilization, vascular health, oral hygiene, skin health, hair health, intestinal environment, mental health, depression, and stress, work engagement, eye health, headaches, lower back pain, stiff shoulders, fatigue, bone health, ear health, joint health, body temperature homeostasis (sensitivity to cold), appetite stimulation, male menopause, high blood pressure (reduced salt intake), weather-related pain, and excessive sitting.

[0101] 25A to 25C show examples of other health conditions and examples of unique data entered by the customer for each health condition. Those skilled in the art can appropriately set one or more thresholds for these unique data to create multiple segmentations. Those skilled in the art can then cause the information processing device 100 to present an explanation of the health condition according to each segmentation and / or associate and present to the customer products to further improve that health condition. While there are countless possible health conditions, by adopting a similar approach for all health conditions, it is possible to provide the customer with an explanation of their own health condition and products to improve that health condition.

[0102] By using personal data to determine which of multiple predetermined segmentations a customer belongs to, it is possible to make suggestions to improve the customer's health symptoms. This makes it possible to make suggestions to prevent the target customer's underlying health condition from worsening or to improve it further than it is now.

[0103] The above describes various aspects of providing information using the information processing device 100. The aspects described so far can be summarized in the process shown in FIG.

[0104] 26 is a flowchart showing the processing steps according to this embodiment. In step S31, the CPU 121 acquires personal data of the target customer. In step S32, the CPU 121 determines, from among multiple segmentations, a segmentation to which the target customer belongs, based on the personal data and classification criteria. In step S33, the CPU 121 provides health information associated with the determined segmentation.

[0105] The flowcharts executed by the CPU 121 of the information processing device 100 described in the above embodiment can be realized as a computer program.

[0106] (Embodiment 2) The information provision system according to this embodiment provides various health-related information to customers or potential customers, or provides opportunities for receiving support from others, thereby enabling the customers to continuously maintain and manage their health in a way that suits them, or enabling the customers to continuously be provided with products and opportunities that are beneficial to their health.

[0107] FIG. 27A shows a schematic diagram of various types of information and opportunities that customers can receive. As an example, customers can have the opportunity to receive information and / or support as shown in items (1) to (6). Specifically, they are as follows. Note that the items in parentheses are examples of approaches from the information provision system to customers. Item (1) Opportunity to receive health information through reading material (direction to health information site) Item (2) Opportunity to receive health information through video (direction to operation site) Item (3) Opportunity to receive information through online seminars (direction to seminar reservation site) Item (4) Opportunity to receive support through consultation via email or chat (launch of email or chat application software) Item (5) Opportunity to receive online support from a pharmacist or nutritionist (direction to reservation site) Item (6) Opportunity to receive face-to-face support from a pharmacist or nutritionist (direction to reservation site)

[0108] The above items (1) and (2) are one-way, providing opportunities for customers to passively receive information. Item (3) is two-way, providing opportunities for customers to actively receive information. Item (4) is an opportunity for customers to have one-on-one consultations. Items (5) and (6) are opportunities for customers to receive one-on-one support from expert pharmacists and nutritionists, both online and in person. Customers can select any one of the above items (1) to (6) via their own information terminal, such as a smartphone or PC, and enjoy the information and opportunities provided by the selected item. Links or URLs (Uniform Resource Locators) may be provided to transition from one item to another.

[0109] FIG. 27B shows examples of services provided to customers, specifically as follows: (7) Staff support, (8) Digital support, (9) Supplements, and (10) Doctor introductions.

[0110] A customer who receives the information and opportunity shown in FIG. 27A via their own information terminal, such as a smartphone or PC, clicks on a link or URL for receiving one of the services (7) to (10) provided by the information system. The display on the information terminal then transitions to a display for receiving one of the services (7) to (10). This allows the customer to receive the service they need.

[0111] There can be a variety of such opportunities. However, it is thought that the appropriate way to receive information and opportunities varies depending on the customer. For a customer, whether the information and opportunities provided suit them will affect whether they will want to continue receiving such information and opportunities. Therefore, it is necessary for the information provision system to provide appropriate information and opportunities for each customer. Below, we will explain an information provision system that can provide appropriate information and opportunities for each customer.

