System

Abstract

To provide a system for ordering insufficient foodstuffs on-line by proposing an optimum menu on the basis of stock information of a home.SOLUTION: A system includes a reception section, a proposal section, and an ordering section. The reception section inputs stock information. The proposal section proposes a menu on the basis of the stock information inputted by the reception section. The ordering section orders insufficient foodstuffs on-line on the basis of the menu proposed by the proposal section.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology cannot suggest optimal menus based on household inventory information or automatically order missing ingredients, so there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal menus based on home inventory information and to order missing ingredients online. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, and an order unit. The reception unit inputs inventory information. The proposal unit proposes a menu based on the inventory information input by the reception unit. The order unit orders missing ingredients online based on the menu proposed by the proposal unit. Effect of the Invention

[0007] The system according to the embodiment proposes optimal menus based on home inventory information and can order missing ingredients online. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A health management and diet support system according to an embodiment of the present invention is a system in which a user inputs inventory information for a refrigerator or a food storehouse, and an AI proposes a menu suitable for an individual or a household based on the inventory information. This system can order missing ingredients online and deliver them to the home, taking into account the user's preset budget, allergy information, and information on disliked ingredients. For example, a user inputs a list of ingredients in the refrigerator and ingredients stored in the food storehouse. This information is input to the AI. The AI ​​then analyzes the input inventory information and proposes a menu suitable for the individual or household. The AI ​​generates an optimal menu taking into account the user's health management and diet goals. For example, a menu that emphasizes calorie restriction and specific nutrients is proposed. In addition, the user's preset budget, allergy information, and information on disliked ingredients are also considered. For example, if a user is allergic to a specific ingredient, a menu that does not include that ingredient is proposed. Also, if the user sets a disliked ingredient, a menu that avoids that ingredient is proposed. Missing ingredients are ordered online and delivered to the home. The AI ​​identifies missing ingredients based on the proposed menu, and places an online order within a preset budget. For example, if the user sets a budget, the optimal ingredients within that range are ordered. This will greatly reduce the trouble and effort that housewives have to make daily menu plans. Users only need to input the inventory information of their refrigerator and pantry, and menus optimal for health management and dieting will be suggested, and missing ingredients will be automatically ordered, saving time and effort. This allows the health management and diet support system to suggest menus that match the user's health management and dieting goals, and to automatically order missing ingredients.

[0029] The health management and diet support system according to the embodiment includes a reception unit, a proposal unit, and an order unit. The reception unit inputs inventory information of a refrigerator or a food storehouse by a user. The inventory information input by the user includes, for example, the type, quantity, and expiration date of ingredients, but is not limited to such examples. The reception unit can provide, for example, a method in which the user manually inputs the information, or a method in which the information is automatically input using a barcode scanner. The reception unit can also input the inventory information through a smartphone app. For example, the user can scan the barcode of the ingredients using the camera of the smartphone and automatically input the inventory information. The proposal unit uses AI to propose a menu based on the inventory information input by the reception unit. The proposal unit generates an optimal menu, for example, taking into account the user's health management and diet goals. For example, the proposal unit proposes a menu that emphasizes calorie restrictions and specific nutrients. The proposal unit also considers the user's preset budget, allergy information, and information on disliked ingredients. For example, if the user is allergic to a specific ingredient, the proposal unit proposes a menu that does not include that ingredient. If the user sets an ingredient that he or she dislikes, the proposal unit proposes a menu that avoids that ingredient. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest a menu using an AI model that inputs the user's health management and diet goals, budget, allergy information, and information on disliked ingredients and outputs an optimal menu. The ordering unit orders missing ingredients online based on the menu proposed by the suggestion unit. The ordering unit, for example, identifies missing ingredients based on the proposed menu and places an online order within a preset budget. For example, if the user sets a budget, the ordering unit orders optimal ingredients within that range. In addition, the ordering unit can order ingredients through an online ordering platform and deliver them to the user's home. For example, the ordering unit orders ingredients using an online shopping site and delivers them to the user's home through a delivery service.As a result, the health management and diet support system according to the embodiment can propose a menu according to the user's health management and diet goals, and automatically order missing ingredients. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI. For example, the ordering unit can identify missing ingredients based on the menu proposed by the suggestion unit, and order ingredients using an AI model that performs online ordering. As a result, the health management and diet support system can propose a menu according to the user's health management and diet goals, and automatically order missing ingredients.

[0030] The reception unit allows the user to input inventory information of the refrigerator or food storage. The inventory information input by the user includes, for example, the type, quantity, and expiration date of ingredients, but is not limited to such examples. The reception unit can provide, for example, a method in which the user manually inputs the information, or a method in which the information is automatically input using a barcode scanner. Specifically, the user can manually input the type, quantity, and expiration date of ingredients using a smartphone or tablet while checking the contents of the refrigerator. In addition, when a barcode scanner is used, the information of the ingredients is automatically input into the system by simply scanning the barcode of the ingredients. Furthermore, the reception unit can also input inventory information through a smartphone app. For example, the user can scan the barcode of the ingredients using the camera of the smartphone and automatically input the inventory information. This allows the user to easily manage the inventory information without any hassle. Furthermore, the reception unit has a voice input function, and the user can also input the information of ingredients by voice. For example, when the user inputs "1 liter of milk, expiration date is next Monday" by voice, the information is automatically reflected in the system. This allows the user to input inventory information without using his / her hands, improving convenience. The reception unit can also be linked to a smart refrigerator. A smart refrigerator uses cameras and sensors installed inside to automatically detect the type and quantity of ingredients and their expiration date, and sends that information to the system. This saves users the trouble of having to check the contents of their refrigerator, and allows them to always have the latest inventory information. Furthermore, the reception unit is linked to a cloud service, allowing users to check and edit inventory information from multiple devices. For example, inventory information entered by a user on a smartphone can also be checked and edited on a tablet or PC. This allows users to easily manage inventory information from any device.

[0031] The suggestion unit uses AI to suggest a menu based on the inventory information input by the reception unit. The suggestion unit generates an optimal menu, for example, taking into account the user's health management and diet goals. Specifically, the suggestion unit suggests a menu that emphasizes the calorie limit and specific nutrients set by the user. For example, if the user sets the daily calorie intake to 1500 kcal, the suggestion unit suggests a balanced menu within that range. The suggestion unit also considers the budget, allergy information, and information on disliked ingredients set by the user in advance. For example, if the user is allergic to a specific ingredient, the suggestion unit suggests a menu that does not include that ingredient. Also, if the user sets an ingredient that he or she dislikes, the suggestion unit suggests a menu that avoids that ingredient. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can suggest a menu using an AI model that inputs the user's health management and diet goals, budget, allergy information, and disliked ingredient information and outputs an optimal menu. Specifically, the AI ​​model uses past data and nutritional knowledge to generate an optimal menu based on the user's input information. For example, the AI ​​model can suggest new menus by referring to the menus that the user liked in the past and nutritionally balanced recipes. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy of the suggestions. For example, the user can evaluate the proposed menus and re-learn the AI ​​model based on the evaluation, so that a menu that better suits the user's preferences can be suggested. Furthermore, the suggestion unit can also suggest appropriate menus taking into account the season, weather, and regional characteristics. For example, hot soups and stews are suggested in cold seasons, and refreshing salads and cold dishes are suggested in hot seasons. In this way, the suggestion unit can suggest optimal menus according to the user's health management and diet goals, and support the user's eating habits.

