System

Abstract

An object of a system according to an embodiment is to integrally perform a process from providing a cooking recipe to supporting building.SOLUTION: A system according to an embodiment includes a recipe providing unit, a process guide unit, a meal optimization unit, an image providing unit, and a building support unit. A recipe providing part provides a cooking recipe and food material information by using the generated AI. The process guide part guides a cooking process based on the recipe provided by the recipe providing part. The diet optimizer optimizes a nutritionally balanced diet based on the steps guided by the step guide. The image providing unit provides a completion drawing or a process of the meal optimized by the meal optimization unit as an image or a moving image. The body-building support unit supports diet, muscle training, and body-building based on the information provided by the image providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology provides recipes and ingredient information, guides users through cooking processes, optimizes nutritionally balanced meals, and supports dieting and muscle training individually, and there was a lack of a system that provides integrated support.

[0005] The system according to the embodiment aims to provide an integrated service ranging from providing cooking recipes to supporting body building. [Means for solving the problem]

[0006] The system according to the embodiment includes a recipe providing unit, a process guide unit, a meal optimization unit, an image providing unit, and a body building support unit. The recipe providing unit provides cooking recipes and ingredient information using a generative AI. The process guide unit guides the user through the cooking process based on the recipe provided by the recipe providing unit. The meal optimization unit optimizes a nutritionally balanced meal based on the process guided by the process guide unit. The image providing unit provides images and videos of the finished meal and the process optimized by the meal optimization unit. The body building support unit supports dieting, muscle training, and body building based on the information provided by the image providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an integrated range of services, from providing cooking recipes to supporting body building. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Reborn Gourmet System according to an embodiment of the present invention is a system that supports users in enriching their lives and living a healthy lifestyle through "food." This system provides cooking recipes and ingredient information, guides users through cooking processes, optimizes nutritionally balanced meals, and works with image AI to provide images and videos of finished dishes and cooking processes, supporting dieting, muscle training, and body building. In this way, the Reborn Gourmet System enables users to enrich their lives and live a healthy lifestyle through "food."

[0029] The Reborn Gourmet system according to the embodiment includes a recipe provider, a process guider, a meal optimization unit, an image provider, and a body building support unit. The recipe provider uses a generation AI to provide cooking recipes and ingredient information. For example, when a user inputs a prompt such as "Tell me an easy dinner recipe," the generation AI suggests an appropriate recipe based on the instruction. The generation AI also provides information about specific ingredients. For example, in response to a prompt such as "Tell me a recipe for a dish using tomatoes," the generation AI suggests various recipes using tomatoes. The process guider guides the user through the cooking process based on the recipe provided by the recipe provider. For example, in response to a user prompt such as "What should I do next?", the generation AI provides specific instructions such as "Next, chop the onion." This allows even beginners to cook with confidence. The meal optimization unit optimizes nutritionally balanced meals based on the process guided by the process guider. For example, the generation AI suggests a nutritionally balanced meal plan based on the user's health condition and goals. When a user inputs a prompt such as "Tell me a suitable diet plan," the generation AI proposes a low-calorie, nutritious meal plan. The image providing unit provides images and videos of the finished meal plan and its process, optimized by the meal optimization unit. For example, the generation AI works with the image AI to provide images and videos of the finished dish and each process. When a user inputs a prompt such as "Show me the finished dish," the generation AI uses the image AI to generate a finished dish plan and provides it to the user. The body building support unit supports dieting, muscle training, and body building based on the information provided by the image providing unit. For example, the generation AI provides meal plans and advice tailored to the user's diet, muscle training, and other body building goals. When a user inputs a prompt such as "Tell me a suitable diet plan for muscle training," the generation AI proposes a high-protein meal plan that supports muscle growth. In this way, the Reborn Gourmet system according to the embodiment allows users to enrich their lives and live healthy lives through "food."For example, you can rediscover the joy of cooking, improve your health through healthy eating, and receive support to achieve your weight loss and fitness goals.

[0030] The recipe providing unit can suggest personalized recipes based on the user's past eating history and preferences. In the recipe providing unit, for example, the generation AI analyzes the user's past eating history and suggests recipes based on preferences. For example, new recipes are generated based on data on the user's favorite dishes in the past. The recipe providing unit also suggests personalized recipes based on the user's favorite ingredients and cooking style entered by the user. For example, it may provide recipes tailored to a user who likes vegetarian recipes or spicy dishes. The recipe providing unit also learns the user's eating history and suggests recipes tailored to the season or event. For example, it may suggest cold dishes in the summer and hot dishes in the winter. This improves satisfaction by providing recipes tailored to the user's preferences.

[0031] The recipe providing unit can suggest recipes that take into account seasonal and regional specialties. For example, the generation AI of the recipe providing unit takes into account seasonal specialties and suggests recipes that are best suited to the season. For example, it might suggest a salad using fresh vegetables in spring, or a dish using mushrooms in autumn. The generation AI of the recipe providing unit also suggests recipes that utilize regional specialties. For example, it might provide recipes using fresh seafood to a user in Hokkaido, and recipes using local specialties to a user in Kyushu. The generation AI of the recipe providing unit also suggests recipes that utilize local specialties based on the user's location information. For example, it might suggest recipes that use ingredients that are available at the market in the area where the user lives. This makes it possible to provide users with new cooking ideas by utilizing seasonal and regional specialties.

[0032] The recipe providing unit can suggest substitutes for specific ingredients to accommodate allergies and dietary restrictions. For example, the recipe providing unit's generation AI takes into account allergy information for specific ingredients and suggests substitutes. For example, it suggests recipes using soy milk to a user with a dairy allergy. The recipe providing unit also suggests substitutes for specific ingredients to accommodate dietary restrictions. For example, it provides recipes using rice flour to a gluten-free user. The recipe providing unit also generates recipes using substitutes based on the user's allergy information and dietary restrictions. For example, it suggests recipes that do not use nuts to a user with a nut allergy. This allows the user's health to be supported by accommodating allergies and dietary restrictions.

[0033] The recipe providing unit can customize recipes based on the ingredients the user has when the user inputs them. For example, when the recipe providing unit inputs the ingredients the user has, the generation AI suggests recipes using those ingredients. For example, when the ingredients in the refrigerator are input, a recipe is generated based on that. The recipe providing unit also analyzes the ingredients the user has and customizes the optimal recipe. For example, if a particular ingredient is low, it will suggest a substitute. The recipe providing unit also customizes recipes in real time based on the ingredients the user has. For example, it adjusts the cooking method and seasonings to match the ingredients input by the user. This allows the user to cook more efficiently and reduce waste by making use of the ingredients they have.

