JP2024082279A - Health food or drug recommendation device, health food or drug recommendation program, health food recommendation method, drug recommendation device operation method, and health food or drug recommendation system - Google Patents

Abstract

[Problem] To provide a device that suggests health foods that are recommended for intake to a user. [Solution] A health food or drug suggestion device used in a health food or drug suggestion system, in which a customer device and the health food or drug suggestion device are connected via a network, calculates the average blood turbidity and standard deviation or confidence interval from blood turbidity at a specific time previously received from the customer device, and calculates a personal blood turbidity level based on the calculated average blood turbidity and standard deviation or confidence interval from blood turbidity at a specific time received from the customer device. [Selected Figure] Figure 1

Description

The present invention relates to a health food or drug suggestion device, a health food or drug suggestion program, a health food suggestion method, an operation method of a drug suggestion device, and a health food or drug suggestion system.

There is a technique for calculating lipid concentration from changes in scattering intensity of blood (see, for example, Patent Document 1). This technique makes it possible to measure changes in blood components over time after a meal.

Patent No. 6241853 Patent Publication No. 6991634

However, it is difficult for users to interpret the measurement results. Furthermore, the time course of changes in blood components after a meal varies depending on the food eaten. This makes it even more difficult for users to select the best supplements and diet for themselves based on the measurement results.

The present invention has been made to solve the above-mentioned problems in the conventional art, and aims to provide a health food or drug suggestion device, health food or drug suggestion program, health food suggestion method, operating method of the drug suggestion device, and health food or drug suggestion system which suggest health foods and drugs that are recommended for intake to a user by analyzing the lipid metabolism status during a measurement period from different measurement results of blood turbidity, etc., taken on two or more measurement dates.

The present invention is a health food or drug proposal device used in a health food or drug proposal system in which a customer device and the health food or drug proposal device are connected via a network, the health food or drug proposal device calculates an average blood turbidity and standard deviation or confidence interval from blood turbidity at a specific time received in advance from the customer device, and calculates a personal blood turbidity level based on the calculated average blood turbidity and standard deviation or confidence interval from the blood turbidity at a specified time received from the customer device.

The present invention is a health food or drug proposal program that causes a computer of a health food or drug proposal device used in a health food or drug proposal system in which a customer device and the health food or drug proposal device are connected via a network to execute a process of calculating an average blood turbidity and a standard deviation or a confidence interval from blood turbidity at a specific time received in advance from the customer device, and a process of calculating a personal blood turbidity level based on the calculated average blood turbidity and a standard deviation or a confidence interval from the blood turbidity at a specific time received from the customer device.

The present invention is a health food proposal method in a health food proposal system in which a customer device and a health food proposal device are connected via a network, the health food proposal method calculating an average blood turbidity and a standard deviation or a confidence interval from blood turbidity at a specific time previously received from the customer device, and calculating a personal blood turbidity level based on the calculated average blood turbidity and a standard deviation or a confidence interval from the blood turbidity at a specified time received from the customer device.

The present invention is a method for operating a drug proposal device in a drug proposal system in which a customer device and a drug proposal device are connected via a network, the method calculating an average blood turbidity and a standard deviation or a confidence interval from blood turbidity at a specific time previously received from the customer device, and calculating a personal blood turbidity level based on the calculated average blood turbidity and a standard deviation or a confidence interval from the blood turbidity at a specified time received from the customer device.

The present invention is a health food or drug proposal system in which a customer device and a health food or drug proposal device are connected via a network, and the health food or drug proposal system calculates an average blood turbidity and standard deviation or confidence interval from blood turbidity at a specific time received in advance from the customer device, and calculates a personal blood turbidity level based on the calculated average blood turbidity and standard deviation or confidence interval from the blood turbidity at a specified time received from the customer device.

The health food or drug proposal device, health food or drug proposal program, health food proposal method, operating method of the drug proposal device, and health food or drug proposal system of the present invention make it possible to suggest to the user health foods or drugs that are recommended for intake.

Example of a health food or medicine proposal system Block diagram of customer terminal Block diagram of blood turbidity measuring device Block diagram of the health food or medicine recommendation device Data table of health food or medicine database Data table of health food or medicine database Data table of health food or medicine database Data table of health food or medicine database Flowchart of moving average processing Example of calculating standard deviation Example of calculating standard deviation Example of calculating standard deviation A graph showing the moving average of the mean and standard deviation of blood turbidity Flowchart of personal blood turbidity level classification process Diagram showing personal blood turbidity level classification process Conversion table of recommended levels from personal blood turbidity levels Flowchart of health food or medicine proposal processing Recommendation level and correction coefficient table Examples of recommended intakes for each health food Flowchart of EC site transfer process

Hereinafter, an embodiment will be described with reference to the drawings.

