System

Abstract

An object of a system according to an embodiment is to easily realize a healthy meal for a family.SOLUTION: A system includes an information collection part, a preference management part, a nutrition balance part, a recipe proposal part, and a food expense management part. The information collection unit collects bargain information of the nearest supermarket. The preference management unit manages food preferences and allergy information of the user. The nutrition balance unit manages the nutrition balance based on the nutrition balance. The recipe proposing unit proposes a recipe based on the family structure. The food expense management unit manages food expenses.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 technologies have had the problem that collecting and managing information to easily ensure healthy meals for the family is complicated and difficult to do efficiently.

[0005] The system according to the embodiment aims to make it easy for families to eat healthy meals. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a preference management unit, a nutritional balance unit, a recipe suggestion unit, and a food cost management unit. The information collection unit collects bargain information from the nearest supermarket. The preference management unit manages the user's food preferences and allergy information. The nutritional balance unit manages nutritional balance based on nutritional balance. The recipe suggestion unit suggests recipes based on family composition. The food cost management unit manages food costs. [Effects of the Invention]

[0007] The system according to the embodiment makes it easy to provide healthy meals for the family. [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 My Home Chef system according to an embodiment of the present invention is a system in which AI proposes a menu for each night by combining bargain information from the nearest supermarket with the user's food preferences, allergy information, etc. This allows the My Home Chef system to easily provide healthy meals for the family.

[0029] The My Home Chef system according to the embodiment includes an information collection unit, a preference management unit, a nutritional balance unit, a recipe suggestion unit, and a food cost management unit. The information collection unit collects bargain information from the nearest supermarket. For example, it obtains data from the supermarket's website or app to ascertain the latest price information. The information collection unit also obtains real-time inventory information from the supermarket and proposes menus that take into account the risk of out-of-stock. For example, it avoids ingredients with low inventory and suggests alternative ingredients. The information collection unit also analyzes past price fluctuation data from the supermarket to suggest optimal purchase times. For example, it predicts when a particular ingredient will be cheaper and suggests purchasing it at that time. The preference management unit manages the user's food preferences and allergy information. For example, if a user inputs information such as "I like tomatoes" or "I have a nut allergy," the preference management unit takes this information into consideration when proposing menus. The preference management unit also automatically updates the user's food preferences and allergy information based on past meal history. For example, it registers frequently eaten ingredients as preferences. The preference management unit also dynamically adjusts the user's food preferences in response to seasonal and weather changes. For example, cold dishes are suggested in the summer. The nutritional balance unit assembles menus taking into consideration nutrient balance. For example, the unit assembles menus to include a balanced amount of nutrients such as protein, carbohydrates, lipids, vitamins, and minerals. The nutritional balance unit also links the user's health and fitness data to suggest menus that meet individual nutritional needs. For example, it suggests low-calorie recipes for a user who is on a diet. The nutritional balance unit also adjusts the nutritional balance based on the user's long-term health goals. For example, it suggests low-calorie menus for a user who is trying to lose weight. The recipe suggestion unit suggests recipes based on family composition. For example, it suggests children's menus for families with children and adult menus for families with only adults. The recipe suggestion unit also suggests recipes that take into account specific nutritional needs based on the age and health condition of family members. For example, it suggests low-salt recipes for the elderly. The recipe suggestion unit also suggests special recipes for special family events (e.g., birthdays and anniversaries). For example, it suggests a special cake recipe for a birthday.The food expense management unit manages the user's food expenses. For example, it sets a monthly food budget and suggests menus within that budget. The food expense management unit also visualizes savings effects by comparing with past expenditure data. For example, it displays monthly food expense fluctuations in a graph. Furthermore, the food expense management unit provides advice by comparing with the average food expenses of other households. For example, it provides advice on saving money if the food expenses are higher than the average. In this way, the My Home Chef system according to the embodiment makes it easy to provide healthy meals for the family. For example, it purchases ingredients at a bargain price at the nearest supermarket and suggests nutritionally balanced menus that take into consideration the preferences and allergies of the family. It also provides recipes tailored to the family composition and supports food expense management. This allows the whole family to live a healthy and happy diet.

[0030] The information collection unit can obtain data from the supermarket's website or app and grasp the latest price information. For example, the information collection unit obtains data from the supermarket's website or app to grasp the latest price information. For example, the information collection unit collects data from the supermarket's official website or price comparison site to obtain the latest price information. The information collection unit also obtains the supermarket's inventory status in real time and proposes menus that take into account the risk of out-of-stock. For example, it avoids ingredients that are in low stock and proposes alternative ingredients. This allows the user to purchase the most suitable ingredients by grasping the latest price information.

[0031] The preference management unit can suggest menus based on the user's food preferences and allergy information. For example, when a user inputs information such as "I like tomatoes" or "I have a nut allergy," the preference management unit takes this information into consideration when suggesting menus. For example, it can suggest dishes that use tomatoes and dishes that do not contain nuts. The preference management unit also automatically updates the user's food preferences and allergy information based on past meal history. For example, it can register ingredients that are frequently eaten as preferences. This allows it to suggest menus that take the user's preferences and allergies into consideration.

[0032] The nutritional balance unit can create menus based on nutrient balance. The nutritional balance unit creates menus that include a good balance of nutrients, such as protein, carbohydrates, lipids, vitamins, and minerals. For example, it suggests balanced meals to prevent excess or deficiency of nutrients. The nutritional balance unit also links the user's health status and fitness data to suggest menus that meet individual nutritional needs. For example, it suggests low-calorie recipes to users who are on a diet. This makes it possible to provide menus with a good nutritional balance.

[0033] The recipe suggestion unit can suggest recipes based on family composition. For example, the recipe suggestion unit suggests a children's menu for a household with children, and an adult menu for a household with only adults. For example, it suggests nutritionally balanced meals for children and low-calorie meals for adults. The recipe suggestion unit also suggests recipes that take into account specific nutritional needs according to the age and health status of family members. For example, it suggests low-salt recipes for elderly people. This makes it possible to provide appropriate recipes according to family composition.

[0034] The food expense management unit can manage the user's food expenses and suggest menus within a monthly budget. For example, the food expense management unit sets a monthly food budget and suggests menus within that range. For example, it selects ingredients to stay within the budget and reduces waste. The food expense management unit also visualizes the savings effect by comparing with past expenditure data. For example, it displays monthly food expense fluctuations in a graph. This allows the user to effectively manage their food expenses and suggest menus within the budget.