[0034] The process guide unit can adjust the level of detail of the process according to the user's cooking skill level. For example, the generation AI analyzes the user's cooking skill level and provides a detailed process guide for beginners. For example, it provides detailed explanations on how to use a knife and basic cooking methods. The generation AI also provides a concise process guide for intermediate and advanced cooks. For example, it omits basic steps and explains only the important points. The generation AI also adjusts the level of detail of the process in real time according to the user's skill level. For example, it adds detailed explanations when the user asks questions. This allows the process guide to be provided according to the user's skill level, making it suitable for a wide range of cooks, from beginners to advanced cooks.

[0035] The process guide unit can monitor the user's progress in real time and provide advice as needed. For example, the generation AI in the process guide unit monitors the user's progress in real time and instructs them on the next step. For example, once the user has finished cutting the onions, it instructs them on what to do next. The process guide unit also provides correction advice in real time if the user makes a mistake in a step. For example, if the amount of seasoning is incorrect, it suggests an appropriate correction method. The process guide unit also monitors the user's progress and adjusts the timing as needed. For example, it suggests adding more time if the simmering time is insufficient. In this way, advice based on the user's progress can be provided, reducing cooking mistakes and allowing the process to proceed smoothly.

[0036] The process guide unit can provide a guide tailored to the user's kitchen equipment and tools. For example, the generation AI analyzes the user's kitchen equipment and provides a guide tailored to it. For example, if the user does not have an oven, the generation AI will suggest an alternative cooking method. The process guide unit also suggests the optimal cooking method based on the tools the user has. For example, if the user does not have a food processor, the generation AI will explain how to cook manually. The process guide unit also customizes the process guide based on the user's kitchen equipment and tools. For example, it will explain in detail how to cook using specific tools. This allows the user to cook more efficiently by providing a guide tailored to the user's kitchen equipment and tools.

[0037] The process guide unit can provide an efficient process guide for cooking multiple dishes simultaneously. For example, the process guide unit provides an efficient process guide for the generation AI to cook multiple dishes simultaneously. For example, it suggests processes that allow cooking to be done simultaneously, saving time. Furthermore, when a user cooks multiple dishes, the process guide unit guides the generation AI to proceed with the processes in the optimal order. For example, it instructs the user to start with the dish that takes the longest time to cook. Furthermore, the process guide unit provides a time schedule for the generation AI to cook multiple dishes simultaneously. For example, it suggests an efficient schedule taking into account the cooking time of each dish. This provides an efficient guide for cooking multiple dishes simultaneously, saving time.

[0038] The meal optimization unit can monitor the user's health data (e.g., blood sugar level, blood pressure) in real time and adjust the meal plan based on that. For example, the generation AI in the meal optimization unit monitors the user's blood sugar level in real time and adjusts the meal plan based on that. For example, if the blood sugar level is high, a low-carbohydrate meal is suggested. The meal optimization unit also proposes an optimal meal plan based on the user's blood pressure data. For example, if the blood pressure is high, a low-salt meal is provided. The meal optimization unit also analyzes the user's health data in real time and dynamically adjusts the meal plan. For example, it suggests meals based on blood sugar fluctuations after exercise. In this way, a healthy diet can be provided by adjusting the meal plan based on the user's health data.

[0039] The diet optimization unit can propose an optimal nutritional balance taking into account the user's genetic information. For example, the generation AI analyzes the user's genetic information and proposes an optimal nutritional balance based on that information. For example, it provides a meal plan that corresponds to a specific genetic risk. The generation AI also proposes an individualized nutritional balance based on the user's genetic information. For example, for a user who has genetically poor vitamin D absorption, it proposes a diet rich in vitamin D. The generation AI also proposes a meal plan that takes into account the genetic information to reduce the user's health risks. For example, for a user who is at high risk of heart disease, it provides recipes using ingredients that are good for the heart. This makes it possible to provide an optimal nutritional balance based on the user's genetic information, enabling individualized health management.

[0040] The meal optimization unit can customize meal plans based on the user's lifestyle (e.g., amount of exercise, sleep patterns). For example, the generation AI analyzes the user's amount of exercise and customizes the meal plan based on that. For example, on days when the user exercises a lot, it suggests high-calorie meals. The meal optimization unit also suggests an optimal meal plan based on the user's sleep patterns. For example, on days when the user does not get enough sleep, it provides meals that replenish energy. The meal optimization unit also analyzes the user's lifestyle data in real time and dynamically adjusts the meal plan. For example, on days when the user is under a lot of stress, it suggests meals that will help them relax. This allows for personalized health management by customizing meal plans based on the user's lifestyle.

[0041] The diet optimization unit can propose a meal plan according to a specific health goal (e.g., improving immunity, anti-aging). In the diet optimization unit, for example, the generation AI proposes a meal plan according to the user's health goal. For example, a user aiming to improve immunity may be provided with meals rich in vitamin C. In addition, the diet optimization unit proposes a meal plan rich in antioxidants to a user aiming for anti-aging. For example, recipes using blueberries and nuts may be provided. In addition, the generation AI adjusts the meal plan according to the specific health goal in real time. For example, the meal content may be changed according to the user's progress. In this way, by providing a meal plan according to the specific health goal, the user can be supported in achieving their health goal.

[0042] The image providing unit can analyze the user's cooking progress in real time and provide correction instructions as needed. For example, the image providing unit uses image AI to analyze the user's cooking progress in real time and provide correction instructions as needed. For example, if the food is not cooked enough, the image providing unit will suggest adding more cooking time. Furthermore, if the user makes a mistake in the cooking process, the image providing unit will provide correction instructions in real time. For example, if the cutting method is incorrect, the image providing unit will instruct the user on the correct way to cut the food. Furthermore, the image providing unit uses image AI to monitor the user's cooking progress and adjust the timing as needed. For example, if the simmering time is insufficient, the image providing unit will suggest adding more time. In this way, by providing correction instructions according to the user's cooking progress, cooking mistakes can be reduced and cooking can proceed smoothly.

[0043] The image providing unit can evaluate the appearance of the user's dish and suggest improvements to the presentation. For example, the image providing unit uses image AI to evaluate the appearance of the user's dish and suggest improvements to the presentation. For example, it can provide advice on adjusting the balance of the plating. The image providing unit also uses image AI to analyze the appearance of the dish made by the user and suggest visually beautiful presentations. For example, it can devise ways to improve color balance and placement. The image providing unit also uses image AI to evaluate the appearance of the user's dish in real time and suggest improvements to the presentation. For example, it can provide decoration ideas. This allows the appearance of the dish to be improved by evaluating the appearance of the user's dish and suggesting improvements to the presentation.

[0044] The image providing unit can provide the user with new cooking ideas by providing finished images of dishes from different cultures and regions. For example, the image providing unit uses image AI to provide finished images of dishes from different cultures and regions, providing the user with new cooking ideas. For example, it displays finished images of Italian or Indian dishes. The image providing unit also uses image AI to analyze dishes from different cultures that the user is interested in and provides the finished images. For example, if the user is interested in Asian cuisine, it displays finished images of Asian cuisine. The image providing unit also uses image AI to provide finished images of dishes from different regions in real time, suggesting new cooking ideas to the user. For example, it displays finished images of Mediterranean cuisine or South American cuisine. In this way, by providing finished images of dishes from different cultures and regions, it is possible to provide the user with new cooking ideas.

