System

Abstract

An object of a system according to an embodiment is to efficiently collect information about dishes and food ingredients around the world and provide the information to users.SOLUTION: A system includes a data collection unit, a data organization unit, and a proposal unit. The data collection unit collects data related to dishes and food ingredients around the world. The data organizer organizes the data collected by the data collector. The suggestion unit provides the user with an idea of a dish or information on a food culture of a different culture on the basis of the data organized by the data organization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to efficiently collect information about cuisines and ingredients from around the world and provide it to users, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect information about dishes and ingredients from around the world and provide it to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data organization unit, and a suggestion unit. The data collection unit collects data on cuisines and ingredients from around the world. The data organization unit systematically organizes the data collected by the data collection unit. The suggestion unit provides users with cooking ideas and information on food cultures in different cultures based on the data organized by the data organization unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect information about dishes and ingredients from around the world and provide it to users. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 Food Explorer AI system according to an embodiment of the present invention is a system that collects and organizes data on cuisines and ingredients from around the world and provides users with cooking ideas and information on food cultures of different cultures. As a result, the Food Explorer AI system can provide users with a variety of cooking ideas and information, helping them to deepen their understanding of food cultures of different cultures.

[0029] A food explorer AI system according to an embodiment includes a data collection unit, a data organization unit, and a suggestion unit. The data collection unit collects data on cuisines and ingredients from around the world. For example, it collects information on traditional cuisines and local specialties of each country, the nutritional value of ingredients, and cooking methods. The data collection unit can also collect information from public databases on the Internet and specialized books. For example, the data collection unit can collect data from recipe websites on the Internet using web scraping technology. The data collection unit can also scan specialized books and convert them into digital data using OCR technology. The data organization unit systematically organizes the data collected by the data collection unit. For example, it can categorize and tag the collected data. The data organization unit can also design a database structure and efficiently manage the data. For example, the data organization unit can manage data using an SQL database and search data using queries. The data organization unit can also efficiently manage large amounts of data using a NoSQL database. The suggestion unit provides the user with cooking ideas and information on food cultures in different cultures based on the data organized by the data organization unit. For example, the suggestion unit can suggest new recipes in response to a user's request. The suggestion unit can also provide information about the food cultures of other cultures that interest the user. For example, when a user inputs a prompt such as "Please tell me some easy-to-make Italian recipes," the suggestion unit can cause the generation AI to suggest appropriate recipes based on the user's request. When a user inputs a prompt such as "Please tell me about traditional Japanese breakfasts," the suggestion unit can cause the generation AI to provide information about traditional Japanese breakfasts based on the user's request. In this way, the food explorer AI system according to the embodiment can provide users with a variety of cooking ideas and information, helping them deepen their understanding of food cultures of other cultures. For example, the system can provide users with information that is useful when trying new dishes, and can broaden their enjoyment of food by learning about food cultures of other cultures. Furthermore, the system is expected to improve the quality of users' diets by suggesting healthy recipes.

[0030] The data collection unit can also collect the historical background and cultural significance of each dish and incorporate them into the database. For example, when collecting recipes for traditional dishes from various countries using generative AI, the data collection unit can also collect the historical background and cultural significance of the dish. For example, the origin and cultural significance of Italian pizza can be incorporated into the database. The data collection unit can also collect historical documents and materials related to the dish, convert them into digital data, and incorporate them into the database. For example, the data collection unit can scan historical cookbooks and convert them into digital data using OCR technology. The data collection unit can also collect interviews and documentary footage related to the dish and incorporate them into the database. For example, the data collection unit can record interviews with chefs and culinary experts and incorporate the content into the database. This allows users to understand the historical background and cultural significance of the dish.

[0031] The data organization unit can visualize the interrelationships between dishes and ingredients and provide an interactive map that allows users to intuitively understand. For example, the data organization unit creates an interactive map that visualizes the interrelationships between dishes and ingredients from various countries based on data collected by the generative AI. For example, the data organization unit visually displays the similarities and differences between Italian and French cuisine. The data organization unit also visualizes ingredient combinations and commonalities between dishes to allow users to intuitively understand. For example, the data organization unit creates graphs and charts showing the interrelationships between ingredients and provides them as an interactive map. The data organization unit can also filter information about dishes and ingredients that interest the user and display it on the interactive map. For example, when a user selects a specific ingredient, dishes and other ingredients related to that ingredient are displayed. This allows users to intuitively understand the interrelationships between dishes and ingredients.

[0032] The suggestion unit can analyze the user's past search history and preferences and provide personalized cooking ideas based on the analysis. The suggestion unit can, for example, use generative AI to analyze the user's past search history and provide personalized cooking ideas based on the analysis. For example, it can suggest new recipes based on recipes the user has previously searched for. The suggestion unit can also analyze the user's preferences and provide personalized cooking ideas based on the analysis. For example, it can suggest new recipes based on the user's favorite ingredients and cooking methods. The suggestion unit can also provide information on food cultures of different cultures that may interest the user based on the user's past search history and preferences. For example, it can provide information on food cultures of different cultures related to dishes the user has previously searched for. This allows the user to obtain cooking ideas based on their own preferences.

