System

Abstract

An object of a system according to an embodiment is to propose an optimal recipe in consideration of an individual's health condition and automatically incorporate the recipe into a diet management application.SOLUTION: A system according to an embodiment includes a keyword input unit, a recipe proposing unit, and a recipe importing unit. The keyword input unit allows a user to input a keyword. A recipe proposing part proposes an optimum recipe in which a generation AI considers a personal health state based on the keyword inputted by the keyword inputting part. The recipe importing unit automatically imports the recipe information proposed by the recipe proposing unit into the meal management application.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not suggest optimal recipes that take into account an individual's health condition and automatically incorporate them into a diet management app, so there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal recipes that take into account an individual's health condition and automatically incorporate the recipes into a dietary management app. [Means for solving the problem]

[0006] The system according to the embodiment includes a keyword input unit, a recipe suggestion unit, and a recipe import unit. The keyword input unit allows a user to input keywords. The recipe suggestion unit uses a generation AI to suggest optimal recipes that take into account an individual's health condition based on the keywords input by the keyword input unit. The recipe import unit automatically imports recipe information suggested by the recipe suggestion unit into a diet management app. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal recipes that take into account an individual's health condition and automatically import them into a dietary management app. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In the recipe suggestion system according to an embodiment of the present invention, a user inputs keywords, and a generation AI proposes optimal recipes that take into account the individual's health condition, and the proposed recipes are automatically imported into a diet management app. This allows the recipe suggestion system to help users eat healthy meals without having to worry about their daily meal menu.

[0029] A recipe suggestion system according to an embodiment includes a keyword input unit, a recipe suggestion unit, and a recipe import unit. The keyword input unit allows a user to input keywords. For example, a user can input keywords such as "diet" or "high protein" via LINE. The keyword input unit also transmits the keywords input by the user to a generation AI. The recipe suggestion unit allows the generation AI to propose optimal recipes that take into account the user's health condition based on the keywords input by the keyword input unit. For example, the generation AI may propose low-sugar or low-calorie recipes taking into account the user's health condition and dietary history. The generation AI may also propose allergen-free recipes taking into account the user's allergy information. The recipe import unit automatically imports recipe information proposed by the recipe suggestion unit into a diet management app. For example, recipes proposed by the generation AI are automatically registered in the diet management app, allowing the user to easily check the contents of the day's diet. The diet management app can also record the user's diet history and track changes in health condition. This allows the recipe suggestion system according to an embodiment to propose recipes tailored to the user's health condition and automatically import them into the diet management app simply by the user entering keywords.

[0030] The recipe suggestion unit learns the user's past search history and preferences to suggest more personalized recipes. For example, the recipe suggestion unit learns the user's past search keywords and recipe choices, and the generation AI uses that data to suggest new recipes. For example, if a user has frequently searched for "pasta" and "salad" in the past, the generation AI will suggest new recipes based on these preferences. To learn the user's preferences, the generation AI collects user ratings and feedback and suggests recipes based on that. For example, the generation AI can learn the characteristics of recipes that users rated as "delicious" and suggest similar recipes. The recipe suggestion unit also suggests recipes tailored to the season or event based on the user's past search history and preferences. For example, if a user has previously searched for "Christmas dinner" or "summer barbecue," the generation AI will suggest recipes that match those. This allows the generation AI to suggest more personalized recipes based on the user's past search history and preferences.

[0031] The recipe suggestion unit can automatically generate cooking videos and cooking steps related to keywords and suggest them in a visually easy-to-understand format. For example, based on keywords entered by a user, the generation AI automatically generates related cooking videos and suggests them in a visually easy-to-understand format. For example, if the user enters "how to make pasta," the generation AI will show a video of the pasta cooking steps. The recipe suggestion unit also displays step-by-step cooking steps for keywords, making it easy for users to cook. For example, if the user enters "chicken curry," the generation AI can display the cooking steps for chicken curry in order. The recipe suggestion unit also uses keywords to explain cooking tips and tricks in videos, allowing users to make more delicious dishes. For example, if the user enters "how to grill steak," the generation AI will show a video of tips for grilling a delicious steak. This allows the generation AI to suggest cooking videos and cooking steps related to keywords in a visually easy-to-understand format.

[0032] The recipe suggestion unit suggests recipes according to the season and weather in response to keyword input, making it possible to provide meals that incorporate a seasonal feel. For example, the recipe suggestion unit uses the generation AI to suggest recipes that correspond to the season based on keywords entered by the user. For example, if "salad" is entered, a cold salad will be suggested in the summer and a warm salad in the winter. The recipe suggestion unit also references weather information and the generation AI suggests recipes that match the weather of the day. For example, it can suggest a hot soup on a rainy day and a barbecue recipe on a sunny day. The recipe suggestion unit also uses the generation AI to suggest recipes that utilize seasonal ingredients. For example, it suggests recipes using fresh vegetables in the spring and recipes using mushrooms and pumpkins in the fall. This makes it possible to suggest recipes that correspond to the season and weather, and provide meals that incorporate a seasonal feel.