[0112] 28 is a block diagram showing the configuration of an information providing system 10 according to this embodiment. The information providing system 10 includes a server device 200 and an information terminal 300.

[0113] The configuration of the server device 200 is generally the same as that of the information processing device 100 shown in Fig. 1. Specifically, the server device 200 may be, for example, a PC or a server computer. Specifically, the server device 200 includes a CPU 121, an input interface (I / F) 122, a storage device 123, and an output interface (I / F) 124.

[0114] The CPU 121 is an example of an arithmetic circuit of the server device 200 in this embodiment. The CPU 121 realizes the operations described below by executing a control program 226 stored in the storage device 223. In other words, the CPU 221 realizes the functions of the server device 200 in this embodiment by executing the control program 226. The arithmetic circuit configured as the CPU 221 in this embodiment may be realized by various processors such as an MPU or a GPU, or may be configured by one or more processors.

[0115] The input I / F 222 is an input device for accepting input of risk data indicating the degree of health risk of a target customer. In this embodiment, the input I / F 222 is a communication terminal or communication circuit that is connected to the communication network 54 or the like and is capable of performing data communication. The input I / F 222 receives risk data indicating the degree of health risk of a customer that is transmitted by the customer from the information terminal 300. The risk data is typically data representing the segmentation described in embodiment 1.

[0116] The storage device 223 is a storage medium that stores a computer program 226 required to operate the server device 200, as well as multiple pieces of content and provision order information 227. The storage device 223 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD), which is a semiconductor storage device. The storage device 223 may include a temporary storage element configured, for example, by RAM such as DRAM or SRAM, and may function as a work area for the CPU 221. The control program 226 is an example of a computer program in this embodiment. Details of the multiple pieces of content and provision order information 227 will be described later.

[0117] The output I / F 224 is a communication circuit and / or a communication terminal connected to various devices external to the server device 200. For example, the output I / F 224 is a video output terminal connected to the display 52. ​​The CPU 221 of the server device 200 transmits information to be provided to the customer from the output I / F 224 to the information terminal 300 via the communication network 54. As a result, the information is displayed on the display of the information terminal 300 owned by the customer in a content and order appropriate for that customer. The customer can check the content displayed on the display of the information terminal 300 and obtain opportunities to receive health information and support from experts, etc.

[0118] When the input I / F 222 and the output I / F 224 are communication terminals or communication circuits connected to a communication network 54 or the like and capable of performing data communication, the hardware of the output I / F 224 and the input I / F 222 may be the same. In this case, the input I / F 222 and the output I / F 224 may be collectively referred to as a communication interface (I / F). The communication I / F may be, for example, an Ethernet (registered trademark) communication terminal or a USB (registered trademark) terminal. Alternatively, the communication I / F may be a communication circuit that communicates in accordance with standards such as IEEE 802.11, 4G, or 5G. The communication I / F may be connectable to a communication network such as an intranet or the Internet. Furthermore, the server device 200 may communicate directly with other devices via the communication I / F, or may communicate via an access point.

[0119] 29 is a block diagram showing the configuration of the information terminal 300. The information terminal 300 has an arithmetic circuit 302, a display 304, a communication interface (I / F) 306, an input device 308, and a memory 310.

[0120] The arithmetic circuit 302 is a semiconductor integrated circuit known as a CPU. The CPU 302 is a computer implemented in the information terminal 300. Hereinafter, the arithmetic circuit 302 will be referred to as the "CPU 302."

[0121] The communication I / F 306 performs wireless communication conforming to a communication standard such as 4G (Generation) or 5G, etc. The communication I / F 306 can also receive data from the outside via the communication network 1 and a wireless base station.

[0122] The input device 308 is a device for operating the information terminal 300. The input device 308 is, for example, a touch screen panel or hardware buttons superimposed on the display 304. The display 304 is a display device that displays the processing results of the CPU 302. The display device is configured using, for example, a liquid crystal display panel or an organic EL (Electro Luminescence) display panel.

