Individualized proposal device for physical condition-related component and individualized proposal method for physical condition-related component

Abstract

The purpose of the present invention is to propose a physical condition-related component in consideration of the purpose of a user's eating habits. This individualized proposal device (10) for a physical condition-related component is a food design system comprising: an input information acquisition unit (32) that acquires input information including a purpose relating to the eating habits of a user; a deficiency amount calculation unit (34) that uses a recommended intake amount for a plurality of physical condition-related components, which includes a functional component specified on the basis of the input information, and an existing intake amount for each physical condition-related component for the user to calculate a deficiency amount for each physical condition-related component; and an importance determination unit (35) that uses an AI analysis, which includes the degree of contribution of each physical condition-related component to the purpose, the deficiency amount of each physical condition-related component for the user, and a probability logic model to determine the importance and precedence of each physical condition-related component for the user.

Description

Device for individually suggesting health-related ingredients and method for individually suggesting health-related ingredients

[0001] This disclosure relates to an individual suggestion device for health-related ingredients and a method for individual suggestion of health-related ingredients.

[0002] In recent years, it is predicted that lifestyle-related diseases and mild physical and mental ailments will increase due to irregular eating habits and unbalanced nutrition, which is becoming a serious issue that will lead to a decline in overall labor productivity and an increase in medical expenses.

[0003] To mitigate such nutritional imbalances, systems have been proposed that provide food ingredients and meal menus that offer the necessary nutrients for health purposes (see, for example, Patent Document 1). Furthermore, Patent Document 2 discloses a system that estimates the nutrients a user needs based on their physical data and, based on the estimated nutrients, determines a suitable product (such as a functional food) from among a variety of commercially available products.

[0004] Patent No. 6805079 Publication Special Publication No. 2024-545265

[0005] The necessary nutrients vary not only by age and gender, but also by the individual user's dietary goals and objectives. For example, users who want to improve their skin condition, improve sleep quality, enhance memory, reduce stress, or lose weight will all have different priorities for consuming nutrients.

[0006] This disclosure is made in view of these circumstances and aims to provide a device and method for individually suggesting health-related ingredients that can suggest health-related ingredients that take into account the user's dietary goals.

[0007] A device for individually suggesting health-related components according to one aspect of this disclosure comprises: an input information acquisition means for acquiring input information including the user's dietary goals; a deficiency calculation means for calculating the deficiency amount of each health-related component using the recommended intake amount of a plurality of health-related components identified based on the input information and the user's past intake amount of each of the health-related components; and an importance determination means for determining the importance of each health-related component to the user using the contribution of each health-related component to the stated goals and the user's deficiency amount of each of the health-related components. Here, "dietary goals" is a broad concept that also includes objectives.

[0008] A device for individually suggesting health-related components according to one aspect of this disclosure comprises: an input information acquisition means for acquiring input information including the user's dietary goals; a deficiency calculation means for calculating the deficiency amount of each health-related component using the recommended intake amount of each health-related component identified from the user's attribute data and dietary goals, and the user's past intake amount of each health-related component; and a priority determination means for determining the priority of each health-related component for the user using the contribution level of each health-related component and the user's deficiency amount of each health-related component, wherein the contribution level of each health-related component is set based on literature describing the health effects of the health-related component.

[0009] A health-related ingredient individual suggestion system according to one aspect of the present disclosure is a health-related ingredient individual suggestion system comprising a client terminal and a health-related ingredient individual suggestion device, comprising: input information acquisition means for acquiring input information including the user's dietary goals; deficiency amount calculation means for calculating the deficiency amount of each health-related ingredient using the recommended intake amount of a plurality of health-related ingredients identified from the input information and the user's past intake amount of each of the health-related ingredients; and importance determination means for determining the importance of each health-related ingredient to the user using the contribution of each health-related ingredient to the goals and the user's deficiency amount of each of the health-related ingredients.

[0010] A method for individually suggesting health-related components according to one aspect of this disclosure involves a computer performing the following steps: acquiring input information including the user's dietary goals; calculating the amount of each health-related component that is deficient using the recommended intake of a plurality of health-related components identified from the input information and the user's past intake of each health-related component; and determining the importance of each health-related component to the user using the contribution of each health-related component to the goals and the user's deficient amount of each health-related component.

[0011] A method for individually suggesting health-related components according to one aspect of this disclosure involves a computer performing the following steps: acquiring input information including the user's dietary goals; calculating the deficiency of each health-related component using the recommended intake of each health-related component identified from the user's dietary goals and the user's past intake of each health-related component; and determining the priority of each health-related component for the user using the contribution level of each health-related component and the user's deficiency. The contribution level of each health-related component is set based on literature describing the health effects of that health-related component.

[0012] A program for individually suggesting health-related components according to one aspect of this disclosure causes a computer to function as an individual suggestion device for the health-related components.

[0013] According to the individual health-related ingredient suggestion device and individual health-related ingredient suggestion method of this disclosure, it is possible to suggest health-related ingredients that take into account the user's dietary goals.

[0014] This figure shows an example of the network configuration of the individual proposal system for health-related components according to the first embodiment of this disclosure. This schematic configuration diagram shows an example of the hardware configuration of the individual proposal device for health-related components according to the first embodiment of this disclosure. This functional configuration diagram shows an example of the functions provided by the individual proposal device for health-related components according to the first embodiment of this disclosure. This figure shows an example of the input screen according to the first embodiment of this disclosure. This figure shows an example of the input screen according to the first embodiment of this disclosure. This flowchart shows an example of the processing procedure for the individual proposal method for health-related components according to the first embodiment of this disclosure. This functional configuration diagram shows an example of the functions provided by the individual proposal device for health-related components according to the second embodiment of this disclosure. This flowchart shows an example of the procedure for the method of calculating the health contribution of health-related components according to the second embodiment of this disclosure. This flowchart shows an example of the procedure for the method of calculating the health contribution of necessary nutrients according to the second embodiment of this disclosure. This functional configuration diagram shows an example of the functions provided by the individual proposal device for health-related components according to Modification 4 of this disclosure.

[0015] In this disclosure, "food" means edible food and beverages, including dishes and ingredients. "Food" also includes food and beverages that become edible after processing. For example, it includes food and beverages that are inedible in their raw state but can be consumed after being cooked or otherwise prepared.

[0016] Furthermore, in this disclosure, "food ingredients" means the units of raw materials that constitute food.

[0017] Examples of "food items" include sushi, ramen, tomato pasta, sliced ​​bread, milk, and sugar. Examples of "ingredients" include rice, tuna, wheat flour, carrots, pork, tomatoes, and shimeji mushrooms.

[0018] In this disclosure, "health-related ingredients" refers to ingredients that have been shown to have a certain effect on improving health. Essential nutrients are elements for which reference values ​​have been established, and in Japan, this refers to nutrients listed in the nutrient intake standards set by the Ministry of Health, Labour and Welfare.

[0019] Essential nutrients include carbohydrates, proteins, lipids, saturated fatty acids, n-6 fatty acids, n-3 fatty acids, dietary fiber, various vitamins, and various minerals. Vitamins include vitamin A, vitamin D, vitamin E, vitamin K, vitamin B1 (thiamine), vitamin B2 (riboflavin), niacin, vitamin B6, vitamin B12, folic acid, pantothenic acid, biotin, and vitamin C. Minerals include sodium, potassium, calcium, magnesium, phosphorus, iron, zinc, copper, manganese, iodine, selenium, chromium, and molybdenum.

