JP7803508B2 - Physical information evaluation device, physical information evaluation method, program, and recording medium - Google Patents

Description

The present invention relates to a physical information evaluation device, a physical information evaluation method, a program, and a recording medium.

Systems that evaluate a user's physical information are known. For example, the system described in Patent Document 1 discloses technology that estimates a user's health status by taking into account the reliability of the information, thereby increasing the reliability of the estimation results.

Japanese Patent Application Laid-Open No. 2005-250583

However, conventional technology still leaves room for improvement in assessing the physical information of users who have consumed ingested foods.

The present invention was made in consideration of these circumstances, and aims to provide a physical information evaluation device, physical information evaluation method, program, and recording medium that can evaluate the physical information of a user who has ingested an ingested substance.

A physical information evaluation device according to one aspect of the present invention includes a reception unit that receives a user's physical information in association with identification information of ingested items; a memory unit that stores the information received by the reception unit, in association with the user's physical information and the identification information of the ingested items, as a database; and an evaluation unit that calculates, based on the database stored in the memory unit, evaluation information of the user's current body and evaluation information of the user's future body if the user ingests the ingested items.

A physical information evaluation method according to one aspect of the present invention includes the steps of: receiving a user's physical information in association with identification information of an ingested item; storing information associating the user's physical information with the identification information of the ingested item as a database in a memory unit; and calculating, based on the database stored in the memory unit, current evaluation information of the user's body and future evaluation information of the user's body if the user ingests the ingested item.

A program according to one aspect of the present invention causes one or more computers to perform the following processes: receive a user's physical information in association with identification information of an ingested item; store information associating the user's physical information with the identification information of the ingested item as a database in a memory unit; and evaluate the user's current physical evaluation information and future physical evaluation information of the user if the user ingests the ingested item based on the database stored in the memory unit.

The present invention makes it possible to evaluate the physical information of a user who has consumed an ingested substance.

FIG. 1 is a diagram illustrating an example of a system for utilizing physical evaluation information. FIG. 1 illustrates an example of a physical information evaluation device according to an embodiment. FIG. 10 illustrates an example of a user management table. FIG. 10 is a diagram illustrating an example of a physical evaluation information table. FIG. 10 is a diagram illustrating an example of an intake table. FIG. 10 is a diagram illustrating an example of a user preference table. FIG. 10 is a diagram illustrating an example of a company information table. FIG. 10 is a diagram illustrating an example of a multivariate analysis process. FIG. 10 is a diagram illustrating an example of processing in the learning phase and execution phase of a learning model. 10 is a sequence chart showing an example of a flow of a process executed by a physical information evaluation device in cooperation with a mobile information terminal according to an embodiment. FIG. 10 is a diagram illustrating an example of the processing flow of a physical evaluation web service. FIG. 10 is a diagram showing an example of display of body evaluation information in the body evaluation web service. FIG. 10 is a diagram showing an example of time progression of future predicted values of physical evaluation information. FIG. 10 is a diagram showing an example of time progression of future predicted values of evaluation information of a user's body when ingestion of ingested foods and exercise are combined. FIG. 10 is a diagram for explaining an example of behavioral change for a user. FIG. 10 is a diagram illustrating an example of a method for proposing an exercise menu to encourage a user to change their behavior. FIG. 10 is a diagram illustrating an example of a method for proposing an exercise menu to encourage a user to change their behavior. FIG. 10 is a diagram showing an example of statistical data of body evaluation information provided to a manufacturer. FIG. 10 is a diagram showing an example of statistical data of body evaluation information provided to a manufacturer. FIG. 10 is a diagram showing an example of statistical data of body evaluation information provided to a manufacturer.

The following describes one embodiment of a physical information evaluation device.

Figure 1 shows an example of a system for utilizing physical evaluation information.