[0045] The image providing unit can automatically post images of the user's finished dish to social media and collect feedback from other users. For example, the image providing unit uses an image AI to automatically post images of the user's finished dish to social media and collect feedback from other users. For example, the image providing unit posts the images to Instagram or Twitter. The image providing unit also builds a system in which the image AI analyzes images of the finished dish made by the user and automatically posts them to social media. For example, the image is posted with a hashtag. The image providing unit also uses an image AI to post images of the user's finished dish to social media in real time and collect feedback from other users. For example, it collects comments and likes. This automatically posts images of the user's finished dish to social media and collects feedback from other users, thereby improving the enjoyment of cooking.

[0046] The body building support unit can analyze the user's exercise data in real time and adjust the meal plan based on that. For example, the generation AI in the body building support unit analyzes the user's exercise data in real time and adjusts the meal plan based on that. For example, it may suggest a high-protein meal after exercise. The body building support unit also uses the generation AI to suggest an optimal meal plan based on the user's exercise data. For example, it may provide a meal with a high calorie intake on days when the amount of exercise is heavy. The body building support unit also uses the generation AI to analyze the user's exercise data in real time and dynamically adjust the meal plan. For example, it may change the content of meals before and after exercise. This allows for effective support in body building by adjusting the meal plan based on the user's exercise data.

[0047] The body building support unit can propose an optimal meal plan taking into account the user's body composition data (e.g., muscle mass, fat mass). For example, the generation AI in the body building support unit analyzes the user's muscle mass and fat mass and proposes an optimal meal plan based on that. For example, it provides a high-protein meal to increase muscle mass. The body building support unit also proposes an individualized meal plan based on the user's body composition data. For example, it provides a low-calorie meal to reduce fat. The body building support unit also analyzes the user's body composition data in real time and dynamically adjusts the meal plan. For example, it changes the meal content according to changes in muscle mass. This allows for effective body building support by providing an optimal meal plan based on the user's body composition data.

[0048] The body building support unit works in conjunction with the user's exercise plan to optimize the balance between diet and exercise. For example, the generation AI in the body building support unit analyzes the user's exercise plan and adjusts the meal plan based on that. For example, it may suggest meals that replenish energy before exercise. The body building support unit also works in conjunction with the user's exercise plan, and the generation AI suggests the optimal balance between diet and exercise. For example, it may provide meals that promote muscle recovery after exercise. The body building support unit also analyzes the user's exercise plan in real time, and dynamically adjusts the meal plan. For example, it may change the meal content depending on the amount of exercise. This works in conjunction with the user's exercise plan to optimize the balance between diet and exercise, thereby supporting effective body building.

[0049] The body building support unit can visualize the user's body building progress and provide feedback toward achieving goals. In the body building support unit, for example, the generation AI visualizes the user's body building progress and provides feedback toward achieving goals. For example, it displays changes in weight and muscle mass in a graph. In addition, the body building support unit has the generation AI provide specific feedback based on the user's body building progress data. For example, it displays advice and encouraging messages toward achieving goals. In addition, the body building support unit has the generation AI analyze the user's body building progress in real time and provide feedback toward achieving goals. For example, it adjusts meal plans and exercise plans according to progress. In this way, the user's body building progress can be visualized and feedback toward achieving goals can be provided, thereby supporting effective body building.

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

[0051] The Reborn Gourmet System can further include a storage management unit that monitors the storage status of the user's ingredients. For example, the storage management unit analyzes the expiration dates of ingredients in the refrigerator and suggests recipes that prioritize ingredients with upcoming expiration dates. For example, the generation AI obtains data on ingredients in the refrigerator and provides recipes using ingredients with upcoming expiration dates. The storage management unit can also guide the user on how to store ingredients purchased. For example, it suggests the optimal storage method for specific ingredients and provides advice on maintaining freshness. The storage management unit can also suggest recipes using leftover ingredients to prevent the user from wasting ingredients. For example, the generation AI analyzes leftover ingredients in the refrigerator and provides new recipes using them. This allows the user to use ingredients efficiently and reduce waste.

[0052] The Reborn Gourmet System can further include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal. The satisfaction evaluation unit, for example, analyzes feedback entered by the user after a meal and evaluates the satisfaction with the meal. For example, the generation AI analyzes the user's feedback data and identifies recipes that provide high satisfaction. The satisfaction evaluation unit can also improve recipes based on the user's feedback. For example, if the user enters feedback such as "I wish it was a little spicier," the generation AI will suggest a recipe that meets that request. The satisfaction evaluation unit can also suggest recommended recipes to other users based on the user's satisfaction data. For example, it can display recipes with high satisfaction in a ranking format. This can improve the user's satisfaction with the meal.

[0053] The Reborn Gourmet System can further include a time zone response unit that suggests recipes according to the user's mealtimes. The time zone response unit suggests recipes suitable for breakfast, lunch, and dinner, for example. For example, the generation AI analyzes the user's mealtimes and provides recipes that are optimal for those time zones. The time zone response unit can also suggest mealtimes that suit the user's lifestyle. For example, for a user who eats late at night, it suggests light recipes that are easy to digest. The time zone response unit can also suggest recipes that take nutritional balance into consideration according to the user's mealtimes. For example, it can provide high-calorie recipes for breakfast to replenish energy. This allows the user to eat the optimal meal according to the time zone.

[0054] The Reborn Gourmet System can further include a health effect evaluation unit that evaluates the health effects of the user's meals. The health effect evaluation unit, for example, analyzes the user's health data and evaluates the health effects of the meals. For example, the generation AI analyzes the user's blood sugar and blood pressure data and evaluates the effects of the meals. The health effect evaluation unit can also suggest meal plans based on the user's health goals. For example, a user aiming to improve their immune system could be provided with meals rich in vitamin C. The health effect evaluation unit can also evaluate the effects of meals in real time based on the user's health data. For example, it could suggest meals based on blood sugar fluctuations after exercise. This allows the user to experience the health effects of the meals.

[0055] The Reborn Gourmet System can further include a cost management unit that manages the user's meal costs. The cost management unit, for example, analyzes the costs of ingredients purchased by the user and proposes meal plans within a budget. For example, the generative AI analyzes the user's budget data and provides cost-effective recipes. The cost management unit can also propose recipes using leftover ingredients to prevent the user from wasting ingredients. For example, it can analyze leftover ingredients in the refrigerator and provide new recipes using them. The cost management unit can also manage the user's meal costs in real time and propose meal plans within a budget. For example, it can provide recipes that utilize sale information. This allows the user to manage meal costs and reduce waste.