[0020] Health-related components refer to food components that are involved in biological regulatory functions as a tertiary function of food, and functional components are one example of this. Ingredients related to health include polyphenols, carotenoids, isothiocyanates, GABA (gamma-aminobutyric acid), L-ornithine hydrochloride, L-serine, L-theanine, alpha-linolenic acid, astaxanthin, ornithine, glycine, glucosylceramide, crocetin, chlorogenic acids derived from coffee beans, sulforaphane glucosinolate, zeaxanthin, vanillic acid, xanthophyll derived from paprika, pantothenic acid, biotin, beta-carotene, lycopene, procyanidin derived from apples, lutein, rosmarinic acid, soy isoflavones, catechin, flavonoids, quercetin, rutin, ellagic acid, tannin, fucoxanthin, soy peptides, lactic acid bacteria peptides, inulin, fructooligosaccharides, galactooligosaccharides, soy oligosaccharides, lactin Examples include glucose, indigestible dextrin, glucomannan, β-glucan, lactic acid bacteria (Lactobacillus genus), bifidobacteria (Bifidobacterium genus), Lactococcus bacteria, Enterococcus bacteria, probiotic bacteria, DHA (docosahexaenoic acid), EPA (eicosapentaenoic acid), γ-linolenic acid, oleic acid, medium-chain triglycerides (MCT), BCAAs (leucine, isoleucine, valine), arginine, glutamine, citrulline, coenzyme Q10, glucosamine, chondroitin, hyaluronic acid, melatonin, ergothioneine, saponins, chlorophyll, sesamin, caffeine, gingerol, shogaol, sulforaphane, fucoidan, pterostilbene, and hesperidin.

[0021] The definitions of essential nutrients and health-related components may vary depending on national regulations and international standards (e.g., CODEX, EFSA, ISO, etc.). Therefore, essential nutrients and health-related components can be appropriately modified in accordance with various regulations and standards, such as those established in the country where the service is provided or international standards. If there are no regulations regarding essential nutrients or health-related components in the country where the service is provided, regulations from other countries or private organizations may be used. Even if the country where the service is provided has its own regulations, it may be appropriate to refer to information from other countries or organizations as needed and set the essential nutrients and health-related components accordingly. In this way, when providing services not only in Japan but also overseas, it is possible to set the requirements appropriately by referring to the regulations of the country where the service is provided, international regulations, private regulations, etc. For example, essential nutrients may include sugars and polyunsaturated fatty acids.

[0022] [First Embodiment] Below, an individual body condition-related component suggestion device 10 and an individual body condition-related component suggestion method according to the first embodiment of this disclosure will be described with reference to the drawings. Figure 1 is a diagram showing an example of the network configuration of an individual body condition-related component suggestion system (hereinafter simply referred to as the "individual suggestion system") 1 according to this embodiment. As shown in Figure 1, the individual suggestion system 1 comprises an individual body condition-related component suggestion device (hereinafter simply referred to as the "individual suggestion device") 10 and a client terminal 70. The individual suggestion device 10 and the client terminal 70 are connected via a network 3 and are configured to send and receive information to each other. Examples of networks include Bluetooth networks, the Internet, Wi-Fi, Li-Fi, mobile communication systems (3G, 4G, 5G, 6G, LTE, etc.), wireless LANs, wired LANs, etc. Connections may also be made via dedicated lines, VPNs, etc.

[0023] Figure 1 shows two client terminals 70 as an example, but the number of client terminals 70 connected to the individual proposal device 10 is not limited to this. Examples of client terminals 70 include desktop PCs, notebook PCs, tablet terminals, mobile phone terminals, smartphones, and wearable devices (smartwatches, smart bands, etc.), both fixed and portable devices.

[0024] The client terminal 70 is, for example, an information processing device used by a user who utilizes the services provided by the individual suggestion device 10. The client terminal 70 may be installed in various types of stores. For example, the client terminal 70 may be installed in a cosmetics store that sells cosmetics, a food store that sells food, etc., and can be used in a way that allows the user to directly input data or for store staff to input data while conversing with the user.

[0025] Figure 2 is a schematic diagram showing an example of the hardware configuration of the custom-designed device 10. The custom-designed device 10 is a so-called server (computer), and as shown in Figure 2 as an example, it is equipped with a CPU (Central Processing Unit: processor) 11, main memory 12, secondary storage 13, communication device 14, etc. These components are connected directly or indirectly via a bus 18.

[0026] The individual suggestion device 10 may include an external interface for connecting external devices, an input device for the user to perform input operations, a display for displaying data, and the like.

[0027] The CPU 11 may consist of one or more CPUs that cooperate with each other to perform processing. In addition to the CPU 11, other processors such as a GPU may also be provided.

[0028] The main storage device 12 is configured by a writable memory such as a RAM (Random Access Memory), for example, and is used as a work area for reading execution programs of the CPU 11, writing processing data by the execution programs, and the like. A plurality of main storage devices 12 may be provided.

[0029] The secondary storage device 13 is a non-transitory computer readable storage medium. Examples of the secondary storage device 13 include semiconductor memory, magnetic disks, magneto-optical disks, optical disks, CD-ROMs, DVD-ROMs, and the like. Specific examples include SSD (Solid State Drive), HDD (Hard Disc Drive), and the like. A part of the secondary storage devices 13 may be provided as cloud storage.

[0030] The secondary storage device 13 stores, for example, an OS, applications, and various data and files for realizing the functions of the individual proposing device 10. For example, as one example, a series of processes for realizing various functions of the individual proposing device 10 described later are stored in the secondary storage device 13 in the form of a program. The CPU 11 reads this program into the main storage device 12 and executes information processing and arithmetic processing, whereby various functions are realized. A plurality of secondary storage devices 13 may be provided, and programs and data for realizing processes (functions) described later may be divided and stored in each secondary storage device 13.

[0031] Furthermore, programs for realizing various functions of the individual proposing device 10 described later may be provided in the following modes: in addition to a mode pre-installed in the secondary storage device 13, a mode installed in a state stored in a computer-readable external storage (recording medium), and a mode distributed via wired or wireless communication means. The computer-readable storage medium is a magnetic disk, an optical disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, a semiconductor memory, or the like.

[0032] Next, functions of the individual proposal apparatus 10 according to the present embodiment will be described. FIG. 3 is a functional configuration diagram illustrating an example of functions provided in the individual proposal apparatus 10 according to the present embodiment.

[0033] As shown in FIG. 3, the individual proposal apparatus 10 includes, for example, a necessary nutrient database 21, a physical condition-related component database 22, a food database 23, and the like. The individual proposal apparatus 10 includes a contribution degree database 24. The individual proposal apparatus 10 includes, for example, a UI generation unit 31, an input information acquisition unit 32, a recommended intake setting unit 33, a deficiency calculation unit 34, and an importance determination unit 35. The individual proposal apparatus 10 may include a storage unit 41. The individual proposal apparatus 10 may include a food extraction unit 36, a model update unit 37, and the like.

[0034] [Necessary Nutrient Database 21] The necessary nutrient database 21 stores necessary nutrient information in which each necessary nutrient and the recommended intake thereof are set. The recommended intake of each necessary nutrient varies depending on user attributes such as age and gender. Accordingly, the recommended intake of each necessary nutrient may be registered for each user attribute such as age and gender.

[0035] Further, the recommended intake of each necessary nutrient may be registered in accordance with the purpose of dietary life. Furthermore, the recommended intake of each necessary nutrient may be registered for a combination of the purpose of dietary life and a user attribute.