[0033] The recipe suggestion unit can refer to the user's ingredient inventory information when a keyword is entered and suggest recipes that use ingredients on hand. For example, based on the keyword entered by the user, the recipe suggestion unit has the generation AI refer to the user's ingredient inventory information and suggest recipes that use ingredients on hand. For example, if "pasta" is entered, the generation AI will suggest pasta recipes that use vegetables and meat on hand. The recipe suggestion unit also suggests recipes that use ingredients without waste based on the ingredient inventory information. For example, it can suggest recipes that use ingredients that are left over in the refrigerator. The recipe suggestion unit also updates the user's ingredient inventory information in real time, and the generation AI suggests recipes based on that information. For example, the inventory information can be updated after shopping and recipes using new ingredients can be suggested. This makes it possible to refer to the user's ingredient inventory information and suggest recipes that use ingredients on hand.

[0034] The recipe suggestion unit acquires the user's health data in real time and can suggest optimal recipes based on that. For example, the recipe suggestion unit acquires the user's blood pressure data in real time, and the generation AI suggests low-salt recipes based on that. For example, if the blood pressure is high, low-salt soups and salads are suggested. The recipe suggestion unit also acquires blood sugar level data in real time, and the generation AI suggests low-carb recipes based on that. For example, if the blood sugar level is high, low-carb desserts and main dishes can be suggested. The recipe suggestion unit also acquires the user's health data in real time, and the generation AI suggests nutritionally balanced recipes based on that. For example, if there is a vitamin deficiency, vitamin-rich recipes are suggested. This makes it possible to acquire the user's health data in real time and suggest optimal recipes based on that.

[0035] The recipe suggestion unit can analyze the user's genetic information and suggest healthy recipes based on genetic risk. For example, the recipe suggestion unit analyzes the user's genetic information and the generation AI suggests healthy recipes based on genetic risk. For example, if there is a high risk of heart disease, the generation AI suggests low-fat recipes. The recipe suggestion unit also uses the genetic information to suggest recipes that are high in specific nutrients. For example, if there is a risk of vitamin D deficiency, the generation AI can suggest recipes that are rich in vitamin D. The recipe suggestion unit also analyzes the user's genetic information and the generation AI suggests recipes that are useful for preventing specific diseases. For example, if there is a high risk of diabetes, the generation AI suggests low-carbohydrate recipes. In this way, the user's genetic information can be analyzed and healthy recipes can be suggested based on genetic risk.

[0036] The recipe suggestion unit can refer to the user's exercise data and suggest nutritionally balanced recipes according to the amount of exercise. For example, the recipe suggestion unit refers to the user's exercise data and the generation AI suggests nutritionally balanced recipes according to the amount of exercise. For example, it suggests high-protein recipes after exercise. The recipe suggestion unit also suggests recipes suitable for replenishing energy based on the exercise data. For example, it can suggest carbohydrate-rich recipes to replenish energy after a long period of exercise. The recipe suggestion unit also acquires the user's exercise data in real time and the generation AI suggests nutritionally balanced recipes based on that data. For example, it suggests light meals to replenish energy before exercise. In this way, the recipe suggestion unit can refer to the user's exercise data and suggest nutritionally balanced recipes according to the amount of exercise.

[0037] The recipe suggestion unit can analyze the user's sleep data and make meal suggestions to improve sleep quality. For example, the recipe suggestion unit analyzes the user's sleep data and the generation AI makes meal suggestions to improve sleep quality. For example, the recipe suggestion unit suggests a recipe for herbal tea that has a relaxing effect when consumed before sleep. Furthermore, the recipe suggestion unit can suggest nutritionally balanced recipes to improve sleep quality based on the sleep data. For example, recipes using ingredients that are high in tryptophan can be suggested. Furthermore, the recipe suggestion unit acquires the user's sleep data in real time and the generation AI makes meal suggestions to improve sleep quality based on that data. For example, it suggests a recipe for soup that has a relaxing effect when consumed before sleep. In this way, the user's sleep data can be analyzed and meal suggestions to improve sleep quality can be made.

[0038] The recipe import unit can automatically import recipe information proposed by the generation AI not only into the user's meal management app, but also into a calendar app and a reminder app. For example, the recipe import unit not only automatically imports recipe information proposed by the generation AI into the user's meal management app, but also into a calendar app to manage meal plans. For example, it automatically adds daily meal plans to a calendar. The recipe import unit also imports the proposed recipe information into a reminder app to notify the user of cooking timings. For example, it can notify the user of dinner preparation time by sending a reminder. The recipe import unit also works with the user's meal management app to automatically import the recipe information proposed by the generation AI into a calendar app and a reminder app. For example, it centrally manages meal plans and cooking timings. This allows the recipe information proposed by the generation AI to be automatically imported into not only the meal management app, but also the calendar app and the reminder app.

