System

Abstract

An object of a system according to an embodiment is to propose a nutritionally balanced menu on the basis of a user's desire and budget, create an efficient ingredient list, and order and deliver the cheapest ingredient.SOLUTION: A system includes a desire input part, a menu proposal part, an ingredient list preparation part, an order part, and a home delivery part. The desire input unit inputs a user's desire and budget. The menu proposal unit proposes a nutritionally balanced menu on the basis of the desire and the budget input by the desire input unit. The food material list creation part creates a food material list without waste based on the menu proposed by the menu proposal part. The ordering part orders the food material of the lowest price in cooperation with the plurality of net supermarkets based on the food material list created by the food material list creation part. The home delivery unit delivers the food material ordered by the ordering unit to the user's home.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback of making it difficult to efficiently carry out the entire process of proposing nutritionally balanced menus based on the user's preferences and budget, creating a list of ingredients that eliminates waste, ordering the cheapest ingredients, and having them delivered.

[0005] The system of the embodiment aims to propose nutritionally balanced menus based on the user's wishes and budget, create a list of ingredients without waste, order the cheapest ingredients, and deliver them to your home. [Means for solving the problem]

[0006] The system according to the embodiment comprises a preference input unit, a menu proposal unit, an ingredient list creation unit, an ordering unit, and a delivery unit. The preference input unit inputs the user's preferences and budget. The menu proposal unit proposes a nutritionally balanced menu based on the preferences and budget input by the preference input unit. The ingredient list creation unit creates an efficient ingredient list based on the menu proposed by the menu proposal unit. The ordering unit cooperates with multiple online supermarkets based on the ingredient list created by the ingredient list creation unit to order the cheapest ingredients. The delivery unit delivers the ingredients ordered by the ordering unit to the user's home. [Effects of the Invention]

[0007] The system according to the embodiment can propose nutritionally balanced menus based on the user's preferences and budget, create a list of ingredients that eliminate waste, order the cheapest ingredients, and have them delivered to your home. [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 personalized menu creation system according to an embodiment of the present invention allows users to input their preferences and budget, and then a generation AI proposes optimal nutritionally balanced menus, creates a list of ingredients that minimize waste, orders the cheapest ingredients, and delivers them to their home. This reduces the burden on users, reduces food waste, and contributes to the global environment.

[0029] A personalized menu creation system according to an embodiment includes a preference input unit, a menu suggestion unit, an ingredient list creation unit, an ordering unit, and a delivery unit. The preference input unit inputs a user's preferences and budget. For example, the user may input preferences such as using specific ingredients, consuming a large amount of specific nutrients, or keeping weekly food expenses within a certain amount. This information is input as prompts to the generation AI. The menu suggestion unit proposes a nutritionally balanced menu based on the preferences and budget input by the preference input unit. For example, the generation AI generates menus that meet the necessary nutrients for breakfast, lunch, and dinner. The generated menu is displayed to the user. The ingredient list creation unit creates a waste-free ingredient list based on the menu proposed by the menu suggestion unit. For example, it lists ingredients needed for a weekly menu and combines overlapping ingredients to minimize waste. This ingredient list is displayed to the user. The ordering unit connects with multiple online supermarkets based on the ingredient list created by the ingredient list creation unit to order the cheapest ingredients. For example, price information from each online supermarket is collected, allowing the user to purchase ingredients at the lowest price. Orders are placed automatically, allowing the user to purchase ingredients without any hassle. The delivery unit delivers the ingredients ordered by the ordering unit to the user's home, thereby saving the user the trouble of going shopping. The delivery schedule is adjusted according to the user's wishes. As a result, the personalized menu creation system according to the embodiment reduces the burden on the user, reduces food waste, and contributes to the global environment.

[0030] The preference input unit uses AI to analyze the user's past dietary history and health data, allowing it to make more accurate suggestions for preferences and budgets. For example, the preference input unit uses AI to analyze the user's past dietary history and identify frequently consumed ingredients and nutrients. This allows it to make suggestions for preferences and budgets based on the user's preferences and health status. The preference input unit also uses AI to analyze the user's health data (e.g., blood pressure, weight, blood sugar level, etc.) and make diet suggestions based on the user's health status. This supports the user in maintaining and improving their health. The preference input unit also integrates the user's past dietary history and health data, allowing AI to make optimal suggestions for preferences and budgets. For example, it can detect imbalances in nutritional balance from past data and make suggestions to correct them. This allows it to make more accurate suggestions for preferences and budgets by analyzing the user's past dietary history and health data.

[0031] The preference input unit uses AI to automatically detect the user's lifestyle and daily activity level, and can suggest optimal preferences and budgets based on that information. The preference input unit, for example, collects daily activity data from the user's smartphone or wearable device, and the AI ​​analyzes it. For example, it calculates calorie consumption based on the number of steps taken and the amount of exercise, and makes meal suggestions based on that. The preference input unit also uses AI to analyze the user's lifestyle (e.g., work schedule and hobby activities) and suggests optimal preferences and budgets based on that analysis. For example, it suggests easy-to-prepare menus on busy days. The preference input unit also integrates the user's activity data with lifestyle information, and the AI ​​suggests optimal meals. For example, it suggests high-protein menus on days when the user exercises a lot, and low-calorie menus on days when the user exercises less. This allows the system to suggest optimal preferences and budgets by taking the user's lifestyle and daily activity level into consideration.

[0032] The request input unit can provide an interface that allows the user to intuitively input their requests and budget using voice input or image recognition. The request input unit, for example, provides an interface that allows the user to input their requests and budget by voice. For example, using voice recognition technology, what the user says is converted into text, which is then analyzed by AI. The request input unit also provides an interface that allows the user to input their requests and budget by uploading images of ingredients. For example, using image recognition technology, the uploaded ingredients are automatically recognized, and then analyzed by AI. The request input unit also provides a graphical user interface (GUI) that the user can operate intuitively. For example, an interface can be developed that allows the user to input their requests and budget using drag and drop. This allows the user to intuitively input their requests and budget using voice input or image recognition.

[0033] The preference input unit can work in conjunction with other health management apps and wearable devices to automatically import the user's health data. The preference input unit can work in conjunction with other health management apps (e.g., fitness apps and diet apps) to automatically import the user's health data. For example, the AI ​​analyzes the amount of exercise and food records. The preference input unit also automatically imports health data from wearable devices (e.g., smart watches and fitness trackers). For example, the AI ​​analyzes heart rate and sleep data. The preference input unit also integrates data obtained from the health management apps and wearable devices, and the AI ​​comprehensively analyzes the user's health condition. For example, it can make optimal dietary suggestions based on the amount of exercise, food records, and sleep data. In this way, the user's health data can be automatically imported by working in conjunction with other health management apps and wearable devices.

[0034] The menu suggestion unit uses AI to refer to the latest nutritional research and provide the optimal nutritional balance for each individual user. For example, the menu suggestion unit uses AI to refer to the latest nutritional research and provide the optimal nutritional balance based on the user's health condition and lifestyle. For example, it adjusts nutrient intake based on the latest research data. The menu suggestion unit also suggests menus based on specific health goals (such as weight loss or muscle building) based on the latest nutritional research. For example, it generates menus that are rich in specific nutrients. The menu suggestion unit also uses AI to analyze the latest nutritional research and provide a nutritional balance that meets the user's individual needs. For example, it suggests menus that include nutrients that are effective in preventing specific diseases. In this way, by referring to the latest nutritional research, it is possible to provide the optimal nutritional balance for each individual user.

[0035] The menu suggestion unit can suggest menus that incorporate seasonal ingredients, taking into consideration the season and local specialties. The menu suggestion unit, for example, suggests menus by taking into consideration seasonal specialties and seasonal ingredients. For example, in spring, it suggests menus that incorporate fresh vegetables and fruits. The menu suggestion unit also suggests menus that utilize local ingredients, taking into consideration local specialties. For example, it suggests traditional dishes that use local specialties. The menu suggestion unit also suggests nutritionally balanced menus based on the season and local specialties. For example, it suggests healthy menus that use seasonal ingredients. In this way, it is possible to suggest menus that incorporate seasonal ingredients by taking into consideration the season and local specialties.

[0036] The menu suggestion unit can reflect the user's allergy information and food preferences in detail when suggesting a menu. For example, the menu suggestion unit suggests a menu that does not contain allergens based on the user's allergy information. For example, if the user is allergic to a specific ingredient, the menu suggestion unit suggests a menu that excludes that ingredient. The menu suggestion unit also suggests a menu that reflects the user's food preferences in detail. For example, the menu is generated taking into consideration favorite and disliked ingredients. The menu suggestion unit also integrates the allergy information and food preferences to suggest an optimal menu for the user. For example, it suggests a menu that avoids allergens while including many favorite ingredients. In this way, by reflecting the user's allergy information and food preferences in detail, it is possible to suggest a menu that meets individual needs.