[0036] Furthermore, the recommended intake of each necessary nutrient may be registered in association with at least one of a health condition, physical information (e.g., body weight, height, and the like), physical activity level, dietary preference, blood data, sleep electroencephalogram, heart rate data, and intestinal flora.

[0037] The nutrient database 21 is not limited to the above examples; it should simply store information for obtaining recommended intake amounts of essential nutrients for the user. For example, it may store a calculation formula that adjusts the recommended intake amount for each essential nutrient based on the recommended intake amount for each essential nutrient according to age and gender, using weighting coefficients for each essential nutrient associated with dietary goals, health status, physical information, physical activity level, food preferences, blood data, sleep electroencephalogram (EEG), heart rate data, and gut microbiota. It may also store a learning model that outputs the recommended intake amount for each essential nutrient from input information including user attributes and at least one of dietary goals, health status, physical information (e.g., weight, height), physical activity level, food preferences, blood data, sleep EEG, heart rate data, and gut microbiota.

[0038] [Health-Related Ingredient Database 22] The Health-Related Ingredient Database 22 stores information on health-related ingredients, including each health-related ingredient and its recommended intake. The recommended intake of each health-related ingredient varies depending on user attributes such as age and gender. Therefore, the recommended intake of each health-related ingredient may be registered separately for each user attribute such as age and gender.

[0039] Furthermore, the health-related component information may include recommended intake amounts for each health-related component according to the purpose of the diet. For example, recommended intake amounts for each health-related component may be registered for combinations of dietary purpose and user attributes. The health-related components registered in the health-related component information may differ depending on the purpose. In other words, for each purpose, health-related components related to that purpose and their recommended intake amounts may be registered.

[0040] Furthermore, recommended intake levels for each health-related component may be registered in association with at least one of the following: health status, physical information (e.g., weight, height), physical activity level, dietary preferences, blood data, sleep electroencephalogram (EEG), heart rate data, and gut microbiota.

[0041] The database of health-related components 22 is not limited to the above examples, but only contains information for obtaining recommended intake amounts of health-related components for the user. For example, it may store a calculation formula that adjusts the recommended intake amount of each health-related component based on the recommended intake amount of each health-related component according to age and gender, using weighting coefficients for each necessary nutrient associated with dietary goals, health status, physical information, physical activity level, food preferences, blood data, sleep electroencephalogram, heart rate data, and gut microbiota. It may also store a learning model that outputs the recommended intake amount of each health-related component from input information including user attributes, dietary goals, health status, physical information (e.g., weight, height), physical activity level, food preferences, blood data, sleep electroencephalogram, heart rate data, and at least one of gut microbiota.

[0042] [Food Database 23] Food Database 23 stores food names associated with health-related components contained in those foods. The content of health-related components may be further associated with the food names. The essential nutrients contained in those foods and their content may also be further associated with the food names. Furthermore, Food Database 23 may further associate food names with recipes for those foods.

[0043] [Contribution Database 24] The contribution database 24 stores contribution information, for example, which associates the purpose of a diet with the contribution (score) of each health-related component. Here, the content of the registered health-related components may differ for each dietary purpose. This is because the health-related components that are effective differ depending on the purpose.

[0044] The contribution of each health-related component is determined, for example, using probabilistic logical inference. For instance, the contribution of each health-related component is determined as a probabilistic score obtained by learning or analyzing a dataset that associates data indicating the achievement of a target state with data on the intake of each health-related component. A learning model is used for this learning, and statistical processing is used for the analysis.

[0045] As an example of a learning model, a probabilistic logic model can be used. Examples of probabilistic logic models include Bayesian models and logistic regression, which are used to probabilistically predict the relationship between intake and goal achievement. In particular, Bayesian models are strong in predictions that take uncertainty into account, and logistic regression is a suitable method for binary classification problems. Machine learning models can also be used for learning. Examples include decision trees, random forests, and support vector machines (SVMs), which are effective in modeling the complex nonlinear relationship between intake and goal achievement.

[0046] The following describes an example of a method for calculating the contribution of each health-related component. For example, if the goal of dietary habits is to achieve quality sleep, health-related components associated with sleep are extracted. For example, health-related components are extracted based on systematic reviews. Examples of health-related components associated with sleep include (A) glycine, lutein, zeaxanthin, (B) GABA, and (C) serine.

[0047] Next, data obtained from multiple users, which associates information on their wakefulness (e.g., a score) with information on the intake of each health-related component, is divided into two groups: a group that wakes up well (the group that achieves the goal) and a group that wakes up poorly (the group that does not achieve the goal). Here, the information on the intake of health-related components may be shown as a binary value, for example, whether or not the recommended daily intake was consumed. The recommended intake may be a different value from the recommended intake mentioned above.

[0048] Next, data normalization and other preprocessing are performed on the information regarding the intake amounts of each health-related component taken by the group that woke up easily and the information regarding the intake amounts of each health-related component taken by the group that woke up poorly. Preprocessing allows for the acquisition of a stable learning model even when the scales of each health-related component differ. Then, a learning model (e.g., logistic regression) is constructed using the above data, and the contribution of each health-related component to the objective is obtained as a probabilistic score. In this way, by setting the contribution as a probabilistic score, it becomes possible to qualitatively and quantitatively evaluate the contribution of each health-related component to the objective.

[0049] The contribution (score) of each health-related component obtained in this way is associated with the purpose of the diet (in the above case, "improvement of sleep") and stored in the contribution database 24. The contribution is set to a value between 0 and 1, for example.

[0050] The contribution calculation method described above is just one example and is not limited to it. For example, the data used in the learning model may be binary data indicating whether or not each of multiple objectives has been achieved. For example, a learning model may be constructed using data that associates information about the user's sleep status, skin condition, etc. (e.g., "1" for good condition, "0" for poor condition) with information about the amount of each health-related component consumed by the user. This makes it possible to construct a learning model that can obtain the contribution of each health-related component to a combination of multiple objectives, and also makes it possible to obtain the contribution of each health-related component to various objectives using a single learning model.

[0051] [UI Generation Unit 31] The UI generation unit 31 generates screen data to be displayed on the client terminal 70 and transmits the generated screen data to the client terminal 70. For example, the UI generation unit 31 generates input screen data to obtain input information necessary to make individual suggestions for health management components to the user. The UI generation unit 31 generates evaluation result screen data that includes at least the importance of each health-related component determined by the importance determination unit 35, which will be described later. The UI generation unit 31 generates food suggestion screen data that includes information on foods extracted by the food extraction unit 36, which will be described later. The screen data generated by the UI generation unit 31 is transmitted to the corresponding client terminal 70. As a result, various screen data are displayed on the display screen of the client terminal 70.

[0052] Figures 4 and 5 show examples of input screens. As shown in Figure 4, the input screen includes an input field R1 for entering user attribute data. Examples of user attribute data include gender and age. The input screen may also include input fields for entering physical data such as height and weight. The input screen may also include an input field for entering health status.

[0053] As shown in Figure 4, the input screen includes an input field R2 for entering the purpose of one's diet. Here, "purpose related to diet" may include goals related to diet. In Figure 4, multiple purposes are provided as options, and the user can select from among them. Note that the input method is not limited to this. For example, the user may directly input using an input device such as a keyboard. The purpose of diet may be configured to allow input of multiple purposes. Figure 4 illustrates a case where two purposes can be entered.

[0054] As shown in Figure 5, the input screen includes an input field R3 for the user to enter nutritional information. Figure 5 shows an example of a field for entering the food eaten in the last two meals, but this is not the only example. For instance, the field could be used to enter foods the user frequently eats.