[0123] The memory 310 stores a computer program 310a executed by the CPU 302. In this specification, the memory 310 refers to a storage device that includes a random access memory (RAM) and a read-only memory (ROM). The computer program 310a stored in the ROM is read by the CPU 302 and loaded into the RAM. This enables the CPU 302 to execute the computer program 310a. The operation of the information terminal 300 described below is realized by a computer program (application program) 310a that is installed and executed on the information terminal 300.

[0124] In this embodiment, the CPU 221 of the server device 200 transmits information to the information terminal 300 via the output I / F 224 and the communication network 54. The information terminal 300 displays the received information on the display 304, thereby presenting the received information to the customer.

[0125] Hereinafter, the processing performed mainly in the server device 200 of the information providing system 10 will be described, taking sleep states (rhythm and insomnia level) as "sleep risk" as an example. Note that in the example of FIG. 6, it has been explained that there are eight segmentations for sleep. However, for the sake of convenience, the following description will be given dividing sleep risk into three stages. The three stages are "light" for mild sleep risk, "moderate" for moderate sleep risk, and "heavy" for severe sleep risk. The method described in relation to the first embodiment can be used to determine which of these segmentations a customer belongs to.

[0126] 30 shows an example in which the CPU 221 of the server device 200 changes the information to be transmitted to the information terminal 300 depending on the level of sleep risk. As shown within the dashed line in FIG. 30, it can be seen that the content and order of the information displayed on the display of the information terminal are changed depending on the level of sleep risk.

[0127] The CPU 221 of the server device 200 changes the content and order of items displayed on the display 304 of the information terminal 300 by modifying the information sent to the information terminal 300 according to the customer's sleep risk. For example, for a customer with a "low" sleep risk, the CPU 221 first transmits to the information terminal 300 the URL of a webpage containing sleep-related reading material and displays the reading material on the display 304. Next, the CPU 221 transmits to the information terminal 300 the URL of a webpage containing reading material explaining tips for improving sleep risk. Subsequently, information for receiving various services and products useful to customers with a "low" sleep risk is provided from the server device 200 to the information terminal 300. The reason for selecting such display content is that, since customers have little knowledge about sleep, it is considered appropriate to start with a comprehensive support program while providing basic and specific information. Product information and introductory videos by key opinion leaders (KOLs) or influencers, such as doctors, who have influence over product sales promotion, are presented later.

[0128] On the other hand, for customers with a "serious" sleep risk, the CPU 221 of the server device 200 first transmits the URL of a webpage containing reading material explaining products that are useful for reducing sleep risk. Next, it transmits a URL that leads to a reservation site for a health consultation service. It is thought that such customers have already obtained information and have tried the product themselves. Therefore, the CPU 221 first introduces the existence of products that make it easier to fall asleep, making it easier for them to purchase them, and also introduces the health consultation service so that they can seek advice from others.

[0129] It is likely that customers with a "medium" sleep risk already know the basic information. Therefore, for such customers, we start by providing more detailed information, such as videos by KOLs and information on ingredients that are good for sleep, and provide information that will later lead to a product purchase.

[0130] The order in which information is displayed depending on the level of a customer's sleep risk may be determined in advance, for example, by conducting a questionnaire. FIG. 31 shows a table summarizing the results of the questionnaire. First, the sleep risk (segmentation) of the people taking the questionnaire is determined, and various pieces of information are presented to each of them, and they are asked to describe whether or not the content of the information interested them, and more specifically, to what extent they were interested. People with the same sleep risk can be divided into groups ("light," "medium," and "heavy"), and the information can be presented in order of the degree of interest to each group.

[0131] 32 is a transition diagram of the display content showing that the order of information provided changes depending on whether the sleep risk is "light," "medium," or "severe." The solid line shows an example of "light" segmentation presented to a customer, the dashed line shows an example of "medium" segmentation presented to a customer, and the dashed-dotted line shows an example of "severe" segmentation presented to a customer.

[0132] In the storage device 223 of the server device 200, information on a plurality of contents is stored, as well as provision order information that defines the order in which information is provided according to segmentation, as shown in FIG. 30 or 32.