1 shows an example of the configuration of a health food or medicine suggestion system including a health food or medicine suggestion device according to an embodiment. The health food or medicine suggestion system 1 includes a health food or medicine suggestion device 100, a customer terminal 200 (customer use device), a blood turbidity measurement device 300 (customer use device), and an EC site server 400, all of which are connected via a network 500.

The term "health foods" used here does not only refer to those that meet the standards of the health functional food system established by the government, but also includes foods and beverages (including those that can be used as ingredients for beverages regardless of their form, such as powder or solid) that are consumed to improve dietary habits.

In Fig. 1, the health food or medicine suggestion device 100 and the EC site server 400 are separate devices, but they may be the same device. In Fig. 1, the customer terminal 200 and the blood turbidity measurement device 300 are separate devices (customer-used devices) used by the customer, but they may be the same device.

In Figure 1, one or two health food or drug proposal devices 100, customer terminals 200, blood turbidity measuring devices 300, and EC site servers 400 are shown, but the health food or drug proposal system 1 may include a large number of health food or drug proposal devices 100, customer terminals 200, blood turbidity measuring devices 300, and EC site servers 400.

The network connection may be made using an API (Application Programming Interface).

The customer terminal 200 (customer-used device) has a network interface that enables wired or wireless communication, and enables each function by loading a program stored on a storage medium into memory and running the program on a CPU (Central Processing Unit).

The hardware configuration of the customer terminal 200 may be that of a known smartphone, tablet PC, PC, or the like.

The configuration of the control system of the customer terminal 200 will be described. Fig. 2 is a block diagram of the customer terminal 200. A CPU 202, a ROM (Read Only Memory) 203, a RAM (Random Access Memory) 204, an external I/F (Interface) 205, an operation panel 206, and a camera 207 are connected via a system bus 208. The CPU 202, the ROM 203, and the RAM 204 constitute a control unit 201.

The ROM 203 stores in advance programs executed by the CPU 202 and threshold values.

In the RAM 204, various memory areas are dynamically formed, such as an area in which the programs executed by the CPU 202 are loaded and a work area which serves as a working area for data processing by the programs.

The external I/F 205 is an interface for communicating with external devices such as the health food or drug proposal device 100, the blood turbidity measurement device 300, and the EC site server 400.

The external I/F 205 may be any interface that performs data communication with an external device. For example, the external I/F 205 may be a network interface for communication via a network. Furthermore, the data communication method may be Wi-Fi (registered trademark) communication or USB communication. The external I/F 205 may include, for example, a wireless communication mechanism conforming to the IEEE802.11 standard or a short-range wireless communication mechanism conforming to the IEEE802.15.1 standard. It may also include a unit conforming to other wireless communication standards.

The operation panel 206 is a user interface in which a display unit and a touch panel are integrated. The operation panel 206 is disposed on the front surface of the main body of the customer terminal 200, and has a display unit equipped with a touch panel. The control unit 201 controls the contents to be displayed on the display unit of the operation panel 206.

The operation panel 206 outputs information inputted via the touch panel to the control unit 201. Information relating to each process inputted from the operation panel 206 is stored as process information in a predetermined area of the RAM 204. In the customer terminal 200 having the above-mentioned configuration, the customer terminal 200 executes screen display based on a preset program.

The camera 207 is an imaging element such as a CCD, a CMOS, etc. The camera 207 outputs captured food image information, etc. to the control unit 201. The food image information input from the camera 207 is stored in a predetermined area of the RAM 204.

The hardware and software configurations of the blood turbidity measurement device 300 are similar to the known configurations described in Patent Document 1, and therefore will be briefly described here.

The blood turbidity measuring device 300 (customer-use device) has a network interface that enables wired or wireless communication, and enables each function by loading a program stored in a storage medium into memory and running the program on a CPU (Central Processing Unit).

The configuration of the control system of blood turbidity measuring device 300 will be described. Fig. 3 is a block diagram of blood turbidity measuring device 300. A CPU 302, a ROM (Read Only Memory) 303, a RAM (Random Access Memory) 304, an external I/F (Interface) 305, an irradiation unit 306, and light receiving units 307 and 308 are connected via a system bus 309. CPU 302, ROM 303, and RAM 304 constitute a control unit 301.

The ROM 303 stores in advance programs and thresholds to be executed by the CPU 302. The ROM 303 stores statistical data indicating the correlation between the scattering coefficient and the difference or ratio of light intensity measured in advance.