[0039] The recipe import unit can link the recipe information proposed by the generation AI with the user's purchasing history and automatically add the necessary ingredients to the online shopping cart. For example, the recipe import unit references the user's purchasing history based on the recipe information proposed by the generation AI and automatically adds the necessary ingredients to the online shopping cart. For example, the ingredients required for the proposed recipe are automatically added to the cart. The recipe import unit also builds a system that automatically adds the ingredients required for the recipe proposed by the generation AI to the online shopping cart based on the user's purchasing history. For example, the necessary ingredients can be automatically selected based on ingredients purchased in the past. The recipe import unit also links the user's purchasing history based on the recipe information proposed by the generation AI and automatically adds the necessary ingredients to the online shopping cart. For example, the ingredients required for the proposed recipe are automatically added to the cart, allowing the user to easily purchase them. This allows the recipe information proposed by the generation AI to be linked with the user's purchasing history and automatically add the necessary ingredients to the online shopping cart.

[0040] The recipe import unit can add a function to share recipe information proposed by the generation AI with the user's family and friends and to collaboratively manage their diet. The recipe import unit, for example, adds a function to share recipe information proposed by the generation AI with the user's family and friends and to collaboratively manage their diet. For example, meal plans for the whole family can be shared and meals can be prepared together. The recipe import unit also adds a function to share suggested recipe information with friends and to collaboratively manage their diet. For example, recipes for cooking together with friends can be shared. The recipe import unit also adds a function to share recipe information proposed by the generation AI with the user's family and friends and to collaboratively manage their diet. For example, the meal histories of the whole family can be shared and health management can be jointly performed. This allows the recipe information proposed by the generation AI to be shared with the user's family and friends and to collaboratively manage their diet.

[0041] The recipe import unit can link the recipe information proposed by the generation AI with the user's fitness app to provide advice for balancing exercise and diet. The recipe import unit, for example, links the recipe information proposed by the generation AI with the user's fitness app to provide advice for balancing exercise and diet. For example, it can suggest nutritionally balanced recipes suitable for after exercise. The recipe import unit also links with the fitness app to provide advice for balancing exercise and diet based on the recipe information proposed by the generation AI. For example, it can suggest snacks to replenish energy before exercise. The recipe import unit also links the recipe information proposed by the generation AI with the user's fitness app to provide advice for balancing exercise and diet. For example, it can suggest recipes that include nutrients needed after exercise. In this way, the recipe information proposed by the generation AI can be linked with the user's fitness app to provide advice for balancing exercise and diet.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The recipe suggestion unit can suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information. For example, if a user enters "gluten-free," the generation AI will suggest gluten-free recipes. The recipe suggestion unit also learns the user's dietary preferences, and the generation AI will suggest new recipes based on that data. For example, if a user has frequently searched for "vegetarian" or "vegan" in the past, the generation AI will suggest new recipes based on these preferences. The recipe suggestion unit can also suggest allergen-free recipes, taking into account the user's allergy information. For example, if a user enters "nut allergy," the generation AI will suggest nut-free recipes. This allows the system to suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information.

[0044] The recipe suggestion unit can suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information. For example, if a user enters "gluten-free," the generation AI will suggest gluten-free recipes. The recipe suggestion unit also learns the user's dietary preferences, and the generation AI will suggest new recipes based on that data. For example, if a user has frequently searched for "vegetarian" or "vegan" in the past, the generation AI will suggest new recipes based on these preferences. The recipe suggestion unit can also suggest allergen-free recipes, taking into account the user's allergy information. For example, if a user enters "nut allergy," the generation AI will suggest nut-free recipes. This allows the system to suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information.

[0045] The recipe suggestion unit can suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information. For example, if a user enters "gluten-free," the generation AI will suggest gluten-free recipes. The recipe suggestion unit also learns the user's dietary preferences, and the generation AI will suggest new recipes based on that data. For example, if a user has frequently searched for "vegetarian" or "vegan" in the past, the generation AI will suggest new recipes based on these preferences. The recipe suggestion unit can also suggest allergen-free recipes, taking into account the user's allergy information. For example, if a user enters "nut allergy," the generation AI will suggest nut-free recipes. This allows the system to suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information.

[0046] The recipe suggestion unit can suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information. For example, if a user enters "gluten-free," the generation AI will suggest gluten-free recipes. The recipe suggestion unit also learns the user's dietary preferences, and the generation AI will suggest new recipes based on that data. For example, if a user has frequently searched for "vegetarian" or "vegan" in the past, the generation AI will suggest new recipes based on these preferences. The recipe suggestion unit can also suggest allergen-free recipes, taking into account the user's allergy information. For example, if a user enters "nut allergy," the generation AI will suggest nut-free recipes. This allows the system to suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information.