[0035] The information collection unit can obtain supermarket inventory status in real time and propose menus based on the risk of out-of-stock. The information collection unit, for example, obtains supermarket inventory data in real time and proposes menus taking into account the risk of out-of-stock. For example, it avoids ingredients that are low in stock and proposes alternative ingredients. The information collection unit also periodically updates the supermarket inventory status and notifies the user of the risk of out-of-stock. For example, it issues an alert to avoid recipes that use ingredients that are low in stock. This makes it possible to propose menus that take into account the risk of out-of-stock.

[0036] The information collection unit can analyze the supermarket's past price fluctuation data and suggest the optimal timing for purchase. The information collection unit, for example, collects the supermarket's past price fluctuation data and suggests the optimal timing for purchase. For example, it predicts when a particular food ingredient will be cheaper and suggests purchasing it at that time. The information collection unit also notifies the user of the best time to purchase a particular food ingredient based on the price fluctuation data. For example, it issues an alert before the price drops to encourage purchase. This allows the user to purchase food ingredients efficiently by suggesting the optimal timing for purchase.

[0037] The information collection unit can collect local event information for supermarkets and notify the user. The information collection unit, for example, collects local event information for supermarkets and notifies the user. For example, it provides information on tasting events and sale events in real time. The information collection unit also periodically updates supermarket event information and notifies the user. For example, it issues an alert the day before a sale event to encourage participation. In this way, notifying the user of local event information makes it easier for the user to participate in the event.

[0038] The information gathering unit can collect eco-friendly product information from supermarkets and propose environmentally friendly menus. The information gathering unit, for example, collects eco-friendly product information from supermarkets and proposes environmentally friendly menus. For example, it proposes recipes using organic vegetables and fair trade products. The information gathering unit also suggests to the user to purchase environmentally friendly ingredients based on the eco-friendly product information. For example, it proposes recipes using eco-friendly ingredients. This makes it possible to propose environmentally friendly menus.

[0039] The preference management unit can automatically update the user's food preferences and allergy information from past meal history. The preference management unit, for example, analyzes the user's past meal history and automatically updates the food preferences and allergy information. For example, it registers ingredients that are frequently eaten as preferences. The preference management unit also updates the user's food preferences and allergy information in real time based on the meal history data. For example, when a new ingredient is tried, it is registered as a preference. This allows the user's preference and allergy information to be automatically updated based on the past meal history.

[0040] The preference management unit can dynamically adjust the user's food preferences based on seasonal and climate changes. The preference management unit dynamically adjusts the user's food preferences in accordance with, for example, seasonal and climate changes. For example, cold dishes are suggested in the summer. The preference management unit also adjusts the user's food preferences in real time based on climate data. For example, hot dishes are suggested in the cold season. This makes it possible to adjust food preferences according to the season and climate.

[0041] The preference management unit can integrate the user's food preferences and allergy information with information about all family members to propose menus that will satisfy everyone. The preference management unit, for example, integrates the food preferences and allergy information of all family members to propose menus that will satisfy everyone. For example, it proposes recipes using ingredients that everyone can eat. The preference management unit also proposes menus that will satisfy everyone in real time based on the food preferences and allergy information of all family members. For example, it proposes recipes using ingredients that everyone likes. This makes it possible to propose menus that take into account the preferences and allergy information of all family members.

[0042] The preference management unit can compare and analyze the food preferences of the user with those of other users and propose menus that reflect trends. The preference management unit, for example, compares and analyzes the food preferences of other users and proposes menus that reflect trends. For example, it proposes recipes using popular ingredients. The preference management unit also compares the food preferences of the user with those of other users and proposes menus that reflect trends in real time. For example, it proposes recipes using popular ingredients. This makes it possible to propose menus that reflect trends.

[0043] The nutritional balance unit can link the user's health condition and fitness data to suggest menus based on individual nutritional needs. The nutritional balance unit, for example, links the user's health condition and fitness data to suggest menus that meet individual nutritional needs. For example, it suggests low-calorie recipes to a user who is on a diet. The nutritional balance unit also suggests menus that take into account the user's exercise volume and calorie expenditure based on fitness data. For example, it suggests recipes that include nutrients needed after exercise. This makes it possible to suggest menus that meet individual nutritional needs.

[0044] The nutritional balance unit can adjust the nutritional balance based on the user's long-term health goals. The nutritional balance unit adjusts the nutritional balance based on the user's long-term health goals, for example. For example, it suggests a low-calorie menu to a user who is trying to lose weight. The nutritional balance unit also adjusts the nutritional balance in real time, taking into account the long-term health goals. For example, it suggests a low-carbohydrate menu to a user who is trying to manage their blood sugar levels. This makes it possible to adjust the nutritional balance based on the user's long-term health goals.

[0045] The nutritional balance section can diversify the nutritional balance by incorporating the eating habits of different cultures and regions. For example, the nutritional balance section proposes nutritionally balanced menus that incorporate the eating habits of different cultures and regions. For example, it proposes recipes that incorporate Mediterranean cuisine or Asian cuisine. The nutritional balance section also diversifies the nutritional balance based on the eating habits of different cultures. For example, it proposes menus that incorporate cuisine from different cultures, such as Japanese cuisine or Italian cuisine. This makes it possible to diversify the nutritional balance by incorporating the eating habits of different cultures and regions.

[0046] The nutritional balance unit can customize the nutritional balance based on the user's lifestyle. For example, the nutritional balance unit customizes the nutritional balance according to the user's lifestyle. For example, it suggests recipes for vegetarians and vegans. The nutritional balance unit also customizes the nutritional balance in real time based on the user's lifestyle data. For example, it suggests gluten-free or low-carbohydrate recipes. This makes it possible to customize the nutritional balance according to the user's lifestyle.

[0047] The recipe suggestion unit can suggest recipes based on specific nutritional needs based on the age and health condition of family members. For example, the recipe suggestion unit suggests recipes that take into account specific nutritional needs according to the age and health condition of family members. For example, it suggests low-salt recipes for elderly people. The recipe suggestion unit also suggests recipes that take into account specific nutritional needs in real time based on the health condition data of family members. For example, it suggests high-calcium recipes for children. This makes it possible to suggest recipes that take into account nutritional needs according to the age and health condition of family members.