In the RAM 304, various memory areas are dynamically formed, such as an area for loading the programs executed by the CPU 302 and a work area which serves as a working area for data processing by the programs.

The external I/F 305 is an interface for communicating with external devices such as the health food or drug proposal device 100, the customer terminal 200, and the EC site server 400.

The external I/F 305 may be any interface that performs data communication with an external device. For example, the external I/F 305 may be a network interface for communication via a network. Furthermore, the data communication method may be Wi-Fi (registered trademark) communication or USB communication. The external I/F 305 may include, for example, a wireless communication mechanism conforming to the IEEE802.11 standard or a short-range wireless communication mechanism conforming to the IEEE802.15.1 standard. It may also include a unit conforming to other wireless communication standards.

The irradiation unit 306 irradiates light from outside the living body (not shown) toward the living body at a predetermined irradiation position. The irradiation unit 206 can freely adjust the wavelength of the irradiated light. The irradiation unit 306 is, for example, a fluorescent lamp, an LED, a laser, an incandescent lamp, an HID, a halogen lamp, or the like. The illuminance of the irradiation unit 306 may be controlled by the control unit 301. Natural light such as sunlight or moonlight may also be used.

The light receiving units 307 and 308 receive light emitted from the living body to outside the living body and detect the light intensity. The light receiving units 307 and 308 are installed at different distances from the irradiation position of the irradiation unit 206 (not shown). The light receiving units 307 and 308 may be photodiodes, CCDs, or CMOSs. The light receiving units 307 and 308 output the detected light intensity to the control unit 304. The arrangement of the irradiation unit and the light receiving unit is not limited to this embodiment, and may be changed as appropriate as long as blood turbidity measurement is possible.

The control unit 301 calculates the light scattering coefficient (blood turbidity) within the living body based on statistical data (or a regression equation or arithmetic equation derived from the statistical data) showing the relationship between the light intensity difference or ratio, blood flow information, and the scattering coefficient, which has been measured in advance and stored in ROM 303, based on the light intensity difference or ratio, blood flow information, etc. detected by the light receiving units 307 and 308.

The calculation of the light scattering coefficient (blood turbidity) may be performed by the health food or drug suggestion device 100. In this case, the control unit 301 transmits the light intensity detected by the light receiving units 307 and 308 to the health food or drug suggestion device 100, and the health food or drug suggestion device 100 calculates the light scattering coefficient (blood turbidity) in the user's living body based on the difference or ratio of the light intensity received from the blood turbidity measurement device 300 and statistical data (or a regression equation or arithmetic equation derived from the statistical data) indicating the correlation between the difference or ratio of the light intensity and the scattering coefficient, which is measured in advance and stored in the memory unit (ROM 103, etc.) of the health food or drug suggestion device 100.

The EC site server 400 has a network interface that enables wired or wireless communication, and enables each function by loading a program stored in a storage medium into memory and running the program on a CPU (Central Processing Unit). The hardware configuration of the EC site server 400 may be that of a known server or the like, so a description of the internal configuration using diagrams will be omitted.

The health food or medicine suggestion device 100 and the customer terminal 200 can access the EC site server 400 by inputting an ID or the like from an operation panel 206 or the like.

The EC site server 400 stores data of customers. The health food or drug proposal device 100 and the customer terminal 200 access the EC site server 400, whereby the customer can purchase health foods such as supplements from the EC site.

FIG. 4 is a block diagram of the health food or medicine suggestion device 100 according to the embodiment.

A CPU (Central Processing Unit) 102, a ROM (Read Only Memory) 103, a RAM (Random Access Memory) 104, a HDD (Hard Disk Drive) 105, an external I/F (Interface) 106, and an input unit 107 are connected via a system bus 108. The CPU 102, the ROM 103, and the RAM 104 constitute a control unit 101.

The ROM 103 stores in advance programs executed by the CPU 102 and threshold values.

The RAM 104 has various memory areas, such as an area for loading the programs executed by the CPU 102 and a work area that serves as a work area for data processing by the programs.

The HDD 105 stores data input from the input unit 107. The HDD 105 stores a health food database 1051. In the embodiment, the health food database 1051 is built into the health food or drug proposal device 100, but it may be stored in an external device (the EC site server 400, another server, etc.).

5 shows a data table of the health food or medicine database 1051. The health food or medicine database 1051 stores information such as the use and quality of health foods or medicines.

The type area of the health food or medicine database 1051 is an area that stores the mechanism of action and target symptoms of health foods such as supplements or medicines.

The active ingredient area of the health food or medicine database 1051 is an area for storing active ingredients of health foods or medicines. The amount of active ingredient is a value disclosed by public organizations such as the Ministry of Health, Labor and Welfare and health food manufacturers.