[0032] The ordering unit orders the missing ingredients online based on the menu proposed by the suggestion unit. For example, the ordering unit identifies the missing ingredients based on the proposed menu and places an online order within a preset budget. Specifically, the ordering unit generates a list of ingredients required for the proposed menu and identifies the missing ingredients by checking against the user's inventory information. For example, if the proposed menu requires "200 grams of chicken" and the user does not have chicken in stock, the ordering unit adds the chicken to the order list. Furthermore, if the user sets a budget, the ordering unit orders optimal ingredients within that range. For example, if the user sets a food budget of 5,000 yen for one week, the ordering unit orders the necessary ingredients within that range. Furthermore, the ordering unit can order ingredients through an online ordering platform and deliver them to the user's home. For example, the ordering unit orders ingredients using an online shopping site and delivers them to the user's home through a delivery service. This allows the user to obtain the necessary ingredients while staying at home. Some or all of the above-mentioned processing in the ordering unit may be performed using AI, for example, or may be performed without using AI. For example, the ordering unit can use an AI model that identifies ingredients that are missing based on the menu proposed by the suggestion unit and places an online order to order ingredients. Specifically, the AI ​​model generates a list of ingredients needed based on the user's inventory information and the proposed menu, selects an optimal online shopping site, and places an order. The ordering unit can also suggest optimal ingredients and shopping sites based on the user's past order history. For example, if the user has ordered ingredients from a specific shopping site in the past, the site is preferentially suggested. This allows the ordering unit to order optimal ingredients according to the user's preferences and budget, improving user convenience. Furthermore, the ordering unit can track the delivery status in real time and notify the user. For example, the ordering unit notifies the user when the order is confirmed, and notifies again when delivery begins. This allows the user to know when the ordered ingredients will arrive, allowing them to use the service with peace of mind.

[0033] The suggestion unit can generate a menu based on the user's health management and diet goals. The suggestion unit generates an optimal menu, for example, taking into account the user's health management and diet goals. For example, the suggestion unit can suggest a menu that emphasizes calorie restriction and specific nutrients. The suggestion unit can also suggest a balanced meal based on the user's health management and diet goals. For example, if the user is restricting calories, the suggestion unit can suggest a menu that uses low-calorie ingredients. If the user emphasizes a specific nutrient, the suggestion unit can also suggest a menu that uses ingredients that contain a lot of that nutrient. This allows the suggestion unit to suggest a menu according to the user's health management and diet goals. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can generate a menu using an AI model that inputs the user's health management and diet goals and outputs an optimal menu.

[0034] The suggestion unit can generate a menu based on the budget, allergy information, and information on disliked ingredients that the user has set in advance. The suggestion unit generates an optimal menu, for example, by considering the budget, allergy information, and information on disliked ingredients that the user has set in advance. For example, if the user is allergic to a specific ingredient, the suggestion unit can suggest a menu that does not include that ingredient. Also, if the user sets an ingredient that he or she dislikes, the suggestion unit can suggest a menu that avoids that ingredient. Furthermore, if the user sets a budget, the suggestion unit can suggest an optimal menu within that range. For example, if the user sets a weekly food expense, the suggestion unit can suggest an optimal menu within that range. This allows the suggestion unit to suggest a menu according to the user's budget, allergy information, and information on disliked ingredients. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can generate a menu using an AI model that inputs the budget, allergy information, and information on disliked ingredients that the user has set in advance and outputs an optimal menu.

[0035] The ordering unit can order appropriate ingredients online within the budget. The ordering unit, for example, identifies ingredients that are missing based on a proposed menu and places an online order within a preset budget. For example, if a user sets a budget, the ordering unit can order optimal ingredients within that range. The ordering unit can also order ingredients through an online ordering platform and deliver them to the home. For example, the ordering unit orders ingredients using an online shopping site and delivers them to the home through a delivery service. This allows the ordering unit to order optimal ingredients within the budget. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI or may be performed without using AI. For example, the ordering unit can order ingredients using an AI model that identifies ingredients that are missing based on a menu proposed by the suggestion unit and places an online order.

[0036] The health management and diet support system includes a management unit that manages allergy information and information on disliked ingredients. The management unit allows, for example, a user to input allergy information and information on disliked ingredients. For example, the management unit can provide a method for the user to input manually or a method for automatically extracting information from past meal history. The management unit can also allow the user to input allergy information and information on disliked ingredients through a smartphone app. For example, the user can use the smartphone camera to scan the barcode of an ingredient and automatically input the allergy information and information on disliked ingredients. This allows the management unit to manage the allergy information and information on disliked ingredients. A part or all of the above-mentioned processing in the management unit may be performed, for example, using AI or may be performed without using AI. For example, the management unit can manage the information using an AI model that stores the allergy information and information on disliked ingredients input by the user in a database and updates the information as necessary.

[0037] The health management and diet support system includes an evaluation unit that evaluates the nutritional balance of a proposed menu. The evaluation unit can, for example, evaluate the nutritional balance of the proposed menu. For example, the evaluation unit can evaluate the nutritional balance of the proposed menu based on the recommended intake of each nutrient and the definition of a balanced diet. The evaluation unit can also evaluate the nutritional balance of the proposed menu based on the user's health condition and diet history. For example, if the user places importance on a specific nutrient, the evaluation unit can evaluate the intake of the nutrient. Furthermore, the evaluation unit has a function of visually displaying the nutritional balance of the proposed menu. For example, the evaluation unit can display the evaluation result of the nutritional balance as a graph or chart and provide it in a visually easy-to-understand format. This allows the evaluation unit to evaluate the nutritional balance of the proposed menu and provide feedback to the user. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can perform the evaluation using an AI model that evaluates the nutritional balance of the proposed menu.

[0038] The reception unit can analyze the user's past inventory input history and select the optimal input method. The reception unit, for example, analyzes the user's past inventory input history and selects the optimal input method. For example, the reception unit automatically displays ingredients that the user frequently inputs in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest ingredients to be used in a specific time period from the user's past input history. This allows the reception unit to select the optimal input method based on the user's past inventory input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can select the input method using an AI model that analyzes the user's past inventory input history and selects the optimal input method.

[0039] The reception unit can filter the stock information based on the user's current meal plan and health condition when inputting the stock information. The reception unit, for example, filters the stock information based on the user's current meal plan and health condition when inputting the stock information. For example, when the user is on a diet, the reception unit preferentially inputs low-calorie ingredients. In addition, when the user places importance on a specific nutrient, the reception unit can also preferentially input ingredients containing the nutrient. Furthermore, when the user has a specific meal plan, the reception unit can also filter and input necessary ingredients based on the plan. This allows the reception unit to filter the stock information based on the user's current meal plan and health condition. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the user's current meal plan and health condition and filters necessary ingredients.

[0040] The reception unit can preferentially input highly relevant stock information based on the geographical location information of the user when inputting stock information. For example, the reception unit preferentially inputs highly relevant stock information in consideration of the geographical location information of the user when inputting stock information. For example, when the user lives in a specific area, the reception unit preferentially inputs ingredients that are easily available in that area. In addition, when the user is traveling, the reception unit can also preferentially input ingredients that are available at the travel destination. Furthermore, when the user shops at a specific store, the reception unit can also preferentially input ingredients handled at that store. This allows the reception unit to preferentially input highly relevant stock information based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can perform input using an AI model that inputs the geographical location information of the user and preferentially inputs highly relevant stock information.

[0041] The reception unit can analyze the social media activity of the user and input the related inventory information when the inventory information is input. For example, the reception unit can analyze the social media activity of the user and input the related inventory information when the inventory information is input. For example, the reception unit can input the necessary ingredients based on a recipe shared by the user on social media. The reception unit can also input the ingredients based on the posts of a cooking account that the user follows on social media. Furthermore, the reception unit can also input the ingredients based on the trends of a cooking community that the user participates in on social media. This allows the reception unit to input the related inventory information based on the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can perform input using an AI model that analyzes the social media activity of the user and inputs the related inventory information.

[0042] The suggestion unit can adjust the level of detail of the menu based on the user's health goal when proposing a menu. For example, the suggestion unit adjusts the level of detail of the menu based on the user's health goal when proposing a menu. For example, if the user is on a diet, the suggestion unit provides detailed information on calories and nutrients. In addition, if the user is aiming to build muscle, the suggestion unit can also provide detailed information on the protein content. Furthermore, if the user has a specific health problem, the suggestion unit can also provide detailed information on a menu corresponding to the problem. This allows the suggestion unit to adjust the level of detail of the menu according to the user's health goal. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can adjust the level of detail using an AI model that inputs the user's health goal and adjusts the level of detail of the menu.

[0043] The suggestion unit can apply different suggestion algorithms according to the user's meal history when suggesting a menu. For example, the suggestion unit applies different suggestion algorithms according to the user's meal history when suggesting a menu. For example, the suggestion unit suggests a similar menu based on a menu that the user liked to eat in the past. The suggestion unit can also suggest a menu that does not include ingredients that the user avoided in the past. Furthermore, the suggestion unit can also suggest a menu that takes nutritional balance into consideration from the user's meal history. This allows the suggestion unit to apply different suggestion algorithms according to the user's meal history. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can suggest a menu using an AI model that inputs the user's meal history and applies different suggestion algorithms.