[0033] The suggestion unit can suggest optimal recipes based on the ingredients and cooking utensils the user has. The suggestion unit, for example, uses generative AI to suggest optimal recipes based on the ingredients the user has. For example, when the ingredients in the refrigerator are input, a recipe based on that is suggested. The suggestion unit can also suggest optimal recipes based on the cooking utensils the user has. For example, a recipe suitable for a frying pan or oven the user has is suggested. The suggestion unit can also suggest optimal recipes by combining ingredients and cooking utensils the user has. For example, when the ingredients and cooking utensils the user has are input, a recipe based on that is suggested. This allows the user to obtain optimal recipes based on the ingredients and cooking utensils they have.

[0034] When suggesting recipes using specific nutrients or ingredients, the suggestion unit can simultaneously provide the health benefits and recommended intake amounts for each nutrient. For example, when suggesting recipes using ingredients containing specific nutrients using a generation AI, the suggestion unit simultaneously provides the health benefits of the nutrient. For example, it may explain recipes using ingredients rich in vitamin C and their health benefits. The suggestion unit can also simultaneously provide recommended intake amounts for each nutrient. For example, it may provide the recommended daily intake amount of vitamin C. When suggesting recipes using specific nutrients or ingredients, the suggestion unit can also automatically retrieve the health benefits and recommended intake amounts for each nutrient from a database and provide them to the user. For example, the generation AI may retrieve the health benefits and recommended intake amount of vitamin C from the database and provide them along with the recipe. This allows the user to select a recipe while understanding the health benefits and recommended intake amounts of specific nutrients or ingredients.

[0035] The suggestion unit can suggest optimal recipes based on the ingredients and cooking utensils the user has. The suggestion unit, for example, uses generative AI to suggest optimal recipes based on the ingredients the user has. For example, when the ingredients in the refrigerator are input, a recipe based on that is suggested. The suggestion unit can also suggest optimal recipes based on the cooking utensils the user has. For example, a recipe suitable for a frying pan or oven the user has is suggested. The suggestion unit can also suggest optimal recipes by combining ingredients and cooking utensils the user has. For example, when the ingredients and cooking utensils the user has are input, a recipe based on that is suggested. This allows the user to obtain optimal recipes based on the ingredients and cooking utensils they have.

[0036] The suggestion unit can automatically generate fusion dish ideas that combine cuisines from different cultures and suggest them to the user. For example, the suggestion unit can use a generation AI to automatically generate fusion dish ideas that combine cuisines from different cultures and suggest them to the user. For example, the suggestion unit can suggest a recipe that combines Italian and Japanese cuisine. The suggestion unit can also customize fusion dish ideas according to the user's request. For example, if the user inputs a prompt such as "Please tell me a dish that combines Mexican and Chinese cuisine," the generation AI can suggest an appropriate recipe based on the request. The suggestion unit can also provide new dish ideas by combining ingredients and cooking methods from different cultures. For example, the suggestion unit can suggest a recipe that combines Asian spices with European cooking techniques. This allows the user to obtain new dish ideas that combine cuisines from different cultures.

[0037] When providing information about food cultures of different cultures, the suggestion unit can simultaneously provide the dining etiquette of each culture and how to obtain ingredients. For example, using a generation AI, the suggestion unit can simultaneously provide the dining etiquette of each culture when providing information about food cultures of different cultures. For example, the suggestion unit can explain Japanese dining etiquette and how to use chopsticks. The suggestion unit can also simultaneously provide how to obtain ingredients of each culture. For example, the suggestion unit can provide information on where to purchase specific ingredients, such as online shops and local markets. The suggestion unit can also customize information about food cultures of different cultures that interest the user, allowing the user to gain a deeper understanding. For example, if a user inputs a prompt such as "Tell me about traditional French cuisine," the generation AI can provide French dining etiquette and how to obtain ingredients based on the user's request. This allows the user to understand dining etiquette and how to obtain ingredients along with information about food cultures of different cultures.

[0038] The suggestion unit can link documentary videos and interview articles about the food culture of each culture to information about the food culture of different cultures, allowing the user to gain a deeper understanding. For example, the suggestion unit can link documentary videos about the food culture of each culture to information provided by the generation AI, allowing the user to gain a deeper understanding. For example, it can provide documentary videos about traditional Japanese cuisine. The suggestion unit can also link interview articles about the food culture of each culture. For example, it can provide interview articles with chefs and food experts. The suggestion unit can also customize information about the food culture of different cultures that interests the user, allowing the user to gain a deeper understanding. For example, if the user inputs a prompt such as "I would like to know more about Italian food culture," the generation AI can provide documentary videos and interview articles based on that request. This allows the user to gain a deeper understanding of information about the food culture of different cultures.

[0039] When suggesting dishes according to a region or season, the suggestion unit can simultaneously provide information about the climate and culture of each region. For example, when suggesting dishes according to a region or season using a generation AI, the suggestion unit can simultaneously provide information about the climate of each region. For example, the suggestion unit can suggest dishes suitable for the hot summer months. The suggestion unit can also simultaneously provide information about the culture and culture of each region. For example, the suggestion unit can provide information about the topography and soil of a specific region. The suggestion unit can also customize suggestions for dishes according to a region or season that interests the user, allowing the user to gain a deeper understanding. For example, if a user inputs a prompt such as "Please tell me some recommended French dishes for autumn," the generation AI can provide information about the climate and culture of France along with recipes for French dishes suitable for autumn based on the user's request. This allows the user to understand information about the climate and culture along with suggestions for dishes according to a region or season.

