System

Abstract

An object of the system according to the embodiment is to optimally plan a dinner menu of a household and support work-life balance of a dual-income household.SOLUTION: A system includes a menu generation part, a meal provision adjustment part, and a feedback reflection part. A menu generation part uses the generation AI to generate an optimum menu on the basis of information on likes and dislikes, allergies, lessons, and home return time of members of the household. The meal service adjustment part performs adjustment so that lunch and dinner do not overlap on the basis of the meal service menu of the nursery school or the school. The feedback reflection unit assembles a menu by reflecting the feedback according to the seasonal ingredient or the satisfaction of the child.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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology makes it difficult to optimally plan a family dinner menu, making it difficult to maintain a work-life balance, especially in dual-income households.

[0005] The system according to the embodiment aims to optimally plan a dinner menu for the home and support the work-life balance of dual-income households. [Means for solving the problem]

[0006] The system according to the embodiment includes a menu generation unit, a school lunch adjustment unit, and a feedback reflection unit. The menu generation unit uses a generation AI to generate an optimal menu based on information about household members' likes and dislikes, allergies, extracurricular activities, and home time. The school lunch adjustment unit adjusts the school lunch menu based on the nursery school or school lunch menu so that lunch and dinner do not overlap. The feedback reflection unit assembles the menu by reflecting feedback on seasonal ingredients or children's satisfaction. [Effects of the Invention]

[0007] The system according to the embodiment can optimally plan a dinner menu for the home and support the work-life balance of dual-income households. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. 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 process 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The dinner support system according to an embodiment of the present invention uses AI to support the family's dinner cycle. The system generates optimal menus by taking into account information such as the likes and dislikes of family members, allergies, extracurricular activities, and home time. It also considers school and nursery school lunch menus to avoid overlapping lunch and dinner menus, and assembles menus that incorporate seasonal ingredients and feedback from children's satisfaction. This allows the dinner support system to streamline family dinner preparation and provide high-quality dinners while maintaining work-life balance.

[0029] A dinner support system according to an embodiment includes a menu generation unit, a school lunch adjustment unit, and a feedback reflection unit. The menu generation unit generates an optimal menu based on information about household members' likes and dislikes, allergies, extracurricular activities, and home time. For example, the generation AI receives information about household members' meals as input and generates a menu based on that information. The school lunch adjustment unit adjusts the school lunch menu at a nursery school or school so that lunch and dinner do not overlap. For example, the generation AI receives information about the school lunch menu as input and adjusts the dinner menu based on that information. The feedback reflection unit assembles a menu by reflecting feedback on seasonal ingredients or children's satisfaction. For example, the generation AI receives information about seasonal ingredients and feedback as input and generates a menu based on that information. This allows the dinner support system according to an embodiment to streamline dinner preparation at home and provide high-quality dinners while maintaining work-life balance.

[0030] The menu generation unit can analyze household members' past eating history and suggest changes in preferences or the introduction of new ingredients. For example, the menu generation unit's generation AI stores the household members' eating history from the past year in a database and analyzes changes in preferences. For example, if a child has recently started to like an ingredient that they previously disliked, the generation AI will suggest a menu that includes that ingredient. The generation AI will also suggest the introduction of new ingredients based on the household members' eating history. For example, it will suggest recipes using ingredients that have not been eaten before, increasing meal variety. The generation AI will also analyze the household members' eating history and, if a particular ingredient is consumed frequently, will suggest a new recipe using that ingredient. For example, if a household often eats chicken, it will suggest new dishes using chicken. This makes it possible to suggest menus that suit the preferences of household members.

[0031] The menu generation unit can generate a menu that is optimal for the health status of household members based on the health data of the household members. For example, the generation AI collects the health data of household members and proposes menus that take into account fluctuations in blood sugar levels and weight. For example, it proposes low-carbohydrate menus for members with high blood sugar levels. The generation AI also generates nutritionally balanced menus based on the health data of household members. For example, it proposes calorie-restricted menus for members who need to manage their weight. The generation AI also analyzes the health data of household members and proposes menus tailored to specific health goals (for example, weight loss or muscle building). For example, it proposes low-calorie, high-protein menus for members who are aiming to lose weight. This makes it possible to propose menus that suit the health status of household members.

[0032] The school lunch adjustment unit can analyze the nutritional balance of the school lunch menu and suggest menus that supplement nutrients that are lacking in home dinners. For example, the generation AI in the school lunch adjustment unit analyzes the nutritional balance of the school lunch menu and suggests menus that supplement nutrients that are lacking in home dinners. For example, it suggests a menu that includes vitamin C, which is lacking in school lunches. The generation AI also suggests a balanced meal for home dinners based on the nutritional balance of the school lunch menu. For example, it suggests a menu that supplements protein that is lacking in school lunches. The generation AI also analyzes the nutritional balance of the school lunch menu and suggests menus that strengthen specific nutrients in home dinners. For example, it suggests a menu that includes calcium, which is lacking in school lunches. This makes it possible to suggest menus that supplement the nutritional balance of home dinners.

[0033] The school lunch adjustment unit can analyze the cooking methods of the school lunch menu and suggest a menu using different cooking methods for dinner at home. In the school lunch adjustment unit, for example, the generation AI analyzes the cooking methods of the school lunch menu and suggests a menu using different cooking methods for dinner at home. For example, if the school lunch contains a lot of fried foods, it suggests steamed dishes for dinner at home. The generation AI also suggests a wide variety of cooking methods for dinner at home based on the cooking methods of the school lunch menu. For example, if the school lunch contains a lot of grilled foods, it suggests simmered dishes for dinner at home. The generation AI also analyzes the cooking methods of the school lunch menu and suggests a menu using different cooking methods for dinner at home. For example, if the school lunch contains a lot of stir-fried foods, it suggests grilled dishes for dinner at home. This makes it possible to suggest menus using different cooking methods for dinner at home.