In this system, an image code (e.g., a QR code (registered trademark) or barcode) is first attached to an ingested product manufactured and sold by a manufacturer. Ingestion products include, for example, snacks, beverages, pharmaceuticals, and health foods. Health foods include, for example, foods for specified health uses, functional foods, dietary supplements, health supplements, fortified foods, nutritionally balanced foods, and supplements. The image code contains, for example, a product code and a URL for a website for accessing the physical evaluation web service. A user reads the image code attached to a purchased ingested product using a mobile information terminal and displays the physical evaluation web service website on the display screen of the mobile information terminal. The user provides the product code of the ingested product obtained from the image code along with the purchaser's physical information to the physical evaluation web service. The physical evaluation web service calculates the user's current physical evaluation information as well as future physical evaluation information for the user if the user consumes the ingested product. The evaluation data from the physical evaluation web service is stored in a physical evaluation information database. Statistical data on the physical evaluation information of multiple users stored in the physical evaluation information database is provided to manufacturers, for example, and used to evaluate the efficacy of ingested products.

Figure 2 is a diagram showing an example of a physical information evaluation device 100.

The physical information evaluation device 100 is connected to, for example, a measurement device 200 and a mobile information terminal 300 via a communication network NW. The communication network NW is composed of, for example, a communication line or a wireless communication network.

The measuring device 200 is a device that measures the user's physical information, and includes, for example, a blood pressure monitor, a weight scale, a body composition monitor, and various wearable devices such as eyeglasses and watches. The user's physical information includes, for example, weight, body fat percentage, basal metabolic rate, average number of steps, and BMI.

The mobile information terminal 300 is a portable information processing terminal such as a smartphone or tablet terminal, and includes, for example, an image recognition unit 310 and a display control unit 320.

The image recognition unit 310 performs image recognition on the image code attached to the ingested item consumed by the user and reads the information written in the image code. The information written in the image code includes, for example, the product code of the ingested item and a URL for accessing the body evaluation web service.

The display control unit 320 displays the user's physical evaluation information evaluated by the physical information evaluation device 100 on the display screen of the mobile information terminal 300. The display control unit 320 displays the user's physical evaluation information, obtained by, for example, entering the user's physical information into a physical evaluation web service, on the display screen of the mobile information terminal 300. The user's physical evaluation information includes, for example, athletic age and actual age.

The physical information assessment device 100 includes, for example, a control unit 110 and a storage unit 120. The control unit 110 is realized by a hardware processor, such as a CPU (Central Processing Unit), executing a program (software). Some or all of these components may be realized by hardware (including circuitry), such as an LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or GPU (Graphics Processing Unit), or by a combination of software and hardware. The program may be stored in advance on a computer-readable storage device, such as the HDD or flash memory, of the physical information assessment device 100, or may be stored on a removable computer-readable storage medium, such as a DVD or CD-ROM, and installed on the HDD or flash memory of the physical information assessment device 100 by inserting the computer-readable storage medium into a drive.

The control unit 110 includes, for example, a reception unit 111 and an evaluation unit 112.

The reception unit 111 receives the user's physical information in association with the product code of the ingested item. The product code of the ingested item is an example of identification information for the ingested item. The reception unit 111 stores information associating the user's physical information with the product code of the ingested item in the storage unit 120 as a physical evaluation information table 122. The physical evaluation information table 122 is an example of a data table.

The evaluation unit 112 calculates the user's current physical evaluation information, as well as future physical evaluation information for the user if the user ingests the ingested food, based on the physical evaluation information table 122 stored in the memory unit 120.

The evaluation unit 112 inputs the user's physical information received by the reception unit 111 and predicts the user's future physical evaluation information when the user ingests the ingested product, for example, using a multivariate analysis method in which the user's physical information at multiple points in time and the product code of the ingested product are used as explanatory variables, and the evaluation information is used as the objective variable.

The evaluation unit 112 may, for example, train the learning model 126 by machine learning using data that associates the user's physical information at multiple points in time with the product code and evaluation information of ingested foods as learning data, input the user's physical information received by the reception unit 111 into the learning model 126, and predict future predicted values of the physical evaluation information when the user ingests foods.

The evaluation unit 112, for example, predicts future physical evaluation information of the user when the user consumes an ingestion for each of the user's health preference levels, and suggests an exercise menu to the user based on the predicted future physical evaluation information of the user. For example, when the user's health preference level is a first preference level, the evaluation unit 112 suggests an exercise menu to the user based on future physical evaluation information of the user corresponding to the first preference level. That is, the evaluation unit 112 suggests an exercise menu to the user that is suitable for the user's health preference level. For example, when the user's health preference level is a first preference level, the evaluation unit 112 may suggest an exercise menu to the user based on future physical evaluation information of the user corresponding to a second preference level that is higher than the first preference level. That is, the evaluation unit 112 may suggest an exercise menu to the user that is more strenuous than an exercise menu that is suitable for the user's health preference level.