[0056] The Reborn Gourmet System can further include an environmental impact assessment unit that evaluates the environmental impact of a user's meals. The environmental impact assessment unit, for example, analyzes the environmental impact of ingredients used by the user and suggests environmentally friendly recipes. For example, the generation AI analyzes the production process and transportation distance of ingredients and suggests recipes using ingredients with low environmental impact. The environmental impact assessment unit can also suggest recipes using leftover ingredients to prevent users from wasting food. For example, it can analyze leftover ingredients in the refrigerator and suggest new recipes using them. The environmental impact assessment unit can also evaluate the environmental impact of a user's meals in real time and suggest environmentally friendly meal plans. For example, it can suggest recipes using local ingredients. This allows users to eat environmentally conscious meals.

[0057] The Reborn Gourmet System can further include a nutritional balance evaluation unit that evaluates the nutritional balance of a user's meals. The nutritional balance evaluation unit, for example, analyzes the user's dietary content and evaluates the nutritional balance. For example, the generation AI analyzes the user's dietary data and identifies nutrient deficiencies and excesses. The nutritional balance evaluation unit can also propose a nutritionally balanced meal plan based on the user's health goals. For example, it can provide low-calorie, nutritious meals to a user on a diet. The nutritional balance evaluation unit can also analyze the user's dietary content in real time and evaluate the nutritional balance. For example, if a specific nutrient is lacking, it can suggest a recipe using ingredients that supplement that nutrient. This allows the user to eat a nutritionally balanced meal.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The recipe provider uses the generative AI to provide cooking recipes and ingredient information. For example, if a user inputs a prompt such as "Tell me an easy dinner recipe," the system will suggest an appropriate recipe based on that instruction. It also provides information about specific ingredients. For example, in response to a prompt such as "Tell me a recipe for a dish using tomatoes," the generative AI will suggest various recipes using tomatoes. Step 2: The process guide unit guides the user through the cooking process based on the recipe provided by the recipe provider. For example, in response to a user prompt such as "What should I do next?", the process guide unit provides specific instructions such as "Next, chop the onion." This allows even beginners to cook with confidence. Step 3: The meal optimization unit optimizes a nutritionally balanced diet based on the process guided by the process guide unit. For example, it proposes a nutritionally balanced meal plan based on the user's health condition and goals. When the user inputs a prompt such as "Tell me about suitable meals for dieting," the generation AI proposes a low-calorie, nutritious meal plan. Step 4: The image provision unit provides images and videos of the finished meal and the process steps optimized by the meal optimization unit. For example, the generation AI works with the image AI to provide images and videos of the finished dish and each process step. When the user inputs a prompt such as "Show me a finished image of this dish," the generation AI uses the image AI to generate an image of the finished dish and provides it to the user. Step 5: The body building support unit supports dieting, muscle training, and body building based on the information provided by the image provider. For example, the generation AI provides meal plans and advice based on the user's diet, muscle training, and other body building goals. When the user inputs a prompt such as "Tell me a meal plan that's suitable for muscle training," the generation AI suggests a meal plan that's high in protein and supports muscle growth.

[0060] (Example 2) The Reborn Gourmet System according to an embodiment of the present invention is a system that supports users in enriching their lives and living a healthy lifestyle through "food." This system provides cooking recipes and ingredient information, guides users through cooking processes, optimizes nutritionally balanced meals, and works with image AI to provide images and videos of finished dishes and cooking processes, supporting dieting, muscle training, and body building. In this way, the Reborn Gourmet System enables users to enrich their lives and live a healthy lifestyle through "food."

[0061] The Reborn Gourmet system according to the embodiment includes a recipe provider, a process guider, a meal optimization unit, an image provider, and a body building support unit. The recipe provider uses a generation AI to provide cooking recipes and ingredient information. For example, when a user inputs a prompt such as "Tell me an easy dinner recipe," the generation AI suggests an appropriate recipe based on the instruction. The generation AI also provides information about specific ingredients. For example, in response to a prompt such as "Tell me a recipe for a dish using tomatoes," the generation AI suggests various recipes using tomatoes. The process guider guides the user through the cooking process based on the recipe provided by the recipe provider. For example, in response to a user prompt such as "What should I do next?", the generation AI provides specific instructions such as "Next, chop the onion." This allows even beginners to cook with confidence. The meal optimization unit optimizes nutritionally balanced meals based on the process guided by the process guider. For example, the generation AI suggests a nutritionally balanced meal plan based on the user's health condition and goals. When a user inputs a prompt such as "Tell me a suitable diet plan," the generation AI proposes a low-calorie, nutritious meal plan. The image providing unit provides images and videos of the finished meal plan and its process, optimized by the meal optimization unit. For example, the generation AI works with the image AI to provide images and videos of the finished dish and each process. When a user inputs a prompt such as "Show me the finished dish," the generation AI uses the image AI to generate a finished dish plan and provides it to the user. The body building support unit supports dieting, muscle training, and body building based on the information provided by the image providing unit. For example, the generation AI provides meal plans and advice tailored to the user's diet, muscle training, and other body building goals. When a user inputs a prompt such as "Tell me a suitable diet plan for muscle training," the generation AI proposes a high-protein meal plan that supports muscle growth. In this way, the Reborn Gourmet system according to the embodiment allows users to enrich their lives and live healthy lives through "food."For example, you can rediscover the joy of cooking, improve your health through healthy eating, and receive support to achieve your weight loss and fitness goals.

[0062] The recipe providing unit can suggest personalized recipes based on the user's past eating history and preferences. In the recipe providing unit, for example, the generation AI analyzes the user's past eating history and suggests recipes based on preferences. For example, new recipes are generated based on data on the user's favorite dishes in the past. The recipe providing unit also suggests personalized recipes based on the user's favorite ingredients and cooking style entered by the user. For example, it may provide recipes tailored to a user who likes vegetarian recipes or spicy dishes. The recipe providing unit also learns the user's eating history and suggests recipes tailored to the season or event. For example, it may suggest cold dishes in the summer and hot dishes in the winter. This improves satisfaction by providing recipes tailored to the user's preferences.

[0063] The recipe providing unit can suggest recipes that take into account seasonal and regional specialties. For example, the generation AI of the recipe providing unit takes into account seasonal specialties and suggests recipes that are best suited to the season. For example, it might suggest a salad using fresh vegetables in spring, or a dish using mushrooms in autumn. The generation AI of the recipe providing unit also suggests recipes that utilize regional specialties. For example, it might provide recipes using fresh seafood to a user in Hokkaido, and recipes using local specialties to a user in Kyushu. The generation AI of the recipe providing unit also suggests recipes that utilize local specialties based on the user's location information. For example, it might suggest recipes that use ingredients that are available at the market in the area where the user lives. This makes it possible to provide users with new cooking ideas by utilizing seasonal and regional specialties.