[0034] The feedback reflection unit can analyze past feedback data, identify popular seasonal menu items, and re-suggest them. In the feedback reflection unit, for example, the generation AI collects past feedback data and identifies popular seasonal menu items. For example, it identifies that menu items using fresh vegetables are popular in spring and re-suggests them. The generation AI also re-suggests popular seasonal menu items based on past feedback data. For example, it identifies that cold desserts are popular in summer and re-suggests them. The generation AI also analyzes past feedback data, identifies popular seasonal menu items, and re-suggests them. For example, it identifies that dishes using mushrooms are popular in autumn and re-suggests them. In this way, popular seasonal menu items can be re-suggested.

[0035] The feedback reflection unit can analyze the nutritional value of seasonal ingredients and suggest menus that maximize the health benefits. For example, the generation AI in the feedback reflection unit analyzes the nutritional value of seasonal ingredients and suggests menus that maximize the health benefits. For example, in spring, it suggests a menu using ingredients rich in vitamin C. The generation AI also suggests menus that maximize the health benefits based on the nutritional value of seasonal ingredients. For example, in summer it suggests a menu using ingredients that are good for hydration. The generation AI also analyzes the nutritional value of seasonal ingredients and suggests menus that maximize the health benefits. For example, in autumn it suggests a menu using ingredients rich in dietary fiber. In this way, it is possible to suggest menus that maximize the nutritional value of seasonal ingredients.

[0036] The feedback reflection unit can incorporate seasonal ingredients from different regions to provide new flavors for home dinners. For example, the generation AI collects seasonal ingredients from different regions and proposes menus that offer new flavors for home dinners. For example, it proposes a menu using spring ingredients from Hokkaido. The generation AI also proposes menus that offer new flavors for home dinners based on seasonal ingredients from different regions. For example, it proposes a menu using summer ingredients from Kyushu. The generation AI also analyzes seasonal ingredients from different regions and proposes menus that offer new flavors for home dinners. For example, it proposes a menu using autumn ingredients from Tohoku. This makes it possible to provide new flavors for home dinners.

[0037] The feedback reflection unit can suggest special menus that match seasonal events or occasions. For example, the generation AI considers seasonal events and occasions and suggests special menus that match them. For example, it suggests a special dinner for Christmas. The generation AI also suggests special menus for home dinners based on seasonal events. For example, it suggests street food-style dishes for summer festivals. The generation AI also analyzes seasonal events and occasions and suggests special menus that match them. For example, it suggests dishes using pumpkins for Halloween. In this way, it is possible to suggest special menus that match seasonal events and occasions.

[0038] The cooking utensil suggestion unit can analyze past cooking data and suggest cooking utensils that optimize cooking time or energy consumption. For example, the generation AI collects past cooking data and suggests cooking utensils that optimize cooking time or energy consumption. For example, it suggests a pressure cooker that can shorten cooking time. The generation AI also suggests cooking utensils that optimize energy consumption based on past cooking data. For example, it suggests an induction range that is highly energy efficient. The generation AI also analyzes past cooking data and suggests specific cooking utensils that optimize cooking time and energy consumption. For example, it suggests a multi-cooker that can shorten cooking time. This makes it possible to suggest cooking utensils that optimize cooking time and energy consumption.

[0039] The cooking utensil suggestion unit can analyze the frequency of use of cooking utensils and suggest the cooking utensil with the best cost performance. For example, the generation AI collects the frequency of use of cooking utensils and suggests the cooking utensil with the best cost performance. For example, it suggests replacing a frequently used frying pan. The generation AI also suggests cooking utensil with the best cost performance based on the frequency of use of cooking utensils. For example, it suggests replacing a frequently used knife. The generation AI also analyzes the frequency of use of cooking utensils and suggests the cooking utensil with the best cost performance. For example, it suggests replacing a frequently used pot. In this way, it is possible to suggest the cooking utensil with the best cost performance.

[0040] The Procurement Support Department can analyze past purchase history and propose the most cost-effective procurement method. In the Procurement Support Department, for example, the generation AI collects past purchase history and proposes the most cost-effective procurement method. For example, it identifies that purchasing from a specific supermarket is the cheapest and suggests purchasing from that supermarket. The generation AI also proposes the most cost-effective procurement method based on past purchase history. For example, it identifies that purchasing from an online store is the cheapest and suggests purchasing from that store. The generation AI also analyzes past purchase history and proposes the most cost-effective procurement method. For example, it suggests supermarkets that offer discounts for purchases made on specific days of the week. This makes it possible to propose the most cost-effective procurement method.

[0041] The Procurement Support Department can suggest the optimal purchase timing based on the shelf life or quality of ingredients. For example, the generation AI analyzes the shelf life and quality of ingredients and suggests the optimal purchase timing. For example, it suggests purchasing vegetables, where freshness is important, on the weekend. The generation AI also suggests the optimal purchase timing based on the shelf life and quality of ingredients. For example, it suggests purchasing dried foods that can be stored for a long time in bulk at the beginning of the month. The generation AI also analyzes the shelf life and quality of ingredients and suggests the optimal purchase timing. For example, it suggests purchasing certain ingredients during sales periods. This makes it possible to suggest the optimal purchase timing.