The evaluation unit 112 predicts the user's future physical evaluation information when the user combines ingesting ingestants and exercising, for example, based on the physical evaluation information table 122 stored in the storage unit 120. The evaluation unit 112 predicts the user's future physical evaluation information when the user combines ingesting ingestants and exercising, for example, for each user's exercise preference level, and suggests an exercise menu to the user based on the predicted future user's physical evaluation information. That is, when the user combines ingesting ingestants and exercising, the evaluation unit 112 suggests to the user an exercise menu that is appropriate for the user's exercise preference level. For example, when the user's exercise preference level is a first preference level, the evaluation unit 112 may suggest to the user an exercise menu that is more demanding than an exercise menu that is appropriate for the user's exercise preference level. That is, when the user combines ingesting ingestants and exercising, the evaluation unit 112 may suggest to the user an exercise menu that is more demanding than an exercise menu that is appropriate for the user's exercise preference level.

The memory unit 120 stores, for example, a user management table 121, a physical evaluation information table 122, an intake table 123, a user preference table 124, a company information table 125, and a learning model 126.

Figure 3 shows an example of the user management table 121. In the user management table 121, for example, a user ID, which serves as identification information for identifying a user, is associated with a password, registration date, date of birth, place of residence, gender, and preference level. The password is used when the user authenticates themselves to the physical evaluation web service. The registration date indicates the date on which the user information was registered in the user management table 121. The preference level indicates the user's preference level for health or exercise, and the category ID of the user preference table 124 is registered.

Figure 4 is a diagram showing an example of the physical evaluation information table 122. In the physical evaluation information table 122, a user ID, which serves as identification information for identifying a user, is associated with a data registration date, a product ID, physical information, and physical evaluation information. The data registration date indicates the date on which the physical evaluation information was registered in the physical evaluation information table 122. The product ID indicates the identification information of the ingested food consumed by the user. The physical information is information measured when the user ingests the ingested food, and includes, for example, weight, body fat percentage, basal metabolic rate, average number of steps, and BMI. The physical evaluation information is evaluation information obtained by inputting the user's physical information into a physical evaluation web service, and includes, for example, athletic age and actual age. The athletic age is data that serves as an indicator of the user's athletic ability. The actual age is data that indicates the user's age. If the athletic age is lower than the actual age, it is an indicator that the user is in good health.

Figure 5 shows an example of the intake table 123. In the intake table 123, a company ID, which serves as identification information for identifying the manufacturer of an intake, is associated with a product ID, nutritional components, and ingredient category. The product ID indicates the identification information for the intake. The nutritional components indicate the nutritional components contained in the intake, and include, for example, energy, protein, lipids, carbohydrates, dietary fiber, salt, potassium, calcium, vitamin C, vitamin K, and folic acid. The ingredient category indicates the category of the intake based on the nutritional components of the dietary supplement.

Figure 6 is a diagram showing an example of the user preference table 124. In the user preference table 124, the content of the user's preferences is associated with a category ID, which serves as identification information for identifying the category of the user's preferences. In the example shown in Figure 6, the user preference table 124 registers the user's level of preference for health and the user's level of preference for exercise, with these being distinguished.

Figure 7 shows an example of the company information table 125. In the company information table 125, a company ID, which serves as identification information for identifying the manufacturer of an ingestible product, is associated with the company name, contact information, and address.

Figure 8 shows an example of multivariate analysis processing.