[0037] The menu suggestion unit can suggest popular menus based on the ratings and reviews of other users. The menu suggestion unit suggests popular menus, for example, based on the ratings and reviews of other users. For example, it preferentially displays highly rated menus. The menu suggestion unit also analyzes user reviews to suggest particularly popular menus. For example, it analyzes the content of reviews to suggest menus with many positive ratings. The menu suggestion unit also displays popular menus in a ranking format based on the rating data of other users. For example, menus with high rating scores are displayed at the top. This makes it possible to suggest popular menus based on the ratings and reviews of other users.

[0038] The ingredient list creation unit uses AI to analyze past purchase history and automatically generate an ingredient list that eliminates waste. For example, the ingredient list creation unit uses AI to analyze a user's past purchase history and automatically generate an ingredient list that eliminates waste. For example, it includes ingredients that are frequently purchased or used in the list. The ingredient list creation unit also reflects duplicate ingredients in the list based on past purchase history, for example, by adjusting the list to avoid purchasing the same ingredient multiple times. The ingredient list creation unit also analyzes purchase history and develops an algorithm to create an ingredient list that eliminates waste. For example, it suggests optimal purchase amounts based on past data. In this way, an ingredient list that eliminates waste can be automatically generated by analyzing past purchase history.

[0039] The ingredient list creation unit can suggest optimal purchase quantities by taking into account the shelf life and frequency of use of ingredients. The ingredient list creation unit, for example, suggests optimal purchase quantities by taking into account the shelf life of ingredients. For example, it suggests purchasing small amounts of ingredients with short shelf lives. The ingredient list creation unit also suggests optimal purchase quantities based on the frequency of use of ingredients. For example, it suggests purchasing larger amounts of ingredients that are used frequently and smaller amounts of ingredients that are used less frequently. The ingredient list creation unit also develops an algorithm that integrates shelf life and frequency of use to suggest optimal purchase quantities. For example, it analyzes shelf life and frequency of use based on past data and calculates the optimal purchase quantity. This makes it possible to suggest optimal purchase quantities by taking into account the shelf life and frequency of use of ingredients.

[0040] The ingredient list creation unit can share the ingredient list with other users, promoting joint purchasing and sharing. The ingredient list creation unit, for example, provides a function for sharing the ingredient list with other users, promoting joint purchasing. For example, ingredients are shared between users living in the same area. The ingredient list creation unit also builds a system for sharing ingredient lists and exchanging leftover ingredients with other users. For example, ingredients that cannot be used up are provided to other users. The ingredient list creation unit also develops a platform for promoting joint purchasing and sharing. For example, a community is created where users can share ingredients with each other. In this way, sharing the ingredient list with other users can promote joint purchasing and sharing.

[0041] The ingredient list creation unit can provide recipe suggestions and cooking method advice based on the ingredient list. For example, the ingredient list creation unit builds a system that suggests optimal recipes based on the ingredient list. For example, it automatically generates recipes using ingredients included in the list. The ingredient list creation unit also provides cooking method advice based on the ingredient list. For example, it suggests simple cooking methods using ingredients included in the list. The ingredient list creation unit also suggests recipes tailored to the user's preferences based on the ingredient list. For example, it suggests dishes that the user likes using ingredients included in the list. This makes it possible to provide recipe suggestions and cooking method advice based on the ingredient list.

[0042] The ordering department uses AI to monitor price fluctuations in real time, allowing ingredients to be ordered at the optimal time. For example, the ordering department uses AI to collect price information from each online supermarket in real time and monitor price fluctuations. For example, ingredients are ordered when the price drops. The ordering department also develops an algorithm to order ingredients at the optimal time based on price fluctuations. For example, it predicts when the price will be lowest and places an order at that time. The ordering department also collects price information in real time, allowing ingredients to be ordered at the optimal time for the user. For example, an order is automatically placed the moment the price drops. This allows for cost reduction by monitoring price fluctuations in real time and ordering ingredients at the optimal time.

[0043] The ordering unit can take into account the inventory status of each online supermarket and propose the most efficient ordering method. For example, the ordering unit collects inventory status of each online supermarket in real time and proposes the most efficient ordering method. For example, it prioritizes ordering from supermarkets with abundant stock. The ordering unit also develops an algorithm that proposes the optimal ordering method based on the inventory status. For example, it distributes orders for ingredients that are low in stock from multiple supermarkets. The ordering unit also collects inventory information in real time and proposes the most efficient ordering method to the user. For example, it suggests ordering ingredients that are low in stock early. In this way, the most efficient ordering method can be proposed by taking into account the inventory status of each online supermarket.

[0044] The ordering department can also work with local markets and farmers other than the online supermarket to promote local production and consumption. The ordering department, for example, works with local markets and farmers other than the online supermarket to build a system that promotes local production and consumption. For example, it prioritizes ordering fresh local ingredients. The ordering department also collects ingredient information from local markets and farmers and suggests it to users. For example, it includes local specialties and seasonal ingredients in the list. The ordering department also develops a platform to promote local production and consumption. For example, it works directly with local markets and farmers to build a system for ordering ingredients. This makes it possible to promote local production and consumption by working with local markets and farmers other than the online supermarket.

[0045] The ordering unit can suggest recommended products that match the user's preferences based on the food ingredient order history. The ordering unit, for example, analyzes the food ingredient order history and suggests recommended products that match the user's preferences. For example, suggestions are made based on frequently purchased or favorite ingredients. The ordering unit also analyzes the user's preferences based on the order history and develops an algorithm that suggests optimal products. For example, it predicts the user's preferences based on past data. The ordering unit also suggests related products based on the user's order history. For example, it suggests seasonings or recipes that go well with purchased ingredients. In this way, it is possible to suggest recommended products that match the user's preferences based on the food ingredient order history.

[0046] The delivery department uses AI to calculate the optimal delivery route, thereby shortening delivery time. For example, the delivery department selects the optimal route based on traffic and weather information. The delivery department also develops algorithms to optimize delivery routes, thereby shortening delivery time. For example, it recalculates routes in real time and selects the shortest route. The delivery department also builds a system to calculate the optimal delivery route and notify the user. For example, shortening delivery time improves user convenience. This allows AI to calculate the optimal delivery route, thereby shortening delivery time.

[0047] The delivery unit can track the delivery status in real time and notify the user. The delivery unit, for example, builds a system that tracks the delivery status in real time and notifies the user. For example, it allows the delivery progress to be checked using a smartphone app. The delivery unit also tracks the delivery status in real time and notifies the user by email or SMS. For example, it sends a notification when delivery is approaching. The delivery unit also tracks the delivery status in real time and allows the user to receive the notification at a time desired by the user. For example, it sends a notification one hour before delivery. In this way, it is possible to track the delivery status in real time and notify the user, thereby improving user convenience.

[0048] The delivery unit can provide advice on how to store and cook ingredients at the time of delivery. The delivery unit, for example, builds a system that gives advice to users on how to store ingredients at the time of delivery. For example, it provides information about ingredients that need to be refrigerated. The delivery unit also gives advice to users on how to cook ingredients at the time of delivery. For example, it provides simple recipes and cooking tips. The delivery unit also develops a system that provides advice on how to store and cook ingredients together at the time of delivery. For example, it displays storage methods and cooking methods together. This allows for providing advice on how to store and cook ingredients at the time of delivery, thereby improving user convenience.

[0049] The delivery department can reduce the environmental burden by working with the delivery service and using recyclable packaging materials. For example, the delivery department works with the delivery service to build a system that uses recyclable packaging materials. For example, paper packaging materials are used instead of plastic. The delivery department can also reduce the environmental burden by using recyclable packaging materials. For example, reusable eco-bags are provided. The delivery department can also work with the delivery service to implement a campaign to promote the use of recyclable packaging materials. For example, discounts are offered if recyclable packaging materials are used. In this way, the use of recyclable packaging materials can reduce the environmental burden.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The preference input unit uses AI to analyze the user's past dietary history and health data, allowing it to make more accurate suggestions about preferences and budgets. For example, AI analyzes the user's past dietary history to identify frequently consumed ingredients and nutrients. This allows it to make suggestions about preferences and budgets based on the user's preferences and health status. The preference input unit also uses AI to analyze the user's health data (e.g., blood pressure, weight, blood sugar level, etc.) and make diet suggestions based on the user's health status. This supports the user in maintaining and improving their health. The preference input unit also integrates the user's past dietary history and health data, allowing AI to make optimal suggestions about preferences and budgets. For example, it can detect imbalances in nutritional balance from past data and make suggestions to correct them. This allows it to make more accurate suggestions about preferences and budgets by analyzing the user's past dietary history and health data.