[0042] The delivery collaboration unit can analyze past delivery history and suggest the delivery service with the highest satisfaction rate. In the delivery collaboration unit, for example, the generation AI collects past delivery history and suggests the delivery service with the highest satisfaction rate. For example, it re-suggests a delivery service that has been highly rated in the past. The generation AI also suggests the delivery service with the highest satisfaction rate based on past delivery history. For example, it suggests a delivery service with a short delivery time. The generation AI also analyzes past delivery history and suggests the delivery service with the highest satisfaction rate. For example, it suggests a delivery service with high food quality. This makes it possible to suggest the delivery service with the highest satisfaction rate.

[0043] The delivery collaboration unit can propose the optimal service based on the delivery time or quality of the delivery service. In the delivery collaboration unit, for example, the generation AI analyzes the delivery time and quality of the delivery service and proposes the optimal service. For example, it proposes a service with a short delivery time and high food quality. The generation AI also proposes the optimal service based on the delivery time and quality of the delivery service. For example, it proposes a service with an accurate delivery time and high food quality. The generation AI also analyzes the delivery time and quality of the delivery service and proposes the optimal service. For example, it proposes a service with a short delivery time and food at the appropriate temperature. This makes it possible to propose the optimal delivery service.

[0044] The delivery collaboration unit can analyze delivery service usage data from other households and suggest the most suitable service. In the delivery collaboration unit, for example, the generation AI collects delivery service usage data from other households and suggests the most suitable service. For example, it refers to delivery services that are highly rated by other households. The generation AI also suggests the most suitable service based on the delivery service usage data from other households. For example, it refers to delivery services that are frequently used by other households. The generation AI also analyzes delivery service usage data from other households and suggests the most suitable service. For example, it refers to delivery services that have a high level of satisfaction among other households. This makes it possible to suggest the most suitable delivery service based on the usage data from other households.

[0045] The delivery collaboration unit can make proposals that incorporate the latest trends or new services in delivery services. For example, the generation AI collects the latest trends and new services in delivery services and makes proposals based on them. For example, it can propose the latest contactless delivery services. The generation AI can also make proposals that suit the needs of households based on the latest trends and new services in delivery services. For example, it can propose a delivery service that offers health-conscious menus. The generation AI can also analyze the latest trends and new services in delivery services and make specific proposals based on that. For example, it can propose an eco-friendly delivery service. This makes it possible to propose delivery services that incorporate the latest trends and new services.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The menu generation unit can also suggest menus that take into account the shelf life of ingredients based on information about the meals of household members. For example, it can manage the expiration dates of ingredients in the refrigerator and suggest menus that prioritize ingredients with upcoming expiration dates. The generation AI can also suggest menus that use ingredients that can be stored for a long period of time based on the shelf life of the ingredients. For example, it can suggest menus that use dried or frozen foods. The generation AI can also analyze the shelf life of ingredients and suggest menus that reduce food waste. For example, it can suggest remade dishes using leftover ingredients. This reduces food waste and enables efficient food management.

[0048] The menu generation unit can also suggest menus that take into account the cost of procuring ingredients, based on information about the meals of household members. For example, the generation AI collects ingredient price information and suggests cost-effective menus. For example, it suggests cost-effective menus that use seasonal ingredients. The generation AI can also suggest menus that make use of special sales items based on ingredient price information. For example, it can suggest menus based on supermarket sale information. The generation AI can also analyze ingredient price information and suggest menus that fit the household budget. For example, it can suggest menus that select ingredients to fit within the budget. This makes it possible to provide balanced meals while keeping household food costs down.

[0049] The menu generation unit can also suggest menus that take into account the cooking time of ingredients based on information about the meals of household members. For example, the generation AI will suggest menus that shorten cooking time. For example, it will suggest menus that can be prepared in a short amount of time on busy weekdays. The generation AI will also suggest menus that reduce the effort of cooking based on cooking time. For example, it will suggest one-pot dishes that can be cooked in one pot. The generation AI will also analyze cooking time and suggest menus that suit the household schedule. For example, it will suggest menus that are easy to make on days when people get home late. This makes it possible to prepare meals efficiently according to the household schedule.

[0050] The menu generation unit can also suggest menus that take into account the nutritional value of ingredients based on information about the diets of household members. For example, the generation AI analyzes the nutritional value of ingredients and suggests nutritionally balanced menus. For example, it suggests menus using ingredients rich in vitamins and minerals. The generation AI can also suggest menus that strengthen specific nutrients based on the nutritional value of ingredients. For example, it can suggest menus using ingredients that are high in iron for members who are iron deficient. The generation AI can also analyze the nutritional value of ingredients and suggest menus that match health goals. For example, it can suggest high-protein menus for members who are aiming to build muscle. This makes it possible to provide nutritionally balanced meals that suit the health status of household members.

[0051] The menu generation unit can also suggest menus that take into account food allergy information based on information about the diets of household members. For example, the generation AI collects allergy information and suggests menus that avoid ingredients that cause allergies. For example, it suggests a nut-free menu for a member with a nut allergy. The generation AI can also suggest menus that use alternative ingredients based on the allergy information. For example, it suggests a menu that uses soy milk for a member with a dairy allergy. The generation AI can also analyze allergy information and suggest safe menus that do not cause allergies. For example, it suggests gluten-free menus. This makes it possible to provide safe meals that take into consideration the allergies of household members.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The menu generation unit generates the optimal menu based on information about household members' likes and dislikes, allergies, hobbies, and home arrival times. For example, the generation AI receives information about household members' dietary habits as input and generates a menu based on that information. Step 2: The school lunch adjustment unit adjusts the lunch and dinner menus based on the nursery school or school lunch menu so that they do not overlap. For example, the generation AI receives school lunch menu information as input and adjusts the dinner menu based on that information. Step 3: The feedback reflection unit assembles the menu by reflecting feedback on seasonal ingredients or children's satisfaction. For example, the generation AI receives seasonal ingredient information and feedback as input and generates a menu based on that information.