[0055] Input screen data, as illustrated in Figures 4 and 5, is generated by the UI generation unit 31 and transmitted to the client terminal 70, whereupon the input screen is displayed on the client terminal 70's display unit. When the user enters the necessary information on the input screen and performs a predetermined transmission operation, the entered information is transmitted to the individual suggestion device 10.

[0056] [Input Information Acquisition Unit 32] The input information acquisition unit 32 acquires input information transmitted from the client terminal 70. Specifically, the input information acquisition unit 32 acquires input information including the user's attribute data and the purpose related to their diet. The input information includes information about the food consumed by the user.

[0057] The input information acquired by the input information acquisition unit 32 is stored in the storage unit 41. At this time, the input information is stored in association with a user ID for identifying the user. The user ID may be entered on the input screen or may be assigned automatically.

[0058] [Recommended Intake Setting Unit 33] The recommended intake setting unit 33 sets the recommended intake for each necessary nutrient and each health-related component identified from the user's input information. Specifically, the recommended intake setting unit 33 sets the recommended intake for each necessary nutrient using the necessary nutrient information stored in the necessary nutrient database 21. For example, it sets the recommended intake for each necessary nutrient using the necessary nutrient information corresponding to the input information (e.g., gender and age) acquired by the input information acquisition unit 32. The recommended intake for each necessary nutrient may also be set considering the purpose of the diet. Furthermore, the recommended intake for each necessary nutrient may be adjusted using at least one of the health status, physical information (e.g., weight, height, etc.), and activity level. Furthermore, a learning model may be used to obtain the recommended intake for each necessary nutrient from input information including user attributes and at least one of the purpose of the diet, health status, physical information (e.g., weight, height, etc.), and activity level.

[0059] Similarly, the recommended intake setting unit 33 sets the recommended intake amount for each health-related component using the health-related component information stored in the health-related component database 22. For example, it sets the recommended intake amount for each health-related component using the health-related component information corresponding to the input information acquired by the input information acquisition unit 32. For example, the recommended intake setting unit 33 may set the recommended intake amount for each health-related component considering the purpose of the diet. Furthermore, the recommended intake amount for each health-related component may be adjusted using at least one of gender, age, health status, physical information (e.g., weight, height, etc.), and activity level. Moreover, a learning model may be used to obtain the recommended intake amount for each health-related component from input information including user attributes and at least one of the purpose of the diet, health status, physical information (e.g., weight, height, etc.), and activity level.

[0060] [Deficiency Calculation Unit 34] The deficiency calculation unit 34 searches the food database 23 based on the food information included in the input information, that is, the food information consumed by the user, and obtains the amount of essential nutrients and health-related components consumed by the user as the amount already consumed. Then, by subtracting the user's amount already consumed from the recommended intake amount of each essential nutrient and health-related component set by the recommended intake amount setting unit 33, it calculates the essential nutrients and health-related components that are deficient and the amount of the deficiency.

[0061] [Importance Determination Unit 35] The importance determination unit 35 determines the importance of each health-related component for the user by using the contribution of each health-related component to the purpose of the diet and the amount of deficiency of the user in each health-related component. For example, the importance is calculated as a score.

[0062] For example, the importance determination unit 35 retrieves the contribution of each health-related component corresponding to the user's objective from the contribution database 24, which stores contribution information that associates the purpose of the diet with the contribution of each health-related component.

[0063] The importance determination unit 35 extracts user physical condition-related components associated with the user's purpose, and calculates a deficiency ratio for each extracted physical condition-related component based on the deficiency amount of said component. Subsequently, the importance determination unit 35 determines the importance using the contribution degree of each physical condition-related component and the corresponding deficiency ratio. The importance is determined such that the higher the contribution degree and the smaller the deficiency ratio, the higher the importance. Further, the contribution degree may be calculated using at least one of an importance level based on a systematic review and a probability score calculated by probabilistic logical inference.

[0064] For example, the importance determination unit 35 calculates the importance of each physical condition-related component using an importance calculation formula that takes the contribution degree of each physical condition-related component and the corresponding deficiency ratio as parameters. For example, the importance can be calculated by the following arithmetic expression.

[0065] Importance = (α １ × Contribution degree) × (β １ × Deficiency ratio) (1)

[0066] In the above formula (1), α １ (0 < α １ ≤ 1), β １ (0 < β １ ≤ 1) are weighting coefficients. The values of the weighting coefficients α １ and β １ can be arbitrarily set depending on whether emphasis is placed on the contribution degree or the deficiency ratio. Further, it is preferable to set such that α １ + β １ = 1 holds.

[0067] Furthermore, the importance determination unit 35 may also set the importance of each required nutrient. The importance of a required nutrient may be determined, for example, using the rank of each required nutrient and the corresponding deficiency ratio. The importance is determined such that the higher the rank and the higher the deficiency ratio, the higher the importance.

[0068] Nutrients are ranked according to their required intake levels, with those listed as recommended intake levels receiving higher ranks and other required nutrients receiving lower ranks. The ranks are set as scores greater than 0 and less than or equal to 1. The ranks may also differ depending on whether or not a recommended intake level is set. In this case, the ranks would be in the order of recommended intake > recommended intake > other nutrients, with higher values ​​in that order.

[0069] The importance determination unit 35 calculates the percentage of deficiency of essential nutrients. The calculation method is the same as that for the health-related components described above, so the explanation is omitted. The importance determination unit 35 then calculates the importance of essential nutrients using an importance calculation formula that uses the rank of the essential nutrient and its deficiency percentage as parameters. For example, importance can be calculated using the following formula.

[0070] Importance = γ(α ２ × rank) × (β ２ (× percentage of shortfall) (2)

[0071] In equation (2) above, α ２ (0 < α) ２ ≤1), β ２ (0 < β) ２ ≤1) is the weighting coefficient. Weighting coefficient α ２ , β ２ This can be set arbitrarily depending on whether you prioritize contribution or the percentage of shortage. Also, α ２ +β ２ It is best to set it so that it equals 1.

[0072] In equation (2) above, γ is a weighting coefficient that can be arbitrarily set depending on whether essential nutrients or health-related components are given more importance. If essential nutrients are prioritized, a value greater than 1 should be set; if health-related components are prioritized, a value of 1 or less should be set.

[0073] [Food Extraction Unit 36] The food extraction unit 36 ​​extracts foods containing one or more essential nutrients and / or health-related components of high importance from the food database 23, which associates foods with essential nutrients and health-related components. It may also further extract information about those foods and their recipes. This extracts information about foods containing health-related components that are important for the user to achieve their goals. The food extraction unit 36 ​​outputs the extracted food information to the UI generation unit 31.

[0074] [Model Update Unit 37] The model update unit 37 updates the learning model using the information acquired by the input information acquisition unit 32. For example, by acquiring the user's current health status (e.g., sleep status, skin condition, etc.) as input information, it becomes possible to understand what goals the user has achieved and what goals they have not achieved. The model update unit 37 performs preprocessing to make the information acquired by the input information acquisition unit 32 into data that is easy to apply to the learning model, and updates the learning model using the preprocessed data. The learning model can be updated repeatedly at predetermined time intervals. By updating the learning model in this way, it is possible to improve the accuracy of calculating the contribution of each health condition-related component.

[0075] Next, the method for individually suggesting health-related components according to this embodiment will be described with reference to Figure 6. The series of processes shown below are stored as a program (for example, an individual suggestion program for health-related components) in the secondary storage device 13 of the individual suggestion device 10, and are realized when the CPU 11 reads this program into the main storage device 12 and executes it.