[0040] When suggesting dishes according to a region or season, the suggestion unit can also include dishes related to traditional events and festivals of each region. For example, the suggestion unit includes dishes related to traditional events and festivals of each region in the dishes suggested by the generation AI. For example, the suggestion unit can suggest Japanese New Year's dishes and osechi dishes. The suggestion unit can also customize dishes related to traditional events and festivals of each region to enable the user to gain a deeper understanding. For example, when a user inputs a prompt such as "Please tell me dishes that are suitable for summer festivals," the generation AI can suggest dishes suitable for summer festivals based on the request. Furthermore, when suggesting dishes according to a region or season that the user is interested in, the suggestion unit can automatically retrieve dishes related to traditional events and festivals of each region from a database and provide them to the user. For example, the generation AI retrieves dishes related to Japanese summer festivals from the database and provides them to the user. This allows the user to understand dishes related to traditional events and festivals along with suggestions for dishes according to the region or season.

[0041] When suggesting recipes using specific nutrients or ingredients, the suggestion unit can simultaneously provide the health benefits and recommended intake amounts for each nutrient. For example, when suggesting recipes using ingredients containing specific nutrients using a generation AI, the suggestion unit simultaneously provides the health benefits of the nutrient. For example, it may explain recipes using ingredients rich in vitamin C and their health benefits. The suggestion unit can also simultaneously provide recommended intake amounts for each nutrient. For example, it may provide the recommended daily intake amount of vitamin C. When suggesting recipes using specific nutrients or ingredients, the suggestion unit can also automatically retrieve the health benefits and recommended intake amounts for each nutrient from a database and provide them to the user. For example, the generation AI may retrieve the health benefits and recommended intake amount of vitamin C from the database and provide them along with the recipe. This allows the user to select a recipe while understanding the health benefits and recommended intake amounts of specific nutrients or ingredients.

[0042] The suggestion unit can suggest recipes that utilize specific nutrients and ingredients, taking into account the user's health condition and allergy information. The suggestion unit, for example, uses a generation AI to suggest recipes that take into account the user's health condition. For example, it can suggest low-carb recipes that are suitable for a diabetic user. The suggestion unit can also suggest recipes that take into account the user's allergy information. For example, if the user is allergic to a specific ingredient, it can suggest recipes that do not include that ingredient. The suggestion unit can also automatically obtain the user's health condition and allergy information from a database and suggest recipes based on that. For example, the generation AI can obtain the user's health condition and allergy information from a database and suggest appropriate recipes based on that. This allows the user to obtain recipes that take into account their own health condition and allergy information.

[0043] The suggestion unit can automatically generate new recipes that combine different nutrients and ingredients and suggest them to the user. For example, the suggestion unit can use a generation AI to automatically generate new recipes that combine ingredients containing different nutrients and suggest them to the user. For example, the suggestion unit can suggest recipes that combine ingredients containing vitamin C and iron. The suggestion unit can also customize new recipes according to the user's requests. For example, if the user inputs a prompt such as "Please tell me a high-protein, low-calorie recipe," the generation AI can suggest an appropriate recipe based on the request. The suggestion unit can also provide new cooking ideas by combining different nutrients and ingredients. For example, the suggestion unit can suggest a recipe that combines ingredients containing vitamin D and calcium. This allows the user to obtain new recipes that combine different nutrients and ingredients.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The Food Explorer AI system can also analyze a user's dietary history and make suggestions aimed at improving their health. For example, if a user has eaten many high-calorie meals in the past, it can suggest low-calorie, nutritionally balanced recipes. Also, if a user is lacking in a particular nutrient, it can provide recipes using ingredients that supplement that nutrient. Furthermore, it can set long-term health goals based on the user's dietary history and suggest meal plans to achieve them. This allows users to choose meals that take their own health into consideration.

[0046] The Food Explorer AI system can also learn the user's food preferences and suggest new cooking ideas based on them. For example, if a user likes a particular ingredient, it can suggest new recipes using that ingredient. Also, if a user likes a particular cooking method, it can provide recipes that utilize that cooking method. Furthermore, based on the user's past choices, it can suggest dishes that the user has not tried yet but is likely to like. This allows users to discover new dishes that suit their tastes.

[0047] The Food Explorer AI system can also learn the user's food preferences and introduce them to different cultural cuisines based on that. For example, if the user likes spicy food, it can suggest spicy dishes from India or Thailand. If the user prefers sweet food, it can also offer French or Italian desserts. Furthermore, based on the user's past choices, it can suggest dishes from different cultures that the user has not tried yet but is likely to like. This allows users to enjoy different cultural cuisines that suit their preferences.