[0048] The recipe suggestion unit can analyze the family's meal history and re-suggest past recipes that were well-received. The recipe suggestion unit, for example, analyzes the family's meal history and re-suggests past recipes that were well-received. For example, it re-suggests recipes that the family particularly liked. The recipe suggestion unit also re-suggests past recipes that were well-received in real time based on the meal history data. For example, it re-suggests recipes that the family gave high ratings to. In this way, by re-suggesting past recipes that were well-received, meals that satisfy the whole family can be provided.

[0049] The recipe suggestion unit can suggest special recipes tailored to special family events. For example, the recipe suggestion unit suggests special recipes tailored to special family events. For example, it suggests a special cake recipe for a birthday. The recipe suggestion unit also suggests recipes tailored to special family events in real time based on special event data. For example, it suggests a special dinner recipe for an anniversary. This makes it possible to suggest special recipes tailored to special events.

[0050] The recipe suggestion unit can suggest time-saving recipes based on the family's mealtimes and schedules. The recipe suggestion unit suggests time-saving recipes according to, for example, the family's mealtimes and schedules. For example, it suggests recipes that can be made in a short time on busy weekdays. The recipe suggestion unit also suggests time-saving recipes in real time that are tailored to the family's mealtimes based on schedule data. For example, it suggests recipes that can be made in a short time for breakfast. This makes it possible to suggest time-saving recipes according to the family's mealtimes and schedules.

[0051] The food expense management unit can visualize the savings effect by comparing food expense management with past expenditure data. The food expense management unit, for example, compares current food expenses based on past expenditure data and visualizes the savings effect. For example, it displays monthly food expense fluctuations in a graph. The food expense management unit also analyzes expenditure data and visualizes the savings effect in real time. For example, it displays the amount of savings within a specific period. In this way, by visualizing the savings effect by comparing with past expenditure data, the user can confirm the results of their savings.

[0052] The food expense management unit can provide advice on managing food expenses by comparing them with the average food expenses of other households. For example, the food expense management unit collects average food expense data of other households and compares it with the user's food expenses to provide advice. For example, if the food expenses are higher than the average, it provides advice on saving money. The food expense management unit also compares the user's food expenses in real time based on the average food expense data and provides advice. For example, if the food expenses are lower than the average, it notifies the user that savings have been successful. In this way, by providing advice by comparing it with the average food expenses of other households, the user can use it as a reference for saving money.

[0053] The food expense management unit can integrate food expense management with different expenditure categories to perform comprehensive household management. The food expense management unit, for example, integrates different expenditure categories to perform comprehensive household management. For example, it provides a household ledger that includes expenditures on eating out and daily necessities. The food expense management unit also analyzes expenditure data and integrates different expenditure categories to perform comprehensive household management in real time. For example, it displays monthly expenditures by category. This makes it possible to integrate different expenditure categories to perform comprehensive household management.

[0054] The food expense management unit can customize food expense management based on the user's income and spending patterns. The food expense management unit customizes food expense management according to, for example, the user's income and spending patterns. For example, it sets a food expense budget according to income. The food expense management unit also customizes food expense management in real time based on income data and spending patterns. For example, it provides advice on saving money in months with high spending. This makes it possible to customize food expense management according to the user's income and spending patterns.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The My Home Chef system can further include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal. The satisfaction evaluation unit collects feedback entered by the user after the meal and evaluates the satisfaction with the meal. For example, if the user enters feedback such as "It was delicious" or "The portion was just right," the satisfaction evaluation unit will score the satisfaction level based on this information. The satisfaction evaluation unit also analyzes past satisfaction data and suggests menus that suit the user's preferences. For example, it may re-suggest recipes that have received high ratings in the past. Furthermore, the satisfaction evaluation unit takes into account the satisfaction of all family members and suggests menus that will satisfy everyone. For example, it may prioritize suggesting recipes that have been highly rated by all family members. This can increase user satisfaction.

[0057] The My Home Chef system can also include an image analysis unit that analyzes photos of a user's meals. When a user takes a photo of a meal and uploads it to the system, the image analysis unit analyzes the photo to identify ingredients and the type of dish. For example, it can identify ingredients such as tomatoes or chicken from a photo taken by the user and suggest recipes based on that. The image analysis unit also evaluates the appearance and presentation of the meal and provides feedback to the user. For example, it can evaluate the presentation as beautiful or the colors are nice. The image analysis unit also analyzes past meal photos to understand the user's preferences and trends. For example, it can register frequently photographed dishes as favorites. This can improve the user's dining experience.

[0058] The My Home Chef system can further include a mealtime management unit that manages the user's mealtimes. The mealtime management unit records the times the user eats meals and suggests appropriate meal times. For example, it can record the times of breakfast, lunch, and dinner and suggest regular meal times. The mealtime management unit also adjusts meal times according to the user's lifestyle. For example, it can suggest nighttime meal times for users who work night shifts. Furthermore, the mealtime management unit analyzes the relationship between mealtimes and health status and suggests healthy meal times. For example, it can advise users to avoid eating late at night. This can support the user's healthy eating habits.

[0059] The My Home Chef system can further include a storage method suggestion unit that suggests ways for the user to store ingredients. The storage method suggestion unit suggests ways to store purchased ingredients and provides advice on how to keep ingredients fresh. For example, it suggests ways to store vegetables and fruits, and advises on how to store them in the refrigerator or freezer. The storage method suggestion unit also manages the expiration dates of ingredients and suggests using ingredients that are close to their expiration date first. For example, it suggests recipes that use ingredients that are close to their expiration date. Furthermore, the storage method suggestion unit monitors the storage conditions of ingredients and provides advice on maintaining an appropriate storage environment. For example, it suggests refrigerator temperature settings. This reduces food waste and enables efficient food management.