In the example shown in FIG. 8 , the attribute of the explanatory variable X in the multivariate analysis is the user's physical information measured when each of multiple users ingested one or more ingested foods, and the attribute of the objective variable Y in the multivariate analysis is the user's physical evaluation information when the user ingested one or more ingested foods. The user's physical information includes, for example, x1 (body weight), x2 (body fat percentage), x3 (basal metabolic rate), x4 (average number of steps), and x5 (BMI). The user's physical evaluation information includes, for example, y1 (exercise age) and y2 (effective age). The combinations of the explanatory variable X and the objective variable Y in the multivariate analysis are managed separately for each combination of one or more ingested foods ingested by the user. In the example shown in FIG. 8 , multiple regression analysis is applied as an example of multivariate analysis, and a function indicating the correlation between the explanatory variable X and the objective variable Y is calculated using, for example, the least squares method. Then, by inputting the user's physical information received by the reception unit 111 into the calculated function, the user's future physical evaluation information when the user ingests the ingested foods is predicted.

Figure 9 shows an example of the processing in the learning phase and execution phase of the learning model 126.

In the example shown in FIG. 9, in the learning phase, the learning model 126 is trained by machine learning using training data. The training data is, for example, data in which first data and second data are associated with each other, and is time-series data including data at multiple points in time (Dt1, Dt2, Dt3, ...). The first data includes, for example, product information and physical information. The product information is information indicating a combination of one or more ingested substances taken by each of multiple users, and includes, for example, information regarding whether or not the user ingested a given substance for parameters set for each ingested substance. The physical information is information measured when each of multiple users ingested one or more ingested substances, and includes, for example, weight, body fat percentage, basal metabolic rate, average number of steps, and BMI. The second data includes, for example, physical evaluation information. The physical evaluation information is information calculated using a method different from the physical evaluation web service, and includes, for example, exercise age and actual age.

Next, in the execution phase, input data of the user to be evaluated is input into the learning model 126. The input data includes, for example, product information and physical information. The product information is information indicating a combination of one or more ingested substances taken by the user to be evaluated, and includes, for example, information regarding whether the user ingested a substance or substances in relation to parameters set for each substance. The physical information is information measured when the user to be evaluated ingested one or more ingested substances, and includes, for example, weight, body fat percentage, basal metabolic rate, average number of steps, and BMI. The learning model 126 is implemented in a physical evaluation web service, and when input data of the user to be evaluated is input, it outputs evaluation information of the user's body. The evaluation information of the user's body includes, for example, athletic age and actual age.

Figure 10 is a sequence chart showing an example of the flow of processing executed by the physical information evaluation device 100 in cooperation with the mobile information terminal 300.

As shown in the figure, first, the mobile information terminal 300 reads the image code attached to the ingested item (step S10). Next, the mobile information terminal 300 displays the web page of the physical evaluation web service based on the URL written in the image code (step S11). Next, when a pre-registered user ID and password are input, the mobile information terminal 300 transmits the input user ID and password to the physical information evaluation device 100, thereby establishing user authentication for the physical evaluation web service. Next, when the user's physical information is input, the mobile information terminal 300 associates the product code of the ingested item written in the image code with the user's physical information and transmits it to the physical information evaluation device 100.

When the physical information evaluation device 100 receives the user's physical information associated with the product code of the ingested item from the mobile information terminal 300, it updates the physical evaluation information table 122 (step S12). Next, the physical information evaluation device 100 updates the learning model 126 based on the information registered in the updated physical evaluation information table 122 (step S13). Next, the physical information evaluation device 100 analyzes the future predicted value of the physical evaluation information using the updated learning model 126 (step S14). Then, the physical information evaluation device 100 transmits the user's physical evaluation information and the future predicted value of the physical evaluation information to the mobile information terminal 300.

When the mobile information terminal 300 receives the user's physical evaluation information and future predicted values of the physical evaluation information from the mobile information terminal 300, it suggests to the user an exercise menu to improve the user's physical evaluation information based on the received information (step S15).

Figure 11 shows an example of the processing flow of the physical evaluation web service.

In the example shown in FIG. 11, the mobile information terminal 300 first reads the image code 400M attached to the ingested item 400 and obtains the URL of the physical evaluation web service. Next, the mobile information terminal 300 displays a login screen for the physical evaluation web service. Next, when a login ID and password are entered on the login screen for the physical evaluation web service, the mobile information terminal 300 performs user authentication for the physical evaluation web service. Next, when user authentication is successful, the mobile information terminal 300 registers the user's physical information measured by the measuring device 200 in association with the product code of the ingested item written on the image code 400M.

Figure 12 shows an example of how body evaluation information is displayed in the body evaluation web service.