[0048] The Food Explorer AI system can also learn the user's food preferences and provide advice on food storage methods based on that. For example, if a user prefers fresh vegetables, it can suggest storage methods to make those vegetables last longer. Also, if a user frequently uses a particular ingredient, it can provide an efficient way to store that ingredient. Furthermore, based on the user's past choices, it can suggest convenient storage methods that the user has not yet tried. This allows users to learn how to store ingredients that suit their preferences.

[0049] The Food Explorer AI system can also learn the user's food preferences and provide advice on how to choose ingredients based on that. For example, if the user prefers organic ingredients, it can suggest how to choose organic ingredients. If the user prefers a particular ingredient, it can also provide a way to distinguish the quality of that ingredient. Furthermore, based on the user's past choices, it can suggest ingredients that the user has not tried yet but is likely to like. This allows the user to choose ingredients that suit their preferences.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The data collection department collects data on cuisines and ingredients from around the world. For example, it collects information on traditional cuisines and specialties of each country, as well as the nutritional value and cooking methods of ingredients. The data collection department can also collect information from public databases on the Internet and specialized books. For example, the data collection department can use web scraping technology to collect data from recipe websites on the Internet. The data collection department can also scan specialized books and convert them into digital data using OCR technology. Step 2: The data organization unit systematically organizes the data collected by the data collection unit. For example, the collected data is categorized and tagged. The data organization unit can also design a database structure and efficiently manage data. For example, the data organization unit can manage data using an SQL database and search data using queries. The data organization unit can also efficiently manage large amounts of data using a NoSQL database. Step 3: The suggestion unit provides the user with cooking ideas and information on food cultures of different cultures based on the data organized by the data organization unit. For example, the suggestion unit suggests new recipes in response to the user's requests. The suggestion unit can also provide information on food cultures of different cultures that interest the user. For example, when the user inputs a prompt such as "Please tell me some easy-to-make Italian recipes," the suggestion unit causes the generation AI to suggest appropriate recipes based on the request. Also, when the user inputs a prompt such as "Please tell me about traditional Japanese breakfasts," the suggestion unit causes the generation AI to provide information on traditional Japanese breakfasts based on the request.

[0052] (Example 2) The Food Explorer AI system according to an embodiment of the present invention is a system that collects and organizes data on cuisines and ingredients from around the world and provides users with cooking ideas and information on food cultures of different cultures. As a result, the Food Explorer AI system can provide users with a variety of cooking ideas and information, helping them to deepen their understanding of food cultures of different cultures.

[0053] A food explorer AI system according to an embodiment includes a data collection unit, a data organization unit, and a suggestion unit. The data collection unit collects data on cuisines and ingredients from around the world. For example, it collects information on traditional cuisines and local specialties of each country, the nutritional value of ingredients, and cooking methods. The data collection unit can also collect information from public databases on the Internet and specialized books. For example, the data collection unit can collect data from recipe websites on the Internet using web scraping technology. The data collection unit can also scan specialized books and convert them into digital data using OCR technology. The data organization unit systematically organizes the data collected by the data collection unit. For example, it can categorize and tag the collected data. The data organization unit can also design a database structure and efficiently manage the data. For example, the data organization unit can manage data using an SQL database and search data using queries. The data organization unit can also efficiently manage large amounts of data using a NoSQL database. The suggestion unit provides the user with cooking ideas and information on food cultures in different cultures based on the data organized by the data organization unit. For example, the suggestion unit can suggest new recipes in response to a user's request. The suggestion unit can also provide information about the food cultures of other cultures that interest the user. For example, when a user inputs a prompt such as "Please tell me some easy-to-make Italian recipes," the suggestion unit can cause the generation AI to suggest appropriate recipes based on the user's request. When a user inputs a prompt such as "Please tell me about traditional Japanese breakfasts," the suggestion unit can cause the generation AI to provide information about traditional Japanese breakfasts based on the user's request. In this way, the food explorer AI system according to the embodiment can provide users with a variety of cooking ideas and information, helping them deepen their understanding of food cultures of other cultures. For example, the system can provide users with information that is useful when trying new dishes, and can broaden their enjoyment of food by learning about food cultures of other cultures. Furthermore, the system is expected to improve the quality of users' diets by suggesting healthy recipes.

[0054] The data collection unit can also collect the historical background and cultural significance of each dish and incorporate them into the database. For example, when collecting recipes for traditional dishes from various countries using generative AI, the data collection unit can also collect the historical background and cultural significance of the dish. For example, the origin and cultural significance of Italian pizza can be incorporated into the database. The data collection unit can also collect historical documents and materials related to the dish, convert them into digital data, and incorporate them into the database. For example, the data collection unit can scan historical cookbooks and convert them into digital data using OCR technology. The data collection unit can also collect interviews and documentary footage related to the dish and incorporate them into the database. For example, the data collection unit can record interviews with chefs and culinary experts and incorporate the content into the database. This allows users to understand the historical background and cultural significance of the dish.