[0044] The suggestion unit can determine the priority order of the menu based on the meal time of the user when proposing the menu. For example, the suggestion unit determines the priority order of the menu based on the meal time of the user when proposing the menu. For example, the suggestion unit preferentially suggests a menu with good nutritional balance at breakfast. The suggestion unit can also preferentially suggest a menu suitable for energy replenishment at lunch. Furthermore, the suggestion unit can also preferentially suggest a menu that is easy to digest at dinner. This allows the suggestion unit to determine the priority order of the menu based on the meal time of the user. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can determine the priority order using an AI model that inputs the meal time of the user and determines the priority order of the menu.

[0045] The suggestion unit can adjust the order of the menu based on the relevance of the user's meals when proposing a menu. For example, the suggestion unit adjusts the order of the menu based on the relevance of the user's meals when proposing a menu. For example, the suggestion unit preferentially suggests a menu that is highly related to a menu that the user has eaten in the past. In addition, if the user likes a specific ingredient, the suggestion unit can also preferentially suggest a menu that includes the ingredient. Furthermore, if the user likes a specific cooking style, the suggestion unit can also preferentially suggest a menu based on the style. This allows the suggestion unit to adjust the order of the menu based on the relevance of the user's meals. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can adjust the order using an AI model that uses the relevance of the user's meals as input and adjusts the order of the menu.

[0046] The ordering unit can select the optimal ordering method at the time of ordering by analyzing the user's past order history. For example, the ordering unit can select the optimal ordering method at the time of ordering by analyzing the user's past order history. For example, the ordering unit can automatically display ingredients that the user has frequently ordered in the past as candidates. The ordering unit can also preferentially suggest ordering methods (such as voice and text) that the user has used in the past. Furthermore, the ordering unit can predict and suggest ingredients to be ordered in a specific time period from the user's past order history. This allows the ordering unit to select the optimal ordering method based on the user's past order history. A part or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI. For example, the ordering unit can select the ordering method using an AI model that analyzes the user's past order history and selects the optimal ordering method.

[0047] The ordering unit can customize the order contents based on the user's current budget situation at the time of ordering. The ordering unit customizes the order contents based on the user's current budget situation at the time of ordering, for example. For example, the ordering unit suggests optimal ingredients within the budget set by the user. The ordering unit can also suggest order contents taking discounts and benefits into consideration according to the user's budget situation. Furthermore, the ordering unit can also suggest ingredients with high cost performance based on the user's budget situation. This allows the ordering unit to customize the order contents based on the user's budget situation. A part or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can perform customization using an AI model that uses the user's budget situation as an input and customizes the order contents.

[0048] The ordering unit can select an appropriate ordering method based on the geographical location information of the user at the time of ordering. For example, the ordering unit selects the optimal ordering method in consideration of the geographical location information of the user at the time of ordering. For example, when the user lives in a specific area, the ordering unit preferentially orders ingredients that are easily available in that area. In addition, when the user is traveling, the ordering unit can also preferentially order ingredients that are available at the travel destination. Furthermore, when the user shops at a specific store, the ordering unit can also preferentially order ingredients that are handled at that store. This allows the ordering unit to select the optimal ordering method based on the geographical location information of the user. Part or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI. For example, the ordering unit can select the ordering method using an AI model that inputs the geographical location information of the user and selects the optimal ordering method.

[0049] The ordering unit can analyze the social media activity of the user at the time of ordering to suggest the order contents. For example, the ordering unit can analyze the social media activity of the user at the time of ordering to suggest the order contents. For example, the ordering unit orders the necessary ingredients based on a recipe shared by the user on social media. The ordering unit can also order ingredients based on posts of cooking accounts that the user follows on social media. Furthermore, the ordering unit can also order ingredients based on trends of cooking communities that the user participates in on social media. This allows the ordering unit to suggest the order contents based on the user's social media activity. A part or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI or may be performed without using AI. For example, the ordering unit can make suggestions using an AI model that analyzes the social media activity of the user and suggests the order contents.

[0050] The management unit can select the optimal management method by referring to the user's past meal history during management. The management unit, for example, selects the optimal management method by referring to the user's past meal history during management. For example, the management unit automatically updates allergy information and disliked ingredients based on ingredients that the user has avoided in the past. The management unit can also predict and manage allergic reactions to specific ingredients from the user's past meal history. Furthermore, the management unit can analyze the user's past meal history and propose the most efficient management method. This allows the management unit to select the optimal management method based on the user's past meal history. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI or may be performed without using AI. For example, the management unit can select a management method by referring to the user's past meal history and using an AI model that selects the optimal management method.

[0051] The management unit can select the optimal management method in consideration of the geographical location information of the user at the time of management. For example, the management unit selects the optimal management method in consideration of the geographical location information of the user at the time of management. For example, when the user lives in a specific area, the management unit preferentially manages ingredients that are easily available in that area. In addition, when the user is traveling, the management unit can also preferentially manage ingredients that are available at the travel destination. Furthermore, when the user shops at a specific store, the management unit can also preferentially manage ingredients handled at that store. This allows the management unit to select the optimal management method based on the geographical location information of the user. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can select the management method using an AI model that inputs the geographical location information of the user and selects the optimal management method.

[0052] The evaluation unit can select the optimal evaluation method by referring to the user's past meal history at the time of evaluation. The evaluation unit, for example, selects the optimal evaluation method by referring to the user's past meal history at the time of evaluation. For example, the evaluation unit evaluates the nutritional balance based on the menu the user has eaten in the past. The evaluation unit can also evaluate the intake status of specific nutrients from the user's past meal history. Furthermore, the evaluation unit can analyze the user's past meal history and propose the most efficient evaluation method. This allows the evaluation unit to select the optimal evaluation method based on the user's past meal history. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI. For example, the evaluation unit can select the evaluation method by referring to the user's past meal history and using an AI model that selects the optimal evaluation method.

[0053] The evaluation unit can select the optimal evaluation method in consideration of the geographical location information of the user at the time of evaluation. For example, the evaluation unit selects the optimal evaluation method in consideration of the geographical location information of the user at the time of evaluation. For example, when the user lives in a specific area, the evaluation unit evaluates the nutritional balance based on ingredients that are easily available in that area. In addition, when the user is traveling, the evaluation unit can also evaluate the nutritional balance based on ingredients available at the travel destination. Furthermore, when the user shops at a specific store, the evaluation unit can also evaluate the nutritional balance based on ingredients handled at the store. This allows the evaluation unit to select the optimal evaluation method based on the geographical location information of the user. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can select the evaluation method using an AI model that inputs the geographical location information of the user and selects the optimal evaluation method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The suggestion unit can analyze the user's meal history and suggest menus according to the season and weather. For example, cold dishes and light meals can be suggested in summer, and hot dishes and nutritious meals can be suggested in winter. It can also suggest hot soups and stews on rainy days, and salads and grilled dishes on sunny days. In this way, the suggestion unit can suggest menus according to the season and weather.

[0056] The suggestion unit can analyze the user's meal history and suggest menus that match specific events or occasions. For example, it can suggest a special dinner for a birthday and a traditional dish for Christmas. It can also suggest light meals or party foods on sporting event days and relaxing menus on holidays. In this way, the suggestion unit can suggest menus that match specific events or occasions.

[0057] The suggestion unit can analyze the user's meal history and suggest menus based on the expiration dates of ingredients. For example, it is possible to suggest a menu that prioritizes the use of ingredients that are close to their expiration date, thereby reducing the waste of ingredients. It is also possible to suggest a menu that uses ingredients efficiently based on the inventory status of the refrigerator or pantry. This allows the suggestion unit to suggest menus based on the expiration dates of ingredients.

[0058] The suggestion unit can analyze the user's dietary history and suggest menus based on the intake status of specific nutrients. For example, if the user is lacking in vitamin C intake, a menu using ingredients that are high in vitamin C can be suggested. Also, if the user wants to increase protein intake, a menu high in protein can be suggested. This allows the suggestion unit to suggest menus based on the intake status of specific nutrients.