[0076] Figure 6 is a flowchart showing an example of the processing procedure for the individual proposal method for health-related components according to this embodiment.

[0077] First, the individual suggestion device 10 transmits input screen data (see Figures 4 and 5) to the client terminal 70 (SA1). As a result, the input screen is displayed on the client terminal 70's display screen. On this input screen, the user enters the necessary information and performs a transmission operation. As a result, the individual suggestion device 10 acquires input information such as the user's attribute data, dietary goals, and food information (information about the food the user has eaten) (SA2). This acquired information is associated with a user ID that identifies the user and stored in the storage unit 41.

[0078] Next, the individual suggestion device 10 sets the recommended intake amounts for each essential nutrient and each health-related component identified from the user's input information. For example, the individual suggestion device 10 identifies essential nutrient information corresponding to the input information from the essential nutrient database 21 and sets the recommended intake amount for each essential nutrient using the identified essential nutrient information. The individual suggestion device 10 identifies health-related component information corresponding to the input information from the health-related component database 22 and sets the recommended intake amount for each health-related component using the identified health-related component information (SA3). Here, the input information includes, for example, the purpose related to diet. The input information may also further include at least one of health status or user attributes (e.g., gender, age, physical information, etc.).

[0079] Next, the individual suggestion device 10 searches the food database 23 based on the food information included in the input information, for example, the food information consumed by the user, and obtains the amount of essential nutrients and health-related components consumed by the user as the amount already consumed (SA4). Then, by subtracting the amount already consumed by the user from the recommended intake amount of each essential nutrient and health-related component set by the recommended intake amount setting unit 33, it calculates the essential nutrients and health-related components that are deficient and the amount of the deficiency (SA5).

[0080] Next, the individual suggestion device 10 determines the importance of each health-related component related to the purpose of the diet (SA6). Specifically, the individual suggestion device 10 obtains the contribution of each health-related component related to the purpose of the diet from the contribution database 24, and calculates the deficiency rate from the deficiency amount of each health-related component related to the purpose of the diet. Then, it determines the importance of each health-related component using the obtained contribution and deficiency rate of each health-related component.

[0081] The individual suggestion device 10 determines the importance of the necessary nutrients (SA7). Specifically, the individual suggestion device 10 determines the importance of each necessary nutrient using its rank and the percentage of deficiency.

[0082] Next, the individual suggestion device 10 extracts information on ingredients containing one or more essential nutrients and / or health-related components of high importance from the food database 23 (SA8), generates food suggestion screen data including the extracted ingredient information, and transmits it to the client terminal 70 (SA9). This food suggestion screen may include evaluation results, including the importance of each essential nutrient and each health-related component calculated in steps SA6 and SA7. As a result, the display screen of the client terminal 70 displays information on ingredients containing essential nutrients and / or health-related components of high importance for the user to achieve their dietary goals (e.g., improving sleep, improving skin condition, etc.). By displaying health-related components related to dietary goals and their importance, it becomes possible to provide the user with an indicator of which health-related components they should actively consume.

[0083] Next, the individual suggestion device 10 updates the learning model used to calculate the contribution using the input information stored in the memory unit 41, and then terminates this process (SA10).

[0084] As described above, the individual health-related component suggestion device 10 according to this embodiment includes: an input information acquisition unit 32 that acquires input information including the user's dietary goals; a deficiency calculation unit 34 that calculates the deficiency amount of each health-related component using the recommended intake amounts of a plurality of health-related components identified from the input information and the user's past intake amounts of each health-related component; and an importance determination unit 35 that determines the importance of each health-related component for the user using the contribution of each health-related component to the goal and the user's deficiency amount of each health-related component.

[0085] According to this embodiment, it is possible to determine health-related components that are of high importance for the user's dietary goals (e.g., improving sleep, improving skin condition, etc.). By providing the user with the determined high-importance health-related components, it is possible to suggest health-related components that take into account the user's dietary goals. By providing information on foods that contain a large amount of high-importance health-related components, useful information that helps achieve dietary goals can be provided.

[0086] In the embodiment described above, we explained a case where contribution information, which associates the purpose of dietary habits with the contribution (score) of each health-related component, is pre-stored in the contribution database 24. However, the invention is not limited to this example. For example, the contribution database 24 may store a dataset that associates the achievement status of the purpose with information on the intake of health-related components. Then, depending on the purpose of the user's diet obtained as input information, data related to the purpose may be extracted from the dataset, and the contribution of each health-related component corresponding to the purpose of the user's diet may be obtained by constructing a learning model as described above using the extracted data. The method for constructing the learning model is as described above.

[0087] [Second Embodiment] Next, the individual suggestion device 10a and the individual suggestion method for health-related components according to the second embodiment of this disclosure will be described with reference to the drawings. In the first embodiment described above, the contribution of each health-related component stored in the contribution database 24 was set as a probabilistic score obtained by using a learning model or statistical processing to associate data indicating the achievement of the objective with data relating to the intake amount of each health-related component. In contrast, the main difference in this embodiment is that the contribution of each health-related component is set without using a learning model. Hereinafter, components identical to those in the first embodiment will be denoted by the same reference numerals and their descriptions will be omitted, and the differences will be described in detail.

[0088] Figure 7 is a functional configuration diagram showing an example of the functions provided by the individually proposed device 10a according to this embodiment.

[0089] [Contribution Database 24a] The contribution database 24a has scores assigned to each essential nutrient and each health-related component. An example of the method for calculating the contribution score is described below.

[0090] First, the contribution of health-related components is determined based on literature describing the health effects of these components. For example, if there is supporting literature such as health-related papers, the contribution is set according to the degree of health effects discussed in those papers, such as whether it is effective, effective but with negative effects, or the effect is unknown. In other words, the more effective it is, the higher the contribution is set. If there is no supporting literature, the contribution is set according to whether or not statistical analysis data is available. In this case, the contribution will be lower than when there is supporting literature.

[0091] Figure 8 is a flowchart illustrating an example of the procedure for calculating the health contribution of health-related components. The health contribution of each health-related component is determined by following the flowchart shown in Figure 8. The contribution values ​​shown below are examples and can be set as appropriate.

[0092] First, for the health-related component being evaluated, it is determined whether or not there is any supporting evidence (SB1). If at least one supporting document exists (SB1: YES), "1.0" is added to the contribution score (SB2). Next, it is determined whether or not all supporting documents indicate a health effect (SB3). If all supporting documents indicate a health effect (SB3: YES), 1 is added to the contribution score (SB4). As a result, the final contribution score for this health-related component is "2.0".

[0093] If the result in step SB3 is negative (SB3: NO), then it is determined whether or not a negative effect is stated (SB5). If a negative effect is stated (SB5: YES), "0.5" is added to the contribution (SB6). As a result, the final contribution of this health-related component becomes "1.5".

[0094] If the result in step SB5 is negative (SB5: NO), the effect is determined to be unknown, no additional points are added to the contribution score, and the final contribution score for this health-related component becomes "1.0" (SB7).

[0095] On the other hand, if it is determined in step SB1 that there are no supporting documents (SB1: NO), it is determined whether or not there is statistical analysis data (SB9). If statistical analysis data related to health is found (SB9: YES), the contribution is set to "0.25" (SB10). On the other hand, if there is no statistical analysis data (SB9: NO), the contribution is set to 0 (SB11).

[0096] For health-related components with equal contributions, a more detailed contribution level may be determined by comprehensively evaluating factors such as the total number of supporting documents, the total number of documents stating effectiveness, and whether the documents describe research based on data from the country providing the service.