[0055] The data organization unit can visualize the interrelationships between dishes and ingredients and provide an interactive map that allows users to intuitively understand. For example, the data organization unit creates an interactive map that visualizes the interrelationships between dishes and ingredients from various countries based on data collected by the generative AI. For example, the data organization unit visually displays the similarities and differences between Italian and French cuisine. The data organization unit also visualizes ingredient combinations and commonalities between dishes to allow users to intuitively understand. For example, the data organization unit creates graphs and charts showing the interrelationships between ingredients and provides them as an interactive map. The data organization unit can also filter information about dishes and ingredients that interest the user and display it on the interactive map. For example, when a user selects a specific ingredient, dishes and other ingredients related to that ingredient are displayed. This allows users to intuitively understand the interrelationships between dishes and ingredients.

[0056] The suggestion unit can analyze the user's past search history and preferences and provide personalized cooking ideas based on the analysis. The suggestion unit can, for example, use generative AI to analyze the user's past search history and provide personalized cooking ideas based on the analysis. For example, it can suggest new recipes based on recipes the user has previously searched for. The suggestion unit can also analyze the user's preferences and provide personalized cooking ideas based on the analysis. For example, it can suggest new recipes based on the user's favorite ingredients and cooking methods. The suggestion unit can also provide information on food cultures of different cultures that may interest the user based on the user's past search history and preferences. For example, it can provide information on food cultures of different cultures related to dishes the user has previously searched for. This allows the user to obtain cooking ideas based on their own preferences.

[0057] The suggestion unit can suggest optimal recipes based on the ingredients and cooking utensils the user has. The suggestion unit, for example, uses generative AI to suggest optimal recipes based on the ingredients the user has. For example, when the ingredients in the refrigerator are input, a recipe based on that is suggested. The suggestion unit can also suggest optimal recipes based on the cooking utensils the user has. For example, a recipe suitable for a frying pan or oven the user has is suggested. The suggestion unit can also suggest optimal recipes by combining ingredients and cooking utensils the user has. For example, when the ingredients and cooking utensils the user has are input, a recipe based on that is suggested. This allows the user to obtain optimal recipes based on the ingredients and cooking utensils they have.

[0058] When suggesting recipes using specific nutrients or ingredients, the suggestion unit can simultaneously provide the health benefits and recommended intake amounts for each nutrient. For example, when suggesting recipes using ingredients containing specific nutrients using a generation AI, the suggestion unit simultaneously provides the health benefits of the nutrient. For example, it may explain recipes using ingredients rich in vitamin C and their health benefits. The suggestion unit can also simultaneously provide recommended intake amounts for each nutrient. For example, it may provide the recommended daily intake amount of vitamin C. When suggesting recipes using specific nutrients or ingredients, the suggestion unit can also automatically retrieve the health benefits and recommended intake amounts for each nutrient from a database and provide them to the user. For example, the generation AI may retrieve the health benefits and recommended intake amount of vitamin C from the database and provide them along with the recipe. This allows the user to select a recipe while understanding the health benefits and recommended intake amounts of specific nutrients or ingredients.

[0059] The suggestion unit can estimate the user's emotions and optimize the next suggestion based on those emotions. For example, the suggestion unit uses an emotion estimation function to analyze the user's emotions toward the suggested dishes and optimize the next suggestion based on those emotions. For example, the suggestion unit can prioritize suggesting dishes for which the user has positive emotions. The suggestion unit can also monitor the user's emotions in real time and dynamically change the suggestion content based on those emotions. For example, the suggestion content can be changed to avoid dishes for which the user has negative emotions. The suggestion unit can also personalize the suggestion content based on the user's emotions and make suggestions that satisfy the user. For example, the suggestion unit can suggest a new recipe based on a dish for which the user has had positive emotions in the past. This makes it possible to make optimal suggestions based on the user's emotions.

[0060] The suggestion unit can suggest optimal recipes based on the ingredients and cooking utensils the user has. The suggestion unit, for example, uses generative AI to suggest optimal recipes based on the ingredients the user has. For example, when the ingredients in the refrigerator are input, a recipe based on that is suggested. The suggestion unit can also suggest optimal recipes based on the cooking utensils the user has. For example, a recipe suitable for a frying pan or oven the user has is suggested. The suggestion unit can also suggest optimal recipes by combining ingredients and cooking utensils the user has. For example, when the ingredients and cooking utensils the user has are input, a recipe based on that is suggested. This allows the user to obtain optimal recipes based on the ingredients and cooking utensils they have.

[0061] The suggestion unit can automatically generate fusion dish ideas that combine cuisines from different cultures and suggest them to the user. For example, the suggestion unit can use a generation AI to automatically generate fusion dish ideas that combine cuisines from different cultures and suggest them to the user. For example, the suggestion unit can suggest a recipe that combines Italian and Japanese cuisine. The suggestion unit can also customize fusion dish ideas according to the user's request. For example, if the user inputs a prompt such as "Please tell me a dish that combines Mexican and Chinese cuisine," the generation AI can suggest an appropriate recipe based on the request. The suggestion unit can also provide new dish ideas by combining ingredients and cooking methods from different cultures. For example, the suggestion unit can suggest a recipe that combines Asian spices with European cooking techniques. This allows the user to obtain new dish ideas that combine cuisines from different cultures.