[0059] The suggestion unit can analyze the user's meal history and suggest menus based on the price fluctuations of ingredients. For example, if the price of a particular ingredient is rising sharply, the suggestion unit can suggest a menu that avoids that ingredient and suggests a menu that uses ingredients whose prices are stable. It can also suggest menus that use ingredients that are on sale or discounted. This allows the suggestion unit to suggest menus based on the price fluctuations of ingredients.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception unit allows the user to input inventory information for the refrigerator or food storage. The inventory information input by the user includes the type of food ingredient, the quantity, the expiration date, etc. The reception unit provides input methods such as manual input, a barcode scanner, and an input method via a smartphone app. For example, the user can use the camera on their smartphone to scan the barcode of an ingredient and automatically input the inventory information. Step 2: The suggestion unit uses AI to suggest menus based on the inventory information entered by the reception unit. The suggestion unit generates an optimal menu taking into consideration the user's health management and diet goals, budget, allergy information, and information on disliked ingredients. For example, it suggests menus that emphasize calorie restrictions or specific nutrients. The processing in the suggestion unit may be performed using AI or may be performed without using AI. Step 3: The ordering department orders the missing ingredients online based on the menu proposed by the suggestion department. The ordering department identifies the missing ingredients based on the proposed menu and places an online order within a preset budget. For example, ingredients are ordered using an online shopping site and delivered to the home via a delivery service. The processing in the ordering department may be performed with or without the use of AI.

[0062] (Example 2) A health management and diet support system according to an embodiment of the present invention is a system in which a user inputs inventory information for a refrigerator or a food storehouse, and an AI proposes a menu suitable for an individual or a household based on the inventory information. This system can order missing ingredients online and deliver them to the home, taking into account the user's preset budget, allergy information, and information on disliked ingredients. For example, a user inputs a list of ingredients in the refrigerator and ingredients stored in the food storehouse. This information is input to the AI. The AI ​​then analyzes the input inventory information and proposes a menu suitable for the individual or household. The AI ​​generates an optimal menu taking into account the user's health management and diet goals. For example, a menu that emphasizes calorie restriction and specific nutrients is proposed. In addition, the user's preset budget, allergy information, and information on disliked ingredients are also considered. For example, if a user is allergic to a specific ingredient, a menu that does not include that ingredient is proposed. Also, if the user sets a disliked ingredient, a menu that avoids that ingredient is proposed. Missing ingredients are ordered online and delivered to the home. The AI ​​identifies missing ingredients based on the proposed menu, and places an online order within a preset budget. For example, if the user sets a budget, the optimal ingredients within that range are ordered. This will greatly reduce the trouble and effort that housewives have to make daily menu plans. Users only need to input the inventory information of their refrigerator and pantry, and menus optimal for health management and dieting will be suggested, and missing ingredients will be automatically ordered, saving time and effort. This allows the health management and diet support system to suggest menus that match the user's health management and dieting goals, and to automatically order missing ingredients.

[0063] The health management and diet support system according to the embodiment includes a reception unit, a proposal unit, and an order unit. The reception unit inputs inventory information of a refrigerator or a food storehouse by a user. The inventory information input by the user includes, for example, the type, quantity, and expiration date of ingredients, but is not limited to such examples. The reception unit can provide, for example, a method in which the user manually inputs the information, or a method in which the information is automatically input using a barcode scanner. The reception unit can also input the inventory information through a smartphone app. For example, the user can scan the barcode of the ingredients using the camera of the smartphone and automatically input the inventory information. The proposal unit uses AI to propose a menu based on the inventory information input by the reception unit. The proposal unit generates an optimal menu, for example, taking into account the user's health management and diet goals. For example, the proposal unit proposes a menu that emphasizes calorie restrictions and specific nutrients. The proposal unit also considers the user's preset budget, allergy information, and information on disliked ingredients. For example, if the user is allergic to a specific ingredient, the proposal unit proposes a menu that does not include that ingredient. If the user sets an ingredient that he or she dislikes, the proposal unit proposes a menu that avoids that ingredient. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest a menu using an AI model that inputs the user's health management and diet goals, budget, allergy information, and information on disliked ingredients and outputs an optimal menu. The ordering unit orders missing ingredients online based on the menu proposed by the suggestion unit. The ordering unit, for example, identifies missing ingredients based on the proposed menu and places an online order within a preset budget. For example, if the user sets a budget, the ordering unit orders optimal ingredients within that range. In addition, the ordering unit can order ingredients through an online ordering platform and deliver them to the user's home. For example, the ordering unit orders ingredients using an online shopping site and delivers them to the user's home through a delivery service.As a result, the health management and diet support system according to the embodiment can propose a menu according to the user's health management and diet goals, and automatically order missing ingredients. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI. For example, the ordering unit can identify missing ingredients based on the menu proposed by the suggestion unit, and order ingredients using an AI model that performs online ordering. As a result, the health management and diet support system can propose a menu according to the user's health management and diet goals, and automatically order missing ingredients.

[0064] The reception unit allows the user to input inventory information of the refrigerator or food storage. The inventory information input by the user includes, for example, the type, quantity, and expiration date of ingredients, but is not limited to such examples. The reception unit can provide, for example, a method in which the user manually inputs the information, or a method in which the information is automatically input using a barcode scanner. Specifically, the user can manually input the type, quantity, and expiration date of ingredients using a smartphone or tablet while checking the contents of the refrigerator. In addition, when a barcode scanner is used, the information of the ingredients is automatically input into the system by simply scanning the barcode of the ingredients. Furthermore, the reception unit can also input inventory information through a smartphone app. For example, the user can scan the barcode of the ingredients using the camera of the smartphone and automatically input the inventory information. This allows the user to easily manage the inventory information without any hassle. Furthermore, the reception unit has a voice input function, and the user can also input the information of ingredients by voice. For example, when the user inputs "1 liter of milk, expiration date is next Monday" by voice, the information is automatically reflected in the system. This allows the user to input inventory information without using his / her hands, improving convenience. The reception unit can also be linked to a smart refrigerator. A smart refrigerator uses cameras and sensors installed inside to automatically detect the type and quantity of ingredients and their expiration date, and sends that information to the system. This saves users the trouble of having to check the contents of their refrigerator, and allows them to always have the latest inventory information. Furthermore, the reception unit is linked to a cloud service, allowing users to check and edit inventory information from multiple devices. For example, inventory information entered by a user on a smartphone can also be checked and edited on a tablet or PC. This allows users to easily manage inventory information from any device.

[0065] The suggestion unit uses AI to suggest a menu based on the inventory information input by the reception unit. The suggestion unit generates an optimal menu, for example, taking into account the user's health management and diet goals. Specifically, the suggestion unit suggests a menu that emphasizes the calorie limit and specific nutrients set by the user. For example, if the user sets the daily calorie intake to 1500 kcal, the suggestion unit suggests a balanced menu within that range. The suggestion unit also considers the budget, allergy information, and information on disliked ingredients set by the user in advance. For example, if the user is allergic to a specific ingredient, the suggestion unit suggests a menu that does not include that ingredient. Also, if the user sets an ingredient that he or she dislikes, the suggestion unit suggests a menu that avoids that ingredient. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can suggest a menu using an AI model that inputs the user's health management and diet goals, budget, allergy information, and disliked ingredient information and outputs an optimal menu. Specifically, the AI ​​model uses past data and nutritional knowledge to generate an optimal menu based on the user's input information. For example, the AI ​​model can suggest new menus by referring to the menus that the user liked in the past and nutritionally balanced recipes. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy of the suggestions. For example, the user can evaluate the proposed menus and re-learn the AI ​​model based on the evaluation, so that a menu that better suits the user's preferences can be suggested. Furthermore, the suggestion unit can also suggest appropriate menus taking into account the season, weather, and regional characteristics. For example, hot soups and stews are suggested in cold seasons, and refreshing salads and cold dishes are suggested in hot seasons. In this way, the suggestion unit can suggest optimal menus according to the user's health management and diet goals, and support the user's eating habits.