[0052] The preference input unit uses AI to automatically detect the user's lifestyle and daily activity level, and can then use that information to suggest optimal preferences and budgets. For example, the AI ​​collects daily activity data from the user's smartphone or wearable device and analyzes it. For example, it calculates calorie consumption based on the number of steps taken and the amount of exercise, and makes meal suggestions based on that. The preference input unit also uses AI to analyze the user's lifestyle (e.g., work schedule and hobby activities) and suggests optimal preferences and budgets based on that analysis. For example, it suggests easy-to-prepare menus on busy days. The preference input unit also integrates the user's activity data with lifestyle information, and the AI ​​then makes optimal meal suggestions. For example, it suggests high-protein menus on days when the user exercises a lot, and low-calorie menus on days when the user exercises less. This allows the system to suggest optimal preferences and budgets by taking the user's lifestyle and daily activity level into consideration.

[0053] The request input unit can provide an interface that allows users to intuitively input their requests and budget using voice input or image recognition. For example, an interface is provided that allows users to input their requests and budget by voice. For example, voice recognition technology is used to convert what the user says into text, which is then analyzed by AI. The request input unit also provides an interface that allows users to input their requests and budget by uploading images of ingredients. For example, image recognition technology is used to automatically recognize the uploaded ingredients, which are then analyzed by AI. The request input unit also provides a graphical user interface (GUI) that users can operate intuitively. For example, an interface is developed that allows users to input their requests and budget using drag and drop. This allows users to intuitively input their requests and budget using voice input or image recognition.

[0054] The preference input unit can work with other health management apps and wearable devices to automatically import the user's health data. For example, it can work with other health management apps (e.g., fitness apps or diet apps) to automatically import the user's health data. For example, the AI ​​analyzes the amount of exercise and food records. The preference input unit also automatically imports health data from wearable devices (e.g., smartwatches and fitness trackers). For example, the AI ​​analyzes heart rate and sleep data. The preference input unit also integrates data obtained from the health management apps and wearable devices, and the AI ​​comprehensively analyzes the user's health condition. For example, it can make optimal dietary suggestions based on the amount of exercise, food records, and sleep data. This allows the user's health data to be automatically imported by working with other health management apps and wearable devices.

[0055] The menu suggestion unit uses AI to refer to the latest nutritional research and provide the optimal nutritional balance for each individual user. For example, the AI ​​may refer to the latest nutritional research and provide the optimal nutritional balance based on the user's health condition and lifestyle. For example, it may adjust nutrient intake based on the latest research data. The menu suggestion unit also uses the latest nutritional research to suggest menus based on specific health goals (such as weight loss or muscle building). For example, it may generate menus that are rich in specific nutrients. The menu suggestion unit also uses AI to analyze the latest nutritional research and provide a nutritional balance that meets the individual needs of the user. For example, it may suggest a menu that includes nutrients that are effective in preventing specific diseases. In this way, by referring to the latest nutritional research, it is possible to provide the optimal nutritional balance for each individual user.

[0056] The menu suggestion unit can propose menus that incorporate seasonal ingredients, taking into consideration the season and local specialties. For example, a menu is proposed taking into consideration seasonal specialties and seasonal ingredients. For example, in spring, a menu incorporating fresh vegetables and fruits is proposed. The menu suggestion unit also proposes menus that utilize local ingredients, taking into consideration local specialties. For example, it proposes traditional dishes that use local specialties. The menu suggestion unit also proposes nutritionally balanced menus based on the season and local specialties. For example, it proposes healthy menus that use seasonal ingredients. In this way, a menu incorporating seasonal ingredients can be proposed by taking into consideration the season and local specialties.

[0057] The ingredient list creation unit uses AI to analyze past purchase history and automatically generate an ingredient list that eliminates waste. For example, AI analyzes a user's past purchase history and automatically generates an ingredient list that eliminates waste. For example, it includes ingredients that are frequently purchased or used on the list. The ingredient list creation unit also reflects duplicate ingredients in the list based on past purchase history, for example, by adjusting the list to prevent the same ingredient from being purchased multiple times. The ingredient list creation unit also analyzes purchase history and develops an algorithm to create an ingredient list that eliminates waste. For example, it suggests optimal purchase amounts based on past data. This makes it possible to automatically generate an ingredient list that eliminates waste by analyzing past purchase history.

[0058] The ingredient list creation unit can suggest optimal purchase quantities by taking into account the shelf life and frequency of use of ingredients. For example, it suggests optimal purchase quantities by taking into account the shelf life of ingredients. For example, it suggests purchasing small amounts of ingredients with short shelf lives. The ingredient list creation unit also suggests optimal purchase quantities based on the frequency of use of ingredients. For example, it suggests purchasing larger amounts of ingredients that are used frequently and smaller amounts of ingredients that are used less frequently. The ingredient list creation unit also develops an algorithm that integrates shelf life and frequency of use to suggest optimal purchase quantities. For example, it analyzes shelf life and frequency of use based on past data and calculates the optimal purchase quantity. This makes it possible to suggest optimal purchase quantities by taking into account the shelf life and frequency of use of ingredients.

[0059] The ingredient list creation unit can share ingredient lists with other users to promote joint purchasing and sharing. For example, it can provide a function for sharing ingredient lists with other users to promote joint purchasing. For example, ingredients can be shared between users living in the same area. The ingredient list creation unit can also build a system for sharing ingredient lists and exchanging leftover ingredients with other users. For example, ingredients that cannot be used up can be provided to other users. The ingredient list creation unit can also develop a platform for promoting joint purchasing and sharing. For example, it can create a community where users can share ingredients with each other. This can promote joint purchasing and sharing by sharing ingredient lists with other users.

[0060] The ingredient list creation unit can provide recipe suggestions and cooking advice based on the ingredient list. For example, a system can be built that suggests optimal recipes based on the ingredient list. For example, recipes using ingredients included in the list can be automatically generated. The ingredient list creation unit can also provide cooking advice based on the ingredient list. For example, it can suggest simple cooking methods using ingredients included in the list. The ingredient list creation unit can also suggest recipes tailored to the user's preferences based on the ingredient list. For example, it can suggest dishes that the user likes using ingredients included in the list. This makes it possible to provide recipe suggestions and cooking advice based on the ingredient list.

[0061] The ordering department uses AI to monitor price fluctuations in real time, allowing ingredients to be ordered at the optimal time. For example, AI collects price information from each online supermarket in real time and monitors price fluctuations. For example, ingredients are ordered when the price drops. The ordering department also develops an algorithm to order ingredients at the optimal time based on price fluctuations. For example, it predicts when the price will be lowest and places an order at that time. The ordering department also collects price information in real time, allowing ingredients to be ordered at the optimal time for the user. For example, an order is automatically placed the moment the price drops. This allows costs to be reduced by monitoring price fluctuations in real time and ordering ingredients at the optimal time.

[0062] The ordering department can take into account the inventory status of each online supermarket and propose the most efficient ordering method. For example, it can collect inventory status from each online supermarket in real time and propose the most efficient ordering method. For example, it can prioritize ordering from supermarkets with abundant stock. The ordering department also develops an algorithm that proposes the optimal ordering method based on the inventory status. For example, it can distribute orders for ingredients that are low in stock from multiple supermarkets. The ordering department also collects inventory information in real time and proposes the most efficient ordering method to the user. For example, it can suggest ordering ingredients that are low in stock early. This allows the most efficient ordering method to be proposed by taking into account the inventory status of each online supermarket.

[0063] The ordering department can also work with local markets and farmers other than the online supermarket to promote local production and consumption. For example, a system can be built to work with local markets and farmers other than the online supermarket to promote local production and consumption. For example, local fresh ingredients can be given priority in ordering. The ordering department can also collect ingredient information from local markets and farmers and suggest it to users. For example, local specialties and seasonal ingredients can be included in the list. The ordering department can also develop a platform to promote local production and consumption. For example, a system can be built to work directly with local markets and farmers to order ingredients. This can promote local production and consumption by working with local markets and farmers other than the online supermarket.

[0064] The ordering unit can suggest recommended products that match the user's preferences based on the food ingredient order history. For example, the ordering unit analyzes the food ingredient order history and suggests recommended products that match the user's preferences. For example, suggestions are made based on frequently purchased ingredients or favorite ingredients. The ordering unit also analyzes the user's preferences based on the order history and develops an algorithm that suggests optimal products. For example, it predicts the user's preferences based on past data. The ordering unit also suggests related products based on the user's order history. For example, it suggests seasonings or recipes that go well with purchased ingredients. In this way, it is possible to suggest recommended products that match the user's preferences based on the food ingredient order history.