[0047] The recipe suggestion unit can suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information. For example, if a user enters "gluten-free," the generation AI will suggest gluten-free recipes. The recipe suggestion unit also learns the user's dietary preferences, and the generation AI will suggest new recipes based on that data. For example, if a user has frequently searched for "vegetarian" or "vegan" in the past, the generation AI will suggest new recipes based on these preferences. The recipe suggestion unit can also suggest allergen-free recipes, taking into account the user's allergy information. For example, if a user enters "nut allergy," the generation AI will suggest nut-free recipes. This allows the system to suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The keyword input unit allows the user to input keywords. For example, the user can input keywords such as "diet" or "high protein" via LINE. The keyword input unit also sends the keywords entered by the user to the generation AI. Step 2: In the recipe suggestion section, the generation AI proposes optimal recipes that take into account the individual's health condition based on the keywords entered by the keyword input section. For example, the generation AI proposes low-sugar and low-calorie recipes taking into account the user's health condition and dietary history. The generation AI can also propose allergen-free recipes taking into account the user's allergy information. Step 3: The recipe import unit automatically imports the recipe information suggested by the recipe suggestion unit into the diet management app. For example, the recipes suggested by the generation AI are automatically registered in the diet management app, allowing the user to easily check the diet contents of the day. The diet management app can also record the user's diet history and track changes in health status.

[0050] (Example 2) In the recipe suggestion system according to an embodiment of the present invention, a user inputs keywords, and a generation AI proposes optimal recipes that take into account the individual's health condition, and the proposed recipes are automatically imported into a diet management app. This allows the recipe suggestion system to help users eat healthy meals without having to worry about their daily meal menu.

[0051] A recipe suggestion system according to an embodiment includes a keyword input unit, a recipe suggestion unit, and a recipe import unit. The keyword input unit allows a user to input keywords. For example, a user can input keywords such as "diet" or "high protein" via LINE. The keyword input unit also transmits the keywords input by the user to a generation AI. The recipe suggestion unit allows the generation AI to propose optimal recipes that take into account the user's health condition based on the keywords input by the keyword input unit. For example, the generation AI may propose low-sugar or low-calorie recipes taking into account the user's health condition and dietary history. The generation AI may also propose allergen-free recipes taking into account the user's allergy information. The recipe import unit automatically imports recipe information proposed by the recipe suggestion unit into a diet management app. For example, recipes proposed by the generation AI are automatically registered in the diet management app, allowing the user to easily check the contents of the day's diet. The diet management app can also record the user's diet history and track changes in health condition. This allows the recipe suggestion system according to an embodiment to propose recipes tailored to the user's health condition and automatically import them into the diet management app simply by the user entering keywords.

[0052] The recipe suggestion unit learns the user's past search history and preferences to suggest more personalized recipes. For example, the recipe suggestion unit learns the user's past search keywords and recipe choices, and the generation AI uses that data to suggest new recipes. For example, if a user has frequently searched for "pasta" and "salad" in the past, the generation AI will suggest new recipes based on these preferences. To learn the user's preferences, the generation AI collects user ratings and feedback and suggests recipes based on that. For example, the generation AI can learn the characteristics of recipes that users rated as "delicious" and suggest similar recipes. The recipe suggestion unit also suggests recipes tailored to the season or event based on the user's past search history and preferences. For example, if a user has previously searched for "Christmas dinner" or "summer barbecue," the generation AI will suggest recipes that match those. This allows the generation AI to suggest more personalized recipes based on the user's past search history and preferences.

[0053] The recipe suggestion unit can automatically generate cooking videos and cooking steps related to keywords and suggest them in a visually easy-to-understand format. For example, based on keywords entered by a user, the generation AI automatically generates related cooking videos and suggests them in a visually easy-to-understand format. For example, if the user enters "how to make pasta," the generation AI will show a video of the pasta cooking steps. The recipe suggestion unit also displays step-by-step cooking steps for keywords, making it easy for users to cook. For example, if the user enters "chicken curry," the generation AI can display the cooking steps for chicken curry in order. The recipe suggestion unit also uses keywords to explain cooking tips and tricks in videos, allowing users to make more delicious dishes. For example, if the user enters "how to grill steak," the generation AI will show a video of tips for grilling a delicious steak. This allows the generation AI to suggest cooking videos and cooking steps related to keywords in a visually easy-to-understand format.

[0054] The recipe suggestion unit can use the emotion estimation function to analyze the emotions associated with keywords entered by the user and suggest recipes that elicit positive emotions. For example, the recipe suggestion unit can analyze the emotions associated with keywords entered by the user and suggest recipes that elicit positive emotions. For example, if the user enters "tired," the generation AI can suggest recipes that have a relaxing effect. The recipe suggestion unit can also use the emotion estimation function to suggest recipes that match the user's emotional state. For example, if the user enters "happy," the generation AI can suggest recipes for special occasions. The recipe suggestion unit can also analyze the user's emotions and suggest recipes that elicit positive emotions. For example, if the user enters "stress," the generation AI can suggest recipes that have a stress-reducing effect. This makes it possible to suggest recipes that elicit positive emotions based on the user's emotions.

[0055] The recipe suggestion unit suggests recipes according to the season and weather in response to keyword input, making it possible to provide meals that incorporate a seasonal feel. For example, the recipe suggestion unit uses the generation AI to suggest recipes that correspond to the season based on keywords entered by the user. For example, if "salad" is entered, a cold salad will be suggested in the summer and a warm salad in the winter. The recipe suggestion unit also references weather information and the generation AI suggests recipes that match the weather of the day. For example, it can suggest a hot soup on a rainy day and a barbecue recipe on a sunny day. The recipe suggestion unit also uses the generation AI to suggest recipes that utilize seasonal ingredients. For example, it suggests recipes using fresh vegetables in the spring and recipes using mushrooms and pumpkins in the fall. This makes it possible to suggest recipes that correspond to the season and weather, and provide meals that incorporate a seasonal feel.