[0064] The recipe providing unit can use the emotion estimation function to suggest recipes that match the user's current mood. For example, the recipe providing unit uses the emotion estimation function to analyze the user's current mood and suggest recipes that match that mood. For example, it suggests relaxing dishes to a user who is feeling stressed. The recipe providing unit also generates recipes that match the user's mood based on the user's emotion data. For example, it suggests dishes that will replenish energy when the user is feeling down, and dishes suitable for parties when the user is in a happy mood. The recipe providing unit also uses the emotion estimation function to suggest recipes that match the user's mood in real time. For example, it suggests dishes that are easy to make when the user is tired. In this way, by providing recipes that match the user's mood, meal satisfaction is improved.

[0065] The recipe providing unit can suggest substitutes for specific ingredients to accommodate allergies and dietary restrictions. For example, the recipe providing unit's generation AI takes into account allergy information for specific ingredients and suggests substitutes. For example, it suggests recipes using soy milk to a user with a dairy allergy. The recipe providing unit also suggests substitutes for specific ingredients to accommodate dietary restrictions. For example, it provides recipes using rice flour to a gluten-free user. The recipe providing unit also generates recipes using substitutes based on the user's allergy information and dietary restrictions. For example, it suggests recipes that do not use nuts to a user with a nut allergy. This allows the user's health to be supported by accommodating allergies and dietary restrictions.

[0066] The recipe providing unit can customize recipes based on the ingredients the user has when the user inputs them. For example, when the recipe providing unit inputs the ingredients the user has, the generation AI suggests recipes using those ingredients. For example, when the ingredients in the refrigerator are input, a recipe is generated based on that. The recipe providing unit also analyzes the ingredients the user has and customizes the optimal recipe. For example, if a particular ingredient is low, it will suggest a substitute. The recipe providing unit also customizes recipes in real time based on the ingredients the user has. For example, it adjusts the cooking method and seasonings to match the ingredients input by the user. This allows the user to cook more efficiently and reduce waste by making use of the ingredients they have.

[0067] The recipe providing unit can use the emotion estimation function to suggest recommended recipes when the user feels a specific emotion. For example, the recipe providing unit uses the emotion estimation function to suggest recommended recipes when the user feels a specific emotion. For example, when the user is sad, it suggests dishes that will lift their spirits. The recipe providing unit also generates recipes that match the user's specific emotion based on the user's emotion data. For example, when the user feels a strong emotion of joy, it suggests celebratory dishes. The recipe providing unit also uses the emotion estimation function to suggest recipes in real time when the user feels a specific emotion. For example, when the user is feeling stressed, it suggests dishes that will help them relax. In this way, by providing recipes that match the user's emotions, meal satisfaction is improved.

[0068] The process guide unit can adjust the level of detail of the process according to the user's cooking skill level. For example, the generation AI analyzes the user's cooking skill level and provides a detailed process guide for beginners. For example, it provides detailed explanations on how to use a knife and basic cooking methods. The generation AI also provides a concise process guide for intermediate and advanced cooks. For example, it omits basic steps and explains only the important points. The generation AI also adjusts the level of detail of the process in real time according to the user's skill level. For example, it adds detailed explanations when the user asks questions. This allows the process guide to be provided according to the user's skill level, making it suitable for a wide range of cooks, from beginners to advanced cooks.

[0069] The process guide unit can monitor the user's progress in real time and provide advice as needed. For example, the generation AI in the process guide unit monitors the user's progress in real time and instructs them on the next step. For example, once the user has finished cutting the onions, it instructs them on what to do next. The process guide unit also provides correction advice in real time if the user makes a mistake in a step. For example, if the amount of seasoning is incorrect, it suggests an appropriate correction method. The process guide unit also monitors the user's progress and adjusts the timing as needed. For example, it suggests adding more time if the simmering time is insufficient. In this way, advice based on the user's progress can be provided, reducing cooking mistakes and allowing the process to proceed smoothly.

[0070] The process guide unit can use the emotion estimation function to provide relaxation advice to the user when he or she feels stressed. The process guide unit, for example, uses the emotion estimation function to provide relaxation advice to the user when he or she feels stressed. For example, it displays a message encouraging the user to take a deep breath. The process guide unit also generates advice to reduce stress based on the user's emotion data. For example, it suggests simple stretching or relaxing music. The process guide unit also uses the emotion estimation function to provide relaxation advice in real time when the user feels stressed. For example, it encourages the user to take a short break. In this way, the enjoyment of cooking can be maintained by providing relaxation advice to the user when he or she feels stressed.

[0071] The process guide unit can provide a guide tailored to the user's kitchen equipment and tools. For example, the generation AI analyzes the user's kitchen equipment and provides a guide tailored to it. For example, if the user does not have an oven, the generation AI will suggest an alternative cooking method. The process guide unit also suggests the optimal cooking method based on the tools the user has. For example, if the user does not have a food processor, the generation AI will explain how to cook manually. The process guide unit also customizes the process guide based on the user's kitchen equipment and tools. For example, it will explain in detail how to cook using specific tools. This allows the user to cook more efficiently by providing a guide tailored to the user's kitchen equipment and tools.

[0072] The process guide unit can provide an efficient process guide for cooking multiple dishes simultaneously. For example, the process guide unit provides an efficient process guide for the generation AI to cook multiple dishes simultaneously. For example, it suggests processes that allow cooking to be done simultaneously, saving time. Furthermore, when a user cooks multiple dishes, the process guide unit guides the generation AI to proceed with the processes in the optimal order. For example, it instructs the user to start with the dish that takes the longest time to cook. Furthermore, the process guide unit provides a time schedule for the generation AI to cook multiple dishes simultaneously. For example, it suggests an efficient schedule taking into account the cooking time of each dish. This provides an efficient guide for cooking multiple dishes simultaneously, saving time.

[0073] The process guide unit can incorporate entertainment elements into the guide using the emotion estimation function to help the user enjoy cooking. The process guide unit, for example, uses the emotion estimation function to incorporate entertainment elements into the guide to help the user enjoy cooking. For example, music can be played while cooking. The process guide unit also suggests entertainment elements to increase enjoyment based on the user's emotion data. For example, cooking-related quizzes and games can be provided. The process guide unit also uses the emotion estimation function to add entertainment elements in real time to help the user enjoy cooking. For example, cooking trivia can be provided. This increases the enjoyment of cooking by providing entertainment elements that help the user enjoy cooking.

[0074] The meal optimization unit can monitor the user's health data (e.g., blood sugar level, blood pressure) in real time and adjust the meal plan based on that. For example, the generation AI in the meal optimization unit monitors the user's blood sugar level in real time and adjusts the meal plan based on that. For example, if the blood sugar level is high, a low-carbohydrate meal is suggested. The meal optimization unit also proposes an optimal meal plan based on the user's blood pressure data. For example, if the blood pressure is high, a low-salt meal is provided. The meal optimization unit also analyzes the user's health data in real time and dynamically adjusts the meal plan. For example, it suggests meals based on blood sugar fluctuations after exercise. In this way, a healthy diet can be provided by adjusting the meal plan based on the user's health data.