[0065] The delivery department uses AI to calculate the optimal delivery route, thereby shortening delivery times. For example, AI calculates the optimal delivery route and shortens delivery times. For example, it selects the optimal route based on traffic and weather information. The delivery department also develops algorithms to optimize delivery routes and shorten delivery times. For example, it recalculates routes in real time and selects the shortest route. The delivery department also calculates the optimal delivery route and builds a system to notify users. For example, shortening delivery times improves user convenience. This allows AI to calculate the optimal delivery route and shorten delivery times.

[0066] The delivery unit can track the delivery status in real time and notify the user. For example, a system can be built that tracks the delivery status in real time and notifies the user. For example, a system can be built that allows the delivery progress to be checked using a smartphone app. The delivery unit can also track the delivery status in real time and notify the user by email or SMS. For example, a notification can be sent when delivery is approaching. The delivery unit can also track the delivery status in real time and allow the user to receive the notification at a time of their choice. For example, a notification can be sent one hour before delivery. This makes it possible to track the delivery status in real time and notify the user, thereby improving user convenience.

[0067] The delivery unit can provide advice on how to store and cook ingredients at the time of delivery. For example, a system is constructed that provides advice to users on how to store ingredients at the time of delivery. For example, it provides information about ingredients that need to be refrigerated. The delivery unit also provides advice to users on how to cook ingredients at the time of delivery. For example, it provides simple recipes and cooking tips. The delivery unit also develops a system that provides advice on how to store and cook ingredients at the time of delivery, integrating the advice on how to store and cook ingredients. For example, it displays the storage method and cooking method together. This makes it possible to improve user convenience by providing advice on how to store and cook ingredients at the time of delivery.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The user's preferences and budget are entered in the preference input section. For example, they may want to use specific ingredients, consume a lot of specific nutrients, or keep their weekly food expenses within a certain amount. This information is entered as a prompt for the generation AI. Step 2: The menu suggestion unit proposes a nutritionally balanced menu based on the preferences and budget entered in the preference input unit. For example, the generation AI generates menus that meet the necessary nutrients for breakfast, lunch, and dinner. The generated menu is displayed to the user. Step 3: The ingredient list creation unit creates an ingredient list that minimizes waste based on the menu proposed by the menu suggestion unit. For example, it lists ingredients needed for a week's worth of menus and combines duplicate ingredients to minimize waste. This ingredient list is displayed to the user. Step 4: The ordering unit links with multiple online supermarkets and orders the cheapest ingredients based on the ingredient list created by the ingredient list creation unit. For example, it collects price information from each online supermarket, allowing the user to purchase ingredients at the cheapest price. The order is placed automatically, allowing the user to purchase ingredients hassle-free. Step 5: The delivery unit delivers the ingredients ordered by the ordering unit to the user's home. This saves the user the trouble of going shopping. The delivery schedule is adjusted according to the user's wishes.

[0070] (Example 2) The personalized menu creation system according to an embodiment of the present invention allows users to input their preferences and budget, and then a generation AI proposes optimal nutritionally balanced menus, creates a list of ingredients that minimize waste, orders the cheapest ingredients, and delivers them to their home. This reduces the burden on users, reduces food waste, and contributes to the global environment.

[0071] A personalized menu creation system according to an embodiment includes a preference input unit, a menu suggestion unit, an ingredient list creation unit, an ordering unit, and a delivery unit. The preference input unit inputs a user's preferences and budget. For example, the user may input preferences such as using specific ingredients, consuming a large amount of specific nutrients, or keeping weekly food expenses within a certain amount. This information is input as prompts to the generation AI. The menu suggestion unit proposes a nutritionally balanced menu based on the preferences and budget input by the preference input unit. For example, the generation AI generates menus that meet the necessary nutrients for breakfast, lunch, and dinner. The generated menu is displayed to the user. The ingredient list creation unit creates a waste-free ingredient list based on the menu proposed by the menu suggestion unit. For example, it lists ingredients needed for a weekly menu and combines overlapping ingredients to minimize waste. This ingredient list is displayed to the user. The ordering unit connects with multiple online supermarkets based on the ingredient list created by the ingredient list creation unit to order the cheapest ingredients. For example, price information from each online supermarket is collected, allowing the user to purchase ingredients at the lowest price. Orders are placed automatically, allowing the user to purchase ingredients without any hassle. The delivery unit delivers the ingredients ordered by the ordering unit to the user's home, thereby saving the user the trouble of going shopping. The delivery schedule is adjusted according to the user's wishes. As a result, the personalized menu creation system according to the embodiment reduces the burden on the user, reduces food waste, and contributes to the global environment.

[0072] The preference input unit uses AI to analyze the user's past dietary history and health data, allowing it to make more accurate suggestions for preferences and budgets. For example, the preference input unit uses AI to analyze the user's past dietary history and identify frequently consumed ingredients and nutrients. This allows it to make suggestions for preferences and budgets based on the user's preferences and health status. The preference input unit also uses AI to analyze the user's health data (e.g., blood pressure, weight, blood sugar level, etc.) and make diet suggestions based on the user's health status. This supports the user in maintaining and improving their health. The preference input unit also integrates the user's past dietary history and health data, allowing AI to make optimal suggestions for preferences and budgets. For example, it can detect imbalances in nutritional balance from past data and make suggestions to correct them. This allows it to make more accurate suggestions for preferences and budgets by analyzing the user's past dietary history and health data.

[0073] The desire input unit can use the emotion estimation function to consider the user's emotional state at the time of input and suggest a menu that reduces stress and fatigue. For example, when the user inputs their desires and budget, the desire input unit uses the emotion estimation function to analyze the user's emotional state in real time. For example, the desire input unit analyzes the user's facial expression and tone of voice using a camera or microphone. The desire input unit also uses the emotion estimation function to suggest ingredients and menus that have a relaxing effect when the user is feeling stressed or tired. For example, the desire input unit suggests herbal tea that is effective in reducing stress and a menu that includes ingredients with a relaxing effect. The desire input unit also suggests ingredients and menus that will lift the user's mood according to the user's emotional state. For example, if the user is tired, the unit suggests a menu that includes ingredients that are suitable for replenishing energy. In this way, a menu that reduces stress and fatigue can be suggested by considering the user's emotional state.

[0074] The preference input unit uses AI to automatically detect the user's lifestyle and daily activity level, and can suggest optimal preferences and budgets based on that information. The preference input unit, for example, collects daily activity data from the user's smartphone or wearable device, and the AI ​​analyzes it. For example, it calculates calorie consumption based on the number of steps taken and the amount of exercise, and makes meal suggestions based on that. The preference input unit also uses AI to analyze the user's lifestyle (e.g., work schedule and hobby activities) and suggests optimal preferences and budgets based on that analysis. For example, it suggests easy-to-prepare menus on busy days. The preference input unit also integrates the user's activity data with lifestyle information, and the AI ​​suggests optimal meals. For example, it suggests high-protein menus on days when the user exercises a lot, and low-calorie menus on days when the user exercises less. This allows the system to suggest optimal preferences and budgets by taking the user's lifestyle and daily activity level into consideration.

[0075] The request input unit can provide an interface that allows the user to intuitively input their requests and budget using voice input or image recognition. The request input unit, for example, provides an interface that allows the user to input their requests and budget by voice. For example, using voice recognition technology, what the user says is converted into text, which is then analyzed by AI. The request input unit also provides an interface that allows the user to input their requests and budget by uploading images of ingredients. For example, using image recognition technology, the uploaded ingredients are automatically recognized, and then analyzed by AI. The request input unit also provides a graphical user interface (GUI) that the user can operate intuitively. For example, an interface can be developed that allows the user to input their requests and budget using drag and drop. This allows the user to intuitively input their requests and budget using voice input or image recognition.

[0076] The preference input unit can work in conjunction with other health management apps and wearable devices to automatically import the user's health data. The preference input unit can work in conjunction with other health management apps (e.g., fitness apps and diet apps) to automatically import the user's health data. For example, the AI ​​analyzes the amount of exercise and food records. The preference input unit also automatically imports health data from wearable devices (e.g., smart watches and fitness trackers). For example, the AI ​​analyzes heart rate and sleep data. The preference input unit also integrates data obtained from the health management apps and wearable devices, and the AI ​​comprehensively analyzes the user's health condition. For example, it can make optimal dietary suggestions based on the amount of exercise, food records, and sleep data. In this way, the user's health data can be automatically imported by working in conjunction with other health management apps and wearable devices.