In the example shown in FIG. 12, the mobile information terminal 300 displays a first icon 300A, a second icon 300B, and a third icon 300C. In this example, the first icon 300A shows the type of food ingested by the user as user information. The second icon 300B shows the user's exercise age and actual age as physical evaluation information. The third icon 300C shows the transition of the user's exercise age when the food is ingested as a future predicted value of physical evaluation information.

Figure 13 shows an example of the time progression of future predicted values of physical evaluation information.

The example shown in Figure 13 shows the time progression of future predicted values of physical evaluation information for each user's level of health preference. In this example, the future predicted values of physical evaluation information tend to gradually decrease over time, regardless of the user's level of health preference. In other words, this shows that if the user continues to ingest foods, the user's physical evaluation information will gradually improve. Also, in this example, the higher the user's level of health preference, the greater the future predicted values of physical evaluation information tend to decrease over time. In other words, this shows that if the user's level of health preference is high, the user will tend to actively ingest foods, and the user's physical evaluation information will improve significantly.

Figure 14 shows an example of the time progression of future predicted values of a user's physical evaluation information when ingesting food and exercising together.

The example shown in Figure 14 shows the time progression of future predicted values of physical evaluation information for each user's level of exercise preference. In this example, the user's level of exercise preference is classified into two levels: "no exercise habit" and "exercise habit," and the time progression of future predicted values of physical evaluation information for each user's health preference in each category is shown. In this example, when a user falls into the "exercise habit" category, the future predicted values of physical evaluation information tend to decrease more significantly over time than when the user falls into the "no exercise habit" category. In other words, this shows that when a user's level of exercise preference is high, the combined effect of ingesting food and exercising tends to be greater.

Figure 15 is a diagram illustrating an example of behavioral change for a user.

In the example shown in Figure 15, the user's health preference level corresponds to "passive efforts," and the user is encouraged to change their behavior to change their health preference level to "normal, casual efforts." Also, in this example, the user's exercise preference level corresponds to "no exercise habit," and the user is encouraged to change their behavior to change their exercise preference level to "exercise habit."

Figure 16 is a diagram illustrating an example of a method for suggesting an exercise menu to encourage behavioral change in a user when combining ingestion of ingested foods with exercise.

The example shown in Figure 16 shows future predicted values of physical evaluation information for each user's health preference level. In this example, the user's health preference level corresponds to "passive effort." A relatively low-impact exercise menu is proposed based on the difference between the future predicted value of physical evaluation information corresponding to the "passive effort" preference level and the current physical evaluation value. In other words, when the user's health preference level is the first preference level, an exercise menu is proposed to the user based on the future user's physical evaluation information corresponding to the first preference level. In this case, an exercise menu appropriate for the user's health preference level is proposed to the user. Furthermore, a relatively high-impact exercise menu is proposed based on the difference between the future predicted value of physical evaluation information corresponding to the "normal, casual effort" preference level and the current physical evaluation value. In other words, when the user's health preference level is the first preference level, an exercise menu is proposed to the user based on the future user's physical evaluation information corresponding to the second preference level, which indicates a higher health preference level than the first preference level. In this case, an exercise menu that encourages the user to improve their health preferences will be suggested to the user.

Figure 17 is a diagram illustrating an example of a method for suggesting an exercise menu to encourage behavioral change in a user when combining ingestion of ingested foods with exercise.

In the example shown in Figure 17, the user's exercise preference level is classified into two levels: "no exercise habit" and "exercise habit," and the graph shows the time progression of the future predicted value of physical assessment information for each user's health preference level for each category. In this example, the user's exercise preference level corresponds to "no exercise habit." A relatively low-impact exercise menu is proposed based on the difference between the future predicted value of physical assessment information corresponding to the preference level of "no exercise habit" and the current physical assessment value. In other words, when the user's exercise preference level is the first preference level, an exercise menu is proposed to the user based on the user's future physical assessment information corresponding to the first preference level. In this case, an exercise menu appropriate for the user's exercise preference level is proposed to the user. Furthermore, a relatively high-impact exercise menu is proposed based on the difference between the future predicted value of physical assessment information corresponding to the preference level of "exercise habit" and the current physical assessment value. In other words, when the user's exercise preference level is a first preference level, an exercise menu is suggested to the user based on future physical evaluation information of the user that corresponds to a second preference level, which indicates a higher preference level for the user's health than the first preference level. In this case, an exercise menu that encourages the user to improve their exercise preference level is suggested to the user.