[0054] (Example 2) The dinner support system according to an embodiment of the present invention uses AI to support the family's dinner cycle. The system generates optimal menus by taking into account information such as the likes and dislikes of family members, allergies, extracurricular activities, and home time. It also considers school and nursery school lunch menus to avoid overlapping lunch and dinner menus, and assembles menus that incorporate seasonal ingredients and feedback from children's satisfaction. This allows the dinner support system to streamline family dinner preparation and provide high-quality dinners while maintaining work-life balance.

[0055] A dinner support system according to an embodiment includes a menu generation unit, a school lunch adjustment unit, and a feedback reflection unit. The menu generation unit generates an optimal menu based on information about household members' likes and dislikes, allergies, extracurricular activities, and home time. For example, the generation AI receives information about household members' meals as input and generates a menu based on that information. The school lunch adjustment unit adjusts the school lunch menu at a nursery school or school so that lunch and dinner do not overlap. For example, the generation AI receives information about the school lunch menu as input and adjusts the dinner menu based on that information. The feedback reflection unit assembles a menu by reflecting feedback on seasonal ingredients or children's satisfaction. For example, the generation AI receives information about seasonal ingredients and feedback as input and generates a menu based on that information. This allows the dinner support system according to an embodiment to streamline dinner preparation at home and provide high-quality dinners while maintaining work-life balance.

[0056] The menu generation unit can analyze household members' past eating history and suggest changes in preferences or the introduction of new ingredients. For example, the menu generation unit's generation AI stores the household members' eating history from the past year in a database and analyzes changes in preferences. For example, if a child has recently started to like an ingredient that they previously disliked, the generation AI will suggest a menu that includes that ingredient. The generation AI will also suggest the introduction of new ingredients based on the household members' eating history. For example, it will suggest recipes using ingredients that have not been eaten before, increasing meal variety. The generation AI will also analyze the household members' eating history and, if a particular ingredient is consumed frequently, will suggest a new recipe using that ingredient. For example, if a household often eats chicken, it will suggest new dishes using chicken. This makes it possible to suggest menus that suit the preferences of household members.

[0057] The menu generation unit can generate a menu that is optimal for the health status of household members based on the health data of the household members. For example, the generation AI collects the health data of household members and proposes menus that take into account fluctuations in blood sugar levels and weight. For example, it proposes low-carbohydrate menus for members with high blood sugar levels. The generation AI also generates nutritionally balanced menus based on the health data of household members. For example, it proposes calorie-restricted menus for members who need to manage their weight. The generation AI also analyzes the health data of household members and proposes menus tailored to specific health goals (for example, weight loss or muscle building). For example, it proposes low-calorie, high-protein menus for members who are aiming to lose weight. This makes it possible to propose menus that suit the health status of household members.

[0058] The menu generation unit can use the emotion estimation function to analyze the emotional state of household members and suggest menus using ingredients that have a stress-reducing or relaxing effect. The menu generation unit, for example, uses the emotion estimation function to analyze the emotional state of household members in real time and suggest menus using ingredients that have a stress-reducing effect. For example, it can suggest dishes using herbs that have a relaxing effect. The emotion estimation function can also be used to suggest menus that have a relaxing effect when household members are tired. For example, it can suggest desserts using chamomile tea or lavender. The emotion estimation function can also be used to analyze the emotional state of household members and suggest menus using ingredients that will lift their spirits. For example, it can suggest desserts using chocolate or bananas. In this way, it is possible to suggest menus that match the emotional state of household members.

[0059] The school lunch adjustment unit can analyze the nutritional balance of the school lunch menu and suggest menus that supplement nutrients that are lacking in home dinners. For example, the generation AI in the school lunch adjustment unit analyzes the nutritional balance of the school lunch menu and suggests menus that supplement nutrients that are lacking in home dinners. For example, it suggests a menu that includes vitamin C, which is lacking in school lunches. The generation AI also suggests a balanced meal for home dinners based on the nutritional balance of the school lunch menu. For example, it suggests a menu that supplements protein that is lacking in school lunches. The generation AI also analyzes the nutritional balance of the school lunch menu and suggests menus that strengthen specific nutrients in home dinners. For example, it suggests a menu that includes calcium, which is lacking in school lunches. This makes it possible to suggest menus that supplement the nutritional balance of home dinners.

[0060] The school lunch adjustment unit can analyze the cooking methods of the school lunch menu and suggest a menu using different cooking methods for dinner at home. In the school lunch adjustment unit, for example, the generation AI analyzes the cooking methods of the school lunch menu and suggests a menu using different cooking methods for dinner at home. For example, if the school lunch contains a lot of fried foods, it suggests steamed dishes for dinner at home. The generation AI also suggests a wide variety of cooking methods for dinner at home based on the cooking methods of the school lunch menu. For example, if the school lunch contains a lot of grilled foods, it suggests simmered dishes for dinner at home. The generation AI also analyzes the cooking methods of the school lunch menu and suggests a menu using different cooking methods for dinner at home. For example, if the school lunch contains a lot of stir-fried foods, it suggests grilled dishes for dinner at home. This makes it possible to suggest menus using different cooking methods for dinner at home.