[0077] The desire input unit can use the emotion estimation function to analyze the user's emotions in real time when entering their desires or budget, and provide an interface that elicits positive emotions. The desire input unit uses the emotion estimation function to analyze emotions in real time, for example, when the user enters their desires or budget. For example, it uses a camera or microphone to analyze the user's facial expressions and tone of voice. The desire input unit also uses the emotion estimation function to design an interface that encourages the user to have positive emotions. For example, it displays positive messages or words of encouragement. The desire input unit also dynamically changes the design and color of the interface depending on the user's emotional state. For example, it provides an interface with calm colors to help the user relax. In this way, the user's emotions are analyzed in real time, and an interface that elicits positive emotions is provided, thereby improving user satisfaction.

[0078] The menu suggestion unit uses AI to refer to the latest nutritional research and provide the optimal nutritional balance for each individual user. For example, the menu suggestion unit uses AI to refer to the latest nutritional research and provide the optimal nutritional balance based on the user's health condition and lifestyle. For example, it adjusts nutrient intake based on the latest research data. The menu suggestion unit also suggests menus based on specific health goals (such as weight loss or muscle building) based on the latest nutritional research. For example, it generates menus that are rich in specific nutrients. The menu suggestion unit also uses AI to analyze the latest nutritional research and provide a nutritional balance that meets the user's individual needs. For example, it suggests menus that include nutrients that are effective in preventing specific diseases. In this way, by referring to the latest nutritional research, it is possible to provide the optimal nutritional balance for each individual user.

[0079] The menu suggestion unit uses the emotion estimation function to suggest meals according to the user's emotional state, thereby improving mental health. The menu suggestion unit, for example, analyzes the user's emotional state in real time and suggests meals according to the emotion. For example, if the user is feeling stressed, the menu suggestion unit suggests a menu including ingredients that have a relaxing effect. The menu suggestion unit also uses the emotion estimation function to suggest meals to improve the user's mental health. For example, the menu suggestion unit suggests a menu including ingredients that have a mood-boosting effect. The menu suggestion unit also customizes the meal suggestions based on the user's emotional state. For example, if the user is feeling tired, the menu suggestion unit suggests a menu including ingredients that are suitable for replenishing energy. In this way, by suggesting meals according to the user's emotional state, mental health can be improved.

[0080] The menu suggestion unit can suggest menus that incorporate seasonal ingredients, taking into consideration the season and local specialties. The menu suggestion unit, for example, suggests menus by taking into consideration seasonal specialties and seasonal ingredients. For example, in spring, it suggests menus that incorporate fresh vegetables and fruits. The menu suggestion unit also suggests menus that utilize local ingredients, taking into consideration local specialties. For example, it suggests traditional dishes that use local specialties. The menu suggestion unit also suggests nutritionally balanced menus based on the season and local specialties. For example, it suggests healthy menus that use seasonal ingredients. In this way, it is possible to suggest menus that incorporate seasonal ingredients by taking into consideration the season and local specialties.

[0081] The menu suggestion unit can reflect the user's allergy information and food preferences in detail when suggesting a menu. For example, the menu suggestion unit suggests a menu that does not contain allergens based on the user's allergy information. For example, if the user is allergic to a specific ingredient, the menu suggestion unit suggests a menu that excludes that ingredient. The menu suggestion unit also suggests a menu that reflects the user's food preferences in detail. For example, the menu is generated taking into consideration favorite and disliked ingredients. The menu suggestion unit also integrates the allergy information and food preferences to suggest an optimal menu for the user. For example, it suggests a menu that avoids allergens while including many favorite ingredients. In this way, by reflecting the user's allergy information and food preferences in detail, it is possible to suggest a menu that meets individual needs.

[0082] The menu suggestion unit can suggest popular menus based on the ratings and reviews of other users. The menu suggestion unit suggests popular menus, for example, based on the ratings and reviews of other users. For example, it preferentially displays highly rated menus. The menu suggestion unit also analyzes user reviews to suggest particularly popular menus. For example, it analyzes the content of reviews to suggest menus with many positive ratings. The menu suggestion unit also displays popular menus in a ranking format based on the rating data of other users. For example, menus with high rating scores are displayed at the top. This makes it possible to suggest popular menus based on the ratings and reviews of other users.

[0083] The menu suggestion unit uses the emotion estimation function to suggest meal scenes that the user will enjoy most, thereby improving meal satisfaction. The menu suggestion unit, for example, uses the emotion estimation function to suggest meal scenes that the user will enjoy most. For example, it suggests meal scenes that correspond to a specific emotional state. The menu suggestion unit also analyzes the user's emotional state and makes suggestions to improve meal satisfaction. For example, it suggests relaxing meal scenes or enjoyable meal scenes. The menu suggestion unit also customizes the meal scenes that the user will enjoy most based on the emotion estimation data. For example, it suggests the atmosphere and environment of the meal depending on the emotional state. In this way, meal satisfaction can be improved by suggesting meal scenes that the user will enjoy most.

[0084] The ingredient list creation unit uses AI to analyze past purchase history and automatically generate an ingredient list that eliminates waste. For example, the ingredient list creation unit uses AI to analyze a user's past purchase history and automatically generate an ingredient list that eliminates waste. For example, it includes ingredients that are frequently purchased or used in the list. The ingredient list creation unit also reflects duplicate ingredients in the list based on past purchase history, for example, by adjusting the list to avoid purchasing the same ingredient multiple times. The ingredient list creation unit also analyzes purchase history and develops an algorithm to create an ingredient list that eliminates waste. For example, it suggests optimal purchase amounts based on past data. In this way, an ingredient list that eliminates waste can be automatically generated by analyzing past purchase history.

[0085] The ingredient list creation unit uses the emotion estimation function to create an ingredient list according to the user's emotional state and suggest ingredients that will reduce stress. The ingredient list creation unit, for example, analyzes the user's emotional state in real time and creates an ingredient list according to the emotion. For example, if the user is feeling stressed, ingredients that have a relaxing effect are included in the list. The ingredient list creation unit also uses the emotion estimation function to create an ingredient list based on the user's emotional state. For example, if the user is feeling tired, ingredients that are suitable for replenishing energy are included in the list. The ingredient list creation unit also analyzes the user's emotional state and suggests ingredients that will reduce stress. For example, herbs with a relaxing effect and ingredients that are effective in reducing stress are included in the list. This allows the ingredient list to be created according to the user's emotional state and suggests ingredients that will reduce stress.

[0086] The ingredient list creation unit can suggest optimal purchase quantities by taking into account the shelf life and frequency of use of ingredients. The ingredient list creation unit, for example, suggests optimal purchase quantities by taking into account the shelf life of ingredients. For example, it suggests purchasing small amounts of ingredients with short shelf lives. The ingredient list creation unit also suggests optimal purchase quantities based on the frequency of use of ingredients. For example, it suggests purchasing larger amounts of ingredients that are used frequently and smaller amounts of ingredients that are used less frequently. The ingredient list creation unit also develops an algorithm that integrates shelf life and frequency of use to suggest optimal purchase quantities. For example, it analyzes shelf life and frequency of use based on past data and calculates the optimal purchase quantity. This makes it possible to suggest optimal purchase quantities by taking into account the shelf life and frequency of use of ingredients.

[0087] The ingredient list creation unit can share the ingredient list with other users, promoting joint purchasing and sharing. The ingredient list creation unit, for example, provides a function for sharing the ingredient list with other users, promoting joint purchasing. For example, ingredients are shared between users living in the same area. The ingredient list creation unit also builds a system for sharing ingredient lists and exchanging leftover ingredients with other users. For example, ingredients that cannot be used up are provided to other users. The ingredient list creation unit also develops a platform for promoting joint purchasing and sharing. For example, a community is created where users can share ingredients with each other. In this way, sharing the ingredient list with other users can promote joint purchasing and sharing.

[0088] The ingredient list creation unit can provide recipe suggestions and cooking method advice based on the ingredient list. For example, the ingredient list creation unit builds a system that suggests optimal recipes based on the ingredient list. For example, it automatically generates recipes using ingredients included in the list. The ingredient list creation unit also provides cooking method advice based on the ingredient list. For example, it suggests simple cooking methods using ingredients included in the list. The ingredient list creation unit also suggests recipes tailored to the user's preferences based on the ingredient list. For example, it suggests dishes that the user likes using ingredients included in the list. This makes it possible to provide recipe suggestions and cooking method advice based on the ingredient list.

[0089] The ingredient list creation unit uses the emotion estimation function to create an ingredient list that will most satisfy the user, thereby increasing purchasing motivation. The ingredient list creation unit, for example, uses the emotion estimation function to create an ingredient list that will most satisfy the user. For example, the list is customized based on the user's emotional state. The ingredient list creation unit also analyzes the user's emotional state to create an ingredient list that will increase purchasing motivation. For example, ingredients that elicit positive emotions are included in the list. The ingredient list creation unit also suggests an ingredient list that will most satisfy the user based on the emotion estimation data. For example, the contents of the list are dynamically changed depending on the emotional state. In this way, purchasing motivation can be increased by creating an ingredient list that will most satisfy the user.