[0056] The recipe suggestion unit can refer to the user's ingredient inventory information when a keyword is entered and suggest recipes that use ingredients on hand. For example, based on the keyword entered by the user, the recipe suggestion unit has the generation AI refer to the user's ingredient inventory information and suggest recipes that use ingredients on hand. For example, if "pasta" is entered, the generation AI will suggest pasta recipes that use vegetables and meat on hand. The recipe suggestion unit also suggests recipes that use ingredients without waste based on the ingredient inventory information. For example, it can suggest recipes that use ingredients that are left over in the refrigerator. The recipe suggestion unit also updates the user's ingredient inventory information in real time, and the generation AI suggests recipes based on that information. For example, the inventory information can be updated after shopping and recipes using new ingredients can be suggested. This makes it possible to refer to the user's ingredient inventory information and suggest recipes that use ingredients on hand.

[0057] The recipe suggestion unit can use the emotion estimation function to analyze the emotion associated with keywords entered by the user and suggest recipes that have a stress-reducing or relaxing effect. The recipe suggestion unit can, for example, analyze the emotion associated with keywords entered by the user and suggest recipes that have a stress-reducing effect. For example, if the user enters "tired," the generation AI can suggest a recipe for herbal tea that has a relaxing effect. The recipe suggestion unit can also use the emotion estimation function to suggest recipes with a relaxing effect that match the user's emotional state. For example, if the user enters "stress," the generation AI can suggest a smoothie recipe that has a stress-reducing effect. The recipe suggestion unit can also analyze the user's emotion and suggest recipes that have a relaxing effect. For example, if the user enters "anxiety," the generation AI can suggest a soup recipe that has a relaxing effect. In this way, recipes that have a stress-reducing or relaxing effect can be suggested based on the user's emotions.

[0058] The recipe suggestion unit acquires the user's health data in real time and can suggest optimal recipes based on that. For example, the recipe suggestion unit acquires the user's blood pressure data in real time, and the generation AI suggests low-salt recipes based on that. For example, if the blood pressure is high, low-salt soups and salads are suggested. The recipe suggestion unit also acquires blood sugar level data in real time, and the generation AI suggests low-carb recipes based on that. For example, if the blood sugar level is high, low-carb desserts and main dishes can be suggested. The recipe suggestion unit also acquires the user's health data in real time, and the generation AI suggests nutritionally balanced recipes based on that. For example, if there is a vitamin deficiency, vitamin-rich recipes are suggested. This makes it possible to acquire the user's health data in real time and suggest optimal recipes based on that.

[0059] The recipe suggestion unit can analyze the user's genetic information and suggest healthy recipes based on genetic risk. For example, the recipe suggestion unit analyzes the user's genetic information and the generation AI suggests healthy recipes based on genetic risk. For example, if there is a high risk of heart disease, the generation AI suggests low-fat recipes. The recipe suggestion unit also uses the genetic information to suggest recipes that are high in specific nutrients. For example, if there is a risk of vitamin D deficiency, the generation AI can suggest recipes that are rich in vitamin D. The recipe suggestion unit also analyzes the user's genetic information and the generation AI suggests recipes that are useful for preventing specific diseases. For example, if there is a high risk of diabetes, the generation AI suggests low-carbohydrate recipes. In this way, the user's genetic information can be analyzed and healthy recipes can be suggested based on genetic risk.

[0060] The recipe suggestion unit can use the emotion estimation function to analyze the user's emotions regarding their health condition and suggest healthy recipes that elicit positive emotions. The recipe suggestion unit, for example, analyzes the user's emotions regarding their health condition and suggests healthy recipes that elicit positive emotions. For example, if the user is feeling "tired," it suggests recipes that replenish energy. The recipe suggestion unit can also use the emotion estimation function to analyze the user's emotions regarding their health condition and suggest healthy recipes that have a relaxing effect. For example, if the user is feeling "stressed," it can suggest recipes that have a relaxing effect. The recipe suggestion unit can also analyze the user's emotions and suggest healthy recipes that elicit positive emotions. For example, if the user is feeling "anxious," it suggests recipes that give a sense of security. In this way, it is possible to analyze the user's emotions regarding their health condition and suggest healthy recipes that elicit positive emotions.

[0061] The recipe suggestion unit can refer to the user's exercise data and suggest nutritionally balanced recipes according to the amount of exercise. For example, the recipe suggestion unit refers to the user's exercise data and the generation AI suggests nutritionally balanced recipes according to the amount of exercise. For example, it suggests high-protein recipes after exercise. The recipe suggestion unit also suggests recipes suitable for replenishing energy based on the exercise data. For example, it can suggest carbohydrate-rich recipes to replenish energy after a long period of exercise. The recipe suggestion unit also acquires the user's exercise data in real time and the generation AI suggests nutritionally balanced recipes based on that data. For example, it suggests light meals to replenish energy before exercise. In this way, the recipe suggestion unit can refer to the user's exercise data and suggest nutritionally balanced recipes according to the amount of exercise.