[0061] The school lunch adjustment unit can use the emotion estimation function to analyze a child's emotional response to the school lunch and suggest a menu for dinner at home that will make up for the dissatisfaction with the school lunch. The school lunch adjustment unit, for example, uses the emotion estimation function to analyze a child's emotional response to the school lunch and suggest a menu for dinner at home that will make up for the dissatisfaction with the school lunch. For example, a menu that avoids ingredients that the child dislikes in the school lunch is suggested. The emotion estimation function can also be used to monitor a child's emotional response to the school lunch in real time and suggest a menu that will elicit positive emotions in the home dinner. For example, a menu that improves on dishes that the child dissatisfied with in the school lunch is suggested. The emotion estimation function can also be used to analyze a child's emotional response to the school lunch and make specific suggestions for making up for the dissatisfaction with the school lunch in the home dinner. For example, a menu that improves on seasonings that the child dissatisfied with in the school lunch is suggested. In this way, a menu can be suggested that will make up for the child's dissatisfaction with the school lunch in the home dinner.

[0062] The feedback reflection unit can analyze past feedback data, identify popular seasonal menu items, and re-suggest them. In the feedback reflection unit, for example, the generation AI collects past feedback data and identifies popular seasonal menu items. For example, it identifies that menu items using fresh vegetables are popular in spring and re-suggests them. The generation AI also re-suggests popular seasonal menu items based on past feedback data. For example, it identifies that cold desserts are popular in summer and re-suggests them. The generation AI also analyzes past feedback data, identifies popular seasonal menu items, and re-suggests them. For example, it identifies that dishes using mushrooms are popular in autumn and re-suggests them. In this way, popular seasonal menu items can be re-suggested.

[0063] The feedback reflection unit can analyze the nutritional value of seasonal ingredients and suggest menus that maximize the health benefits. For example, the generation AI in the feedback reflection unit analyzes the nutritional value of seasonal ingredients and suggests menus that maximize the health benefits. For example, in spring, it suggests a menu using ingredients rich in vitamin C. The generation AI also suggests menus that maximize the health benefits based on the nutritional value of seasonal ingredients. For example, in summer it suggests a menu using ingredients that are good for hydration. The generation AI also analyzes the nutritional value of seasonal ingredients and suggests menus that maximize the health benefits. For example, in autumn it suggests a menu using ingredients rich in dietary fiber. In this way, it is possible to suggest menus that maximize the nutritional value of seasonal ingredients.

[0064] The feedback reflection unit can use the emotion estimation function to analyze the emotional state of the household members by season and suggest a menu that brings out emotions that match the season. The feedback reflection unit, for example, uses the emotion estimation function to analyze the emotional state of the household members by season and suggest a menu that brings out emotions that match the season. For example, a menu that has a refreshing effect is suggested in spring. The emotion estimation function can also be used to monitor the emotional state of the household members by season in real time and suggest a menu that brings out emotions that match the season. For example, a menu that has a relaxing effect is suggested in summer. The emotion estimation function can also be used to analyze the emotional state of the household members by season and suggest a specific menu that brings out emotions that match the season. For example, a warm menu is suggested in autumn. In this way, it is possible to suggest a menu that brings out emotions that match the season.

[0065] The feedback reflection unit can incorporate seasonal ingredients from different regions to provide new flavors for home dinners. For example, the generation AI collects seasonal ingredients from different regions and proposes menus that offer new flavors for home dinners. For example, it proposes a menu using spring ingredients from Hokkaido. The generation AI also proposes menus that offer new flavors for home dinners based on seasonal ingredients from different regions. For example, it proposes a menu using summer ingredients from Kyushu. The generation AI also analyzes seasonal ingredients from different regions and proposes menus that offer new flavors for home dinners. For example, it proposes a menu using autumn ingredients from Tohoku. This makes it possible to provide new flavors for home dinners.

[0066] The feedback reflection unit can suggest special menus that match seasonal events or occasions. For example, the generation AI considers seasonal events and occasions and suggests special menus that match them. For example, it suggests a special dinner for Christmas. The generation AI also suggests special menus for home dinners based on seasonal events. For example, it suggests street food-style dishes for summer festivals. The generation AI also analyzes seasonal events and occasions and suggests special menus that match them. For example, it suggests dishes using pumpkins for Halloween. In this way, it is possible to suggest special menus that match seasonal events and occasions.

[0067] The feedback reflection unit can use the emotion estimation function to monitor the emotional reactions of household members by season in real time and reflect them in the next menu. The feedback reflection unit, for example, uses the emotion estimation function to monitor the emotional reactions of household members by season in real time and adjust the next menu based on that data. For example, in spring, it re-proposes a menu with a refreshing effect. Also, it uses the emotion estimation function to analyze the emotional reactions of household members by season and re-propose a menu that evokes a lot of positive emotions next time. For example, in summer, it re-proposes a menu with a relaxing effect. Also, it uses the emotion estimation function to monitor the emotional reactions of household members by season and re-propose a menu that elicits a specific emotion next time. For example, in autumn, it re-proposes a warm menu. In this way, the emotional reactions of each season can be reflected in the next menu.

[0068] The cooking utensil suggestion unit can analyze past cooking data and suggest cooking utensils that optimize cooking time or energy consumption. For example, the generation AI collects past cooking data and suggests cooking utensils that optimize cooking time or energy consumption. For example, it suggests a pressure cooker that can shorten cooking time. The generation AI also suggests cooking utensils that optimize energy consumption based on past cooking data. For example, it suggests an induction range that is highly energy efficient. The generation AI also analyzes past cooking data and suggests specific cooking utensils that optimize cooking time and energy consumption. For example, it suggests a multi-cooker that can shorten cooking time. This makes it possible to suggest cooking utensils that optimize cooking time and energy consumption.