[0066] The ordering unit orders the missing ingredients online based on the menu proposed by the suggestion unit. For example, the ordering unit identifies the missing ingredients based on the proposed menu and places an online order within a preset budget. Specifically, the ordering unit generates a list of ingredients required for the proposed menu and identifies the missing ingredients by checking against the user's inventory information. For example, if the proposed menu requires "200 grams of chicken" and the user does not have chicken in stock, the ordering unit adds the chicken to the order list. Furthermore, if the user sets a budget, the ordering unit orders optimal ingredients within that range. For example, if the user sets a food budget of 5,000 yen for one week, the ordering unit orders the necessary ingredients within that range. Furthermore, the ordering unit can order ingredients through an online ordering platform and deliver them to the user's home. For example, the ordering unit orders ingredients using an online shopping site and delivers them to the user's home through a delivery service. This allows the user to obtain the necessary ingredients while staying at home. Some or all of the above-mentioned processing in the ordering unit may be performed using AI, for example, or may be performed without using AI. For example, the ordering unit can use an AI model that identifies ingredients that are missing based on the menu proposed by the suggestion unit and places an online order to order ingredients. Specifically, the AI ​​model generates a list of ingredients needed based on the user's inventory information and the proposed menu, selects an optimal online shopping site, and places an order. The ordering unit can also suggest optimal ingredients and shopping sites based on the user's past order history. For example, if the user has ordered ingredients from a specific shopping site in the past, the site is preferentially suggested. This allows the ordering unit to order optimal ingredients according to the user's preferences and budget, improving user convenience. Furthermore, the ordering unit can track the delivery status in real time and notify the user. For example, the ordering unit notifies the user when the order is confirmed, and notifies again when delivery begins. This allows the user to know when the ordered ingredients will arrive, allowing them to use the service with peace of mind.

[0067] The suggestion unit can generate a menu based on the user's health management and diet goals. The suggestion unit generates an optimal menu, for example, taking into account the user's health management and diet goals. For example, the suggestion unit can suggest a menu that emphasizes calorie restriction and specific nutrients. The suggestion unit can also suggest a balanced meal based on the user's health management and diet goals. For example, if the user is restricting calories, the suggestion unit can suggest a menu that uses low-calorie ingredients. If the user emphasizes a specific nutrient, the suggestion unit can also suggest a menu that uses ingredients that contain a lot of that nutrient. This allows the suggestion unit to suggest a menu according to the user's health management and diet goals. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can generate a menu using an AI model that inputs the user's health management and diet goals and outputs an optimal menu.

[0068] The suggestion unit can generate a menu based on the budget, allergy information, and information on disliked ingredients that the user has set in advance. The suggestion unit generates an optimal menu, for example, by considering the budget, allergy information, and information on disliked ingredients that the user has set in advance. For example, if the user is allergic to a specific ingredient, the suggestion unit can suggest a menu that does not include that ingredient. Also, if the user sets an ingredient that he or she dislikes, the suggestion unit can suggest a menu that avoids that ingredient. Furthermore, if the user sets a budget, the suggestion unit can suggest an optimal menu within that range. For example, if the user sets a weekly food expense, the suggestion unit can suggest an optimal menu within that range. This allows the suggestion unit to suggest a menu according to the user's budget, allergy information, and information on disliked ingredients. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can generate a menu using an AI model that inputs the budget, allergy information, and information on disliked ingredients that the user has set in advance and outputs an optimal menu.

[0069] The ordering unit can order appropriate ingredients online within the budget. The ordering unit, for example, identifies ingredients that are missing based on a proposed menu and places an online order within a preset budget. For example, if a user sets a budget, the ordering unit can order optimal ingredients within that range. The ordering unit can also order ingredients through an online ordering platform and deliver them to the home. For example, the ordering unit orders ingredients using an online shopping site and delivers them to the home through a delivery service. This allows the ordering unit to order optimal ingredients within the budget. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI or may be performed without using AI. For example, the ordering unit can order ingredients using an AI model that identifies ingredients that are missing based on a menu proposed by the suggestion unit and places an online order.

[0070] The health management and diet support system includes a management unit that manages allergy information and information on disliked ingredients. The management unit allows, for example, a user to input allergy information and information on disliked ingredients. For example, the management unit can provide a method for the user to input manually or a method for automatically extracting information from past meal history. The management unit can also allow the user to input allergy information and information on disliked ingredients through a smartphone app. For example, the user can use the smartphone camera to scan the barcode of an ingredient and automatically input the allergy information and information on disliked ingredients. This allows the management unit to manage the allergy information and information on disliked ingredients. A part or all of the above-mentioned processing in the management unit may be performed, for example, using AI or may be performed without using AI. For example, the management unit can manage the information using an AI model that stores the allergy information and information on disliked ingredients input by the user in a database and updates the information as necessary.

[0071] The health management and diet support system includes an evaluation unit that evaluates the nutritional balance of a proposed menu. The evaluation unit can, for example, evaluate the nutritional balance of the proposed menu. For example, the evaluation unit can evaluate the nutritional balance of the proposed menu based on the recommended intake of each nutrient and the definition of a balanced diet. The evaluation unit can also evaluate the nutritional balance of the proposed menu based on the user's health condition and diet history. For example, if the user places importance on a specific nutrient, the evaluation unit can evaluate the intake of the nutrient. Furthermore, the evaluation unit has a function of visually displaying the nutritional balance of the proposed menu. For example, the evaluation unit can display the evaluation result of the nutritional balance as a graph or chart and provide it in a visually easy-to-understand format. This allows the evaluation unit to evaluate the nutritional balance of the proposed menu and provide feedback to the user. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can perform the evaluation using an AI model that evaluates the nutritional balance of the proposed menu.

[0072] The reception unit can estimate the user's emotion and adjust the timing of inputting the inventory information based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of inputting the inventory information based on the estimated user's emotion. For example, when the user is stressed, the reception unit simplifies the input of the inventory information so that the input can be performed with a minimum number of steps. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input the inventory information. This allows the reception unit to adjust the timing of inputting the inventory information according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can adjust the input timing using an AI model that estimates the user's emotions and adjusts the input timing of inventory information based on the estimated emotions.

[0073] The reception unit can analyze the user's past inventory input history and select the optimal input method. The reception unit, for example, analyzes the user's past inventory input history and selects the optimal input method. For example, the reception unit automatically displays ingredients that the user frequently inputs in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest ingredients to be used in a specific time period from the user's past input history. This allows the reception unit to select the optimal input method based on the user's past inventory input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can select the input method using an AI model that analyzes the user's past inventory input history and selects the optimal input method.

[0074] The reception unit can filter the stock information based on the user's current meal plan and health condition when inputting the stock information. The reception unit, for example, filters the stock information based on the user's current meal plan and health condition when inputting the stock information. For example, when the user is on a diet, the reception unit preferentially inputs low-calorie ingredients. In addition, when the user places importance on a specific nutrient, the reception unit can also preferentially input ingredients containing the nutrient. Furthermore, when the user has a specific meal plan, the reception unit can also filter and input necessary ingredients based on the plan. This allows the reception unit to filter the stock information based on the user's current meal plan and health condition. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the user's current meal plan and health condition and filters necessary ingredients.

[0075] The reception unit can estimate the user's emotion and determine the priority of the inventory information to be input based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of the inventory information to be input based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit preferentially inputs important ingredients. Furthermore, when the user is relaxed, the reception unit can also input detailed inventory information. Furthermore, when the user is in a hurry, the reception unit can also preferentially input the minimum necessary inventory information. This allows the reception unit to determine the priority of the inventory information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can determine the priority using an AI model that estimates the user's emotion and determines the priority of the inventory information based on the estimated emotion.

[0076] The reception unit can preferentially input highly relevant stock information based on the geographical location information of the user when inputting stock information. For example, the reception unit preferentially inputs highly relevant stock information in consideration of the geographical location information of the user when inputting stock information. For example, when the user lives in a specific area, the reception unit preferentially inputs ingredients that are easily available in that area. In addition, when the user is traveling, the reception unit can also preferentially input ingredients that are available at the travel destination. Furthermore, when the user shops at a specific store, the reception unit can also preferentially input ingredients handled at that store. This allows the reception unit to preferentially input highly relevant stock information based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can perform input using an AI model that inputs the geographical location information of the user and preferentially inputs highly relevant stock information.