[0090] The ordering department uses AI to monitor price fluctuations in real time, allowing ingredients to be ordered at the optimal time. For example, the ordering department uses AI to collect price information from each online supermarket in real time and monitor price fluctuations. For example, ingredients are ordered when the price drops. The ordering department also develops an algorithm to order ingredients at the optimal time based on price fluctuations. For example, it predicts when the price will be lowest and places an order at that time. The ordering department also collects price information in real time, allowing ingredients to be ordered at the optimal time for the user. For example, an order is automatically placed the moment the price drops. This allows for cost reduction by monitoring price fluctuations in real time and ordering ingredients at the optimal time.

[0091] The ordering unit can take into account the inventory status of each online supermarket and propose the most efficient ordering method. For example, the ordering unit collects inventory status of each online supermarket in real time and proposes the most efficient ordering method. For example, it prioritizes ordering from supermarkets with abundant stock. The ordering unit also develops an algorithm that proposes the optimal ordering method based on the inventory status. For example, it distributes orders for ingredients that are low in stock from multiple supermarkets. The ordering unit also collects inventory information in real time and proposes the most efficient ordering method to the user. For example, it suggests ordering ingredients that are low in stock early. In this way, the most efficient ordering method can be proposed by taking into account the inventory status of each online supermarket.

[0092] The ordering department can also work with local markets and farmers other than the online supermarket to promote local production and consumption. The ordering department, for example, works with local markets and farmers other than the online supermarket to build a system that promotes local production and consumption. For example, it prioritizes ordering fresh local ingredients. The ordering department also collects ingredient information from local markets and farmers and suggests it to users. For example, it includes local specialties and seasonal ingredients in the list. The ordering department also develops a platform to promote local production and consumption. For example, it works directly with local markets and farmers to build a system for ordering ingredients. This makes it possible to promote local production and consumption by working with local markets and farmers other than the online supermarket.

[0093] The ordering unit can suggest recommended products that match the user's preferences based on the food ingredient order history. The ordering unit, for example, analyzes the food ingredient order history and suggests recommended products that match the user's preferences. For example, suggestions are made based on frequently purchased or favorite ingredients. The ordering unit also analyzes the user's preferences based on the order history and develops an algorithm that suggests optimal products. For example, it predicts the user's preferences based on past data. The ordering unit also suggests related products based on the user's order history. For example, it suggests seasonings or recipes that go well with purchased ingredients. In this way, it is possible to suggest recommended products that match the user's preferences based on the food ingredient order history.

[0094] The ordering unit uses the emotion estimation function to provide the user with the most satisfying purchasing experience and increase the repeat rate. The ordering unit, for example, uses the emotion estimation function to provide the user with the most satisfying purchasing experience. For example, the ordering unit customizes the purchasing experience based on the user's emotional state. The ordering unit also analyzes the user's emotional state and proposes measures to increase the repeat rate. For example, the ordering unit provides promotions that elicit positive emotions. The ordering unit also builds a system that provides the user with the most satisfying purchasing experience based on the emotion estimation data. For example, the ordering unit dynamically changes the purchasing experience depending on the emotional state. In this way, by using the emotion estimation function, the ordering unit can provide the user with the most satisfying purchasing experience and increase the repeat rate.

[0095] The delivery department uses AI to calculate the optimal delivery route, thereby shortening delivery time. For example, the delivery department selects the optimal route based on traffic and weather information. The delivery department also develops algorithms to optimize delivery routes, thereby shortening delivery time. For example, it recalculates routes in real time and selects the shortest route. The delivery department also builds a system to calculate the optimal delivery route and notify the user. For example, shortening delivery time improves user convenience. This allows AI to calculate the optimal delivery route, thereby shortening delivery time.

[0096] The delivery unit uses the emotion estimation function to suggest a delivery time slot desired by the user, thereby reducing stress. The delivery unit, for example, uses the emotion estimation function to suggest a delivery time slot desired by the user. For example, the delivery unit selects an optimal time slot based on the user's emotional state. The delivery unit also analyzes the user's emotional state and suggests a delivery time slot to reduce stress. For example, deliveries are made during times when the user can relax. The delivery unit also builds a system that suggests a delivery time slot desired by the user based on the emotion estimation data. For example, the delivery time slot is dynamically changed depending on the emotional state. In this way, by using the emotion estimation function, a delivery time slot desired by the user can be suggested, thereby reducing stress.

[0097] The delivery unit can track the delivery status in real time and notify the user. The delivery unit, for example, builds a system that tracks the delivery status in real time and notifies the user. For example, it allows the delivery progress to be checked using a smartphone app. The delivery unit also tracks the delivery status in real time and notifies the user by email or SMS. For example, it sends a notification when delivery is approaching. The delivery unit also tracks the delivery status in real time and allows the user to receive the notification at a time desired by the user. For example, it sends a notification one hour before delivery. In this way, it is possible to track the delivery status in real time and notify the user, thereby improving user convenience.

[0098] The delivery unit can provide advice on how to store and cook ingredients at the time of delivery. The delivery unit, for example, builds a system that gives advice to users on how to store ingredients at the time of delivery. For example, it provides information about ingredients that need to be refrigerated. The delivery unit also gives advice to users on how to cook ingredients at the time of delivery. For example, it provides simple recipes and cooking tips. The delivery unit also develops a system that provides advice on how to store and cook ingredients together at the time of delivery. For example, it displays storage methods and cooking methods together. This allows for providing advice on how to store and cook ingredients at the time of delivery, thereby improving user convenience.

[0099] The delivery department can reduce the environmental burden by working with the delivery service and using recyclable packaging materials. For example, the delivery department works with the delivery service to build a system that uses recyclable packaging materials. For example, paper packaging materials are used instead of plastic. The delivery department can also reduce the environmental burden by using recyclable packaging materials. For example, reusable eco-bags are provided. The delivery department can also work with the delivery service to implement a campaign to promote the use of recyclable packaging materials. For example, discounts are offered if recyclable packaging materials are used. In this way, the use of recyclable packaging materials can reduce the environmental burden.

[0100] The delivery unit uses the emotion estimation function to provide a delivery experience that is most satisfying to the user, thereby improving the quality of service. The delivery unit, for example, uses the emotion estimation function to provide a delivery experience that is most satisfying to the user. For example, the delivery experience is customized based on the user's emotional state. The delivery unit also analyzes the user's emotional state and proposes measures to improve the quality of service. For example, a delivery experience that elicits positive emotions is provided. The delivery unit also builds a system that provides a delivery experience that is most satisfying to the user based on the emotion estimation data. For example, the delivery experience is dynamically changed depending on the emotional state. In this way, by using the emotion estimation function, it is possible to provide a delivery experience that is most satisfying to the user, thereby improving the quality of service.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The preference input unit uses AI to analyze the user's past dietary history and health data, allowing it to make more accurate suggestions about preferences and budgets. For example, AI analyzes the user's past dietary history to identify frequently consumed ingredients and nutrients. This allows it to make suggestions about preferences and budgets based on the user's preferences and health status. The preference input unit also uses AI to analyze the user's health data (e.g., blood pressure, weight, blood sugar level, etc.) and make diet suggestions based on the user's health status. This supports the user in maintaining and improving their health. The preference input unit also integrates the user's past dietary history and health data, allowing AI to make optimal suggestions about preferences and budgets. For example, it can detect imbalances in nutritional balance from past data and make suggestions to correct them. This allows it to make more accurate suggestions about preferences and budgets by analyzing the user's past dietary history and health data.

[0103] The preference input unit can use the emotion estimation function to consider the user's emotional state at the time of input and suggest a menu that reduces stress and fatigue. For example, when a user inputs their preferences and budget, the emotion estimation function is used to analyze the user's emotional state in real time. For example, a camera or microphone is used to analyze the user's facial expression and tone of voice. The preference input unit also uses the emotion estimation function to suggest ingredients and menus that have a relaxing effect when the user is feeling stressed or tired. For example, the preference input unit suggests herbal tea that is effective in reducing stress and a menu that includes ingredients with a relaxing effect. The preference input unit also suggests ingredients and menus that will lift the user's mood according to the user's emotional state. For example, if the user is tired, the unit suggests a menu that includes ingredients that are suitable for replenishing energy. In this way, a menu that reduces stress and fatigue can be suggested by considering the user's emotional state.