[0060] The My Home Chef system can further include a nutrient tracking unit that tracks the nutrients in the user's meals. The nutrient tracking unit records the nutrients in the meals the user consumes and manages nutritional balance. For example, it records the intake of proteins, carbohydrates, fats, vitamins, minerals, etc., and suggests balanced meals. The nutrient tracking unit also works with the user's health and fitness data to provide advice based on individual nutritional needs. For example, it suggests low-calorie recipes to a user who is on a diet. Furthermore, the nutrient tracking unit adjusts the nutritional balance based on long-term health goals. For example, it suggests low-calorie menus to a user who is trying to lose weight. This supports the user's healthy eating habits.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The information gathering unit collects bargain information from the nearest supermarket. For example, it obtains data from the supermarket's website or app to ascertain the latest price information. The information gathering unit also obtains the supermarket's inventory status in real time and makes menu suggestions that take into account the risk of out-of-stock. Furthermore, the information gathering unit analyzes the supermarket's past price fluctuation data and suggests the optimal timing for purchases. Step 2: The preference management unit manages the user's food preferences and allergy information. For example, if the user inputs information such as "I like tomatoes" or "I have a nut allergy," the preference management unit will take this information into consideration when proposing a menu. The preference management unit also automatically updates the user's food preferences and allergy information based on past meal history. Furthermore, the preference management unit dynamically adjusts the user's food preferences according to seasonal and weather changes. Step 3: The nutritional balance section creates a menu that takes into account the balance of nutrients. For example, the menu is created to include a balanced amount of nutrients such as protein, carbohydrates, lipids, vitamins, and minerals. The nutritional balance section also integrates the user's health and fitness data to suggest menus that meet individual nutritional needs. Furthermore, the nutritional balance section adjusts the nutritional balance based on the user's long-term health goals. Step 4: The recipe suggestion unit suggests recipes based on the family structure. For example, it suggests children's menus for families with children and adult menus for families with only adults. The recipe suggestion unit also suggests recipes that take into account the specific nutritional needs of family members based on their age and health status. Furthermore, the recipe suggestion unit suggests special recipes for special family events. Step 5: The food cost management unit manages the user's food expenses. For example, it sets a monthly food budget and suggests menus within that range. The food cost management unit also visualizes the savings effect by comparing with past expenditure data. Furthermore, the food cost management unit provides advice by comparing with the average food expenses of other households.

[0063] (Example 2) The My Home Chef system according to an embodiment of the present invention is a system in which AI proposes a menu for each night by combining bargain information from the nearest supermarket with the user's food preferences, allergy information, etc. This allows the My Home Chef system to easily provide healthy meals for the family.

[0064] The My Home Chef system according to the embodiment includes an information collection unit, a preference management unit, a nutritional balance unit, a recipe suggestion unit, and a food cost management unit. The information collection unit collects bargain information from the nearest supermarket. For example, it obtains data from the supermarket's website or app to ascertain the latest price information. The information collection unit also obtains real-time inventory information from the supermarket and proposes menus that take into account the risk of out-of-stock. For example, it avoids ingredients with low inventory and suggests alternative ingredients. The information collection unit also analyzes past price fluctuation data from the supermarket to suggest optimal purchase times. For example, it predicts when a particular ingredient will be cheaper and suggests purchasing it at that time. The preference management unit manages the user's food preferences and allergy information. For example, if a user inputs information such as "I like tomatoes" or "I have a nut allergy," the preference management unit takes this information into consideration when proposing menus. The preference management unit also automatically updates the user's food preferences and allergy information based on past meal history. For example, it registers frequently eaten ingredients as preferences. The preference management unit also dynamically adjusts the user's food preferences in response to seasonal and weather changes. For example, cold dishes are suggested in the summer. The nutritional balance unit assembles menus taking into consideration nutrient balance. For example, the unit assembles menus to include a balanced amount of nutrients such as protein, carbohydrates, lipids, vitamins, and minerals. The nutritional balance unit also links the user's health and fitness data to suggest menus that meet individual nutritional needs. For example, it suggests low-calorie recipes for a user who is on a diet. The nutritional balance unit also adjusts the nutritional balance based on the user's long-term health goals. For example, it suggests low-calorie menus for a user who is trying to lose weight. The recipe suggestion unit suggests recipes based on family composition. For example, it suggests children's menus for families with children and adult menus for families with only adults. The recipe suggestion unit also suggests recipes that take into account specific nutritional needs based on the age and health condition of family members. For example, it suggests low-salt recipes for the elderly. The recipe suggestion unit also suggests special recipes for special family events (e.g., birthdays and anniversaries). For example, it suggests a special cake recipe for a birthday.The food expense management unit manages the user's food expenses. For example, it sets a monthly food budget and suggests menus within that budget. The food expense management unit also visualizes savings effects by comparing with past expenditure data. For example, it displays monthly food expense fluctuations in a graph. Furthermore, the food expense management unit provides advice by comparing with the average food expenses of other households. For example, it provides advice on saving money if the food expenses are higher than the average. In this way, the My Home Chef system according to the embodiment makes it easy to provide healthy meals for the family. For example, it purchases ingredients at a bargain price at the nearest supermarket and suggests nutritionally balanced menus that take into consideration the preferences and allergies of the family. It also provides recipes tailored to the family composition and supports food expense management. This allows the whole family to live a healthy and happy diet.

[0065] The information collection unit can obtain data from the supermarket's website or app and grasp the latest price information. For example, the information collection unit obtains data from the supermarket's website or app to grasp the latest price information. For example, the information collection unit collects data from the supermarket's official website or price comparison site to obtain the latest price information. The information collection unit also obtains the supermarket's inventory status in real time and proposes menus that take into account the risk of out-of-stock. For example, it avoids ingredients that are in low stock and proposes alternative ingredients. This allows the user to purchase the most suitable ingredients by grasping the latest price information.

[0066] The preference management unit can suggest menus based on the user's food preferences and allergy information. For example, when a user inputs information such as "I like tomatoes" or "I have a nut allergy," the preference management unit takes this information into consideration when suggesting menus. For example, it can suggest dishes that use tomatoes and dishes that do not contain nuts. The preference management unit also automatically updates the user's food preferences and allergy information based on past meal history. For example, it can register ingredients that are frequently eaten as preferences. This allows it to suggest menus that take the user's preferences and allergies into consideration.

[0067] The nutritional balance unit can create menus based on nutrient balance. The nutritional balance unit creates menus that include a good balance of nutrients, such as protein, carbohydrates, lipids, vitamins, and minerals. For example, it suggests balanced meals to prevent excess or deficiency of nutrients. The nutritional balance unit also links the user's health status and fitness data to suggest menus that meet individual nutritional needs. For example, it suggests low-calorie recipes to users who are on a diet. This makes it possible to provide menus with a good nutritional balance.

[0068] The recipe suggestion unit can suggest recipes based on family composition. For example, the recipe suggestion unit suggests a children's menu for a household with children, and an adult menu for a household with only adults. For example, it suggests nutritionally balanced meals for children and low-calorie meals for adults. The recipe suggestion unit also suggests recipes that take into account specific nutritional needs according to the age and health status of family members. For example, it suggests low-salt recipes for elderly people. This makes it possible to provide appropriate recipes according to family composition.