Figure 18 shows an example of statistical data on physical evaluation information provided to manufacturers.

The example shown in Figure 18 shows statistical values of future predicted values of physical evaluation information for each ingredient category of ingested food. Statistical values include, for example, averages and medians. In this example, the future predicted value of physical evaluation information corresponding to ingredient category 2 of ingested food has decreased significantly over time compared to the future predicted value of physical evaluation information corresponding to ingredient category 1 of ingested food. In other words, this shows that ingredient category 2 of ingested food contributes significantly to the efficacy of ingested food compared to ingredient category 1 of ingested food.

Figure 19 shows an example of statistical data on physical evaluation information provided to manufacturers.

The example shown in Figure 19 shows statistical values for future predicted values of physical evaluation information for each ingredient category of ingested foods, categorized based on the user's age. In this example, the future predicted values of physical evaluation information for users aged 40 or older decrease significantly over time compared to the future predicted values of physical evaluation information for users under 40. In other words, this shows that when a user aged 40 or older ingests an ingested food, the efficacy of the ingested food is greater than when a user under 40 ingests the food.

Figure 20 shows an example of statistical data on physical evaluation information provided to manufacturers.

The example shown in Figure 20 shows the amount of change in physical evaluation information for each ingested item. This example shows the amount of change in a user's physical evaluation information when ingesting ingested item A or ingested item B between two points, time t0 and time t1. For example, the amount of change in physical evaluation information for each user may be displayed as a list, or statistics of the amount of change in physical evaluation information for multiple users may be displayed. In this example, the amount of change in physical evaluation information corresponding to ingested item A tends to be greater than the amount of change in physical evaluation information corresponding to ingested item B. In other words, this shows that ingested item A has greater health benefits than ingested item B.

The above embodiment provides the following advantages:

(1) The physical information evaluation device 100 calculates the user's current physical evaluation information, as well as the user's future physical evaluation information if the user ingests an ingested food, based on a database that associates the user's physical information with the product code of the ingested food. This allows for a high degree of flexibility in evaluating the physical information of a user who has ingested an ingested food.

(2) The physical information evaluation device 100 inputs the user's physical information received by the reception unit 111 and predicts the user's future physical evaluation information when the user ingests the ingested food, using a multivariate analysis method with the user's physical information at multiple points in time and the product code of the ingested food as explanatory variables and the user's physical evaluation information as the objective variable. This makes it possible to calculate the user's future physical evaluation information when the user ingests the ingested food with a high degree of reliability.

(3) The physical information evaluation device 100 inputs the user's physical information received by the reception unit 111 into a learning model 126 trained by machine learning using data that associates the user's physical information at multiple points in time with the product code and evaluation information of ingested foods as learning data, and predicts future evaluation information about the user's body when the user ingests the ingested foods. This makes it possible to calculate with high reliability future evaluation information about the user's body when the user ingests the ingested foods.

(4) The physical information evaluation device 100 predicts the user's future physical evaluation information when the user combines ingesting an ingested product with exercise, based on a database that associates the user's physical information with the product code of the ingested product. This allows for greater flexibility in evaluating the physical information of users who have ingested an ingested product.

(5) The physical information evaluation device 100 predicts the user's future physical evaluation information when the user combines ingestion of ingested foods and exercise for each level of the user's health preferences, and suggests an exercise menu to the user based on the predicted future physical evaluation information. This makes it possible to suggest an exercise menu that takes the user's health preferences into consideration.

(6) When the user's health preference level is the first preference level, the physical information evaluation device 100 suggests an exercise menu to the user based on the future user's physical evaluation information corresponding to the first preference level. This makes it possible to suggest an exercise menu suited to the user's health preferences.

(7) When the user's health preference level is a first preference level, the physical information evaluation device 100 suggests an exercise menu to the user based on the future user's physical evaluation information corresponding to a second preference level that is higher than the first preference level. This makes it possible to suggest an exercise menu in a manner that changes the user's health preference level.