The product name area of the health food or medicine database 1051 is an area for storing the product names of health foods or medicines.

The recommended intake area of the health food or medicine database 1051 is an area for storing the recommended intake amount of health food or medicine. It is preferable to refer to the recommended amount disclosed by a public institution such as the Ministry of Health, Labor and Welfare as the recommended amount.

The area of the amount of active ingredients in the health food or medicine database 1051 is an area for storing the product name of the health food or medicine and the amount of active ingredients.

The RM (Recommendation) area of the health food or medicine database 1051 is an area that stores one-third of the recommended daily amount of the ingredient itself.

The price area of the health food or medicine database 1051 is an area that stores the prices of health foods or medicines.

The identification code area of the health food or medicine database 1051 is an area for storing identification codes of health foods or medicines that are sequentially set when the health food or medicine database is created. The identification codes also serve as information for checking against health foods or medicines handled on the EC site.

The health foods in the health food or medicine database 1051 may include supplements, nutritional supplements, and other foods with health benefits, such as foods for specified health uses, lactic acid bacteria drinks, fermented foods, etc. The medicines in the health food or medicine database 1051 may include drugs such as drugs for treating hyperlipidemia, drugs for treating diabetes, and drugs for treating hypertension.

The health foods in the health food or medicine database 1051 may include foods such as bento boxes and bread for customers with the aim of preventing obesity and improving eating habits.

The health food or medicine database 1051 of the embodiment is grouped into recommendation levels (R1 to R5) determined in STEP 205 described later.

5A shows a portion of the health food database 1051 with a recommendation level of R. The health food or medicine database 1051 also contains portions with recommendation levels of G, P, and F (G, P, and F correspond to FIGS. 5B, 5C, and 5D, respectively).

The health foods or medicines listed in G, P, and F should be used in addition to the "turbidity only" classification in Figure 21, and if the ingredients are the same, select different ingredients. Also, adjustments may be made appropriately depending on financial circumstances, the presence or absence of underlying diseases, etc.

The external I/F 106 is an interface for communicating with an external device such as a client terminal (PC), etc. The external I/F 106 is connected to the customer terminal 200, the blood turbidity measurement device 300, and the EC site server 400.

The external I/F 106 may be any interface that performs data communication with an external device, and may be, for example, a device (such as a USB memory) that is locally connected to the external device, or a network interface for communication via a network.

The external I/F 106 is connected to a printing device (not shown) such as a printer, etc. The external I/F 106 is connected to a display device (not shown) such as a liquid crystal display, etc.

The input unit 107 is an input device for inputting a keyboard, a mouse, a scanner (reading device), a voice input, or the like.

In the health food or drug suggestion device 100 having the above-mentioned configuration, the health food or drug suggestion device 100 executes a health food or drug suggestion job based on a preset program.

6 is a flow chart of the moving average process for classifying blood turbidity levels. STEP 101 to STEP 107 are steps for performing pre-processing and storage of moving average data required for classifying blood turbidity into personal blood turbidity levels in STEP 201 and thereafter.

In STEP 101, blood turbidity measurement device 300 owned by a user measures the turbidity of the user's blood and the time when the blood turbidity was obtained.

In STEP 101, the blood turbidity measurement device 300 may measure blood turbidity at any timing according to the convenience of the user.

The blood turbidity measurement device 300 may calculate the blood turbidity by appropriately using a known method (for example, see Patent Document 1) or a known device.

Blood turbidity refers to the degree to which blood plasma becomes cloudy due to particles called lipoproteins, such as chylomicrons and VLDL, in the blood. The unit of blood turbidity is a.u. Any method for measuring blood turbidity may be used, such as using data from fingertip blood, as long as it can measure the change in turbidity due to changes in chylomicrons and VLDL in the blood.

It is a well-known phenomenon that chylomicrons and VLDL remain as turbidity even the next morning due to metabolic delay caused by lipid metabolism disorders, etc. Therefore, analysis of daily variation using data obtained by fingertip blood sampling in a fasting state can also be used as blood turbidity in the present invention.

In STEP 102, the blood turbidity measuring device 300 transmits a plurality of measured blood turbidities and the time when the blood turbidities were obtained to the health food or drug proposing device 100. For example, if blood turbidity is measured 10 times per day at a fixed time between 7:00 and 22:00 for 10 days, 10 blood turbidities per day for the 10 days and the time when the blood turbidity was obtained are transmitted.