[0069] The food expense management unit can manage the user's food expenses and suggest menus within a monthly budget. For example, the food expense management unit sets a monthly food budget and suggests menus within that range. For example, it selects ingredients to stay within the budget and reduces waste. The food expense management unit also visualizes the savings effect by comparing with past expenditure data. For example, it displays monthly food expense fluctuations in a graph. This allows the user to effectively manage their food expenses and suggest menus within the budget.

[0070] The information collection unit can obtain supermarket inventory status in real time and propose menus based on the risk of out-of-stock. The information collection unit, for example, obtains supermarket inventory data in real time and proposes menus taking into account the risk of out-of-stock. For example, it avoids ingredients that are low in stock and proposes alternative ingredients. The information collection unit also periodically updates the supermarket inventory status and notifies the user of the risk of out-of-stock. For example, it issues an alert to avoid recipes that use ingredients that are low in stock. This makes it possible to propose menus that take into account the risk of out-of-stock.

[0071] The information collection unit can analyze the supermarket's past price fluctuation data and suggest the optimal timing for purchase. The information collection unit, for example, collects the supermarket's past price fluctuation data and suggests the optimal timing for purchase. For example, it predicts when a particular food ingredient will be cheaper and suggests purchasing it at that time. The information collection unit also notifies the user of the best time to purchase a particular food ingredient based on the price fluctuation data. For example, it issues an alert before the price drops to encourage purchase. This allows the user to purchase food ingredients efficiently by suggesting the optimal timing for purchase.

[0072] The information collection unit can use the emotion estimation function to preferentially suggest ingredients that elicit positive emotions based on the user's emotions toward specific ingredients. For example, the information collection unit uses the emotion estimation function to analyze the user's emotions toward specific ingredients and suggest ingredients that elicit positive emotions. For example, it preferentially suggests ingredients that the user likes. Furthermore, the information collection unit analyzes the user's emotions toward specific ingredients in real time based on the user's emotion data and suggests recipes that elicit positive emotions. For example, it suggests recipes that use ingredients that the user likes. This makes it possible to suggest ingredients that take the user's emotions into consideration.

[0073] The information collection unit can collect local event information for supermarkets and notify the user. The information collection unit, for example, collects local event information for supermarkets and notifies the user. For example, it provides information on tasting events and sale events in real time. The information collection unit also periodically updates supermarket event information and notifies the user. For example, it issues an alert the day before a sale event to encourage participation. In this way, notifying the user of local event information makes it easier for the user to participate in the event.

[0074] The information gathering unit can collect eco-friendly product information from supermarkets and propose environmentally friendly menus. The information gathering unit, for example, collects eco-friendly product information from supermarkets and proposes environmentally friendly menus. For example, it proposes recipes using organic vegetables and fair trade products. The information gathering unit also suggests to the user to purchase environmentally friendly ingredients based on the eco-friendly product information. For example, it proposes recipes using eco-friendly ingredients. This makes it possible to propose environmentally friendly menus.

[0075] The information collection unit can use the emotion estimation function to analyze the emotions a user has toward a specific supermarket and prioritize suggesting supermarkets with high popularity ratings. The information collection unit, for example, uses the emotion estimation function to analyze the emotions a user has toward a specific supermarket and prioritize suggesting supermarkets with high popularity ratings. For example, it prioritizes providing information about supermarkets that the user likes. The information collection unit also analyzes the emotions toward a specific supermarket in real time based on the user's emotion data and suggests supermarkets with high popularity ratings. For example, it prioritizes providing sale information about supermarkets that the user likes. This makes it possible to suggest supermarkets that take the user's emotions into consideration.

[0076] The preference management unit can automatically update the user's food preferences and allergy information from past meal history. The preference management unit, for example, analyzes the user's past meal history and automatically updates the food preferences and allergy information. For example, it registers ingredients that are frequently eaten as preferences. The preference management unit also updates the user's food preferences and allergy information in real time based on the meal history data. For example, when a new ingredient is tried, it is registered as a preference. This allows the user's preference and allergy information to be automatically updated based on the past meal history.

[0077] The preference management unit can dynamically adjust the user's food preferences based on seasonal and climate changes. The preference management unit dynamically adjusts the user's food preferences in accordance with, for example, seasonal and climate changes. For example, cold dishes are suggested in the summer. The preference management unit also adjusts the user's food preferences in real time based on climate data. For example, hot dishes are suggested in the cold season. This makes it possible to adjust food preferences according to the season and climate.

[0078] The preference management unit can use the emotion estimation function to analyze the user's emotions toward specific ingredients in real time and suggest ingredients that elicit positive emotions. For example, the preference management unit can use the emotion estimation function to analyze the user's emotions toward specific ingredients in real time and suggest ingredients that elicit positive emotions. For example, it can prioritize suggestions of ingredients that the user likes. The preference management unit can also analyze the user's emotions toward specific ingredients in real time based on the user's emotion data and suggest recipes that elicit positive emotions. For example, it can suggest recipes that use ingredients that the user likes. This makes it possible to suggest ingredients that take the user's emotions into consideration.

[0079] The preference management unit can integrate the user's food preferences and allergy information with information about all family members to propose menus that will satisfy everyone. The preference management unit, for example, integrates the food preferences and allergy information of all family members to propose menus that will satisfy everyone. For example, it proposes recipes using ingredients that everyone can eat. The preference management unit also proposes menus that will satisfy everyone in real time based on the food preferences and allergy information of all family members. For example, it proposes recipes using ingredients that everyone likes. This makes it possible to propose menus that take into account the preferences and allergy information of all family members.

[0080] The preference management unit can compare and analyze the food preferences of the user with those of other users and propose menus that reflect trends. The preference management unit, for example, compares and analyzes the food preferences of other users and proposes menus that reflect trends. For example, it proposes recipes using popular ingredients. The preference management unit also compares the food preferences of the user with those of other users and proposes menus that reflect trends in real time. For example, it proposes recipes using popular ingredients. This makes it possible to propose menus that reflect trends.

[0081] The preference management unit can use the emotion estimation function to analyze the emotions a user has toward specific ingredients and suggest ingredients that are likely to resonate emotionally. The preference management unit, for example, uses the emotion estimation function to analyze the emotions a user has toward specific ingredients and suggest ingredients that are likely to resonate emotionally. For example, it prioritizes suggesting ingredients that the user likes. The preference management unit also analyzes emotions toward specific ingredients in real time based on the user's emotion data and suggests recipes that are likely to resonate emotionally. For example, it suggests recipes that use ingredients that the user likes. This makes it possible to suggest ingredients that are likely to resonate emotionally.