[0062] The suggestion unit can monitor the user's emotions in real time and dynamically change the content of suggestions based on those emotions. For example, the suggestion unit uses an emotion estimation function to provide real-time feedback on the user's emotions toward suggested dishes and dynamically change the content of suggestions based on those emotions. For example, the suggestion unit can next suggest a dish that the user likes. The suggestion unit can also monitor the user's emotions in real time and personalize the content of suggestions based on those emotions. For example, it can preferentially suggest dishes that the user has positive emotions about. The suggestion unit can also optimize the content of suggestions based on the user's emotions and make suggestions that satisfy the user. For example, it can suggest a new recipe based on a dish that the user has felt positive about in the past. This makes it possible to dynamically change the content of suggestions based on the user's emotions.

[0063] When providing information about food cultures of different cultures, the suggestion unit can simultaneously provide the dining etiquette of each culture and how to obtain ingredients. For example, using a generation AI, the suggestion unit can simultaneously provide the dining etiquette of each culture when providing information about food cultures of different cultures. For example, the suggestion unit can explain Japanese dining etiquette and how to use chopsticks. The suggestion unit can also simultaneously provide how to obtain ingredients of each culture. For example, the suggestion unit can provide information on where to purchase specific ingredients, such as online shops and local markets. The suggestion unit can also customize information about food cultures of different cultures that interest the user, allowing the user to gain a deeper understanding. For example, if a user inputs a prompt such as "Tell me about traditional French cuisine," the generation AI can provide French dining etiquette and how to obtain ingredients based on the user's request. This allows the user to understand dining etiquette and how to obtain ingredients along with information about food cultures of different cultures.

[0064] The suggestion unit can link documentary videos and interview articles about the food culture of each culture to information about the food culture of different cultures, allowing the user to gain a deeper understanding. For example, the suggestion unit can link documentary videos about the food culture of each culture to information provided by the generation AI, allowing the user to gain a deeper understanding. For example, it can provide documentary videos about traditional Japanese cuisine. The suggestion unit can also link interview articles about the food culture of each culture. For example, it can provide interview articles with chefs and food experts. The suggestion unit can also customize information about the food culture of different cultures that interests the user, allowing the user to gain a deeper understanding. For example, if the user inputs a prompt such as "I would like to know more about Italian food culture," the generation AI can provide documentary videos and interview articles based on that request. This allows the user to gain a deeper understanding of information about the food culture of different cultures.

[0065] The suggestion unit can monitor the user's emotions in real time and dynamically change the content of displayed information based on the emotions. For example, the suggestion unit uses an emotion estimation function to monitor the user's emotions toward food cultures of different cultures in real time and dynamically change the content of displayed information based on the emotions. For example, information about cultures in which the user is interested can be preferentially displayed. The suggestion unit can also provide feedback on the user's emotions in real time and personalize the content of displayed information based on the emotions. For example, information about cultures for which the user has positive emotions can be preferentially displayed. The suggestion unit can also optimize the content of displayed information based on the user's emotions and provide information that satisfies the user. For example, new information can be provided based on information about cultures for which the user has had positive emotions in the past. This makes it possible to dynamically change the content of displayed information based on the user's emotions.

[0066] When suggesting dishes according to a region or season, the suggestion unit can simultaneously provide information about the climate and culture of each region. For example, when suggesting dishes according to a region or season using a generation AI, the suggestion unit can simultaneously provide information about the climate of each region. For example, the suggestion unit can suggest dishes suitable for the hot summer months. The suggestion unit can also simultaneously provide information about the culture and culture of each region. For example, the suggestion unit can provide information about the topography and soil of a specific region. The suggestion unit can also customize suggestions for dishes according to a region or season that interests the user, allowing the user to gain a deeper understanding. For example, if a user inputs a prompt such as "Please tell me some recommended French dishes for autumn," the generation AI can provide information about the climate and culture of France along with recipes for French dishes suitable for autumn based on the user's request. This allows the user to understand information about the climate and culture along with suggestions for dishes according to a region or season.

[0067] When suggesting dishes according to a region or season, the suggestion unit can also include dishes related to traditional events and festivals of each region. For example, the suggestion unit includes dishes related to traditional events and festivals of each region in the dishes suggested by the generation AI. For example, the suggestion unit can suggest Japanese New Year's dishes and osechi dishes. The suggestion unit can also customize dishes related to traditional events and festivals of each region to enable the user to gain a deeper understanding. For example, when a user inputs a prompt such as "Please tell me dishes that are suitable for summer festivals," the generation AI can suggest dishes suitable for summer festivals based on the request. Furthermore, when suggesting dishes according to a region or season that the user is interested in, the suggestion unit can automatically retrieve dishes related to traditional events and festivals of each region from a database and provide them to the user. For example, the generation AI retrieves dishes related to Japanese summer festivals from the database and provides them to the user. This allows the user to understand dishes related to traditional events and festivals along with suggestions for dishes according to the region or season.

[0068] The suggestion unit can monitor the user's emotions in real time and dynamically change the content of suggestions based on those emotions. For example, the suggestion unit uses an emotion estimation function to provide real-time feedback on the user's emotions toward suggested dishes and dynamically change the content of suggestions based on those emotions. For example, the suggestion unit can next suggest a dish that the user likes. The suggestion unit can also monitor the user's emotions in real time and personalize the content of suggestions based on those emotions. For example, it can preferentially suggest dishes that the user has positive emotions about. The suggestion unit can also optimize the content of suggestions based on the user's emotions and make suggestions that satisfy the user. For example, it can suggest a new recipe based on a dish that the user has felt positive about in the past. This makes it possible to dynamically change the content of suggestions based on the user's emotions.