In the embodiment, the blood turbidity is transmitted directly from the blood turbidity measuring device 300 to the health food or drug proposal device 100, but it is also possible to manually input or transmit the blood turbidity measurement value from the blood turbidity measuring device 300 to the user's customer terminal 200 (smartphone, etc.), and then transmit the blood turbidity and the time the blood turbidity was obtained from the user's customer terminal 200 to the health food or drug proposal device 100.

In STEP 103 , the health food or drug proposing device 100 receives blood turbidity for multiple days and the time when the blood turbidity was obtained from the blood turbidity measuring device 300 .

In STEP 104, the health food or drug proposing device 100 stores the received blood turbidity at specific measurement times (eg, 7:00 to 22:00) for multiple days (eg, 10 days) in a storage unit such as the HDD 105.

In STEP 105, the health food or drug proposing device 100 calculates the average value and standard deviation of blood turbidity for each predetermined measurement time (e.g., 10:00) from the stored blood turbidity for multiple days. The standard deviation of blood turbidity is calculated using the following formula 1.

ｓ：標準偏差
ｎ：測定した日数
ｘ_ｉ：特定時刻範囲における個々の血液濁度

s: standard deviation n: number of days measured x _i : individual blood turbidity in a specific time range

Analysis using a confidence interval using Equation 2 is also possible. Although the confidence interval can better reflect the probability of the existence of a true value, in the present invention, the variability itself in complex biomeasurements is important rather than the true value, so in the examples, the standard deviation was used.

ＣＩ：信頼区間

ｔ：ｔ分布のｔ値（自由度ｎ－１）
ｎ：データ数

CI: confidence interval

t: t-value of the t-distribution (n-1 degrees of freedom)
n: number of data

Fig. 7 shows an example of calculating the standard deviation S using Equation 1. Fig. 7 shows an example of analyzing the measurement results of individual blood turbidity measurements taken at a specific time range of 10:00 ± 1:00 (9:00 to 11:00) on any 10-day period.

As shown in FIG. 7, the average value of blood turbidity was 64.61, and the standard deviation S of blood turbidity was 12.48.

Fig. 8 shows another example of calculating the standard deviation S using Equation 1. Fig. 8 shows an example of measurements taken at 10:00 ± 1:00 (9:00 to 11:00) when the number of measurement days is less than 10. Fig. 8 shows an example of analysis of individual blood turbidity measurement results taken at a specific time range of 10:00 ± 1:00 (9:00 to 11:00) on measurement days over an arbitrary number of eight measurement days.

As shown in FIG. 8, the average value of blood turbidity was 63.33, and the standard deviation S of blood turbidity was 9.58.

Fig. 9 shows another example of calculating the standard deviation S using Equation 1. Fig. 9 shows an example of measurements taken multiple times on the same arbitrary day. Fig. 9 shows an example of analysis of individual blood turbidity measurement results taken at a specific time range of 10:00 ± 1:00 (9:00 to 11:00) on arbitrary measurement days over 8 days.

As shown in FIG. 9, the average blood turbidity was 60.60, and the standard deviation S of the blood turbidity was 10.26.

In STEP 106 , the health food or drug proposing device 100 performs moving average processing on the average value and standard deviation of the blood turbidity corresponding to each of the predetermined measurement times obtained in STEP 105 .

Fig. 10 shows the moving average of the average value and standard deviation of blood turbidity for each predetermined measurement time (corresponding to time change) calculated by Equation 1. In Fig. 10, moving average processing is performed to generate moving average lines for the average value and standard deviation of blood turbidity.

X1 in Fig. 10 is the average blood turbidity value + 2SD (standard deviation) calculated from blood turbidity values measured multiple times between 9:00 and 11:00. The +2SD moving average line in the figure is obtained by taking the moving average of the average blood turbidity value + 2SD of the calculation results for each hour. For example, the plot at 9:00 is calculated from the average blood turbidity value + 2SD from 8:00 to 10:00, and the result at 20:00 is the moving average calculated from the average blood turbidity value + 2SD from 19:00 to 21:00.

X2 in Fig. 10 is the average blood turbidity value + 1 SD (standard deviation) calculated from blood turbidity values measured multiple times between 14:00 and 16:00. The +1 SD moving average line in the figure is determined by the moving average of the average blood turbidity value + 1 SD for each hour.

X3 in Fig. 10 is the average value of blood turbidity calculated from the blood turbidity values measured multiple times between 14:00 and 16:00. The moving average line of the average value in the figure is obtained by taking the moving average of the average value of blood turbidity for each hour.

X4 in Fig. 10 is the average blood turbidity minus 1 SD (standard deviation) calculated from blood turbidity values measured multiple times between 11:00 and 13:00. The -1 SD moving average line in the figure is determined by the moving average of the average blood turbidity - 1 SD for each hour.