[0075] The diet optimization unit can propose an optimal nutritional balance taking into account the user's genetic information. For example, the generation AI analyzes the user's genetic information and proposes an optimal nutritional balance based on that information. For example, it provides a meal plan that corresponds to a specific genetic risk. The generation AI also proposes an individualized nutritional balance based on the user's genetic information. For example, for a user who has genetically poor vitamin D absorption, it proposes a diet rich in vitamin D. The generation AI also proposes a meal plan that takes into account the genetic information to reduce the user's health risks. For example, for a user who is at high risk of heart disease, it provides recipes using ingredients that are good for the heart. This makes it possible to provide an optimal nutritional balance based on the user's genetic information, enabling individualized health management.

[0076] The meal optimization unit can use the emotion estimation function to propose a meal plan that will make the user feel positive about meals. For example, the meal optimization unit uses the emotion estimation function to propose a meal plan that will make the user feel positive about meals. For example, it provides recipes that use many of the user's favorite ingredients. The meal optimization unit also generates a meal plan that elicits positive emotions based on the user's emotion data. For example, it suggests colorful dishes that will make the user feel happy. The meal optimization unit also uses the emotion estimation function to adjust the meal plan in real time to make the user feel positive about meals. For example, it changes recipes to match the user's mood. In this way, meal satisfaction is improved by providing a meal plan that will make the user feel positive about meals.

[0077] The meal optimization unit can customize meal plans based on the user's lifestyle (e.g., amount of exercise, sleep patterns). For example, the generation AI analyzes the user's amount of exercise and customizes the meal plan based on that. For example, on days when the user exercises a lot, it suggests high-calorie meals. The meal optimization unit also suggests an optimal meal plan based on the user's sleep patterns. For example, on days when the user does not get enough sleep, it provides meals that replenish energy. The meal optimization unit also analyzes the user's lifestyle data in real time and dynamically adjusts the meal plan. For example, on days when the user is under a lot of stress, it suggests meals that will help them relax. This allows for personalized health management by customizing meal plans based on the user's lifestyle.

[0078] The diet optimization unit can propose a meal plan according to a specific health goal (e.g., improving immunity, anti-aging). In the diet optimization unit, for example, the generation AI proposes a meal plan according to the user's health goal. For example, a user aiming to improve immunity may be provided with meals rich in vitamin C. In addition, the diet optimization unit proposes a meal plan rich in antioxidants to a user aiming for anti-aging. For example, recipes using blueberries and nuts may be provided. In addition, the generation AI adjusts the meal plan according to the specific health goal in real time. For example, the meal content may be changed according to the user's progress. In this way, by providing a meal plan according to the specific health goal, the user can be supported in achieving their health goal.

[0079] The meal optimization unit can use the emotion estimation function to suggest ideas (e.g., meal presentation) to help the user enjoy their meal. For example, the meal optimization unit uses the emotion estimation function to suggest ideas to help the user enjoy their meal. For example, it may improve the presentation of the meal to make it look better. The meal optimization unit also generates ideas for enjoying meals based on the user's emotion data. For example, it may improve the presentation of the meal to make it more visually enjoyable. The meal optimization unit also uses the emotion estimation function to suggest ideas in real time to help the user enjoy their meal. For example, it may set a theme for the meal to increase enjoyment. In this way, by providing ideas to help the user enjoy their meal, meal satisfaction is improved.

[0080] The image providing unit can analyze the user's cooking progress in real time and provide correction instructions as needed. For example, the image providing unit uses image AI to analyze the user's cooking progress in real time and provide correction instructions as needed. For example, if the food is not cooked enough, the image providing unit will suggest adding more cooking time. Furthermore, if the user makes a mistake in the cooking process, the image providing unit will provide correction instructions in real time. For example, if the cutting method is incorrect, the image providing unit will instruct the user on the correct way to cut the food. Furthermore, the image providing unit uses image AI to monitor the user's cooking progress and adjust the timing as needed. For example, if the simmering time is insufficient, the image providing unit will suggest adding more time. In this way, by providing correction instructions according to the user's cooking progress, cooking mistakes can be reduced and cooking can proceed smoothly.

[0081] The image providing unit can evaluate the appearance of the user's dish and suggest improvements to the presentation. For example, the image providing unit uses image AI to evaluate the appearance of the user's dish and suggest improvements to the presentation. For example, it can provide advice on adjusting the balance of the plating. The image providing unit also uses image AI to analyze the appearance of the dish made by the user and suggest visually beautiful presentations. For example, it can devise ways to improve color balance and placement. The image providing unit also uses image AI to evaluate the appearance of the user's dish in real time and suggest improvements to the presentation. For example, it can provide decoration ideas. This allows the appearance of the dish to be improved by evaluating the appearance of the user's dish and suggesting improvements to the presentation.

[0082] The image providing unit can use the emotion estimation function to provide feedback that makes the user satisfied with the appearance of the dish. For example, the image providing unit uses the emotion estimation function to provide feedback that makes the user satisfied with the appearance of the dish. For example, it displays positive comments. The image providing unit also generates feedback on the appearance of the dish based on the user's emotion data. For example, it provides comments praising the good-looking parts. The image providing unit also uses the emotion estimation function to provide feedback in real time that makes the user satisfied with the appearance of the dish. For example, it adds positive comments while pointing out areas for improvement. This provides feedback that makes the user satisfied with the appearance of the dish, thereby increasing the enjoyment of cooking.

[0083] The image providing unit can provide the user with new cooking ideas by providing finished images of dishes from different cultures and regions. For example, the image providing unit uses image AI to provide finished images of dishes from different cultures and regions, providing the user with new cooking ideas. For example, it displays finished images of Italian or Indian dishes. The image providing unit also uses image AI to analyze dishes from different cultures that the user is interested in and provides the finished images. For example, if the user is interested in Asian cuisine, it displays finished images of Asian cuisine. The image providing unit also uses image AI to provide finished images of dishes from different regions in real time, suggesting new cooking ideas to the user. For example, it displays finished images of Mediterranean cuisine or South American cuisine. In this way, by providing finished images of dishes from different cultures and regions, it is possible to provide the user with new cooking ideas.

[0084] The image providing unit can automatically post images of the user's finished dish to social media and collect feedback from other users. For example, the image providing unit uses an image AI to automatically post images of the user's finished dish to social media and collect feedback from other users. For example, the image providing unit posts the images to Instagram or Twitter. The image providing unit also builds a system in which the image AI analyzes images of the finished dish made by the user and automatically posts them to social media. For example, the image is posted with a hashtag. The image providing unit also uses an image AI to post images of the user's finished dish to social media in real time and collect feedback from other users. For example, it collects comments and likes. This automatically posts images of the user's finished dish to social media and collects feedback from other users, thereby improving the enjoyment of cooking.