[0069] The cooking utensil suggestion unit can analyze the frequency of use of cooking utensils and suggest the cooking utensil with the best cost performance. For example, the generation AI collects the frequency of use of cooking utensils and suggests the cooking utensil with the best cost performance. For example, it suggests replacing a frequently used frying pan. The generation AI also suggests cooking utensil with the best cost performance based on the frequency of use of cooking utensils. For example, it suggests replacing a frequently used knife. The generation AI also analyzes the frequency of use of cooking utensils and suggests the cooking utensil with the best cost performance. For example, it suggests replacing a frequently used pot. In this way, it is possible to suggest the cooking utensil with the best cost performance.

[0070] The cooking utensil suggestion unit can use the emotion estimation function to analyze the emotional state of household members while they are cooking and suggest cooking utensils that reduce stress or increase enjoyment. For example, the cooking utensil suggestion unit can use the emotion estimation function to analyze the emotional state of household members while they are cooking in real time and suggest cooking utensils that reduce stress or increase enjoyment. For example, it can suggest easy-to-use kitchen tools. Furthermore, it can use the emotion estimation function to analyze the emotional state of household members while they are cooking and suggest cooking utensils that elicit positive emotions. For example, it can suggest cooking utensils with a high level of design. Furthermore, it can use the emotion estimation function to monitor the emotional state of household members while they are cooking and suggest specific cooking utensils that reduce stress or increase enjoyment. For example, it can suggest easy-to-use electric cooking utensils. In this way, it is possible to suggest cooking utensils that match the emotional state of household members while they are cooking.

[0071] The Procurement Support Department can analyze past purchase history and propose the most cost-effective procurement method. In the Procurement Support Department, for example, the generation AI collects past purchase history and proposes the most cost-effective procurement method. For example, it identifies that purchasing from a specific supermarket is the cheapest and suggests purchasing from that supermarket. The generation AI also proposes the most cost-effective procurement method based on past purchase history. For example, it identifies that purchasing from an online store is the cheapest and suggests purchasing from that store. The generation AI also analyzes past purchase history and proposes the most cost-effective procurement method. For example, it suggests supermarkets that offer discounts for purchases made on specific days of the week. This makes it possible to propose the most cost-effective procurement method.

[0072] The Procurement Support Department can suggest the optimal purchase timing based on the shelf life or quality of ingredients. For example, the generation AI analyzes the shelf life and quality of ingredients and suggests the optimal purchase timing. For example, it suggests purchasing vegetables, where freshness is important, on the weekend. The generation AI also suggests the optimal purchase timing based on the shelf life and quality of ingredients. For example, it suggests purchasing dried foods that can be stored for a long time in bulk at the beginning of the month. The generation AI also analyzes the shelf life and quality of ingredients and suggests the optimal purchase timing. For example, it suggests purchasing certain ingredients during sales periods. This makes it possible to suggest the optimal purchase timing.

[0073] The procurement support unit can use the emotion estimation function to analyze the emotional state of household members at the time of purchase and suggest a procurement method that reduces stress or increases satisfaction. For example, the procurement support unit can use the emotion estimation function to analyze the emotional state of household members at the time of purchase in real time and suggest a procurement method that reduces stress or increases satisfaction. For example, it can suggest online purchase. It can also use the emotion estimation function to analyze the emotional state of household members at the time of purchase and suggest a procurement method that elicits positive emotions. For example, it can suggest a supermarket that offers a fun shopping experience. It can also use the emotion estimation function to monitor the emotional state of household members at the time of purchase and suggest a specific procurement method that reduces stress or increases satisfaction. For example, it can suggest a shopping time that avoids crowds. This makes it possible to suggest a procurement method that suits the emotional state at the time of purchase.

[0074] The delivery collaboration unit can analyze past delivery history and suggest the delivery service with the highest satisfaction rate. In the delivery collaboration unit, for example, the generation AI collects past delivery history and suggests the delivery service with the highest satisfaction rate. For example, it re-suggests a delivery service that has been highly rated in the past. The generation AI also suggests the delivery service with the highest satisfaction rate based on past delivery history. For example, it suggests a delivery service with a short delivery time. The generation AI also analyzes past delivery history and suggests the delivery service with the highest satisfaction rate. For example, it suggests a delivery service with high food quality. This makes it possible to suggest the delivery service with the highest satisfaction rate.

[0075] The delivery collaboration unit can propose the optimal service based on the delivery time or quality of the delivery service. In the delivery collaboration unit, for example, the generation AI analyzes the delivery time and quality of the delivery service and proposes the optimal service. For example, it proposes a service with a short delivery time and high food quality. The generation AI also proposes the optimal service based on the delivery time and quality of the delivery service. For example, it proposes a service with an accurate delivery time and high food quality. The generation AI also analyzes the delivery time and quality of the delivery service and proposes the optimal service. For example, it proposes a service with a short delivery time and food at the appropriate temperature. This makes it possible to propose the optimal delivery service.

[0076] The delivery collaboration unit can use the emotion estimation function to analyze the emotional state when using a delivery service and suggest services that reduce stress or increase satisfaction. The delivery collaboration unit, for example, uses the emotion estimation function to analyze the emotional state when using a delivery service in real time and suggest services that reduce stress or increase satisfaction. For example, it can suggest services with accurate delivery times. It can also use the emotion estimation function to analyze the emotional state when using a delivery service and suggest services that elicit positive emotions. For example, it can suggest services that have high quality food. It can also use the emotion estimation function to monitor the emotional state when using a delivery service and suggest specific services that reduce stress or increase satisfaction. For example, it can suggest services that provide good service from delivery personnel. This makes it possible to suggest services that match the emotional state when using the delivery service.