[0097] In this way, once the contribution level is obtained for each health-related component, contribution level information is generated that associates the health-related component with the contribution level, and this information is stored in the contribution level database 24a. Since the publicly available evidence documents and statistical analysis data are updated daily, the contribution levels in the contribution level database 24a may be updated periodically based on the latest information.

[0098] Next, we will explain the contribution of essential nutrients. For essential nutrients, the contribution level will be set according to the intake levels indicated by national and other organizations. For example, different contribution levels will be set depending on whether the intake level is labeled as a recommended daily allowance or an estimated daily allowance.

[0099] Figure 9 is a flowchart illustrating an example of the procedure for calculating the health contribution of essential nutrients. The health contribution of each essential nutrient is determined by following the flowchart shown in Figure 9. The specific numerical values ​​for contribution shown below are examples and can be set as appropriate.

[0100] First, it is determined whether the intake amount of the required nutrient to be processed is set as a recommended amount (SC1). If it is set as a recommended amount (SC1: YES), the contribution is set to "2.5" (SC2). On the other hand, if it is not set as a recommended amount (SC1: NO), it is determined whether it is set as a guideline amount (SC3). If it is set as a guideline amount (SC3: YES), the contribution is set to "1.5" (SC4). On the other hand, if it is not set as a guideline amount (SC3: NO), the contribution is set to "1.25" as an alternative notation (SC5).

[0101] As described above, in this embodiment, essential nutrients are considered to have a higher contribution than components related to physical condition. This is because essential nutrients are components that are indispensable for maintaining daily health.

[0102] In this way, once the contribution level is obtained for each required nutrient, contribution level information is generated that associates the required nutrient with the contribution level, and this information is stored in the contribution level database 24a.

[0103] [Recommended Intake Setting Unit 33a] The recommended intake setting unit 33a obtains and sets the recommended intake amounts for each necessary nutrient corresponding to the user's dietary goals and / or user attributes (e.g., gender and / or age) obtained by the input information acquisition unit 32 from the necessary nutrient database 21. The recommended intake setting unit 33a also obtains and sets the recommended intake amounts for each health-related component corresponding to the user's dietary goals and / or user attributes (e.g., gender and / or age) obtained by the input information acquisition unit 32 from the health-related component database 22.

[0104] [Importance Determination Unit 35a] The importance determination unit 35a determines the importance using the contribution levels of each health-related component and each required nutrient stored in the contribution database 24a, as well as the deficiency levels of each health-related component and each required nutrient calculated by the deficiency calculation unit 34.

[0105] For example, the importance determination unit 35a calculates the deficiency rate of each health-related component and each required nutrient. Then, it calculates the importance of each health-related component and each required nutrient using an importance calculation formula that uses this deficiency rate and contribution rate as parameters. For example, importance can be calculated using the following calculation formula.

[0106] Importance = (α ３ × Contribution) × (β ３ × (Percentage of shortfall) (3)

[0107] In equation (3) above, α ３ (0 < α) ３ ≤1), β ３ (0 < β) ３ ≤1) is the weighting coefficient. Weighting coefficient α ３ , β ３ This can be set arbitrarily depending on whether you prioritize contribution or the percentage of shortage. Also, α ３ +β ３ It is best to set it so that it equals 1.

[0108] Next, the method for individually suggesting health-related components according to this embodiment will be briefly described. The series of processes shown below are stored as a program (for example, an individual suggestion program for health-related components) in the secondary storage device 13 of the individual suggestion device 10, and are realized when the CPU 11 reads this program into the main storage device 12 and executes it. Processes similar to those in the first embodiment described above will be simplified or omitted.

[0109] First, the personalized suggestion device 10 acquires input information including the user's attribute data and their dietary goals. Next, the personalized suggestion device 10 sets the recommended intake amounts for each necessary nutrient and each health-related component, which are identified from the user's input information (user attributes and dietary goals).

[0110] Next, the individual suggestion device 10 searches the food database 23 based on the food information included in the input information, for example, the food information consumed by the user, and obtains the intake amounts of essential nutrients and health-related components of the food consumed by the user as previously consumed amounts. Then, by subtracting the user's previously consumed amounts from the recommended intake amounts of each essential nutrient and health-related component set by the recommended intake amount setting unit 33a, it calculates the essential nutrients and health-related components that are deficient and the amount of the deficiency.

[0111] Next, the individual suggestion device 10 determines the importance of each health-related component related to the purpose of the diet. Specifically, the individual suggestion device 10 determines the importance using the contribution of each health-related component and each necessary nutrient stored in the contribution database 24a, as well as the deficiency amounts of each health-related component and each necessary nutrient calculated by the deficiency amount calculation unit 34.

[0112] Next, the individual suggestion device 10 extracts information on ingredients containing one or more essential nutrients and / or health-related components with high importance from the food database 23 based on the determined importance level, generates food suggestion screen data including the extracted food information, transmits it to the client terminal 70, and terminates the process.

[0113] As explained above, the individual health-related component suggestion device 10a according to this embodiment does not require reliance on the expertise of registered dietitians or other professionals, as it uses existing officially recognized data (such as supporting documents) to set the contribution level of each health-related component. Furthermore, since no training data is required, it is possible to avoid problems such as insufficient data in the initial stages.

[0114] [Modification 1] For example, in the embodiments described above, various processes are performed in the individual health-related component suggestion devices 10 and 10a, but the invention is not limited to these cases. For example, a part of the processes performed by the individual health-related component suggestion devices 10 and 10a may be performed on the client terminal 70. In this case, data and other information can be sent and received between the individual health-related component suggestion devices 10 and 10a and the client terminal 70, and the data and programs necessary for the processing can be exchanged.

[0115] [Modification 2] In each embodiment, the individual suggestion devices 10, 10a for health-related components were described as examples of cases where they include a necessary nutrient database 21, a health-related component database 22, a food database 23, and a contribution database 24, but the invention is not limited to these. These databases 21 to 24 may be those located on a network, or various data may be obtained by accessing databases located on a server in the cloud.

[0116] [Modification 3] In the individual proposed devices 10 and 10a according to each embodiment described above, the food extraction unit 36 ​​was configured to extract foods containing one or more essential nutrients and / or health-related components of high importance from the food database 23, but it is not limited to this. For example, the food extraction unit 36 ​​may use at least one of the component content of the food, the availability of the food, and the frequency of use of the food as indicators, calculate a numerical score for each indicator, and extract foods based on the calculated scores.

[0117] For example, a food's score based on its nutrient content is given a higher score to foods that contain more of the nutrients the user is lacking. By using nutrient content as one of the indicators, it is possible to prioritize the extraction of foods that can efficiently supplement the nutrients the user is lacking.

[0118] The availability score for food products is calculated based on at least one of the following factors: price range, distribution area, distribution volume, and seasonality. For example, foods with lower prices, nationwide distribution, or high distribution volume are assigned higher scores. Foods with higher prices or those distributed only in specific regions may be assigned lower scores. Furthermore, regarding distribution areas, the score may be assigned considering whether the user's residential area is included in the distribution area. If the user's residential area is included in the distribution area, the food product is available for purchase to the user. In this way, by using availability as one of the indicators, it is possible to prioritize the extraction of foods that are "easy to use" in the user's daily life.