(8) The physical information evaluation device 100 predicts future physical evaluation information for the user when the user combines ingestion of ingested foods and exercise, for each level of the user's exercise preference, and suggests an exercise menu to the user based on the predicted future physical evaluation information for the user. This makes it possible to suggest an exercise menu that takes into account the user's health preferences.

(9) When the user's exercise preference level is a first preference level, the physical information evaluation device 100 suggests an exercise menu to the user based on future evaluation information of the user's body that corresponds to the first preference level. This makes it possible to suggest an exercise menu that is suited to the user's exercise preference level.

(10) When the user's exercise preference level is a first preference level, the physical information evaluation device 100 suggests an exercise menu to the user based on future evaluation information of the user's body that corresponds to a second preference level that is higher than the first preference level. This makes it possible to suggest an exercise menu in a manner that changes the user's exercise preference.

The above embodiment can also be implemented in the following ways:

In the above embodiment, restrictions may be placed on the method of suggesting exercise menus to the user, taking into account the user's exercise preferences, for example, by setting an upper limit on the load or the exercise time.

In the above embodiment, restrictions may be placed on the method of suggesting exercise menus to the user, taking into account the user's health preferences, for example, by setting an upper limit on the load or the exercise time.

In the above embodiment, the information suggested to the user in consideration of the user's level of health preferences is not limited to exercise menus, and may also suggest to the user, for example, how to take ingested foods.

The above-described embodiments are intended to facilitate understanding of the present invention and are not intended to limit the scope of the present invention. The present invention may be modified or improved without departing from its spirit, and equivalents are also included within the scope of the present invention. In other words, designs modified appropriately by a person skilled in the art from each embodiment are also encompassed within the scope of the present invention as long as they incorporate the characteristics of the present invention. For example, the elements of each embodiment and their arrangement, materials, conditions, shape, size, etc. are not limited to those exemplified and can be modified as appropriate. Furthermore, each embodiment is merely an example, and it goes without saying that partial substitution or combination of the configurations shown in different embodiments is possible, and these are also encompassed within the scope of the present invention as long as they incorporate the characteristics of the present invention.

100...physical information evaluation device, 110...control unit, 111...reception unit, 112...evaluation unit, 120...storage unit, 121...user management table, 122...physical evaluation information table, 123...ingestion table, 124...user preference table, 125...company information table, 126...learning model, 200...measuring device, 300...mobile information terminal, 310...image recognition unit, 320...display control unit, NW...communications network.

Claims (11)