[0082] The nutritional balance unit can link the user's health condition and fitness data to suggest menus based on individual nutritional needs. The nutritional balance unit, for example, links the user's health condition and fitness data to suggest menus that meet individual nutritional needs. For example, it suggests low-calorie recipes to a user who is on a diet. The nutritional balance unit also suggests menus that take into account the user's exercise volume and calorie expenditure based on fitness data. For example, it suggests recipes that include nutrients needed after exercise. This makes it possible to suggest menus that meet individual nutritional needs.

[0083] The nutritional balance unit can adjust the nutritional balance based on the user's long-term health goals. The nutritional balance unit adjusts the nutritional balance based on the user's long-term health goals, for example. For example, it suggests a low-calorie menu to a user who is trying to lose weight. The nutritional balance unit also adjusts the nutritional balance in real time, taking into account the long-term health goals. For example, it suggests a low-carbohydrate menu to a user who is trying to manage their blood sugar levels. This makes it possible to adjust the nutritional balance based on the user's long-term health goals.

[0084] The nutritional balance unit can use the emotion estimation function to analyze the emotions a user has toward specific nutrients and suggest a menu that prioritizes nutrients that elicit positive emotions. For example, the nutritional balance unit can use the emotion estimation function to analyze the emotions a user has toward specific nutrients and suggest a menu that prioritizes nutrients that elicit positive emotions. For example, it can suggest recipes that are rich in nutrients that the user prefers. Furthermore, the nutritional balance unit can analyze the emotions toward specific nutrients in real time based on the user's emotion data and suggest recipes that elicit positive emotions. For example, it can suggest recipes that are rich in nutrients that the user prefers. This makes it possible to suggest menus that include nutrients that take the user's emotions into consideration.

[0085] The nutritional balance section can diversify the nutritional balance by incorporating the eating habits of different cultures and regions. For example, the nutritional balance section proposes nutritionally balanced menus that incorporate the eating habits of different cultures and regions. For example, it proposes recipes that incorporate Mediterranean cuisine or Asian cuisine. The nutritional balance section also diversifies the nutritional balance based on the eating habits of different cultures. For example, it proposes menus that incorporate cuisine from different cultures, such as Japanese cuisine or Italian cuisine. This makes it possible to diversify the nutritional balance by incorporating the eating habits of different cultures and regions.

[0086] The nutritional balance unit can customize the nutritional balance based on the user's lifestyle. For example, the nutritional balance unit customizes the nutritional balance according to the user's lifestyle. For example, it suggests recipes for vegetarians and vegans. The nutritional balance unit also customizes the nutritional balance in real time based on the user's lifestyle data. For example, it suggests gluten-free or low-carbohydrate recipes. This makes it possible to customize the nutritional balance according to the user's lifestyle.

[0087] The nutritional balance unit can use the emotion estimation function to analyze the emotions a user has toward specific nutrients and suggest a menu that includes nutrients that are likely to resonate emotionally. The nutritional balance unit, for example, uses the emotion estimation function to analyze the emotions a user has toward specific nutrients and suggest a menu that includes nutrients that are likely to resonate emotionally. For example, it suggests recipes that include a lot of nutrients that the user likes. Furthermore, the nutritional balance unit analyzes the emotions toward specific nutrients in real time based on the user's emotion data and suggests recipes that are likely to resonate emotionally. For example, it suggests recipes that include a lot of nutrients that the user likes. This makes it possible to suggest menus that include nutrients that are likely to resonate emotionally.

[0088] The recipe suggestion unit can suggest recipes based on specific nutritional needs based on the age and health condition of family members. For example, the recipe suggestion unit suggests recipes that take into account specific nutritional needs according to the age and health condition of family members. For example, it suggests low-salt recipes for elderly people. The recipe suggestion unit also suggests recipes that take into account specific nutritional needs in real time based on the health condition data of family members. For example, it suggests high-calcium recipes for children. This makes it possible to suggest recipes that take into account nutritional needs according to the age and health condition of family members.

[0089] The recipe suggestion unit can analyze the family's meal history and re-suggest past recipes that were well-received. The recipe suggestion unit, for example, analyzes the family's meal history and re-suggests past recipes that were well-received. For example, it re-suggests recipes that the family particularly liked. The recipe suggestion unit also re-suggests past recipes that were well-received in real time based on the meal history data. For example, it re-suggests recipes that the family gave high ratings to. In this way, by re-suggesting past recipes that were well-received, meals that satisfy the whole family can be provided.

[0090] The recipe suggestion unit can use the emotion estimation function to analyze the emotions of all family members and suggest recipes that will make everyone feel positive. For example, the recipe suggestion unit can use the emotion estimation function to analyze the emotions of all family members and suggest recipes that will make everyone feel positive. For example, it can suggest recipes that use ingredients that all family members like. Furthermore, the recipe suggestion unit can suggest recipes that will make everyone feel positive in real time based on the emotion data of all family members. For example, it can suggest recipes that use ingredients that all family members like. This makes it possible to suggest recipes that will make all family members feel positive.

[0091] The recipe suggestion unit can suggest special recipes tailored to special family events. For example, the recipe suggestion unit suggests special recipes tailored to special family events. For example, it suggests a special cake recipe for a birthday. The recipe suggestion unit also suggests recipes tailored to special family events in real time based on special event data. For example, it suggests a special dinner recipe for an anniversary. This makes it possible to suggest special recipes tailored to special events.

[0092] The recipe suggestion unit can suggest time-saving recipes based on the family's mealtimes and schedules. The recipe suggestion unit suggests time-saving recipes according to, for example, the family's mealtimes and schedules. For example, it suggests recipes that can be made in a short time on busy weekdays. The recipe suggestion unit also suggests time-saving recipes in real time that are tailored to the family's mealtimes based on schedule data. For example, it suggests recipes that can be made in a short time for breakfast. This makes it possible to suggest time-saving recipes according to the family's mealtimes and schedules.

[0093] The recipe suggestion unit can use the emotion estimation function to analyze the emotions of all family members and suggest recipes that are likely to resonate emotionally. The recipe suggestion unit, for example, uses the emotion estimation function to analyze the emotions of all family members and suggest recipes that are likely to resonate emotionally. For example, it suggests recipes that use ingredients that all family members like. The recipe suggestion unit also suggests recipes that are likely to resonate emotionally in real time based on the emotion data of all family members. For example, it suggests recipes that use ingredients that all family members like. This makes it possible to suggest recipes that are likely to resonate emotionally.