[0119] Furthermore, the score related to the frequency of food use is an indicator of how commonly the food is consumed on a daily basis, and is calculated based on at least one of the following: general consumption statistics, distribution volume data, and the user's past consumption history. Foods that are used more frequently are assigned higher scores. In this way, by using the frequency of food use as one of the indicators, it is possible to prevent the suggestion of foods that are theoretically nutritious but difficult to obtain, and to support dietary improvements that users can easily continue.

[0120] The food extraction unit 36 ​​may also extract food based on a comprehensive score obtained by integrating the scores set for each indicator (and further, for each element within each indicator). The comprehensive score is calculated, for example, as a weighted sum obtained by multiplying the scores of each indicator by a weighting coefficient. For example, the comprehensive score S is calculated by the following formula.

[0121] S = (w1 * component content score) + (w2 * availability score) + (w3 * frequency of use score)

[0122] Here, w1, w2, and w3 are weighting coefficients indicating the importance of each indicator, and can be arbitrarily set according to the user's dietary goals. For example, if the emphasis is on ingredient content, w1 may be set to a large value, while if the emphasis is on ease of use in daily life, w2 or w3 may be set to a large value.

[0123] The method for calculating the overall score is not limited to the above. For example, it may be a method of extracting only the foods that meet a predetermined threshold for each score and then ranking them, or a method of gradually narrowing down the selection by prioritizing certain indicators.

[0124] Furthermore, while the above describes extracting foods containing one or more essential nutrients and / or health-related components of high importance determined by the importance determination unit 35, it is not limited to this. For example, the method described above may be used to extract foods containing essential nutrients and health-related components that are deficient, as calculated by the deficiency calculation unit 34.

[0125] Thus, according to Modification 3, in addition to the content of the food's components, foods can be selected using availability and frequency of use as indicators. This makes it possible to present foods that can efficiently supplement the components that the user is lacking in a way that is easy to use in daily life.

[0126] [Modification 4] In each of the embodiments described above, information about the food consumed by the user was obtained as input information, and the amount of necessary nutrients and health-related components consumed by the user was obtained as the amount already consumed by searching the food database 23 based on this information. However, the embodiment is not limited to this. For example, the input information may be made more diverse, and the amount already consumed may be calculated based on such input information.

[0127] Hereinafter, the individualized health-related component suggestion device 10b according to this modified example will be described with reference to Figure 10. Figure 10 is a functional configuration diagram showing an example of the functions of the individualized health-related component suggestion device 10b according to Modification 4 of this disclosure. Although Figure 10 illustrates a modified example of the individualized health-related component suggestion device 10 according to the first embodiment described above, the configuration according to Modification 4 is also applicable to the individualized health-related component suggestion device 10a according to the second embodiment. Hereinafter, components identical to those in the first embodiment will be denoted by the same reference numerals and their descriptions will be omitted, and the different components will be described mainly.

[0128] For example, the input information acquisition unit 32a acquires information on foods consumed by the user within a predetermined period in the past as input information. Here, the information on foods consumed includes at least one of the following: food name (including dish name and ingredient name), food number, etc. Here, the food number is an input format that uses an identifier that can uniquely identify the food, and for example, identification information (e.g., a number) set in the standard food composition table published by a national organization or institution can be used. In Japan, for example, food numbers set based on the standard food composition table of Japan published by the Ministry of Education, Culture, Sports, Science and Technology can be used.

[0129] As for the input format for food intake information, for example, in the input screen shown in Figure 5, in addition to the field for entering the food name, fields for entering the ingredient name, food number, etc., may be provided, and the user may enter the necessary information in these fields. The user may also enter the amount of the food and / or ingredients consumed in association with this information. For example, for the food name "meat sauce spaghetti," the user may enter information indicating the amount consumed, such as "1 serving."

[0130] The input information acquisition unit 32 acquires information on ingested foods entered by the user and outputs it to the previously consumed amount setting unit 38. The previously consumed amount setting unit 38 acquires the health-related components and their content corresponding to the ingested food information from the food database 23a as the previously consumed amount. The food database 23a is a database that associates food names, food numbers, health-related components contained in the food, and their content. For example, the food database 23a could be a database constructed by adding health-related components and their content contained in each food, based on data from the "Standard Tables of Food Composition in Japan" published by the Ministry of Education, Culture, Sports, Science and Technology.

[0131] The previously consumed amount setting unit 38 searches the food database 23a using the food name included in the consumed food information as a search key. If the food name is registered in the food database 23a as a result, the unit retrieves the health-related components and their content associated with that food name.

[0132] In contrast, if the food name is not stored in the food database 23a, the previously consumed amount setting unit 38 estimates the multiple ingredients that make up that food from the food name. That is, the previously consumed amount setting unit 38 includes an analysis unit 381 for estimating the ingredients that make up that food from the food name. The analysis unit 381 estimates multiple ingredients from the input food name, for example, using a machine learning model or a generative model (for example, a large-scale language model). Subsequently, the previously consumed amount setting unit 38 uses each estimated ingredient name as a search key to obtain the health-related components and their content from the food database 23a. This makes it possible to obtain the health-related components and their content of the food from the information of the ingredients that make up that food, even if the food name corresponding to it is not registered in the food database 23a.

[0133] The previously consumed amount setting unit 38 searches the food database 23a based on the name of the food ingredient or food number included in the ingested food information, and obtains the health-related components and their content corresponding to the food ingredient or food number. In addition to the health-related components mentioned above, the previously consumed amount setting unit 38 may also obtain necessary nutrients and their content.

[0134] The previously consumed amount setting unit 38 calculates the previously consumed amount of each health-related component by adding up the content of each health-related component obtained. This previously consumed amount is output to the deficiency amount calculation unit 34a. The deficiency amount calculation unit 34a calculates the deficient essential nutrients and health-related components and the amount of the deficiency by subtracting the user's previously consumed amount from the recommended intake amount of each essential nutrient and each health-related component set by the recommended intake amount setting unit 33.

[0135] According to this modified example 4, the system allows users to input information about the food they have consumed in various formats, such as food name (including dish name, ingredient name, etc.) and food number, and can calculate the amount consumed based on this input information. As a result, even if a food name not registered in the food database 23a is entered, the system can estimate the constituent ingredients from the food name and use the estimated ingredient name as a search key to obtain health-related components and their content. Consequently, the amount consumed can be calculated for a wide variety of foods, not just registered foods.

[0136] Furthermore, if the user directly inputs the name of an ingredient or food number, the amount already consumed can be calculated using the ingredient information corresponding to that ingredient or food number, thereby simplifying the ingredient calculation process and improving calculation accuracy. Thus, according to this modified example 4, the amount already consumed can be calculated regardless of the format of the input information, making it possible to understand the user's diet more comprehensively and accurately, and improving the accuracy of individual suggestions for health-related ingredients.

[0137] In the modified example 4, the input information may include food information entered by reading a one-dimensional barcode or two-dimensional barcode attached to the food. For example, some food manufacturers display barcodes on the food packaging indicating the ingredients that make up the food. In such cases, food information may be entered by reading the barcode attached to the food using a camera or barcode reader connected to the client terminal 70. The food information may include, for example, the types of ingredients contained in the food, their proportions, the amount used, or ingredient information.

[0138] The previously consumed amount setting unit 38 may obtain the health-related components and their content of the food from the food database 23a based on the food information read. Alternatively, instead of the food database 23a, it may access a database managed by, for example, the food manufacturer, to obtain the health-related components and their content of the food. Thus, the method for calculating the user's previously consumed amount can take various forms, and the system may also be configured to obtain the information by appropriately combining the various forms described above.