a receiving unit that receives the user's physical information in association with identification information of an ingested item, the identification information being associated with information on the manufacturer of the ingested item;
a storage unit that stores, as a database, information in which the physical information and the identification information are associated with each other and the user's health preference level, which information is received by the receiving unit;
an evaluation unit that calculates, based on the database stored in the storage unit, evaluation information on the current user's body and evaluation information on the future user's body based on the user's level of preference for health when the user ingests an ingestion item to which the identification information is assigned;
a display unit that displays a behavioral modification suggestion for the user based on at least the evaluation information of the future user's body;
a providing unit that provides the manufacturer with evaluation information on the future user's body in order to evaluate the efficacy of the ingested product;
Equipped with
Physical information assessment device. The evaluation unit inputs the user's physical information received by the reception unit and predicts future physical evaluation information of the user when the user ingests the ingested food to which the identification information has been assigned, using a multivariate analysis method with the user's physical information at multiple time points and the identification information of the ingested food stored as the database in the storage unit as explanatory variables and the user's physical evaluation information as a target variable.
The physical information evaluation device according to claim 1 . The evaluation unit inputs the user's physical information received by the reception unit into a learning model trained by machine learning using data stored as the database in the storage unit, which data associates the user's physical information at multiple points in time with the identification information and evaluation information of the ingested food, and predicts future evaluation information of the user's body when the user ingests the ingested food to which the identification information has been assigned.
The physical information evaluation device according to claim 1 . The storage unit further stores the user's exercise preference level as a database,
The degree of preference for exercise includes two classifications: whether or not one exercises regularly;
the evaluation unit predicts future evaluation information of the user's body when the user takes the food item to which the identification information is assigned and exercises habitually in combination, based on the database stored in the storage unit;
The physical information evaluation device according to claim 1 . the evaluation unit predicts future evaluation information of the user's body when the user takes the ingestion of the ingestions assigned with the identification information in combination with the habitual exercise, for each level of the user's health preference, and proposes to the user an exercise menu having a load determined based on the difference between the predicted future evaluation information of the user's body and the evaluation information of the user's current body .
The physical information evaluation device according to claim 4 . the evaluation unit, when the user's health preference level is a first preference level, suggests an exercise menu to the user based on evaluation information of the user's future body corresponding to the first preference level;
The physical information evaluation device according to claim 5 . a reception unit that receives the user's physical information in association with identification information of the ingested item;
a storage unit that stores, as a database, information that associates the physical information with the identification information received by the receiving unit and the user's health and exercise preference level;
an evaluation unit that calculates, based on the database stored in the storage unit, evaluation information on the current user's body and evaluation information on the user's future body when the user ingests the ingested food to which the identification information is assigned;
Equipped with
The degree of preference for exercise includes two classifications: whether or not one exercises regularly;
The evaluation unit
predicting future evaluation information of the user's body when the user takes the food to which the identification information is assigned and exercises habitually in combination for each level of the user's health preference ;
a means for proposing to the user an exercise menu having a load determined based on a difference between the future user's physical evaluation information corresponding to a second preference level, the second preference level being higher than the first preference level , and the current user's physical evaluation information, where the user's health preference level is a first preference level ;
Including,
Physical information assessment device. a reception unit that receives the user's physical information in association with identification information of the ingested item;
a storage unit that stores, as a database, information that associates the physical information with the identification information received by the receiving unit and the user's health and exercise preference level;
an evaluation unit that calculates, based on the database stored in the storage unit, evaluation information on the current user's body and evaluation information on the user's future body when the user ingests the ingested food to which the identification information is assigned;
Equipped with
The degree of preference for exercise includes two classifications: whether or not one exercises regularly;
The evaluation unit
a means for inputting the user's physical information received by the receiving unit into a learning model trained by machine learning using data stored as the database in the storage unit, which data associates the user's physical information at multiple points in time with the identification information and evaluation information of an ingested product as learning data, and predicting future evaluation information of the user's physical condition when the user ingests the ingested product to which the identification information has been assigned;
a means for predicting future physical evaluation information of the user when the user takes in an ingestion for each level of preference for exercise of the user, and when the user's level of preference for exercise is no habitual exercise, proposing to the user an exercise menu having a load determined based on the difference between the future user's physical evaluation information corresponding to the preference level of no habitual exercise and the current user's physical evaluation information, and an exercise menu having a load determined based on the difference between the future user's physical evaluation information corresponding to the preference level of habitual exercise and the current user's physical evaluation information ;
Including,
Physical information assessment device. receiving the user's physical information in association with identification information of the ingested item, the identification information being associated with information on the manufacturer of the ingested item;
storing information associating the user's physical information with the identification information of the ingested items and the user 's health preference level as a database in a storage unit;
a step of calculating, based on the database stored in the storage unit, evaluation information on the current user's body and evaluation information on the future user's body based on the user's level of preference for health when the user ingests the ingestion product to which the identification information is assigned;
displaying, on a display unit, a suggestion for behavioral modification to the user based on at least the evaluation information of the future user's body;
providing the manufacturer with evaluation information of the future user's body to evaluate the efficacy of the ingestion product;
Including,
Physical information assessment method. On one or more computers,
A process of accepting the user's physical information in association with identification information of an ingested item, the identification information being associated with information on the manufacturer of the ingested item;
A process of storing information associating the user's physical information with the identification information of the ingested items and the user's health preference level as a database in a storage unit;
a process of evaluating, based on the database stored in the storage unit, the current user's physical evaluation information and the future user's physical evaluation information based on the user's health preference level when the user ingests the ingestion product to which the identification information is assigned;
a process of displaying a behavioral modification suggestion to the user on a display unit based on at least the evaluation information of the future user's body;
providing the manufacturer with evaluation information of the future user's body in order to evaluate the efficacy of the ingestion product;
Execute
program. A computer-readable recording medium storing the program according to claim 10 .