[0094] The food expense management unit can visualize the savings effect by comparing food expense management with past expenditure data. The food expense management unit, for example, compares current food expenses based on past expenditure data and visualizes the savings effect. For example, it displays monthly food expense fluctuations in a graph. The food expense management unit also analyzes expenditure data and visualizes the savings effect in real time. For example, it displays the amount of savings within a specific period. In this way, by visualizing the savings effect by comparing with past expenditure data, the user can confirm the results of their savings.

[0095] The food expense management unit can provide advice on managing food expenses by comparing them with the average food expenses of other households. For example, the food expense management unit collects average food expense data of other households and compares it with the user's food expenses to provide advice. For example, if the food expenses are higher than the average, it provides advice on saving money. The food expense management unit also compares the user's food expenses in real time based on the average food expense data and provides advice. For example, if the food expenses are lower than the average, it notifies the user that savings have been successful. In this way, by providing advice by comparing it with the average food expenses of other households, the user can use it as a reference for saving money.

[0096] The food expense management unit can use the emotion estimation function to analyze the user's emotions toward saving money and suggest ways to save money that will elicit positive emotions. For example, the food expense management unit can use the emotion estimation function to analyze the user's emotions toward saving money and suggest ways to save money that will elicit positive emotions. For example, the food expense management unit can share successful savings experiences. The food expense management unit can also analyze the user's emotions toward saving money in real time based on the user's emotion data and suggest ways to save money that will elicit positive emotions. For example, the food expense management unit can emphasize the sense of accomplishment that comes from saving money. This makes it possible to suggest ways to save money that take the user's emotions into consideration.

[0097] The food expense management unit can integrate food expense management with different expenditure categories to perform comprehensive household management. The food expense management unit, for example, integrates different expenditure categories to perform comprehensive household management. For example, it provides a household ledger that includes expenditures on eating out and daily necessities. The food expense management unit also analyzes expenditure data and integrates different expenditure categories to perform comprehensive household management in real time. For example, it displays monthly expenditures by category. This makes it possible to integrate different expenditure categories to perform comprehensive household management.

[0098] The food expense management unit can customize food expense management based on the user's income and spending patterns. The food expense management unit customizes food expense management according to, for example, the user's income and spending patterns. For example, it sets a food expense budget according to income. The food expense management unit also customizes food expense management in real time based on income data and spending patterns. For example, it provides advice on saving money in months with high spending. This makes it possible to customize food expense management according to the user's income and spending patterns.

[0099] The food expense management unit can use the emotion estimation function to analyze the user's emotions regarding saving money and suggest saving methods that are likely to resonate with the user emotionally. The food expense management unit, for example, uses the emotion estimation function to analyze the user's emotions regarding saving money and suggest saving methods that are likely to resonate with the user emotionally. For example, by sharing successful savings experiences. The food expense management unit also analyzes the user's emotions regarding saving money in real time based on the user's emotion data and suggests saving methods that are likely to resonate with the user emotionally. For example, by emphasizing the sense of accomplishment that comes from saving money. This makes it possible to suggest saving methods that take the user's emotions into consideration.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The My Home Chef system can further include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal. The satisfaction evaluation unit collects feedback entered by the user after the meal and evaluates the satisfaction with the meal. For example, if the user enters feedback such as "It was delicious" or "The portion was just right," the satisfaction evaluation unit will score the satisfaction level based on this information. The satisfaction evaluation unit also analyzes past satisfaction data and suggests menus that suit the user's preferences. For example, it may re-suggest recipes that have received high ratings in the past. Furthermore, the satisfaction evaluation unit takes into account the satisfaction of all family members and suggests menus that will satisfy everyone. For example, it may prioritize suggesting recipes that have been highly rated by all family members. This can increase user satisfaction.

[0102] The My Home Chef system can also include an image analysis unit that analyzes photos of a user's meals. When a user takes a photo of a meal and uploads it to the system, the image analysis unit analyzes the photo to identify ingredients and the type of dish. For example, it can identify ingredients such as tomatoes or chicken from a photo taken by the user and suggest recipes based on that. The image analysis unit also evaluates the appearance and presentation of the meal and provides feedback to the user. For example, it can evaluate the presentation as beautiful or the colors are nice. The image analysis unit also analyzes past meal photos to understand the user's preferences and trends. For example, it can register frequently photographed dishes as favorites. This can improve the user's dining experience.

[0103] The My Home Chef system can further include a mealtime management unit that manages the user's mealtimes. The mealtime management unit records the times the user eats meals and suggests appropriate meal times. For example, it can record the times of breakfast, lunch, and dinner and suggest regular meal times. The mealtime management unit also adjusts meal times according to the user's lifestyle. For example, it can suggest nighttime meal times for users who work night shifts. Furthermore, the mealtime management unit analyzes the relationship between mealtimes and health status and suggests healthy meal times. For example, it can advise users to avoid eating late at night. This can support the user's healthy eating habits.

[0104] The My Home Chef system can further include a storage method suggestion unit that suggests ways for the user to store ingredients. The storage method suggestion unit suggests ways to store purchased ingredients and provides advice on how to keep ingredients fresh. For example, it suggests ways to store vegetables and fruits, and advises on how to store them in the refrigerator or freezer. The storage method suggestion unit also manages the expiration dates of ingredients and suggests using ingredients that are close to their expiration date first. For example, it suggests recipes that use ingredients that are close to their expiration date. Furthermore, the storage method suggestion unit monitors the storage conditions of ingredients and provides advice on maintaining an appropriate storage environment. For example, it suggests refrigerator temperature settings. This reduces food waste and enables efficient food management.

[0105] The My Home Chef system can further include a nutrient tracking unit that tracks the nutrients in the user's meals. The nutrient tracking unit records the nutrients in the meals the user consumes and manages nutritional balance. For example, it records the intake of proteins, carbohydrates, fats, vitamins, minerals, etc., and suggests balanced meals. The nutrient tracking unit also works with the user's health and fitness data to provide advice based on individual nutritional needs. For example, it suggests low-calorie recipes to a user who is on a diet. Furthermore, the nutrient tracking unit adjusts the nutritional balance based on long-term health goals. For example, it suggests low-calorie menus to a user who is trying to lose weight. This supports the user's healthy eating habits.