[0077] The delivery collaboration unit can analyze delivery service usage data from other households and suggest the most suitable service. In the delivery collaboration unit, for example, the generation AI collects delivery service usage data from other households and suggests the most suitable service. For example, it refers to delivery services that are highly rated by other households. The generation AI also suggests the most suitable service based on the delivery service usage data from other households. For example, it refers to delivery services that are frequently used by other households. The generation AI also analyzes delivery service usage data from other households and suggests the most suitable service. For example, it refers to delivery services that have a high level of satisfaction among other households. This makes it possible to suggest the most suitable delivery service based on the usage data from other households.

[0078] The delivery collaboration unit can make proposals that incorporate the latest trends or new services in delivery services. For example, the generation AI collects the latest trends and new services in delivery services and makes proposals based on them. For example, it can propose the latest contactless delivery services. The generation AI can also make proposals that suit the needs of households based on the latest trends and new services in delivery services. For example, it can propose a delivery service that offers health-conscious menus. The generation AI can also analyze the latest trends and new services in delivery services and make specific proposals based on that. For example, it can propose an eco-friendly delivery service. This makes it possible to propose delivery services that incorporate the latest trends and new services.

[0079] The delivery collaboration unit can use the emotion estimation function to monitor emotional reactions when using a delivery service in real time and reflect them in the next recommendation. The delivery collaboration unit, for example, uses the emotion estimation function to monitor emotional reactions when using a delivery service in real time and adjust the next recommendation based on that data. For example, it can suggest a delivery service that is less stressful. It can also use the emotion estimation function to analyze emotional reactions when using a delivery service and recommend delivery services that elicited a lot of positive emotions next time. For example, it can re-recommend a delivery service that offers high quality food. It can also use the emotion estimation function to monitor emotional reactions when using a delivery service and recommend a delivery service that elicits a specific emotion next time. For example, it can re-recommend a delivery service that has a stress-reducing effect. In this way, it can reflect the emotional reactions when using a delivery service in the next recommendation.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The menu generation unit can also suggest menus that take into account the shelf life of ingredients based on information about the meals of household members. For example, it can manage the expiration dates of ingredients in the refrigerator and suggest menus that prioritize ingredients with upcoming expiration dates. The generation AI can also suggest menus that use ingredients that can be stored for a long period of time based on the shelf life of the ingredients. For example, it can suggest menus that use dried or frozen foods. The generation AI can also analyze the shelf life of ingredients and suggest menus that reduce food waste. For example, it can suggest remade dishes using leftover ingredients. This reduces food waste and enables efficient food management.

[0082] The menu generation unit can also suggest menus that take into account the cost of procuring ingredients, based on information about the meals of household members. For example, the generation AI collects ingredient price information and suggests cost-effective menus. For example, it suggests cost-effective menus that use seasonal ingredients. The generation AI can also suggest menus that make use of special sales items based on ingredient price information. For example, it can suggest menus based on supermarket sale information. The generation AI can also analyze ingredient price information and suggest menus that fit the household budget. For example, it can suggest menus that select ingredients to fit within the budget. This makes it possible to provide balanced meals while keeping household food costs down.

[0083] The menu generation unit can also suggest menus that take into account the cooking time of ingredients based on information about the meals of household members. For example, the generation AI will suggest menus that shorten cooking time. For example, it will suggest menus that can be prepared in a short amount of time on busy weekdays. The generation AI will also suggest menus that reduce the effort of cooking based on cooking time. For example, it will suggest one-pot dishes that can be cooked in one pot. The generation AI will also analyze cooking time and suggest menus that suit the household schedule. For example, it will suggest menus that are easy to make on days when people get home late. This makes it possible to prepare meals efficiently according to the household schedule.

[0084] The menu generation unit can also suggest menus that take into account the nutritional value of ingredients based on information about the diets of household members. For example, the generation AI analyzes the nutritional value of ingredients and suggests nutritionally balanced menus. For example, it suggests menus using ingredients rich in vitamins and minerals. The generation AI can also suggest menus that strengthen specific nutrients based on the nutritional value of ingredients. For example, it can suggest menus using ingredients that are high in iron for members who are iron deficient. The generation AI can also analyze the nutritional value of ingredients and suggest menus that match health goals. For example, it can suggest high-protein menus for members who are aiming to build muscle. This makes it possible to provide nutritionally balanced meals that suit the health status of household members.

[0085] The menu generation unit can also suggest menus that take into account food allergy information based on information about the diets of household members. For example, the generation AI collects allergy information and suggests menus that avoid ingredients that cause allergies. For example, it suggests a nut-free menu for a member with a nut allergy. The generation AI can also suggest menus that use alternative ingredients based on the allergy information. For example, it suggests a menu that uses soy milk for a member with a dairy allergy. The generation AI can also analyze allergy information and suggest safe menus that do not cause allergies. For example, it suggests gluten-free menus. This makes it possible to provide safe meals that take into consideration the allergies of household members.

[0086] The menu generation unit can use the emotion estimation function to analyze the emotional state of household members and suggest menus that elicit specific emotions. For example, the emotion estimation function can be used to suggest a menu that has a refreshing effect when a household member is tired. For example, a menu using fruits rich in vitamin C can be suggested. Also, the emotion estimation function can be used to suggest a menu that has a relaxing effect when a household member is feeling stressed. For example, a menu using herbal tea or ingredients with a relaxing effect can be suggested. Also, the emotion estimation function can be used to suggest a menu that will replenish energy when a household member wants to cheer up. For example, a high-calorie, nutritious menu can be suggested. In this way, meals can be provided that suit the emotional state of household members.