[0062] The recipe suggestion unit can analyze the user's sleep data and make meal suggestions to improve sleep quality. For example, the recipe suggestion unit analyzes the user's sleep data and the generation AI makes meal suggestions to improve sleep quality. For example, the recipe suggestion unit suggests a recipe for herbal tea that has a relaxing effect when consumed before sleep. Furthermore, the recipe suggestion unit can suggest nutritionally balanced recipes to improve sleep quality based on the sleep data. For example, recipes using ingredients that are high in tryptophan can be suggested. Furthermore, the recipe suggestion unit acquires the user's sleep data in real time and the generation AI makes meal suggestions to improve sleep quality based on that data. For example, it suggests a recipe for soup that has a relaxing effect when consumed before sleep. In this way, the user's sleep data can be analyzed and meal suggestions to improve sleep quality can be made.

[0063] The recipe suggestion unit can use the emotion estimation function to analyze the user's emotions regarding their health condition and suggest healthy recipes that have a stress-reducing or relaxing effect. The recipe suggestion unit, for example, analyzes the user's emotions regarding their health condition and suggests healthy recipes that have a stress-reducing effect. For example, if the user feels "tired," it suggests a recipe for herbal tea that has a relaxing effect. The recipe suggestion unit can also use the emotion estimation function to analyze the user's emotions regarding their health condition and suggest healthy recipes that have a relaxing effect. For example, if the user feels "stressed," it can suggest a smoothie recipe that has a relaxing effect. The recipe suggestion unit can also analyze the user's emotions and suggest healthy recipes that have a stress-reducing or relaxing effect. For example, if the user feels "anxious," it suggests a soup recipe that gives a sense of security. In this way, the user's emotions regarding their health condition can be analyzed and healthy recipes that have a stress-reducing or relaxing effect can be suggested.

[0064] The recipe import unit can automatically import recipe information proposed by the generation AI not only into the user's meal management app, but also into a calendar app and a reminder app. For example, the recipe import unit not only automatically imports recipe information proposed by the generation AI into the user's meal management app, but also into a calendar app to manage meal plans. For example, it automatically adds daily meal plans to a calendar. The recipe import unit also imports the proposed recipe information into a reminder app to notify the user of cooking timings. For example, it can notify the user of dinner preparation time by sending a reminder. The recipe import unit also works with the user's meal management app to automatically import the recipe information proposed by the generation AI into a calendar app and a reminder app. For example, it centrally manages meal plans and cooking timings. This allows the recipe information proposed by the generation AI to be automatically imported into not only the meal management app, but also the calendar app and the reminder app.

[0065] The recipe import unit can link the recipe information proposed by the generation AI with the user's purchasing history and automatically add the necessary ingredients to the online shopping cart. For example, the recipe import unit references the user's purchasing history based on the recipe information proposed by the generation AI and automatically adds the necessary ingredients to the online shopping cart. For example, the ingredients required for the proposed recipe are automatically added to the cart. The recipe import unit also builds a system that automatically adds the ingredients required for the recipe proposed by the generation AI to the online shopping cart based on the user's purchasing history. For example, the necessary ingredients can be automatically selected based on ingredients purchased in the past. The recipe import unit also links the user's purchasing history based on the recipe information proposed by the generation AI and automatically adds the necessary ingredients to the online shopping cart. For example, the ingredients required for the proposed recipe are automatically added to the cart, allowing the user to easily purchase them. This allows the recipe information proposed by the generation AI to be linked with the user's purchasing history and automatically add the necessary ingredients to the online shopping cart.

[0066] The recipe import unit can use the emotion estimation function to customize recipe information based on the user's emotion and import it into the meal management app in a way that elicits positive emotion. The recipe import unit, for example, uses the emotion estimation function to customize recipe information based on the user's emotion and import it into the meal management app in a way that elicits positive emotion. For example, if the user feels "tired," it can suggest recipes that have a relaxing effect. The recipe import unit can also analyze the user's emotion, customize recipe information to elicit positive emotion, and import it into the meal management app. For example, if the user feels "stressed," it can suggest recipes that have a stress-reducing effect. The recipe import unit can also use the emotion estimation function to customize recipe information based on the user's emotion and import it into the meal management app in a way that elicits positive emotion. For example, if the user feels "anxious," it can suggest recipes that give a sense of security. In this way, the emotion estimation function can be used to customize recipe information based on the user's emotion and import it into the meal management app in a way that elicits positive emotion.

[0067] The recipe import unit can add a function to share recipe information proposed by the generation AI with the user's family and friends and to collaboratively manage their diet. The recipe import unit, for example, adds a function to share recipe information proposed by the generation AI with the user's family and friends and to collaboratively manage their diet. For example, meal plans for the whole family can be shared and meals can be prepared together. The recipe import unit also adds a function to share suggested recipe information with friends and to collaboratively manage their diet. For example, recipes for cooking together with friends can be shared. The recipe import unit also adds a function to share recipe information proposed by the generation AI with the user's family and friends and to collaboratively manage their diet. For example, the meal histories of the whole family can be shared and health management can be jointly performed. This allows the recipe information proposed by the generation AI to be shared with the user's family and friends and to collaboratively manage their diet.