X5 in Fig. 10 is the average blood turbidity minus 2SD (standard deviation) calculated from blood turbidity values measured multiple times between 11:00 and 13:00. The -2SD moving average line in the figure is determined by the moving average of the average blood turbidity - 2SD for each hour.

In STEP 107, blood turbidity measuring device 300 stores the calculated average blood turbidity for each predetermined measurement time (according to time changes) and the moving average of the standard deviation (+2 SD to -2 SD) in a storage unit such as HDD 105.

FIG. 11 is a flowchart of the personal blood turbidity level classification process.

In STEP 201, blood turbidity measurement device 300 owned by a user measures the turbidity of the user's blood and the time when the blood turbidity was obtained.

The blood turbidity measurement device 300 may calculate the blood turbidity by appropriately using a known method (for example, see Patent Document 1).

In STEP 202 , the blood turbidity measurement device 300 transmits the measured blood turbidity and the time when the blood turbidity was obtained to the health food or drug proposal device 100 .

In STEP 203 , the health food or drug proposing device 100 receives the blood turbidity and the acquisition time of the blood turbidity from the blood turbidity measuring device 300 .

In STEP 204, the health food or medicine proposal device 100 classifies the blood turbidity at the specified time received in STEP 203 into one of six personal blood turbidity levels based on the moving average of the average blood turbidity and standard deviation (+2 SD to -2 SD) at the specified measurement time stored in STEP 107.

For example, if the blood turbidity received at 13:00 is less than or equal to the moving average of -2SD of the average blood turbidity, the health food or medicine proposal device 100 classifies the received blood turbidity as personal blood turbidity level A (A in Figure 12).

For example, if the blood turbidity received at 12:00 is greater than or equal to the moving average of -2 SD of the average blood turbidity and less than the moving average of -1 SD of the average blood turbidity, the health food or medicine proposal device 100 classifies the received blood turbidity as personal blood turbidity level B (B in Figure 12).

For example, if the blood turbidity received at 12:00 is greater than or equal to the moving average of -1 SD of the average blood turbidity and less than or equal to the moving average of the average, the health food or medicine proposal device 100 classifies the received blood turbidity as personal blood turbidity level C (C in Figure 12).

For example, if the blood turbidity received at 15:00 is greater than or equal to the moving average of the average blood turbidity and less than the moving average of +1 SD of the average blood turbidity, the health food or medicine proposal device 100 classifies the received blood turbidity as personal blood turbidity level D (D in Figure 12).

For example, if the blood turbidity received at 15:00 is greater than or equal to the moving average of +1 SD of the average blood turbidity and less than the moving average of +2 SD of the average blood turbidity, the health food or medicine proposal device 100 classifies the received blood turbidity as personal blood turbidity level E (E in Figure 12).

For example, if the blood turbidity received at 13:00 is equal to or greater than the moving average of +2 SD of the average blood turbidity, the health food or medicine proposal device 100 classifies the received blood turbidity as personal blood turbidity level F (F in Figure 12).

The health food or medicine suggestion device 100 of the embodiment classifies personal blood turbidity levels into six levels, but it may also be possible to classify into more levels, such as ten or more levels, by making the standard deviation levels finer.

In STEP 205 , the health food or medicine suggestion device 100 calculates a recommendation level from the personal blood turbidity level determined in STEP 204 .

FIG. 13 is a conversion table of recommended levels from personal blood turbidity levels.

If the personal blood turbidity level is A in STEP 204, the health food or medicine suggestion device 100 calculates the recommendation level to be R1 in STEP 205.

If the personal blood turbidity level is B in STEP 204, the health food or medicine suggestion device 100 calculates the recommendation level to be R1 in STEP 205.

If the personal blood turbidity level is C in STEP 204, the health food or medicine proposal device 100 calculates the recommendation level to be R2 in STEP 205.

If the personal blood turbidity level is D in STEP 204, the health food or medicine proposal device 100 calculates the recommendation level to be R3 in STEP 205.

If the personal blood turbidity level is E in STEP 204, the health food or medicine proposal device 100 calculates the recommendation level to be R4 in STEP 205.

If the personal blood turbidity level is F in STEP 204, the health food or medicine suggestion device 100 calculates the recommendation level as R5 in STEP 205. The recommendation level can also be replaced with a reference value, a reference standard range, a reference value, a normal value, a high normal value, a border zone, an abnormal value, etc. In other words, it is sufficient if the measurement results can be classified into two or more states. In addition, the personal blood turbidity level may be further subdivided as long as it can be compared with a health food or medicine database.

FIG. 14 is a flowchart of the health food or medicine suggestion process.