[0077] The reception unit can analyze the social media activity of the user and input the related inventory information when the inventory information is input. For example, the reception unit can analyze the social media activity of the user and input the related inventory information when the inventory information is input. For example, the reception unit can input the necessary ingredients based on a recipe shared by the user on social media. The reception unit can also input the ingredients based on the posts of a cooking account that the user follows on social media. Furthermore, the reception unit can also input the ingredients based on the trends of a cooking community that the user participates in on social media. This allows the reception unit to input the related inventory information based on the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can perform input using an AI model that analyzes the social media activity of the user and inputs the related inventory information.

[0078] The suggestion unit can estimate the user's emotion and adjust the way of expressing the menu based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the way of expressing the menu based on the estimated user's emotion. For example, the suggestion unit provides detailed recipes and cooking procedures when the user is relaxed. In addition, the suggestion unit can also suggest a menu that is easy and quick to make when the user is in a hurry. Furthermore, the suggestion unit can also suggest a visually attractive menu when the user is excited. This allows the suggestion unit to adjust the way of expressing the menu according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit can adjust the way of expressing the menu using an AI model that estimates the user's emotion and adjusts the way of expressing the menu based on the estimated emotion.

[0079] The suggestion unit can adjust the level of detail of the menu based on the user's health goal when proposing a menu. For example, the suggestion unit adjusts the level of detail of the menu based on the user's health goal when proposing a menu. For example, if the user is on a diet, the suggestion unit provides detailed information on calories and nutrients. In addition, if the user is aiming to build muscle, the suggestion unit can also provide detailed information on the protein content. Furthermore, if the user has a specific health problem, the suggestion unit can also provide detailed information on a menu corresponding to the problem. This allows the suggestion unit to adjust the level of detail of the menu according to the user's health goal. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can adjust the level of detail using an AI model that inputs the user's health goal and adjusts the level of detail of the menu.

[0080] The suggestion unit can apply different suggestion algorithms according to the user's meal history when suggesting a menu. For example, the suggestion unit applies different suggestion algorithms according to the user's meal history when suggesting a menu. For example, the suggestion unit suggests a similar menu based on a menu that the user liked to eat in the past. The suggestion unit can also suggest a menu that does not include ingredients that the user avoided in the past. Furthermore, the suggestion unit can also suggest a menu that takes nutritional balance into consideration from the user's meal history. This allows the suggestion unit to apply different suggestion algorithms according to the user's meal history. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can suggest a menu using an AI model that inputs the user's meal history and applies different suggestion algorithms.

[0081] The suggestion unit can estimate the user's emotion and adjust the length of the menu based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the length of the menu based on the estimated user's emotion. For example, the suggestion unit can suggest a menu that can be made in a short time when the user is in a hurry. In addition, the suggestion unit can also suggest a menu that can be made over a long time when the user is relaxed. Furthermore, the suggestion unit can also suggest a menu that combines multiple dishes when the user is excited. This allows the suggestion unit to adjust the length of the menu according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can adjust the length using an AI model that estimates the user's emotion and adjusts the length of the menu based on the estimated emotion.

[0082] The suggestion unit can determine the priority order of the menu based on the meal time of the user when proposing the menu. For example, the suggestion unit determines the priority order of the menu based on the meal time of the user when proposing the menu. For example, the suggestion unit preferentially suggests a menu with good nutritional balance at breakfast. The suggestion unit can also preferentially suggest a menu suitable for energy replenishment at lunch. Furthermore, the suggestion unit can also preferentially suggest a menu that is easy to digest at dinner. This allows the suggestion unit to determine the priority order of the menu based on the meal time of the user. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can determine the priority order using an AI model that inputs the meal time of the user and determines the priority order of the menu.

[0083] The suggestion unit can adjust the order of the menu based on the relevance of the user's meals when proposing a menu. For example, the suggestion unit adjusts the order of the menu based on the relevance of the user's meals when proposing a menu. For example, the suggestion unit preferentially suggests a menu that is highly related to a menu that the user has eaten in the past. In addition, if the user likes a specific ingredient, the suggestion unit can also preferentially suggest a menu that includes the ingredient. Furthermore, if the user likes a specific cooking style, the suggestion unit can also preferentially suggest a menu based on the style. This allows the suggestion unit to adjust the order of the menu based on the relevance of the user's meals. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can adjust the order using an AI model that uses the relevance of the user's meals as input and adjusts the order of the menu.

[0084] The ordering unit can estimate the user's emotions and adjust the timing of the order based on the estimated user's emotions. The ordering unit, for example, estimates the user's emotions and adjusts the timing of the order based on the estimated user's emotions. For example, the ordering unit simplifies and speeds up the ordering procedure when the user is stressed. In addition, the ordering unit can provide detailed ordering options and suggest a customizable ordering method when the user is relaxed. Furthermore, the ordering unit can prioritize voice input and enable the user to quickly place an order when the user is in a hurry. This allows the ordering unit to adjust the timing of the order according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the ordering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the ordering unit can adjust the timing using an AI model that estimates the user's emotions and adjusts the timing of the order based on the estimated emotions.

[0085] The ordering unit can select the optimal ordering method at the time of ordering by analyzing the user's past order history. For example, the ordering unit can select the optimal ordering method at the time of ordering by analyzing the user's past order history. For example, the ordering unit can automatically display ingredients that the user has frequently ordered in the past as candidates. The ordering unit can also preferentially suggest ordering methods (such as voice and text) that the user has used in the past. Furthermore, the ordering unit can predict and suggest ingredients to be ordered in a specific time period from the user's past order history. This allows the ordering unit to select the optimal ordering method based on the user's past order history. A part or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI. For example, the ordering unit can select the ordering method using an AI model that analyzes the user's past order history and selects the optimal ordering method.

[0086] The ordering unit can customize the order contents based on the user's current budget situation at the time of ordering. The ordering unit customizes the order contents based on the user's current budget situation at the time of ordering, for example. For example, the ordering unit suggests optimal ingredients within the budget set by the user. The ordering unit can also suggest order contents taking discounts and benefits into consideration according to the user's budget situation. Furthermore, the ordering unit can also suggest ingredients with high cost performance based on the user's budget situation. This allows the ordering unit to customize the order contents based on the user's budget situation. A part or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can perform customization using an AI model that uses the user's budget situation as an input and customizes the order contents.

[0087] The ordering unit can estimate the user's emotions and determine the priority of the order based on the estimated user's emotions. The ordering unit, for example, estimates the user's emotions and determines the priority of the order based on the estimated user's emotions. For example, the ordering unit prioritizes ordering important ingredients when the user is stressed. The ordering unit can also input detailed order contents when the user is relaxed. Furthermore, the ordering unit can also prioritize ordering the minimum necessary ingredients when the user is in a hurry. This allows the ordering unit to determine the priority of the order according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the ordering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the ordering unit can determine the priority using an AI model that estimates the user's emotions and determines the priority of the order based on the estimated emotions.

[0088] The ordering unit can select an appropriate ordering method based on the geographical location information of the user at the time of ordering. For example, the ordering unit selects the optimal ordering method in consideration of the geographical location information of the user at the time of ordering. For example, when the user lives in a specific area, the ordering unit preferentially orders ingredients that are easily available in that area. In addition, when the user is traveling, the ordering unit can also preferentially order ingredients that are available at the travel destination. Furthermore, when the user shops at a specific store, the ordering unit can also preferentially order ingredients that are handled at that store. This allows the ordering unit to select the optimal ordering method based on the geographical location information of the user. Part or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI. For example, the ordering unit can select the ordering method using an AI model that inputs the geographical location information of the user and selects the optimal ordering method.

[0089] The ordering unit can analyze the social media activity of the user at the time of ordering to suggest the order contents. For example, the ordering unit can analyze the social media activity of the user at the time of ordering to suggest the order contents. For example, the ordering unit orders the necessary ingredients based on a recipe shared by the user on social media. The ordering unit can also order ingredients based on posts of cooking accounts that the user follows on social media. Furthermore, the ordering unit can also order ingredients based on trends of cooking communities that the user participates in on social media. This allows the ordering unit to suggest the order contents based on the user's social media activity. A part or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI or may be performed without using AI. For example, the ordering unit can make suggestions using an AI model that analyzes the social media activity of the user and suggests the order contents.