[0068] The recipe import unit can link the recipe information proposed by the generation AI with the user's fitness app to provide advice for balancing exercise and diet. The recipe import unit, for example, links the recipe information proposed by the generation AI with the user's fitness app to provide advice for balancing exercise and diet. For example, it can suggest nutritionally balanced recipes suitable for after exercise. The recipe import unit also links with the fitness app to provide advice for balancing exercise and diet based on the recipe information proposed by the generation AI. For example, it can suggest snacks to replenish energy before exercise. The recipe import unit also links the recipe information proposed by the generation AI with the user's fitness app to provide advice for balancing exercise and diet. For example, it can suggest recipes that include nutrients needed after exercise. In this way, the recipe information proposed by the generation AI can be linked with the user's fitness app to provide advice for balancing exercise and diet.

[0069] The recipe import unit can use the emotion estimation function to customize recipe information based on the user's emotion and import it into the meal management app in a form that has a stress-reducing or relaxing effect. The recipe import unit, for example, uses the emotion estimation function to customize recipe information based on the user's emotion and import it into the meal management app in a form that has a stress-reducing effect. For example, if the user feels "tired," it suggests recipes that have a relaxing effect. The recipe import unit also analyzes the user's emotion, customizes recipe information that has a stress-reducing effect, and imports it into the meal management app. For example, if the user feels "stressed," it can suggest recipes that have a stress-reducing effect. The recipe import unit also uses the emotion estimation function to customize recipe information based on the user's emotion and import it into the meal management app in a form that has a stress-reducing effect. For example, if the user feels "anxious," it suggests recipes that give a sense of security. In this way, the emotion estimation function can be used to customize recipe information based on the user's emotion and import it into the meal management app in a form that has a stress-reducing or relaxing effect.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The recipe suggestion unit can suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information. For example, if a user enters "gluten-free," the generation AI will suggest gluten-free recipes. The recipe suggestion unit also learns the user's dietary preferences, and the generation AI will suggest new recipes based on that data. For example, if a user has frequently searched for "vegetarian" or "vegan" in the past, the generation AI will suggest new recipes based on these preferences. The recipe suggestion unit can also suggest allergen-free recipes, taking into account the user's allergy information. For example, if a user enters "nut allergy," the generation AI will suggest nut-free recipes. This allows the system to suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information.

[0072] The recipe suggestion unit can use the emotion estimation function to analyze the user's emotions and suggest recipes that correspond to specific emotions. For example, if the user inputs "sad," the generation AI will suggest recipes to brighten the mood. The recipe suggestion unit also uses the emotion estimation function to suggest recipes that match the user's emotional state. For example, if the user inputs "excited," the generation AI can suggest recipes that have a relaxing effect. The recipe suggestion unit can also analyze the user's emotions and suggest recipes that correspond to specific emotions. For example, if the user inputs "depressed," the generation AI will suggest recipes to lift the mood. This makes it possible to suggest recipes that correspond to specific emotions based on the user's emotions.

[0073] The recipe suggestion unit can suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information. For example, if a user enters "gluten-free," the generation AI will suggest gluten-free recipes. The recipe suggestion unit also learns the user's dietary preferences, and the generation AI will suggest new recipes based on that data. For example, if a user has frequently searched for "vegetarian" or "vegan" in the past, the generation AI will suggest new recipes based on these preferences. The recipe suggestion unit can also suggest allergen-free recipes, taking into account the user's allergy information. For example, if a user enters "nut allergy," the generation AI will suggest nut-free recipes. This allows the system to suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information.

[0074] The recipe suggestion unit can use the emotion estimation function to analyze the user's emotions and suggest recipes that correspond to specific emotions. For example, if the user inputs "sad," the generation AI will suggest recipes to brighten the mood. The recipe suggestion unit also uses the emotion estimation function to suggest recipes that match the user's emotional state. For example, if the user inputs "excited," the generation AI can suggest recipes that have a relaxing effect. The recipe suggestion unit can also analyze the user's emotions and suggest recipes that correspond to specific emotions. For example, if the user inputs "depressed," the generation AI will suggest recipes to lift the mood. This makes it possible to suggest recipes that correspond to specific emotions based on the user's emotions.

[0075] The recipe suggestion unit can suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information. For example, if a user enters "gluten-free," the generation AI will suggest gluten-free recipes. The recipe suggestion unit also learns the user's dietary preferences, and the generation AI will suggest new recipes based on that data. For example, if a user has frequently searched for "vegetarian" or "vegan" in the past, the generation AI will suggest new recipes based on these preferences. The recipe suggestion unit can also suggest allergen-free recipes, taking into account the user's allergy information. For example, if a user enters "nut allergy," the generation AI will suggest nut-free recipes. This allows the system to suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information.