In STEP 301 , the health food or medicine suggestion device 100 extracts one or more relevant health foods or medicines from the health food or medicine database 1051 based on the recommendation level (R1 to R5) determined in STEP 205 .

In STEP 302, the health food or drug proposal device 100 divides the RM of one or more health foods extracted in STEP 301 by the amount of active ingredient, and multiplies it by a correction coefficient corresponding to the recommendation level to calculate a recommended intake amount of each of the one or more health foods.

FIG. 15 is a table showing the correspondence between recommendation levels and correction coefficients.

The correction coefficient was set by conducting questionnaires and interviews with a total of 80 people, including diabetes specialists and medical professionals qualified as Japanese Diabetes Care Educators, about how they would calculate the appropriate amount of health food when providing nutritional guidance as part of their job.

In addition, in cases where it is difficult to conduct interviews of all kinds, the intake amount of commercially available health foods may be set based on general average data and a correction coefficient may be set.

The correction coefficient is an index for converting the generally recommended intake amount of health foods to an individual level from the blood turbidity obtained by the blood turbidity measuring device 300.

FIG. 16 shows an example of the recommended intake amount for each health food or medicine calculated by the health food or medicine suggestion device 100 in STEP 302.

In STEP 303, the health food or medicine suggestion device 100 extracts (the identification code of) the health food or medicine whose recommended intake amount calculated in STEP 302 is closest to 1.0. In the example of FIG. 16, S003 is extracted.

In STEP 303, if there are multiple extracted health foods or medicines, the health food or medicine proposal device 100 recommends them in order of lowest price, and if the prices are the same, extracts the health food (or its identification code) that is more popular (with larger shipping volume).

In STEP 304, the health food or medicine suggestion device 100 excludes health foods or medicines whose calculated recommended intake amount exceeds twice the intake amount recommended by the product or falls below 0.5.

In STEP 305, the health food or drug suggestion device 100 selects foods that will absorb calories equivalent to the calorie intake amount disclosed by the Ministry of Health, Labor and Welfare. For example, it is desirable to eat three meals evenly, with reference to the 2000-2400 kcal/day announced by the Ministry of Health, Labor and Welfare. Furthermore, it is desirable for blood turbidity between meals to be B, and foods with a similar nutritional distribution are suggested with reference to the nutritional distribution when blood turbidity is B.

This function assumes that the calorie content of the meal is known in advance, and provides food with an appropriate amount of absorbed calories based on the calories contained in the meal and the amount of calories absorbed from the fat. The absorbed calories used here are calculated from turbidity measurements.

In the initial phase, suggestions will be made based on the desired price range, and as the system operates, data will be collected on the calories, protein, fat, carbohydrates, dietary fiber, sugar, and salt content of the lunch boxes, as well as the amount of calories absorbed. This will then be used as a recommendation function to find lunch boxes (combinations of nutrients) with an absorption rate of 40-70%.

In STEP 306 , the health food or medicine proposal device 100 transmits the extracted information on the health food or medicine (such as an identification code) to the customer terminal 200 .

In addition, transmission does not mean that the information needs to be downloaded to the customer terminal 200 itself, but rather that the information can be viewed via an application.

In STEP 307 , the customer terminal 200 displays on the operation panel 206 the information on the health food having the identification code transmitted from the health food or drug proposal device 100 .

FIG. 17 is a flowchart of the EC site transfer process.

The user checks the health foods or medicines displayed on the operation panel 206 of the customer terminal 200 and selects the health foods or medicines (that he/she wishes to purchase) on the operation panel 206.

In STEP 401 , the customer terminal 200 transmits (the identification code) of the health food or medicine selected by the user to the health food recommendation device 100 .

In STEP 402, the health food or medicine suggestion device 100 transmits the received identification code of the health food or medicine and the user's identification information (email address, etc.) to the server 400 of the EC site.

In STEP 403 , the EC site server 400 receives the identification code of the health food or medicine and the user's identification information from the health food or medicine proposal device 100 .

The EC site server 400 stores the identification information of users who have expressed a desire to use the automatic recommendation service and registered their personal information in advance.

The EC site server 400 stores the user's identification information, as well as the name, address, and shipping and payment method such as credit card information, in the registered user information.

In STEP 404, the EC site server 400 adds the health food or medicine with the identification code to the cart with the user's identification information.

The health food or drug information transmitted in STEP 307 is displayed by the cart function of the EC site of the customer terminal 200. The display period can be selected to be one month, one week, or updated at any time from the start of the initial recommendation display.

Any products that are not purchased within the displayed period will be removed from the cart.