[0090] The management unit can estimate the user's emotions and adjust the management method of allergy information and disliked ingredients based on the estimated user's emotions. The management unit, for example, estimates the user's emotions and adjusts the management method of allergy information and disliked ingredients based on the estimated user's emotions. For example, when the user is stressed, the management unit simplifies the input of allergy information and disliked ingredients so that they can be input with a minimum number of steps. In addition, when the user is relaxed, the management unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the management unit can prioritize voice input so that the allergy information and disliked ingredients can be input quickly. This allows the management unit to adjust the management method of allergy information and disliked ingredients according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management unit can adjust the management method using an AI model that estimates the user's emotions and adjusts the management method of allergy information or disliked ingredients based on the estimated emotions.

[0091] The management unit can select the optimal management method by referring to the user's past meal history during management. The management unit, for example, selects the optimal management method by referring to the user's past meal history during management. For example, the management unit automatically updates allergy information and disliked ingredients based on ingredients that the user has avoided in the past. The management unit can also predict and manage allergic reactions to specific ingredients from the user's past meal history. Furthermore, the management unit can analyze the user's past meal history and propose the most efficient management method. This allows the management unit to select the optimal management method based on the user's past meal history. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI or may be performed without using AI. For example, the management unit can select a management method by referring to the user's past meal history and using an AI model that selects the optimal management method.

[0092] The management unit can estimate the user's emotion and determine the priority of management based on the estimated user's emotion. The management unit, for example, estimates the user's emotion and determines the priority of management based on the estimated user's emotion. For example, when the user is feeling stressed, the management unit prioritizes management of important allergy information. In addition, when the user is relaxed, the management unit can also input detailed management information. Furthermore, when the user is in a hurry, the management unit can also prioritize input of the minimum necessary management information. This allows the management unit to determine the priority of management according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management unit can determine the priority using an AI model that estimates the user's emotion and determines the priority of management based on the estimated emotion.

[0093] The management unit can select the optimal management method in consideration of the geographical location information of the user at the time of management. For example, the management unit selects the optimal management method in consideration of the geographical location information of the user at the time of management. For example, when the user lives in a specific area, the management unit preferentially manages ingredients that are easily available in that area. In addition, when the user is traveling, the management unit can also preferentially manage ingredients that are available at the travel destination. Furthermore, when the user shops at a specific store, the management unit can also preferentially manage ingredients handled at that store. This allows the management unit to select the optimal management method based on the geographical location information of the user. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can select the management method using an AI model that inputs the geographical location information of the user and selects the optimal management method.

[0094] The evaluation unit can estimate the user's emotion and adjust the evaluation method of the nutritional balance of the menu based on the estimated user's emotion. The evaluation unit, for example, estimates the user's emotion and adjusts the evaluation method of the nutritional balance of the menu based on the estimated user's emotion. For example, the evaluation unit provides a detailed evaluation of the nutritional balance when the user is relaxed. The evaluation unit can also provide a concise evaluation of the nutritional balance when the user is in a hurry. Furthermore, the evaluation unit can also provide a visually attractive evaluation of the nutritional balance when the user is excited. This allows the evaluation unit to adjust the evaluation method of the nutritional balance of the menu according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the evaluation unit can adjust the evaluation method using an AI model that estimates the user's emotion and adjusts the evaluation method of the nutritional balance of the menu based on the estimated emotion.

[0095] The evaluation unit can select the optimal evaluation method by referring to the user's past meal history at the time of evaluation. The evaluation unit, for example, selects the optimal evaluation method by referring to the user's past meal history at the time of evaluation. For example, the evaluation unit evaluates the nutritional balance based on the menu the user has eaten in the past. The evaluation unit can also evaluate the intake status of specific nutrients from the user's past meal history. Furthermore, the evaluation unit can analyze the user's past meal history and propose the most efficient evaluation method. This allows the evaluation unit to select the optimal evaluation method based on the user's past meal history. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI. For example, the evaluation unit can select the evaluation method by referring to the user's past meal history and using an AI model that selects the optimal evaluation method.

[0096] The evaluation unit can estimate the user's emotion and determine the priority of the evaluation based on the estimated user's emotion. The evaluation unit, for example, estimates the user's emotion and determines the priority of the evaluation based on the estimated user's emotion. For example, when the user is feeling stressed, the evaluation unit prioritizes the evaluation of important nutrients. In addition, when the user is relaxed, the evaluation unit can also perform a detailed evaluation of nutritional balance. Furthermore, when the user is in a hurry, the evaluation unit can also prioritize the evaluation of the minimum necessary nutritional balance. This allows the evaluation unit to determine the priority of the evaluation according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can determine the priority using an AI model that estimates the user's emotion and determines the priority of the evaluation based on the estimated emotion.

[0097] The evaluation unit can select the optimal evaluation method in consideration of the geographical location information of the user at the time of evaluation. For example, the evaluation unit selects the optimal evaluation method in consideration of the geographical location information of the user at the time of evaluation. For example, when the user lives in a specific area, the evaluation unit evaluates the nutritional balance based on ingredients that are easily available in that area. In addition, when the user is traveling, the evaluation unit can also evaluate the nutritional balance based on ingredients available at the travel destination. Furthermore, when the user shops at a specific store, the evaluation unit can also evaluate the nutritional balance based on ingredients handled at the store. This allows the evaluation unit to select the optimal evaluation method based on the geographical location information of the user. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can select the evaluation method using an AI model that inputs the geographical location information of the user and selects the optimal evaluation method.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The suggestion unit can estimate the user's emotion and adjust the frequency of menu suggestions based on the estimated emotion. For example, when the user is stressed, the suggestion frequency can be reduced, and when the user is relaxed, the suggestion frequency can be increased. In addition, when the user is in a hurry, simple menus can be frequently suggested, and when the user has time, detailed menus can be suggested. In this way, the suggestion unit can adjust the frequency of menu suggestions according to the user's emotion.

[0100] The suggestion unit can analyze the user's meal history and suggest menus according to the season and weather. For example, cold dishes and light meals can be suggested in summer, and hot dishes and nutritious meals can be suggested in winter. It can also suggest hot soups and stews on rainy days, and salads and grilled dishes on sunny days. In this way, the suggestion unit can suggest menus according to the season and weather.

[0101] The suggestion unit can estimate the user's emotions and adjust the difficulty of the menu based on the estimated emotions. For example, if the user is stressed, a simple and hassle-free menu can be suggested, and if the user is relaxed, a challenging menu can be suggested. In addition, if the user is in a hurry, a menu that can be made in a short time can be suggested, and if the user has time, a menu that can be made over a long time can be suggested. In this way, the suggestion unit can adjust the difficulty of the menu according to the user's emotions.

[0102] The suggestion unit can analyze the user's meal history and suggest menus that match specific events or occasions. For example, it can suggest a special dinner for a birthday and a traditional dish for Christmas. It can also suggest light meals or party foods on sporting event days and relaxing menus on holidays. In this way, the suggestion unit can suggest menus that match specific events or occasions.

[0103] The suggestion unit can estimate the user's emotion and adjust the menu presentation method based on the estimated emotion. For example, if the user is stressed, a simple and easy-to-understand presentation can be made, and if the user is relaxed, a detailed and visually appealing presentation can be made. Also, if the user is in a hurry, a presentation that briefly summarizes the main points can be made, and if the user has time, a presentation that includes detailed explanations can be made. In this way, the suggestion unit can adjust the menu presentation method according to the user's emotion.

[0104] The suggestion unit can analyze the user's meal history and suggest menus based on the expiration dates of ingredients. For example, it is possible to suggest a menu that prioritizes the use of ingredients that are close to their expiration date, thereby reducing the waste of ingredients. It is also possible to suggest a menu that uses ingredients efficiently based on the inventory status of the refrigerator or pantry. This allows the suggestion unit to suggest menus based on the expiration dates of ingredients.

[0105] The suggestion unit can estimate the user's emotions and adjust the menu variations based on the estimated emotions. For example, if the user is stressed, a standard menu can be suggested, and if the user is relaxed, a new recipe or a different cooking style can be suggested. In addition, if the user is in a hurry, a simple and easy-to-prepare menu can be suggested, and if the user has time, a menu that combines multiple dishes can be suggested. In this way, the suggestion unit can adjust the menu variations according to the user's emotions.