[0139] Although the present disclosure has been described above using embodiments and modifications, the technical scope of this disclosure is not limited to the scope described in each embodiment and modification. Various changes or improvements can be made to the above embodiments and modifications without departing from the gist of the disclosure, and such changed or improved forms are also included in the technical scope of this disclosure. Furthermore, the processing flow described in the above embodiments is just one example, and unnecessary steps may be deleted, new steps added, or the processing order changed without departing from the spirit of this disclosure.

[0140] 1: Individual suggestion system for health-related components 3: Network 10: Individual suggestion device for health-related components 10a: Individual suggestion device for health-related components 10b: Individual suggestion device for health-related components 11: CPU 12: Main memory 13: Secondary memory 14: Communication device 18: Bus 21: Required nutrient database 22: Health-related component database 23: Food database 23a: Food database 24: Contribution database 24a: Contribution database 31: UI generation unit (UI generation means) 32: Input information acquisition unit (input information acquisition means) 33: Recommended intake amount setting unit 33a: Recommended intake amount setting unit 34: Deficiency amount calculation unit (deficiency amount calculation means) 35: Importance determination unit (importance determination means) 35a: Importance determination unit (importance determination means) 36: Food extraction unit (food extraction means) 37: Model update unit (model update means) 38: Previous intake amount setting unit 41: Memory unit 70: Client terminal 381: Analysis unit

Claims

1. A device for individually suggesting health-related components, comprising: an input information acquisition means for acquiring input information including the user's dietary goals; a deficiency calculation means for calculating the deficiency amount of each health-related component using the recommended intake amount of a plurality of health-related components identified based on the input information and the user's past intake amount of each of the health-related components; and an importance determination means for determining the importance of each health-related component to the user using the contribution of each health-related component to the goals and the user's deficiency amount of each of the health-related components.

2. The individual health-related component suggestion device according to claim 1, wherein the importance determination means obtains the contribution of each health-related component corresponding to the user's objective from a contribution database which stores contribution information relating each of the plurality of objectives to the contribution of each health-related component.

3. The individual health-related component suggestion device according to claim 1, wherein the contribution of each health-related component is a probabilistic score obtained by constructing a learning model using a dataset that associates data indicating the achievement status of each objective with data on the intake of each health-related component.

4. The individual suggestion device for health-related components according to claim 3, further comprising a model update means for updating the learning model using the information acquired by the input information acquisition means.

5. The individual health-related component suggestion device according to claim 1, wherein the importance determination means takes input information including the user's dietary goals as input and uses a learning model that outputs the contribution of a plurality of health-related components related to the goals as a probabilistic score to obtain the contribution of each health-related component corresponding to the user's goals.

6. The individual health-related component suggestion device according to claim 1, wherein the deficiency amount calculation means calculates the deficiency amount of each required nutrient using the recommended intake amounts of a plurality of required nutrients identified from the input information and the user's past intake amounts of each of the required nutrients, and the importance determination means determines the importance of each required nutrient for the user using a pre-set rank for each of the required nutrients and the user's deficiency amount for each of the required nutrients.

7. The individual health-related component suggestion device according to claim 1, comprising UI generation means for generating screen data to be displayed on a client terminal, wherein the UI generation means generates input screen data including an input field for inputting the objective and transmits it to the client terminal.

8. The individual health-related component suggestion device according to claim 1, wherein the UI generation means generates input screen data including input fields for inputting user attribute data and transmits it to the client terminal, and the input information acquisition means acquires the input information including the user attribute data and the purpose related to dietary habits.

9. The individual health-related component suggestion device according to claim 7, wherein the UI generation means generates evaluation result screen data that includes at least the importance of each health-related component determined by the importance determination means, and transmits it to the client terminal.

10. The individual health-related component suggestion device according to claim 7, comprising a food extraction means for extracting foods containing one or more health-related components of high importance from a food database in which foods are associated with each health-related component, wherein the UI generation means generates food suggestion screen data including information on the extracted foods and transmits it to the client terminal.

11. The individual suggestion device for health-related components according to claim 10, wherein the food extraction means uses at least one of the content of health-related components in the food, the availability of the food, and the frequency of use of the food as indicators, calculates a numerical score for each indicator, and extracts one or more health-related components of high importance based on the calculated scores.

12. The individual health-related component suggestion device according to claim 1, comprising a recommended intake setting means for obtaining the recommended intake of the health-related component corresponding to the input information, using a health-related component database in which health-related component information is registered, in which health-related components and their recommended intake amounts are set for each purpose related to the user's diet.

13. The individual suggestion device for health-related components according to claim 11, wherein the input information further includes at least one of the user's attribute data, health status, physical information, and activity level, and the recommended intake setting means adjusts the recommended intake of the health-related component using at least one of the user's attribute data, health status, physical information, and activity level.

14. The individual suggestion device for health-related components according to claim 1, wherein the input information further includes at least one of the user's attribute data, health status, physical information, and activity level, and the device comprises a recommended intake setting means for obtaining the recommended intake of the health-related components from the input information using a learning model that takes the input information as input and outputs the recommended intake of the health-related components.

15. The individual suggestion device for health-related components according to claim 1, wherein the input information includes information on foods consumed by the user within a predetermined period in the past, the information on foods consumed includes at least one of a food name and a food number, and the device further includes means for obtaining previously consumed amounts from a food database associated with the food name, the food number of the food, and each health-related component of the food and its content, corresponding to the information on foods consumed, as previously consumed amounts.

16. The device for individually suggesting health-related components according to claim 15, wherein the means for obtaining previously consumed amounts comprises an analysis means for estimating the ingredients that make up a food from the name of the food, and the health-related components and their content corresponding to each ingredient estimated by the analysis means are obtained from the food database.

17. A device for individually suggesting health-related components, comprising: an input information acquisition means for acquiring input information including the user's dietary goals; a deficiency calculation means for calculating the deficiency amount of each health-related component using the recommended intake amount of each health-related component identified from the user's dietary goals and the user's past intake amount of each health-related component; and a priority determination means for determining the priority of each health-related component for the user using the contribution level of each health-related component and the user's deficiency amount of each health-related component, wherein the contribution level of each health-related component is set based on literature describing the health effects of the health-related component.

18. A system for individually suggesting health-related components comprising a client terminal and a device for individually suggesting health-related components, the system comprising: an input information acquisition means for acquiring input information including the user's dietary goals; a deficiency amount calculation means for calculating the deficiency amount of each health-related component using the recommended intake amounts of a plurality of health-related components identified from the input information and the user's past intake amounts of each of the health-related components; and an importance determination means for determining the importance of each health-related component to the user using the contribution of each health-related component to the goals and the user's deficiency amount of each of the health-related components.

19. A method for individually suggesting health-related components, wherein a computer performs the following steps: acquiring input information including the user's dietary goals; calculating the amount of each health-related component that is deficient using the recommended intake of a plurality of health-related components identified from the input information and the user's past intake of each health-related component; and determining the importance of each health-related component to the user using the contribution of each health-related component to the goals and the user's deficient amount of each health-related component.

20. A method for individually suggesting health-related components, wherein a computer performs the following steps: acquiring input information including the user's dietary goals; calculating the amount of each health-related component that is deficient using the recommended intake of each health-related component identified from the user's attribute data and dietary goals, and the user's past intake of each health-related component; and determining the priority of each health-related component for the user using the contribution level of each health-related component and the user's health-related component deficiency, wherein the contribution level of each health-related component is set based on literature describing the health effects of the health-related component.

21. A program for causing a computer to function as an individual suggestion device for health-related components as described in any one of claims 1 to 17.

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