[0106] The My Home Chef system can further include an emotion estimation unit that estimates the user's emotions and suggests ingredients based on the estimated emotions. The emotion estimation unit analyzes the user's facial expressions and tone of voice to estimate their current emotional state. For example, if the user is feeling stressed, it suggests ingredients that have a relaxing effect. The emotion estimation unit also analyzes the relationship between the user's past emotional state and meals based on the user's emotional data. For example, if it determines that a particular ingredient improves the user's mood, it will preferentially suggest that ingredient. Furthermore, the emotion estimation unit adjusts recipes according to the user's emotional state. For example, if the user is tired, it will suggest easy-to-make recipes. This makes it possible to suggest meals that take the user's emotions into consideration.

[0107] The My Home Chef system can further include an emotion estimation unit that estimates the user's emotions and suggests meal times based on the estimated emotions. The emotion estimation unit analyzes the user's facial expressions and tone of voice to estimate their current emotional state. For example, if the user is feeling hungry, it suggests meal times. The emotion estimation unit also analyzes the relationship between the user's past emotional state and meal times based on the user's emotional data. For example, if it is determined that eating at a specific time of day improves the user's mood, it suggests eating at that time of day. Furthermore, the emotion estimation unit adjusts meal times according to the user's emotional state. For example, if the user is feeling stressed, it suggests eating at a time when the user is able to relax. This makes it possible to suggest meal times that take the user's emotions into consideration.

[0108] The My Home Chef system can further include an emotion estimation unit that estimates the user's emotions and adjusts the amount of food consumed based on the estimated emotions. The emotion estimation unit analyzes the user's facial expressions and tone of voice to estimate their current emotional state. For example, if the user feels full, it suggests reducing the amount of food consumed. The emotion estimation unit also analyzes the relationship between the user's past emotional state and the amount of food consumed based on the user's emotional data. For example, if it determines that adjusting the amount of food consumed in a specific emotional state will increase the user's satisfaction, it suggests that amount. Furthermore, the emotion estimation unit adjusts the amount of food consumed in real time according to the user's emotional state. For example, if the user is feeling stressed, it suggests eating a smaller amount of food. This makes it possible to adjust the amount of food consumed while taking the user's emotions into consideration.

[0109] The My Home Chef system can further include an emotion estimation unit that estimates the user's emotions and suggests meal types based on the estimated emotions. The emotion estimation unit analyzes the user's facial expressions and tone of voice to estimate their current emotional state. For example, if the user is tired, it suggests a meal that has a refreshing effect. The emotion estimation unit also analyzes the relationship between the user's past emotional state and meal types based on the user's emotional data. For example, if it is determined that a particular meal improves the user's mood, it will preferentially suggest that meal. Furthermore, the emotion estimation unit adjusts the meal types according to the user's emotional state. For example, if the user is feeling stressed, it will suggest a meal that will help them relax. This makes it possible to suggest meal types that take the user's emotions into consideration.

[0110] The My Home Chef system can further include an emotion estimation unit that estimates the user's emotions and suggests a dining environment based on the estimated emotions. The emotion estimation unit analyzes the user's facial expressions and tone of voice to estimate their current emotional state. For example, if the user wants to relax, it suggests dining in a quiet environment. The emotion estimation unit also analyzes the relationship between the user's past emotional state and the dining environment based on the user's emotional data. For example, if it is determined that eating in a specific environment improves the user's mood, it suggests that environment. Furthermore, the emotion estimation unit adjusts the dining environment according to the user's emotional state. For example, if the user is feeling stressed, it suggests dining in a relaxing environment. This makes it possible to suggest dining environments that take the user's emotions into consideration.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The information gathering unit collects bargain information from the nearest supermarket. For example, it obtains data from the supermarket's website or app to ascertain the latest price information. The information gathering unit also obtains the supermarket's inventory status in real time and makes menu suggestions that take into account the risk of out-of-stock. Furthermore, the information gathering unit analyzes the supermarket's past price fluctuation data and suggests the optimal timing for purchases. Step 2: The preference management unit manages the user's food preferences and allergy information. For example, if the user inputs information such as "I like tomatoes" or "I have a nut allergy," the preference management unit will take this information into consideration when proposing a menu. The preference management unit also automatically updates the user's food preferences and allergy information based on past meal history. Furthermore, the preference management unit dynamically adjusts the user's food preferences according to seasonal and weather changes. Step 3: The nutritional balance section creates a menu that takes into account the balance of nutrients. For example, the menu is created to include a balanced amount of nutrients such as protein, carbohydrates, lipids, vitamins, and minerals. The nutritional balance section also integrates the user's health and fitness data to suggest menus that meet individual nutritional needs. Furthermore, the nutritional balance section adjusts the nutritional balance based on the user's long-term health goals. Step 4: The recipe suggestion unit suggests recipes based on the family structure. For example, it suggests children's menus for families with children and adult menus for families with only adults. The recipe suggestion unit also suggests recipes that take into account the specific nutritional needs of family members based on their age and health status. Furthermore, the recipe suggestion unit suggests special recipes for special family events. Step 5: The food cost management unit manages the user's food expenses. For example, it sets a monthly food budget and suggests menus within that range. The food cost management unit also visualizes the savings effect by comparing with past expenditure data. Furthermore, the food cost management unit provides advice by comparing with the average food expenses of other households.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 AI 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.

[0130] 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.

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

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

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

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

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

[0136] 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).

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

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

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

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0141] 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.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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 AI 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.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may 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.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0158] 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.

[0159] 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.

[0160] 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 AI 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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, in order to avoid confusion and to 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.

[0179] 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]

[0180] 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. An information gathering department that collects bargain information from the nearest supermarket, a preference management unit that manages the user's food preferences and allergy information; Based on nutritional balance, A recipe suggestion section that suggests recipes based on family structure; A food expense management unit that manages food expenses. A system characterized by:

2. The information collecting unit Get data from the supermarket's website or app to keep up to date with prices 2. The system of claim 1.

3. The preference management unit Suggesting a menu based on the user's food preferences and allergy information 2. The system of claim 1.

4. The nutritional balance section Plan your meals based on nutrient balance 2. The system of claim 1.

5. The recipe suggestion unit Suggest the recipe based on the family structure 2. The system of claim 1.

6. The food expense management department Manage the user's food expenses and suggest menus within a monthly budget 2. The system of claim 1.

Images