[0106] The suggestion unit can analyze the user's dietary history and suggest menus based on the intake status of specific nutrients. For example, if the user is lacking in vitamin C intake, a menu using ingredients that are high in vitamin C can be suggested. Also, if the user wants to increase protein intake, a menu high in protein can be suggested. This allows the suggestion unit to suggest menus based on the intake status of specific nutrients.

[0107] The suggestion unit can estimate the user's emotion and provide menu customization options based on the estimated emotion. For example, if the user is stressed, a simple customization option can be provided, and if the user is relaxed, a detailed customization option can be provided. In addition, if the user is in a hurry, an option that allows quick customization can be provided, and if the user has time, an option that allows detailed customization can be provided. In this way, the suggestion unit can provide menu customization options according to the user's emotion.

[0108] The suggestion unit can analyze the user's meal history and suggest menus based on the price fluctuations of ingredients. For example, if the price of a particular ingredient is rising sharply, the suggestion unit can suggest a menu that avoids that ingredient and suggests a menu that uses ingredients whose prices are stable. It can also suggest menus that use ingredients that are on sale or discounted. This allows the suggestion unit to suggest menus based on the price fluctuations of ingredients.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The reception unit allows the user to input inventory information for the refrigerator or food storage. The inventory information input by the user includes the type of food ingredient, the quantity, the expiration date, etc. The reception unit provides input methods such as manual input, a barcode scanner, and an input method via a smartphone app. For example, the user can use the camera on their smartphone to scan the barcode of an ingredient and automatically input the inventory information. Step 2: The suggestion unit uses AI to suggest menus based on the inventory information entered by the reception unit. The suggestion unit generates an optimal menu taking into consideration the user's health management and diet goals, budget, allergy information, and information on disliked ingredients. For example, it suggests menus that emphasize calorie restrictions or specific nutrients. The processing in the suggestion unit may be performed using AI or may be performed without using AI. Step 3: The ordering department orders the missing ingredients online based on the menu proposed by the suggestion department. The ordering department identifies the missing ingredients based on the proposed menu and places an online order within a preset budget. For example, ingredients are ordered using an online shopping site and delivered to the home via a delivery service. The processing in the ordering department may be performed with or without the use of AI.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned reception unit, suggestion unit, order unit, management unit, and evaluation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the user inputs inventory information of the refrigerator and the food storage. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal menu using AI. The order unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and orders missing ingredients online. The management unit is realized, for example, by the control unit 46A of the smart device 14, and manages allergy information and information on disliked ingredients. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the nutritional balance of the proposed menu. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the reception unit, suggestion unit, order unit, management unit, and evaluation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the user inputs inventory information of the refrigerator and the food storage. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal menu using AI. The order unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and orders missing ingredients online. The management unit is realized, for example, by the control unit 46A of the smart glasses 214, and manages allergy information and information on disliked ingredients. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the nutritional balance of the proposed menu. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned reception unit, suggestion unit, order unit, management unit, and evaluation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and the user inputs inventory information of the refrigerator and the food storage. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal menu using AI. The order unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and orders missing ingredients online. The management unit is realized, for example, by the control unit 46A of the headset type terminal 314, and manages allergy information and information on disliked ingredients. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the nutritional balance of the proposed menu. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] Each of the multiple elements including the reception unit, suggestion unit, order unit, management unit, and evaluation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user inputs inventory information of the refrigerator and the food storage. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal menu using AI. The order unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and orders missing ingredients online. The management unit is realized, for example, by the control unit 46A of the robot 414, and manages allergy information and information on disliked ingredients. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the nutritional balance of the proposed menu. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration in which a processor is used that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A reception section for inputting inventory information; a suggestion unit that suggests a menu based on the inventory information input by the reception unit; an ordering unit that orders missing ingredients online based on the menu suggested by the suggestion unit; Equipped A system characterized by: (Appendix 2) The suggestion unit, Generate menus based on the user's health and diet goals 2. The system of claim 1, (Appendix 3) The suggestion unit, Generates menus based on user-specified budgets, allergy information, and disliked ingredients 2. The system of claim 1, (Appendix 4) The ordering unit: Order the right ingredients online within your budget 2. The system of claim 1, (Appendix 5) Equipped with a management section for managing information on allergies and disliked ingredients 2. The system of claim 1, (Appendix 6) Equipped with an evaluation section that evaluates the nutritional balance of proposed menus 2. The system of claim 1, (Appendix 7) The reception unit is To estimate a user's emotion and adjust the timing of inputting inventory information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past inventory input history and select the appropriate input method 2. The system of claim 1, (Appendix 9) The reception unit is Filtering inventory input based on the user's current diet plan and health status 2. The system of claim 1, (Appendix 10) The reception unit is Infer user emotions and determine the priority of stock information to be input based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The reception unit is When entering inventory information, prioritize the most relevant inventory information based on the user's geographic location 2. The system of claim 1, (Appendix 12) The reception unit is When entering inventory information, analyze the user's social media activity and enter relevant inventory information 2. The system of claim 1, (Appendix 13) The suggestion unit, Infer user emotions and adjust the menu display based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The suggestion unit, When suggesting menu items, adjust the menu details based on the user's health goals 2. The system of claim 1, (Appendix 15) The suggestion unit, When suggesting menu items, different suggestion algorithms are applied depending on the user's meal history. 2. The system of claim 1, (Appendix 16) The suggestion unit, Infer user emotion and adjust menu length based on the estimated user emotion 2. The system of claim 1, (Appendix 17) The suggestion unit, When suggesting menu items, prioritize menu items based on the user's mealtime 2. The system of claim 1, (Appendix 18) The suggestion unit, When making menu suggestions, adjust the order of menu items based on the user's dietary relevance 2. The system of claim 1, (Appendix 19) The ordering unit: Infer user emotions and adjust order timing based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The ordering unit: When placing an order, analyze the user's past order history to select the optimal ordering method. 2. The system of claim 1, (Appendix 21) The ordering unit: At the time of order, customize the order based on the user's current budget situation 2. The system of claim 1, (Appendix 22) The ordering unit: Infer user sentiment and prioritize orders based on the inferred sentiment 2. The system of claim 1, (Appendix 23) The ordering unit: When placing an order, select the appropriate ordering method based on the user's geographic location 2. The system of claim 1, (Appendix 24) The ordering unit: When placing an order, analyze your social media activity to suggest order options 2. The system of claim 1, (Appendix 25) The management unit Inferring the user's emotions and adjusting the management of allergy information and disliked ingredients based on the estimated user's emotions 3. The system of claim 2, (Appendix 26) The management unit When managing food, the system selects the optimal management method by referring to the user's past meal history. 3. The system of claim 2, (Appendix 27) The management unit To estimate a user's emotion and determine a management priority based on the estimated user's emotion. 3. The system of claim 2, (Appendix 28) The management unit During management, the optimal management method is selected taking into account the user's geographic location information. 3. The system of claim 2, (Appendix 29) The evaluation unit is To estimate a user's emotion and adjust a method for evaluating the nutritional balance of a menu based on the estimated user's emotion. 4. The system of claim 3. (Appendix 30) The evaluation unit is When evaluating, the optimal evaluation method is selected by referring to the user's past meal history. 4. The system of claim 3. (Appendix 31) The evaluation unit is Estimate user sentiment and prioritize ratings based on the estimated user sentiment 4. The system of claim 3. (Appendix 32) The evaluation unit is During evaluation, the optimal evaluation method is selected taking into account the user's geographic location information. 4. The system of claim 3. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section for inputting inventory information; a suggestion unit that suggests a menu based on the inventory information input by the reception unit; an ordering unit that orders missing ingredients online based on the menu suggested by the suggestion unit; Equipped A system characterized by:

2. The suggestion unit, Generate menus based on the user's health and diet goals 2. The system of claim 1 .

3. The suggestion unit, Generates menus based on user-specified budgets, allergy information, and disliked ingredients 2. The system of claim 1 .

4. The ordering unit: Order the right ingredients online within your budget 2. The system of claim 1 .

5. Equipped with a management section for managing information on allergies and disliked ingredients 2. The system of claim 1 .

6. Equipped with an evaluation section that evaluates the nutritional balance of proposed menus 2. The system of claim 1 .

7. The reception unit is To estimate a user's emotion and adjust the timing of inputting inventory information based on the estimated user's emotion.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past inventory input history and select the appropriate input method 2. The system of claim 1 .

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