[0085] The image providing unit can use the emotion estimation function to provide advice that helps the user to feel positive about the appearance of the food. For example, the image providing unit uses the emotion estimation function to provide advice that helps the user to feel positive about the appearance of the food. For example, it suggests creative ways to arrange the food. The image providing unit also generates positive advice about the appearance of the food based on the user's emotion data. For example, it provides ideas for improving color balance. The image providing unit also uses the emotion estimation function to provide advice in real time that helps the user to feel positive about the appearance of the food. For example, it suggests decoration ideas. In this way, by providing advice that helps the user to feel positive about the appearance of the food, the enjoyment of cooking is improved.

[0086] The body building support unit can analyze the user's exercise data in real time and adjust the meal plan based on that. For example, the generation AI in the body building support unit analyzes the user's exercise data in real time and adjusts the meal plan based on that. For example, it may suggest a high-protein meal after exercise. The body building support unit also uses the generation AI to suggest an optimal meal plan based on the user's exercise data. For example, it may provide a meal with a high calorie intake on days when the amount of exercise is heavy. The body building support unit also uses the generation AI to analyze the user's exercise data in real time and dynamically adjust the meal plan. For example, it may change the content of meals before and after exercise. This allows for effective support in body building by adjusting the meal plan based on the user's exercise data.

[0087] The body building support unit can propose an optimal meal plan taking into account the user's body composition data (e.g., muscle mass, fat mass). For example, the generation AI in the body building support unit analyzes the user's muscle mass and fat mass and proposes an optimal meal plan based on that. For example, it provides a high-protein meal to increase muscle mass. The body building support unit also proposes an individualized meal plan based on the user's body composition data. For example, it provides a low-calorie meal to reduce fat. The body building support unit also analyzes the user's body composition data in real time and dynamically adjusts the meal plan. For example, it changes the meal content according to changes in muscle mass. This allows for effective body building support by providing an optimal meal plan based on the user's body composition data.

[0088] The body building support unit can use the emotion estimation function to propose a meal plan that will help the user maintain motivation for body building. The body building support unit, for example, uses the emotion estimation function to propose a meal plan that will help the user maintain motivation for body building. For example, it provides recipes using ingredients that elicit positive emotions. The body building support unit also generates a meal plan to increase motivation based on the user's emotion data. For example, it proposes recipes that use many of the user's favorite ingredients. The body building support unit also uses the emotion estimation function to adjust the meal plan in real time so that the user can maintain motivation for body building. For example, it changes recipes according to the user's mood. This allows for effective body building support by providing a meal plan that will help the user maintain motivation for body building.

[0089] The body building support unit works in conjunction with the user's exercise plan to optimize the balance between diet and exercise. For example, the generation AI in the body building support unit analyzes the user's exercise plan and adjusts the meal plan based on that. For example, it may suggest meals that replenish energy before exercise. The body building support unit also works in conjunction with the user's exercise plan, and the generation AI suggests the optimal balance between diet and exercise. For example, it may provide meals that promote muscle recovery after exercise. The body building support unit also analyzes the user's exercise plan in real time, and dynamically adjusts the meal plan. For example, it may change the meal content depending on the amount of exercise. This works in conjunction with the user's exercise plan to optimize the balance between diet and exercise, thereby supporting effective body building.

[0090] The body building support unit can visualize the user's body building progress and provide feedback toward achieving goals. In the body building support unit, for example, the generation AI visualizes the user's body building progress and provides feedback toward achieving goals. For example, it displays changes in weight and muscle mass in a graph. In addition, the body building support unit has the generation AI provide specific feedback based on the user's body building progress data. For example, it displays advice and encouraging messages toward achieving goals. In addition, the body building support unit has the generation AI analyze the user's body building progress in real time and provide feedback toward achieving goals. For example, it adjusts meal plans and exercise plans according to progress. In this way, the user's body building progress can be visualized and feedback toward achieving goals can be provided, thereby supporting effective body building.

[0091] The body building support unit can use the emotion estimation function to incorporate entertainment elements into the meal plan that will allow the user to enjoy the body building process. The body building support unit, for example, uses the emotion estimation function to incorporate entertainment elements into the meal plan that will allow the user to enjoy the body building process. For example, it suggests music or videos that can be enjoyed while eating. The body building support unit also suggests entertainment elements to help the user enjoy the body building process based on the user's emotion data. For example, it provides quizzes or games related to meals. The body building support unit also uses the emotion estimation function to add entertainment elements in real time to help the user enjoy the body building process. For example, it sets a meal theme to increase the enjoyment. This allows for effective body building support by providing entertainment elements that will allow the user to enjoy the body building process.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The Reborn Gourmet System can further include a storage management unit that monitors the storage status of the user's ingredients. For example, the storage management unit analyzes the expiration dates of ingredients in the refrigerator and suggests recipes that prioritize ingredients with upcoming expiration dates. For example, the generation AI obtains data on ingredients in the refrigerator and provides recipes using ingredients with upcoming expiration dates. The storage management unit can also guide the user on how to store ingredients purchased. For example, it suggests the optimal storage method for specific ingredients and provides advice on maintaining freshness. The storage management unit can also suggest recipes using leftover ingredients to prevent the user from wasting ingredients. For example, the generation AI analyzes leftover ingredients in the refrigerator and provides new recipes using them. This allows the user to use ingredients efficiently and reduce waste.

[0094] The Reborn Gourmet System can further include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal. The satisfaction evaluation unit, for example, analyzes feedback entered by the user after a meal and evaluates the satisfaction with the meal. For example, the generation AI analyzes the user's feedback data and identifies recipes that provide high satisfaction. The satisfaction evaluation unit can also improve recipes based on the user's feedback. For example, if the user enters feedback such as "I wish it was a little spicier," the generation AI will suggest a recipe that meets that request. The satisfaction evaluation unit can also suggest recommended recipes to other users based on the user's satisfaction data. For example, it can display recipes with high satisfaction in a ranking format. This can improve the user's satisfaction with the meal.

[0095] The Reborn Gourmet System can further include a time zone response unit that suggests recipes according to the user's mealtimes. The time zone response unit suggests recipes suitable for breakfast, lunch, and dinner, for example. For example, the generation AI analyzes the user's mealtimes and provides recipes that are optimal for those time zones. The time zone response unit can also suggest mealtimes that suit the user's lifestyle. For example, for a user who eats late at night, it suggests light recipes that are easy to digest. The time zone response unit can also suggest recipes that take nutritional balance into consideration according to the user's mealtimes. For example, it can provide high-calorie recipes for breakfast to replenish energy. This allows the user to eat the optimal meal according to the time zone.