[0087] The menu generation unit can use the emotion estimation function to analyze the emotional state of household members and suggest menus that will alleviate specific emotions. For example, the emotion estimation function can be used to suggest a menu that has a relaxing effect when a household member is feeling irritated, such as suggesting a dessert using chamomile tea or lavender. The emotion estimation function can also be used to suggest a menu that will brighten a household member's mood when they are sad, such as suggesting a dessert using chocolate or banana. The emotion estimation function can also be used to suggest a menu that will give a sense of security when a household member is feeling anxious, such as suggesting hot soup or comfort food. This makes it possible to provide meals that suit the emotional state of household members.

[0088] The menu generation unit can use the emotion estimation function to analyze the emotional state of a household member and suggest a menu that enhances a specific emotion. For example, when a household member is feeling happy, the emotion estimation function can be used to suggest a menu that will further enhance that emotion. For example, luxurious desserts or special dishes can be suggested. Also, when a household member is excited, the emotion estimation function can be used to suggest a menu that will maintain that emotion. For example, spicy dishes or menus that replenish energy can be suggested. Also, when a household member is relaxed, the emotion estimation function can be used to suggest a menu that will maintain that emotion. For example, a menu using herbal tea or ingredients that have a relaxing effect can be suggested. In this way, meals can be provided that correspond to the emotional state of the household members.

[0089] The menu generation unit can use the emotion estimation function to analyze the emotional state of a household member and suggest ingredients that will bring out a specific emotion. For example, the emotion estimation function can be used to suggest ingredients that have a relaxing effect when a household member wants to relax. For example, dishes using lavender or chamomile can be suggested. Also, the emotion estimation function can be used to suggest ingredients that will replenish energy when a household member wants to cheer up. For example, dishes using bananas or nuts can be suggested. Also, the emotion estimation function can be used to suggest ingredients that have a stress-reducing effect when a household member is feeling stressed. For example, dishes using dark chocolate or oatmeal can be suggested. In this way, ingredients can be provided that match the emotional state of the household member.

[0090] The menu generation unit can use the emotion estimation function to analyze the emotional state of household members and suggest ingredients to alleviate specific emotions. For example, the emotion estimation function can be used to suggest ingredients that have a relaxing effect when a household member is irritated, such as suggesting dishes using chamomile or lavender. The emotion estimation function can also be used to suggest ingredients that will brighten a household member's mood when they are sad, such as suggesting dishes using chocolate or bananas. The emotion estimation function can also be used to suggest ingredients that will give a sense of security when a household member is feeling anxious, such as suggesting warm soup or comfort food. In this way, ingredients can be provided that match the emotional state of household members.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The menu generation unit generates the optimal menu based on information about household members' likes and dislikes, allergies, hobbies, and home arrival times. For example, the generation AI receives information about household members' dietary habits as input and generates a menu based on that information. Step 2: The school lunch adjustment unit adjusts the lunch and dinner menus based on the nursery school or school lunch menu so that they do not overlap. For example, the generation AI receives school lunch menu information as input and adjusts the dinner menu based on that information. Step 3: The feedback reflection unit assembles the menu by reflecting feedback on seasonal ingredients or children's satisfaction. For example, the generation AI receives seasonal ingredient information and feedback as input and generates a menu based on that information.

[0093] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0094] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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, RAM 30, and 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.

[0099] 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.

[0100] 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 audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] 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 carried out in a secure state.

[0103] Fig. 4 shows an example of the 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.

[0104] 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.

[0105] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0107] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0109] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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, RAM 30, and 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.

[0114] 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.

[0115] 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 audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] 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 carried out in a secure state.

[0118] 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.

[0119] 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.

[0120] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 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 specific processing unit 290 using these models.

[0122] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0124] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the 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.

[0128] 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, RAM 30, and 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.

[0129] 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.

[0130] 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 audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] 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 carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. 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.

[0134] 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.

[0135] 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.

[0136] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. 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.

[0138] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] 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 voice indicating a user input regarding 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.

[0140] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] 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.

[0150] 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 USB (Universal Serial Bus) 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.

[0151] Alternatively, 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 in response to a request from the data processing device 12.

[0152] 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.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0155] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 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. Using generative AI, A menu generator that generates the optimal menu based on information about the likes and dislikes, allergies, hobbies, and home time of household members; The school lunch coordination department adjusts lunch and dinner menus based on the nursery school or school lunch menu so that they do not overlap. A feedback reflection unit that assembles a menu by reflecting feedback on seasonal ingredients or children's satisfaction. A system characterized by:

2. The menu generation unit Analyze the past eating history of the household members and suggest changes in preferences and the introduction of new ingredients 2. The system of claim 1.

3. The menu generation unit Based on the health data of the household members, a menu optimal for their health condition is generated.

2. The system of claim 1.

4. The menu generation unit Analyze the emotional state of the household members and suggest menu items that contain ingredients that have a stress-reducing or relaxing effect.

2. The system of claim 1.

5. The meal preparation unit Analyze the nutritional balance of the school lunch menu and propose menus that supplement nutrients lacking in home dinners 2. The system of claim 1.

6. The meal preparation unit Analyze the cooking methods of the school lunch menu and propose a menu using different cooking methods for home dinner.

2. The system of claim 1.

7. The meal preparation unit Analyze the child's emotional response to school lunches and suggest a menu for home dinner that will make up for the dissatisfaction with the school lunch.

2. The system of claim 1.

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