[0104] The preference input unit uses AI to automatically detect the user's lifestyle and daily activity level, and can then use that information to suggest optimal preferences and budgets. For example, the AI ​​collects daily activity data from the user's smartphone or wearable device and analyzes it. For example, it calculates calorie consumption based on the number of steps taken and the amount of exercise, and makes meal suggestions based on that. The preference input unit also uses AI to analyze the user's lifestyle (e.g., work schedule and hobby activities) and suggests optimal preferences and budgets based on that analysis. For example, it suggests easy-to-prepare menus on busy days. The preference input unit also integrates the user's activity data with lifestyle information, and the AI ​​then makes optimal meal suggestions. For example, it suggests high-protein menus on days when the user exercises a lot, and low-calorie menus on days when the user exercises less. This allows the system to suggest optimal preferences and budgets by taking the user's lifestyle and daily activity level into consideration.

[0105] The request input unit can provide an interface that allows users to intuitively input their requests and budget using voice input or image recognition. For example, an interface is provided that allows users to input their requests and budget by voice. For example, voice recognition technology is used to convert what the user says into text, which is then analyzed by AI. The request input unit also provides an interface that allows users to input their requests and budget by uploading images of ingredients. For example, image recognition technology is used to automatically recognize the uploaded ingredients, which are then analyzed by AI. The request input unit also provides a graphical user interface (GUI) that users can operate intuitively. For example, an interface is developed that allows users to input their requests and budget using drag and drop. This allows users to intuitively input their requests and budget using voice input or image recognition.

[0106] The preference input unit can work with other health management apps and wearable devices to automatically import the user's health data. For example, it can work with other health management apps (e.g., fitness apps or diet apps) to automatically import the user's health data. For example, the AI ​​analyzes the amount of exercise and food records. The preference input unit also automatically imports health data from wearable devices (e.g., smartwatches and fitness trackers). For example, the AI ​​analyzes heart rate and sleep data. The preference input unit also integrates data obtained from the health management apps and wearable devices, and the AI ​​comprehensively analyzes the user's health condition. For example, it can make optimal dietary suggestions based on the amount of exercise, food records, and sleep data. This allows the user's health data to be automatically imported by working with other health management apps and wearable devices.

[0107] The menu suggestion unit uses AI to refer to the latest nutritional research and provide the optimal nutritional balance for each individual user. For example, the AI ​​may refer to the latest nutritional research and provide the optimal nutritional balance based on the user's health condition and lifestyle. For example, it may adjust nutrient intake based on the latest research data. The menu suggestion unit also uses the latest nutritional research to suggest menus based on specific health goals (such as weight loss or muscle building). For example, it may generate menus that are rich in specific nutrients. The menu suggestion unit also uses AI to analyze the latest nutritional research and provide a nutritional balance that meets the individual needs of the user. For example, it may suggest a menu that includes nutrients that are effective in preventing specific diseases. In this way, by referring to the latest nutritional research, it is possible to provide the optimal nutritional balance for each individual user.

[0108] The menu suggestion unit uses the emotion estimation function to suggest meals according to the user's emotional state, thereby improving mental health. For example, the unit analyzes the user's emotional state in real time and suggests meals according to the emotion. For example, if the user is feeling stressed, the unit suggests a menu including ingredients that have a relaxing effect. The menu suggestion unit also uses the emotion estimation function to suggest meals to improve the user's mental health. For example, the unit suggests a menu including ingredients that have a mood-boosting effect. The menu suggestion unit also customizes the meal suggestions based on the user's emotional state. For example, if the user is feeling tired, the unit suggests a menu including ingredients that are suitable for replenishing energy. In this way, by suggesting meals according to the user's emotional state, mental health can be improved.

[0109] The menu suggestion unit can propose menus that incorporate seasonal ingredients, taking into consideration the season and local specialties. For example, a menu is proposed taking into consideration seasonal specialties and seasonal ingredients. For example, in spring, a menu incorporating fresh vegetables and fruits is proposed. The menu suggestion unit also proposes menus that utilize local ingredients, taking into consideration local specialties. For example, it proposes traditional dishes that use local specialties. The menu suggestion unit also proposes nutritionally balanced menus based on the season and local specialties. For example, it proposes healthy menus that use seasonal ingredients. In this way, a menu incorporating seasonal ingredients can be proposed by taking into consideration the season and local specialties.

[0110] The menu suggestion unit can use the emotion estimation function to suggest meal scenes that the user will enjoy most, thereby improving meal satisfaction. For example, the emotion estimation function can be used to suggest meal scenes that the user will enjoy most. For example, meal scenes that correspond to a specific emotional state can be suggested. The menu suggestion unit also analyzes the user's emotional state and makes suggestions to improve meal satisfaction. For example, it can suggest relaxing meal scenes or enjoyable meal scenes. The menu suggestion unit also customizes the meal scenes that the user will enjoy most based on the emotion estimation data. For example, it can suggest the atmosphere and environment for the meal depending on the emotional state. This can improve meal satisfaction by suggesting meal scenes that the user will enjoy most.

[0111] The ingredient list creation unit uses AI to analyze past purchase history and automatically generate an ingredient list that eliminates waste. For example, AI analyzes a user's past purchase history and automatically generates an ingredient list that eliminates waste. For example, it includes ingredients that are frequently purchased or used on the list. The ingredient list creation unit also reflects duplicate ingredients in the list based on past purchase history, for example, by adjusting the list to prevent the same ingredient from being purchased multiple times. The ingredient list creation unit also analyzes purchase history and develops an algorithm to create an ingredient list that eliminates waste. For example, it suggests optimal purchase amounts based on past data. This makes it possible to automatically generate an ingredient list that eliminates waste by analyzing past purchase history.

[0112] The ingredient list creation unit uses the emotion estimation function to create an ingredient list according to the user's emotional state and suggest ingredients that will reduce stress. For example, the ingredient list creation unit analyzes the user's emotional state in real time and creates an ingredient list according to the emotion. For example, if the user is feeling stressed, ingredients that have a relaxing effect are included in the list. The ingredient list creation unit also uses the emotion estimation function to create an ingredient list based on the user's emotional state. For example, if the user is feeling tired, ingredients that are suitable for replenishing energy are included in the list. The ingredient list creation unit also analyzes the user's emotional state and suggests ingredients that will reduce stress. For example, herbs with a relaxing effect and ingredients that are effective in reducing stress are included in the list. This allows the ingredient list to be created according to the user's emotional state and suggests ingredients that will reduce stress.

[0113] The ingredient list creation unit can suggest optimal purchase quantities by taking into account the shelf life and frequency of use of ingredients. For example, it suggests optimal purchase quantities by taking into account the shelf life of ingredients. For example, it suggests purchasing small amounts of ingredients with short shelf lives. The ingredient list creation unit also suggests optimal purchase quantities based on the frequency of use of ingredients. For example, it suggests purchasing larger amounts of ingredients that are used frequently and smaller amounts of ingredients that are used less frequently. The ingredient list creation unit also develops an algorithm that integrates shelf life and frequency of use to suggest optimal purchase quantities. For example, it analyzes shelf life and frequency of use based on past data and calculates the optimal purchase quantity. This makes it possible to suggest optimal purchase quantities by taking into account the shelf life and frequency of use of ingredients.

[0114] The ingredient list creation unit can share ingredient lists with other users to promote joint purchasing and sharing. For example, it can provide a function for sharing ingredient lists with other users to promote joint purchasing. For example, ingredients can be shared between users living in the same area. The ingredient list creation unit can also build a system for sharing ingredient lists and exchanging leftover ingredients with other users. For example, ingredients that cannot be used up can be provided to other users. The ingredient list creation unit can also develop a platform for promoting joint purchasing and sharing. For example, it can create a community where users can share ingredients with each other. This can promote joint purchasing and sharing by sharing ingredient lists with other users.

[0115] The ingredient list creation unit can provide recipe suggestions and cooking advice based on the ingredient list. For example, a system can be built that suggests optimal recipes based on the ingredient list. For example, recipes using ingredients included in the list can be automatically generated. The ingredient list creation unit can also provide cooking advice based on the ingredient list. For example, it can suggest simple cooking methods using ingredients included in the list. The ingredient list creation unit can also suggest recipes tailored to the user's preferences based on the ingredient list. For example, it can suggest dishes that the user likes using ingredients included in the list. This makes it possible to provide recipe suggestions and cooking advice based on the ingredient list.

[0116] The ingredient list creation unit uses the emotion estimation function to create an ingredient list that will most satisfy the user, thereby increasing purchasing motivation. For example, the emotion estimation function is used to create an ingredient list that will most satisfy the user. For example, the list is customized based on the user's emotional state. The ingredient list creation unit also analyzes the user's emotional state to create an ingredient list that will increase purchasing motivation. For example, ingredients that elicit positive emotions are included in the list. The ingredient list creation unit also suggests an ingredient list that will most satisfy the user based on the emotion estimation data. For example, the contents of the list are dynamically changed depending on the emotional state. In this way, the ingredient list that will most satisfy the user can be created, thereby increasing purchasing motivation.