[0096] The Reborn Gourmet System can further include a presentation support unit to improve the presentation of a user's meal. The presentation support unit, for example, provides ideas for plating and decorating food. For example, the generative AI analyzes the appearance of the user's dish and suggests visually beautiful presentations. The presentation support unit can also analyze photos of the dish the user has made and point out areas for improvement. For example, it can provide advice on improving color balance and placement. The presentation support unit can also provide positive feedback to ensure the user is satisfied with the appearance of the dish. For example, it can display comments praising the good-looking parts. This can increase the user's satisfaction with the appearance of the dish.

[0097] The Reborn Gourmet System can further include an entertainment provider to enhance the user's enjoyment of eating. The entertainment provider, for example, suggests music or videos that can be enjoyed while cooking. For example, a generation AI plays music that matches the user's preferences. The entertainment provider can also provide cooking-related quizzes and games. For example, it can present cooking trivia in the form of a quiz. The entertainment provider can also add entertainment elements in real time to help the user enjoy cooking. For example, it can display fun messages as the cooking progresses. This allows the user to enjoy the cooking process.

[0098] The Reborn Gourmet System can further include a health effect evaluation unit that evaluates the health effects of the user's meals. The health effect evaluation unit, for example, analyzes the user's health data and evaluates the health effects of the meals. For example, the generation AI analyzes the user's blood sugar and blood pressure data and evaluates the effects of the meals. The health effect evaluation unit can also suggest meal plans based on the user's health goals. For example, a user aiming to improve their immune system could be provided with meals rich in vitamin C. The health effect evaluation unit can also evaluate the effects of meals in real time based on the user's health data. For example, it could suggest meals based on blood sugar fluctuations after exercise. This allows the user to experience the health effects of the meals.

[0099] The Reborn Gourmet System can further include a cost management unit that manages the user's meal costs. The cost management unit, for example, analyzes the costs of ingredients purchased by the user and proposes meal plans within a budget. For example, the generative AI analyzes the user's budget data and provides cost-effective recipes. The cost management unit can also propose recipes using leftover ingredients to prevent the user from wasting ingredients. For example, it can analyze leftover ingredients in the refrigerator and provide new recipes using them. The cost management unit can also manage the user's meal costs in real time and propose meal plans within a budget. For example, it can provide recipes that utilize sale information. This allows the user to manage meal costs and reduce waste.

[0100] The Reborn Gourmet System can further include an environmental impact assessment unit that evaluates the environmental impact of a user's meals. The environmental impact assessment unit, for example, analyzes the environmental impact of ingredients used by the user and suggests environmentally friendly recipes. For example, the generation AI analyzes the production process and transportation distance of ingredients and suggests recipes using ingredients with low environmental impact. The environmental impact assessment unit can also suggest recipes using leftover ingredients to prevent users from wasting food. For example, it can analyze leftover ingredients in the refrigerator and suggest new recipes using them. The environmental impact assessment unit can also evaluate the environmental impact of a user's meals in real time and suggest environmentally friendly meal plans. For example, it can suggest recipes using local ingredients. This allows users to eat environmentally conscious meals.

[0101] The Reborn Gourmet System can further include a nutritional balance evaluation unit that evaluates the nutritional balance of a user's meals. The nutritional balance evaluation unit, for example, analyzes the user's dietary content and evaluates the nutritional balance. For example, the generation AI analyzes the user's dietary data and identifies nutrient deficiencies and excesses. The nutritional balance evaluation unit can also propose a nutritionally balanced meal plan based on the user's health goals. For example, it can provide low-calorie, nutritious meals to a user on a diet. The nutritional balance evaluation unit can also analyze the user's dietary content in real time and evaluate the nutritional balance. For example, if a specific nutrient is lacking, it can suggest a recipe using ingredients that supplement that nutrient. This allows the user to eat a nutritionally balanced meal.

[0102] The Reborn Gourmet System can further include an emotion evaluation unit that evaluates the emotional satisfaction of a user's meal. The emotion evaluation unit, for example, analyzes the emotions felt by the user after eating and evaluates the emotional satisfaction. For example, the generation AI analyzes the user's emotion data and identifies the emotional satisfaction of the meal. The emotion evaluation unit can also improve recipes based on the user's emotion data. For example, if the user inputs feedback such as "I want to make the meal more enjoyable," the generation AI will suggest a recipe that meets that request. The emotion evaluation unit can also suggest recommended recipes to other users based on the user's emotion data. For example, recipes with high emotional satisfaction can be displayed in a ranking format. This can improve the user's emotional satisfaction of the meal.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The recipe provider uses the generative AI to provide cooking recipes and ingredient information. For example, if a user inputs a prompt such as "Tell me an easy dinner recipe," the system will suggest an appropriate recipe based on that instruction. It also provides information about specific ingredients. For example, in response to a prompt such as "Tell me a recipe for a dish using tomatoes," the generative AI will suggest various recipes using tomatoes. Step 2: The process guide unit guides the user through the cooking process based on the recipe provided by the recipe provider. For example, in response to a user prompt such as "What should I do next?", the process guide unit provides specific instructions such as "Next, chop the onion." This allows even beginners to cook with confidence. Step 3: The meal optimization unit optimizes a nutritionally balanced diet based on the process guided by the process guide unit. For example, it proposes a nutritionally balanced meal plan based on the user's health condition and goals. When the user inputs a prompt such as "Tell me about suitable meals for dieting," the generation AI proposes a low-calorie, nutritious meal plan. Step 4: The image provision unit provides images and videos of the finished meal and the process steps optimized by the meal optimization unit. For example, the generation AI works with the image AI to provide images and videos of the finished dish and each process step. When the user inputs a prompt such as "Show me a finished image of this dish," the generation AI uses the image AI to generate an image of the finished dish and provides it to the user. Step 5: The body building support unit supports dieting, muscle training, and body building based on the information provided by the image provider. For example, the generation AI provides meal plans and advice based on the user's diet, muscle training, and other body building goals. When the user inputs a prompt such as "Tell me a meal plan that's suitable for muscle training," the generation AI suggests a meal plan that's high in protein and supports muscle growth.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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 recipe provider that uses generative AI to provide cooking recipes and ingredient information; a process guide unit that guides the user through a cooking process based on the recipe provided by the recipe providing unit; a meal optimization unit that optimizes a nutritionally balanced meal based on the process guided by the process guide unit; an image providing unit that provides a completed diagram and process of the meal optimized by the meal optimization unit in the form of images and videos; a body building support unit that supports dieting, muscle training, and body building based on the information provided by the image providing unit. A system characterized by:

2. The recipe providing unit Suggesting personalized recipes based on the user's eating history and preferences 2. The system of claim 1.

3. The recipe providing unit Propose recipes that take into account seasonal and local specialties 2. The system of claim 1.

4. The recipe providing unit Suggest recipes that match the user's current mood 2. The system of claim 1.

5. The recipe providing unit Suggest substitutions for specific ingredients to accommodate allergies and dietary restrictions 2. The system of claim 1.

6. The recipe providing unit Users can input ingredients they have and customize the recipe based on those.

2. The system of claim 1.

Images