[0069] When suggesting recipes using specific nutrients or ingredients, the suggestion unit can simultaneously provide the health benefits and recommended intake amounts for each nutrient. For example, when suggesting recipes using ingredients containing specific nutrients using a generation AI, the suggestion unit simultaneously provides the health benefits of the nutrient. For example, it may explain recipes using ingredients rich in vitamin C and their health benefits. The suggestion unit can also simultaneously provide recommended intake amounts for each nutrient. For example, it may provide the recommended daily intake amount of vitamin C. When suggesting recipes using specific nutrients or ingredients, the suggestion unit can also automatically retrieve the health benefits and recommended intake amounts for each nutrient from a database and provide them to the user. For example, the generation AI may retrieve the health benefits and recommended intake amount of vitamin C from the database and provide them along with the recipe. This allows the user to select a recipe while understanding the health benefits and recommended intake amounts of specific nutrients or ingredients.

[0070] The suggestion unit can suggest recipes that utilize specific nutrients and ingredients, taking into account the user's health condition and allergy information. The suggestion unit, for example, uses a generation AI to suggest recipes that take into account the user's health condition. For example, it can suggest low-carb recipes that are suitable for a diabetic user. The suggestion unit can also suggest recipes that take into account the user's allergy information. For example, if the user is allergic to a specific ingredient, it can suggest recipes that do not include that ingredient. The suggestion unit can also automatically obtain the user's health condition and allergy information from a database and suggest recipes based on that. For example, the generation AI can obtain the user's health condition and allergy information from a database and suggest appropriate recipes based on that. This allows the user to obtain recipes that take into account their own health condition and allergy information.

[0071] The suggestion unit can automatically generate new recipes that combine different nutrients and ingredients and suggest them to the user. For example, the suggestion unit can use a generation AI to automatically generate new recipes that combine ingredients containing different nutrients and suggest them to the user. For example, the suggestion unit can suggest recipes that combine ingredients containing vitamin C and iron. The suggestion unit can also customize new recipes according to the user's requests. For example, if the user inputs a prompt such as "Please tell me a high-protein, low-calorie recipe," the generation AI can suggest an appropriate recipe based on the request. The suggestion unit can also provide new cooking ideas by combining different nutrients and ingredients. For example, the suggestion unit can suggest a recipe that combines ingredients containing vitamin D and calcium. This allows the user to obtain new recipes that combine different nutrients and ingredients.

[0072] The suggestion unit can monitor the user's emotions in real time and dynamically change the content of suggestions based on those emotions. For example, the suggestion unit uses an emotion estimation function to provide real-time feedback on the user's emotions toward a suggested recipe and dynamically change the content of suggestions based on those emotions. For example, the suggestion unit may next suggest a recipe that the user likes. The suggestion unit can also monitor the user's emotions in real time and personalize the content of suggestions based on those emotions. For example, it may preferentially suggest recipes that the user has positive emotions about. The suggestion unit can also optimize the content of suggestions based on the user's emotions and make suggestions that satisfy the user. For example, it may suggest a new recipe based on a recipe that the user has felt positive about in the past. This makes it possible to dynamically change the content of suggestions based on the user's emotions.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The Food Explorer AI system can also analyze a user's dietary history and make suggestions aimed at improving their health. For example, if a user has eaten many high-calorie meals in the past, it can suggest low-calorie, nutritionally balanced recipes. Also, if a user is lacking in a particular nutrient, it can provide recipes using ingredients that supplement that nutrient. Furthermore, it can set long-term health goals based on the user's dietary history and suggest meal plans to achieve them. This allows users to choose meals that take their own health into consideration.

[0075] The Food Explorer AI system can also estimate a user's emotions and suggest dishes based on the estimated emotions. For example, if a user is feeling stressed, it can suggest recipes using herbs that have a relaxing effect. Or, if a user wants to feel energized, it can provide high-protein recipes that will replenish energy. Furthermore, it can learn the dishes that users prefer when they are feeling a certain emotion and make personalized suggestions based on that emotion. This allows users to enjoy cooking that suits their emotions.

[0076] The Food Explorer AI system can also learn the user's food preferences and suggest new cooking ideas based on them. For example, if a user likes a particular ingredient, it can suggest new recipes using that ingredient. Also, if a user likes a particular cooking method, it can provide recipes that utilize that cooking method. Furthermore, based on the user's past choices, it can suggest dishes that the user has not tried yet but is likely to like. This allows users to discover new dishes that suit their tastes.

[0077] The Food Explorer AI system can also estimate the user's emotions and provide advice on how to choose ingredients based on the estimated emotions. For example, if the user is tired, it can suggest ingredients that are effective in relieving fatigue. Or, if the user feels like relaxing, it can provide ingredients that have a relaxing effect. Furthermore, it can learn the ingredients that the user prefers when they are feeling a certain emotion and provide personalized advice based on that emotion. This allows the user to choose ingredients that suit their own emotions.