[0133] The above explanation is an example in which the degree of sleep risk is used as segmentation. The same can be applied to other health risks. The CPU 221 changes the information to be sent to the information terminal 300 according to the degree (segmentation) of the customer's health risk, thereby changing the content and order of items displayed on the display 304 of the information terminal 300. This allows the customer to receive information that is useful to them.

[0134] FIG. 33 is a diagram illustrating the relationship between the reasons why each of users A to E wants to continue purchasing products and the factors (presented information) that influence those reasons.

[0135] For example, user B, who belongs to a certain segment, watches a KOL video and decides to continue purchasing a product because he or she thinks, "What a doctor says is trustworthy." By comprehensively preparing a variety of content, measures can be taken to prevent customers belonging to various segments from deciding to purchase products, i.e., to prevent them from dropping out.

[0136] For each health risk, a plurality of contents and provision order information 227 are stored in advance in the storage device 223 of the server device 200. This allows the CPU 221 of the server device 200 to change the contents and order displayed on the information terminal 300 according to the level (segmentation) of each type of health risk of the customer.

[0137] The operation of the server device 200 according to this embodiment can be summarized in the process shown in Fig. 34. Fig. 34 is a flowchart showing the procedure of the process according to this embodiment.

[0138] In step S41, a plurality of contents and provision order information 227 are prepared and stored in storage device 223. In step S42, CPU 221 acquires risk data indicating the level of health risk of the target customer via input I / F 222. In step S43, CPU 221 provides information on the plurality of contents in an order corresponding to the level of health risk of the target customer based on the risk data and provision order information.

[0139] This makes it possible to present appropriate information to each customer.

[0140] An exemplary embodiment of the present invention can be used to build a predictive model that is used to warn subjects about a specific disease or a disorder that is not a disease but has symptoms that interfere with daily life, and to provide information to warn subjects about this.

[0141] 100 Information processing device 121 CPU (arithmetic circuit) 122 Input I / F 123 Storage device 124 Output I / F

Claims

1. Obtain the personal data of the target customer, Based on the personal data and a pre-prepared classification criterion, determine the segment to which the target customer belongs from among a plurality of segmentations, each of which indicates a classification according to at least the degree of health status, An information providing method for providing the target customer with health information corresponding to the determined segment, The personal data is composed of a plurality of unique data regarding the target customer, and at least one of the plurality of unique data is health data reflecting the health status of the target customer, The plurality of segmentations are classified according to the degree of the health status, and health information prepared in advance according to the health status is associated with each segmentation. An information providing method.

2. The degree of the health status is classified into a plurality of numerical ranges, Each of the plurality of segmentations is pre-associated with a numerical range corresponding to the degree of the health status, Express the degree of the potential health status of the target customer numerically, The information providing method according to claim 1, wherein the degree of the potential health status of the target customer is predicted by determining the segmentation corresponding to the numerical value based on the numerical range to which the numerical value belongs.

3. The information providing method according to claim 2, wherein the plurality of segmentations are three or more.

4. A table associating each of the plurality of segmentations with a service and / or product suitable for the health status of each segmentation is prepared in advance, The information providing method according to claim 2, further providing a proposal regarding a service and / or product suitable for the health status of the determined segment together with the health information.

5. The information providing method according to claim 4, wherein the health information and / or the proposal are provided via the text of an email, the display on a display device, the printing on a receipt, or the message presented on an app executed on an electronic device.

6. The information providing method according to any one of claims 1 to 5, wherein the potential health status of the target customer is one or more selected from health problems of elderly women, menopausal symptoms, and premenstrual syndrome (PMS, PMDD) symptoms.

7. The information providing method according to any one of claims 1 to 5, wherein the potential health state of the target customer is one or more selected from health problems due to a predetermined unfavorable sleep state, deterioration of the sleep state, disruption of the sleep rhythm, and inappropriate sleep time.

8. The information providing method according to any one of claims 1 to 5, wherein the potential health state of the target customer is one or more selected from changes in physical condition associated with lifestyle diseases and / or obesity, lack of exercise, obesity, and changes in body weight over a certain period.

9. The information providing method according to any one of claims 1 to 5, wherein the potential health information of the target customer is one or more selected from a health state due to water deficiency, a high value of serum Na, and a high value of the BUN / creatinine ratio.