[0117] The ordering department uses AI to monitor price fluctuations in real time, allowing ingredients to be ordered at the optimal time. For example, AI collects price information from each online supermarket in real time and monitors price fluctuations. For example, ingredients are ordered when the price drops. The ordering department also develops an algorithm to order ingredients at the optimal time based on price fluctuations. For example, it predicts when the price will be lowest and places an order at that time. The ordering department also collects price information in real time, allowing ingredients to be ordered at the optimal time for the user. For example, an order is automatically placed the moment the price drops. This allows costs to be reduced by monitoring price fluctuations in real time and ordering ingredients at the optimal time.

[0118] The ordering department can take into account the inventory status of each online supermarket and propose the most efficient ordering method. For example, it can collect inventory status from each online supermarket in real time and propose the most efficient ordering method. For example, it can prioritize ordering from supermarkets with abundant stock. The ordering department also develops an algorithm that proposes the optimal ordering method based on the inventory status. For example, it can distribute orders for ingredients that are low in stock from multiple supermarkets. The ordering department also collects inventory information in real time and proposes the most efficient ordering method to the user. For example, it can suggest ordering ingredients that are low in stock early. This allows the most efficient ordering method to be proposed by taking into account the inventory status of each online supermarket.

[0119] The ordering department can also work with local markets and farmers other than the online supermarket to promote local production and consumption. For example, a system can be built to work with local markets and farmers other than the online supermarket to promote local production and consumption. For example, local fresh ingredients can be given priority in ordering. The ordering department can also collect ingredient information from local markets and farmers and suggest it to users. For example, local specialties and seasonal ingredients can be included in the list. The ordering department can also develop a platform to promote local production and consumption. For example, a system can be built to work directly with local markets and farmers to order ingredients. This can promote local production and consumption by working with local markets and farmers other than the online supermarket.

[0120] The ordering unit can suggest recommended products that match the user's preferences based on the food ingredient order history. For example, the ordering unit analyzes the food ingredient order history and suggests recommended products that match the user's preferences. For example, suggestions are made based on frequently purchased ingredients or favorite ingredients. The ordering unit also analyzes the user's preferences based on the order history and develops an algorithm that suggests optimal products. For example, it predicts the user's preferences based on past data. The ordering unit also suggests related products based on the user's order history. For example, it suggests seasonings or recipes that go well with purchased ingredients. In this way, it is possible to suggest recommended products that match the user's preferences based on the food ingredient order history.

[0121] The ordering unit can use the emotion estimation function to provide the user with the most satisfying purchasing experience and increase the repeat rate. For example, the emotion estimation function can be used to provide the user with the most satisfying purchasing experience. For example, the purchasing experience can be customized based on the user's emotional state. The ordering unit can also analyze the user's emotional state and propose measures to increase the repeat rate. For example, the ordering unit can provide a promotion that elicits positive emotions. The ordering unit can also build a system that provides the user with the most satisfying purchasing experience based on the emotion estimation data. For example, the purchasing experience can be dynamically changed depending on the emotional state. In this way, the emotion estimation function can be used to provide the user with the most satisfying purchasing experience and increase the repeat rate.

[0122] The delivery department uses AI to calculate the optimal delivery route, thereby shortening delivery times. For example, AI calculates the optimal delivery route and shortens delivery times. For example, it selects the optimal route based on traffic and weather information. The delivery department also develops algorithms to optimize delivery routes and shorten delivery times. For example, it recalculates routes in real time and selects the shortest route. The delivery department also calculates the optimal delivery route and builds a system to notify users. For example, shortening delivery times improves user convenience. This allows AI to calculate the optimal delivery route and shorten delivery times.

[0123] The delivery unit uses the emotion estimation function to suggest a delivery time slot desired by the user, thereby reducing stress. For example, the emotion estimation function is used to suggest a delivery time slot desired by the user. For example, the optimal time slot is selected based on the user's emotional state. The delivery unit also analyzes the user's emotional state and suggests a delivery time slot that reduces stress. For example, delivery is made during a time slot that allows the user to relax. The delivery unit also builds a system that suggests a delivery time slot desired by the user based on the emotion estimation data. For example, the delivery time slot is dynamically changed depending on the emotional state. In this way, the emotion estimation function can be used to suggest a delivery time slot desired by the user, thereby reducing stress.

[0124] The delivery unit can track the delivery status in real time and notify the user. For example, a system can be built that tracks the delivery status in real time and notifies the user. For example, a system can be built that allows the delivery progress to be checked using a smartphone app. The delivery unit can also track the delivery status in real time and notify the user by email or SMS. For example, a notification can be sent when delivery is approaching. The delivery unit can also track the delivery status in real time and allow the user to receive the notification at a time of their choice. For example, a notification can be sent one hour before delivery. This makes it possible to track the delivery status in real time and notify the user, thereby improving user convenience.

[0125] The delivery unit can provide advice on how to store and cook ingredients at the time of delivery. For example, a system is constructed that provides advice to users on how to store ingredients at the time of delivery. For example, it provides information about ingredients that need to be refrigerated. The delivery unit also provides advice to users on how to cook ingredients at the time of delivery. For example, it provides simple recipes and cooking tips. The delivery unit also develops a system that provides advice on how to store and cook ingredients at the time of delivery, integrating the advice on how to store and cook ingredients. For example, it displays the storage method and cooking method together. This makes it possible to improve user convenience by providing advice on how to store and cook ingredients at the time of delivery.

[0126] The delivery unit can use the emotion estimation function to provide a delivery experience that is most satisfying to the user and improve the quality of service. For example, the emotion estimation function can be used to provide a delivery experience that is most satisfying to the user. For example, the delivery experience can be customized based on the user's emotional state. The delivery unit can also analyze the user's emotional state and propose measures to improve the quality of service. For example, a delivery experience that elicits positive emotions can be provided. The delivery unit can also build a system that provides a delivery experience that is most satisfying to the user based on the emotion estimation data. For example, the delivery experience can be dynamically changed depending on the emotional state. In this way, the emotion estimation function can be used to provide a delivery experience that is most satisfying to the user and improve the quality of service.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The user's preferences and budget are entered in the preference input section. For example, they may want to use specific ingredients, consume a lot of specific nutrients, or keep their weekly food expenses within a certain amount. This information is entered as a prompt for the generation AI. Step 2: The menu suggestion unit proposes a nutritionally balanced menu based on the preferences and budget entered in the preference input unit. For example, the generation AI generates menus that meet the necessary nutrients for breakfast, lunch, and dinner. The generated menu is displayed to the user. Step 3: The ingredient list creation unit creates an ingredient list that minimizes waste based on the menu proposed by the menu suggestion unit. For example, it lists ingredients needed for a week's worth of menus and combines duplicate ingredients to minimize waste. This ingredient list is displayed to the user. Step 4: The ordering unit links with multiple online supermarkets and orders the cheapest ingredients based on the ingredient list created by the ingredient list creation unit. For example, it collects price information from each online supermarket, allowing the user to purchase ingredients at the cheapest price. The order is placed automatically, allowing the user to purchase ingredients hassle-free. Step 5: The delivery unit delivers the ingredients ordered by the ordering unit to the user's home. This saves the user the trouble of going shopping. The delivery schedule is adjusted according to the user's wishes.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0131] 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.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0150] 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.

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

[0152] 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).

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

[0154] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[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 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.

[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 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.

[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 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.

[0161] 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.

[0162] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0163] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0174] 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.

[0175] 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.

[0176] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0195] 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]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A request input section for inputting the user's requests and budget; a menu suggestion unit that suggests a nutritionally balanced menu based on the desires and the budget input by the desire input unit; an ingredient list creation unit that creates an ingredient list that does not waste food based on the menu proposed by the menu proposal unit; an ordering unit that links with multiple online supermarkets and orders ingredients at the lowest price based on the ingredient list created by the ingredient list creation unit; a delivery unit that delivers the ingredients ordered by the ordering unit to the user's home. A system characterized by:

2. The desire input unit The AI ​​analyzes the user's past dietary history and health data to make more accurate suggestions about the user's preferences and budget.

2. The system of claim 1.

3. The desire input unit Taking into account the emotional state of the user at the time of input, the menu is proposed to reduce stress and fatigue.

2. The system of claim 1.

4. The desire input unit The AI ​​automatically detects the user's lifestyle and daily activity level, and based on that, suggests the user's optimal wishes and budget.

2. The system of claim 1.

5. The desire input unit An interface is provided that allows the user to intuitively input their wishes and budget using voice input or image recognition.

2. The system of claim 1.

6. The desire input unit Link with other health management apps and wearable devices to automatically collect the user's health data.

2. The system of claim 1.

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