[0078] The Food Explorer AI system can also learn the user's food preferences and introduce them to different cultural cuisines based on that. For example, if the user likes spicy food, it can suggest spicy dishes from India or Thailand. If the user prefers sweet food, it can also offer French or Italian desserts. Furthermore, based on the user's past choices, it can suggest dishes from different cultures that the user has not tried yet but is likely to like. This allows users to enjoy different cultural cuisines that suit their preferences.

[0079] The Food Explorer AI system can also estimate a user's emotions and advise meal timings based on the estimated emotions. For example, if a user is feeling stressed, it can suggest a relaxing meal in the evening. Or, if a user needs a boost, it can provide an energy-boosting meal in the morning. It can also learn the user's preferred meal timings when they are feeling a certain emotion and provide personalized advice based on that emotion. This allows users to choose meal timings that suit their emotions.

[0080] The Food Explorer AI system can also learn the user's food preferences and provide advice on food storage methods based on that. For example, if a user prefers fresh vegetables, it can suggest storage methods to make those vegetables last longer. Also, if a user frequently uses a particular ingredient, it can provide an efficient way to store that ingredient. Furthermore, based on the user's past choices, it can suggest convenient storage methods that the user has not yet tried. This allows users to learn how to store ingredients that suit their preferences.

[0081] The Food Explorer AI system can also estimate a user's emotions and provide advice on meal presentations based on the estimated emotions. For example, if a user feels like celebrating a special occasion, it can suggest recipes with luxurious presentations. On the other hand, if a user feels like relaxing, it can provide recipes with simple and relaxing presentations. Furthermore, it can learn the presentations that users prefer when they are feeling a certain emotion and provide personalized advice based on that emotion. This allows users to enjoy meal presentations that suit their emotions.

[0082] The Food Explorer AI system can also learn the user's food preferences and provide advice on how to choose ingredients based on that. For example, if the user prefers organic ingredients, it can suggest how to choose organic ingredients. If the user prefers a particular ingredient, it can also provide a way to distinguish the quality of that ingredient. Furthermore, based on the user's past choices, it can suggest ingredients that the user has not tried yet but is likely to like. This allows the user to choose ingredients that suit their preferences.

[0083] The Food Explorer AI system can also estimate a user's emotions and provide advice on dining environments based on the estimated emotions. For example, if a user feels like relaxing, it can suggest relaxing music and lighting settings. Or, if a user feels like celebrating a special occasion, it can provide decorations and music to create a special atmosphere. Furthermore, it can learn the environments that users prefer when they feel a certain emotion and provide personalized advice based on that emotion. This allows users to enjoy a dining environment that suits their emotions.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The data collection department collects data on cuisines and ingredients from around the world. For example, it collects information on traditional cuisines and specialties of each country, as well as the nutritional value and cooking methods of ingredients. The data collection department can also collect information from public databases on the Internet and specialized books. For example, the data collection department can use web scraping technology to collect data from recipe websites on the Internet. The data collection department can also scan specialized books and convert them into digital data using OCR technology. Step 2: The data organization unit systematically organizes the data collected by the data collection unit. For example, the collected data is categorized and tagged. The data organization unit can also design a database structure and efficiently manage data. For example, the data organization unit can manage data using an SQL database and search data using queries. The data organization unit can also efficiently manage large amounts of data using a NoSQL database. Step 3: The suggestion unit provides the user with cooking ideas and information on food cultures of different cultures based on the data organized by the data organization unit. For example, the suggestion unit suggests new recipes in response to the user's requests. The suggestion unit can also provide information on food cultures of different cultures that interest the user. For example, when the user inputs a prompt such as "Please tell me some easy-to-make Italian recipes," the suggestion unit causes the generation AI to suggest appropriate recipes based on the request. Also, when the user inputs a prompt such as "Please tell me about traditional Japanese breakfasts," the suggestion unit causes the generation AI to provide information on traditional Japanese breakfasts based on the request.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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]

[0153] 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 data collection department that collects data on cuisines and ingredients from around the world, a data organization unit that systematically organizes the data collected by the data collection unit; a suggestion unit that provides users with cooking ideas and information on food cultures of different cultures based on the data organized by the data organization unit. A system characterized by:

2. The data organization unit Visualize the interrelationships between the dishes and the ingredients, and provide an interactive map that users can intuitively understand.

2. The system of claim 1.

3. The proposal unit Analyzing the user's past search history and preferences and providing personalized cooking ideas based thereon 2. The system of claim 1.

4. The proposal unit When providing the information about the food culture of the different culture, the dining etiquette of the culture and how to obtain the ingredients are also provided at the same time.

2. The system of claim 1.

5. The proposal unit Inferring the user's emotions and optimizing next suggestions based on the emotions.

2. The system of claim 1.

6. The proposal unit Monitoring the user's emotions in real time and dynamically changing the content of the suggestions based on the emotions.

2. The system of claim 1.

7. The proposal unit When suggesting dishes according to the region or season, the information about the climate and natural features of the region is also provided.

2. The system of claim 1.

8. The proposal unit Automatically generate new recipes that combine different nutrients and ingredients and suggest them to the user.

2. The system of claim 1.

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