[0076] The recipe suggestion unit can use the emotion estimation function to analyze the user's emotions and suggest recipes that correspond to specific emotions. For example, if the user inputs "sad," the generation AI will suggest recipes to brighten the mood. The recipe suggestion unit also uses the emotion estimation function to suggest recipes that match the user's emotional state. For example, if the user inputs "excited," the generation AI can suggest recipes that have a relaxing effect. The recipe suggestion unit can also analyze the user's emotions and suggest recipes that correspond to specific emotions. For example, if the user inputs "depressed," the generation AI will suggest recipes to lift the mood. This makes it possible to suggest recipes that correspond to specific emotions based on the user's emotions.

[0077] The recipe suggestion unit can suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information. For example, if a user enters "gluten-free," the generation AI will suggest gluten-free recipes. The recipe suggestion unit also learns the user's dietary preferences, and the generation AI will suggest new recipes based on that data. For example, if a user has frequently searched for "vegetarian" or "vegan" in the past, the generation AI will suggest new recipes based on these preferences. The recipe suggestion unit can also suggest allergen-free recipes, taking into account the user's allergy information. For example, if a user enters "nut allergy," the generation AI will suggest nut-free recipes. This allows the system to suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information.

[0078] The recipe suggestion unit can use the emotion estimation function to analyze the user's emotions and suggest recipes that correspond to specific emotions. For example, if the user inputs "sad," the generation AI will suggest recipes to brighten the mood. The recipe suggestion unit also uses the emotion estimation function to suggest recipes that match the user's emotional state. For example, if the user inputs "excited," the generation AI can suggest recipes that have a relaxing effect. The recipe suggestion unit can also analyze the user's emotions and suggest recipes that correspond to specific emotions. For example, if the user inputs "depressed," the generation AI will suggest recipes to lift the mood. This makes it possible to suggest recipes that correspond to specific emotions based on the user's emotions.

[0079] The recipe suggestion unit can suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information. For example, if a user enters "gluten-free," the generation AI will suggest gluten-free recipes. The recipe suggestion unit also learns the user's dietary preferences, and the generation AI will suggest new recipes based on that data. For example, if a user has frequently searched for "vegetarian" or "vegan" in the past, the generation AI will suggest new recipes based on these preferences. The recipe suggestion unit can also suggest allergen-free recipes, taking into account the user's allergy information. For example, if a user enters "nut allergy," the generation AI will suggest nut-free recipes. This allows the system to suggest recipes that avoid certain ingredients, taking into account the user's dietary preferences and allergy information.

[0080] The recipe suggestion unit can use the emotion estimation function to analyze the user's emotions and suggest recipes that correspond to specific emotions. For example, if the user inputs "sad," the generation AI will suggest recipes to brighten the mood. The recipe suggestion unit also uses the emotion estimation function to suggest recipes that match the user's emotional state. For example, if the user inputs "excited," the generation AI can suggest recipes that have a relaxing effect. The recipe suggestion unit can also analyze the user's emotions and suggest recipes that correspond to specific emotions. For example, if the user inputs "depressed," the generation AI will suggest recipes to lift the mood. This makes it possible to suggest recipes that correspond to specific emotions based on the user's emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The keyword input unit allows the user to input keywords. For example, the user can input keywords such as "diet" or "high protein" via LINE. The keyword input unit also sends the keywords entered by the user to the generation AI. Step 2: In the recipe suggestion section, the generation AI proposes optimal recipes that take into account the individual's health condition based on the keywords entered by the keyword input section. For example, the generation AI proposes low-sugar and low-calorie recipes taking into account the user's health condition and dietary history. The generation AI can also propose allergen-free recipes taking into account the user's allergy information. Step 3: The recipe import unit automatically imports the recipe information suggested by the recipe suggestion unit into the diet management app. For example, the recipes suggested by the generation AI are automatically registered in the diet management app, allowing the user to easily check the diet contents of the day. The diet management app can also record the user's diet history and track changes in health status.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 keyword input section for allowing a user to input a keyword; a recipe suggestion unit that uses a generation AI to suggest an optimal recipe taking into account an individual's health condition based on the keywords input by the keyword input unit; a recipe import unit that automatically imports recipe information proposed by the recipe suggestion unit into a meal management app. A system characterized by:

2. The recipe suggestion unit Learn from the user's past search history and preferences to suggest more personalized recipes 2. The system of claim 1.

3. The recipe suggestion unit The system automatically generates cooking videos and cooking procedures related to the keywords and presents them in a visually easy-to-understand format.

2. The system of claim 1.

4. The recipe suggestion unit Analyzing the emotions associated with the keywords entered by the user and suggesting recipes that elicit positive emotions 2. The system of claim 1.

5. The recipe suggestion unit By inputting keywords, the service suggests recipes according to the season and weather, providing meals that incorporate a seasonal feel.

2. The system of claim 1.

6. The recipe suggestion unit When a user inputs a keyword, the system refers to the user's food inventory information and suggests recipes that use ingredients that the user has on hand.

2. The system of claim 1.

Images