10. The information providing method according to any one of claims 1 to 5, wherein the potential health information of the target customer is one or more selected from health problems due to reduced immunity, susceptibility to colds, SIgA concentration, allergic symptoms, and the state of oral health.

11. The information providing method according to any one of claims 1 to 5, wherein the potential health information of the target customer is one or more selected from health problems related to nutrition, nutritional deficiency, nutritional imbalance, and diet.

12. The target customer is female, The plurality of unique data regarding the target customer are the age of the target customer, and a value indicating the equol production ability of the target customer as the health data The information providing method according to claim 1, comprising.

13. The plurality of segmentations are classified according to a combination of a plurality of life stages determined according to age and the presence or absence of equol production ability, Pre-prepared health information is associated with each segmentation The information providing method according to claim 12.

14. The presence or absence of equol production ability is determined by the relationship between the value indicating equol production ability and a threshold value, The information providing method according to claim 13.

15. The plurality of unique data regarding the target customer are the age, gender, height, weight, change in body weight over a certain period of the target customer, and behavior data regarding the presence or absence of the target customer's smoking habit and the number of steps as the health data The information providing method according to claim 1, comprising.

16. The plurality of segmentations are classified according to a combination of age, gender, body mass index, change in body weight over a certain period, behavior data regarding the number of steps, and the presence or absence of a smoking habit. Each of the segmentations is associated with pre-prepared health information The information providing method according to claim 15

17. The plurality of unique data regarding the target customer The insomnia score of the target customer, a time value indicating the sleep time, and A time value indicating the social jet lag of the target customer as the health data The information providing method according to claim 1, comprising

18. The plurality of segmentations are classified according to a combination of the degree of insomnia and the presence or absence of social jet lag, and Each of the segmentations is associated with pre-prepared health information according to the degree of problems related to sleep as the health state The information providing method according to claim 17

19. The plurality of unique data regarding the target customer The age of the target customer, and As the health data The serum Na value of the target customer, and the BUN / creatinine ratio, or The serum Na value of the target customer, and the urine specific gravity and / or urine color The information providing method according to claim 1, comprising

20. The plurality of segmentations are classified according to a combination of the serum Na value and whether it is above or below a predetermined age, and Each of the segmentations is associated with pre-prepared health information according to the degree of problems related to insufficient water content as the health state The information providing method according to claim 19

21. The plurality of unique data regarding the target customer The ideal weight that the target customer considers to be ideal, and As the health data The age, height and weight of the target customer The information providing method according to claim 1, comprising

22. The plurality of segmentations are classified according to a combination of the age group determined according to age, the current body mass index, and the ideal body mass index calculated from the height and the ideal weight, and Each of the segmentations is associated with pre-prepared health information according to the degree of problems related to nutrition as the health state The information providing method according to claim 21

23. The plurality of unique data regarding the target customer includes salivary IgA, the information providing method according to claim 1

24. The plurality of segmentations are classified according to the value of the salivary IgA, and Each of the segmentations is associated with pre-prepared health information according to the degree of problems related to immunity as the health state The information providing method according to claim 23.

25. The plurality of segmentations further include classifications according to daily activities, The plurality of unique data constituting the personal data further includes health data reflecting the health status of the target customer, and the information providing method according to any one of claims 1 to 5, 12-24.

26. Prepare in advance a plurality of contents for presenting to the customer and order information for providing the plurality of contents according to the degree of the customer's health risk, Obtain risk data indicating the degree of the health risk of the target customer, An information providing method for providing the plurality of contents in an order according to the degree of the health risk of the target customer based on the risk data and the order information.

27. The risk data is Based on the personal data of the target customer and a pre-prepared classification criterion, each is data indicating a segmentation selected from among a plurality of segmentations each indicating at least a classification according to the degree of health status, The personal data is composed of a plurality of unique data related to the target customer, and at least one of the plurality of unique data is health data reflecting the health status of the target customer, The plurality of segmentations are classified according to the degree of the health status, and each segmentation is associated with health information prepared in advance according to the health status. The information providing method according to claim 26.