After the data is deleted, the updated information is displayed. Note that during this display period, even if new measurement results are added using the 300 turbidity meter, the display will not be updated.

The cart here is a function within the EC server that allows users to purchase products. This function can be used on your own EC site or through a business partnership with a cart operated by another company.

In addition, in cases where prescription drugs are provided at pharmacies, the EC site server may be a prescription drug handling system that works in conjunction with a system that supports electronic prescriptions.

Although the embodiment has been described above, this embodiment is presented as an example and is not intended to limit the scope of the invention. This new embodiment can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the gist of the invention. This embodiment and its modifications are included in the scope and gist of the invention, and are included in the scope of the invention and its equivalents described in the claims.

1: Health food or medicine suggestion system 100: Health food or medicine suggestion device 200: Customer terminal 300: Blood turbidity measurement device 400: EC site server

Claims (9)

A health food or drug suggestion device used in a health food or drug suggestion system, in which a customer use device and a health food or drug suggestion device are connected via a network,
calculating an average value and a standard deviation or a confidence interval of the blood turbidity from the blood turbidity at a specific time previously received from the customer use device;
A health food or medicine proposal device that calculates a personal blood turbidity level based on the average value and standard deviation or confidence interval of the blood turbidity at the specific time calculated from the blood turbidity at a specified time received from the customer-used device. Calculating the recommendation level corresponding to the calculated personal blood turbidity level based on a conversion table from the personal blood turbidity level to a recommendation level which is two or more classifications of the personal blood turbidity level;
The health food or medicine suggestion device according to claim 1, wherein health foods or medicines in a data table of the health food or medicine database of the group corresponding to the calculated recommendation level are extracted from the health food or medicine database grouped according to the recommendation level. Calculating a moving average of the average blood turbidity and a moving average of the standard deviation or confidence interval from the stored average blood turbidity at the specific time,
The health food or medicine suggestion device according to claim 1 or 2, wherein the personal blood turbidity level is calculated based on a moving average of the average value of the blood turbidity and a moving average of the standard deviation or confidence interval. Calculate the recommended intake amount of the health food or drug by dividing one-third of the daily recommended amount of the ingredient itself of the health food or drug extracted from the health food or drug database by the amount of the active ingredient and multiplying the result by a correction coefficient corresponding to the recommendation level;
The health food or medicine suggestion device according to claim 2 , wherein the health food or medicine whose recommended intake amount is closest to 1.0 is extracted. The health food or medicine recommendation system further includes an EC site server having an EC site accessible by the customer use device and the health food or medicine recommendation device and where predetermined health foods or medicines can be purchased;
The health food or medicine proposal device of claim 2, which transmits health foods or medicines extracted from the health food or medicine database to the customer use device or a server accessible from the customer use device, and transmits information on the health food or medicine that the customer wishes to purchase received from the customer use device to the EC site server. A customer use device and a health food or drug suggestion device are connected via a network to a computer of the health food or drug suggestion device used in a health food or drug suggestion system.
calculating an average value and a standard deviation or a confidence interval of blood turbidity from the blood turbidity at a specific time previously received from the customer use device;
A health food or pharmaceutical recommendation program that executes a process of calculating a personal blood turbidity level based on the average value and standard deviation or confidence interval of the blood turbidity at a specific time calculated from the blood turbidity at a specified time received from the customer-used device. A method for proposing health foods in a health food proposing system in which a customer use device and a health food proposing device are connected via a network, comprising:
calculating an average value and a standard deviation or a confidence interval of the blood turbidity from the blood turbidity at a specific time previously received from the customer use device;
A health food recommendation method, which calculates a personal blood turbidity level based on the calculated average value and standard deviation or confidence interval of the blood turbidity at the specific time from the blood turbidity at a specified time received from the customer-used device. A method for operating a drug suggestion device in a drug suggestion system in which a customer use device and a drug suggestion device are connected via a network, comprising:
calculating an average value and a standard deviation or a confidence interval of the blood turbidity from the blood turbidity at a specific time previously received from the customer use device;
A method for operating a drug recommendation device, which calculates a personal blood turbidity level from the blood turbidity at a specified time received from the customer-used device based on the calculated average value and standard deviation or confidence interval of the blood turbidity at the specific time. A health food or medicine recommendation system in which a customer use device and a health food or medicine recommendation device are connected via a network,
calculating an average value and a standard deviation or a confidence interval of the blood turbidity from the blood turbidity at a specific time previously received from the customer use device;
A health food or medicine recommendation system that calculates a personal blood turbidity level based on the average value and standard deviation or confidence interval of the blood turbidity at the specific time calculated from the blood turbidity at a specified time received from the customer-used device.