system

Abstract

The system according to this embodiment aims to efficiently create menus tailored to individual dietary preferences and health conditions, and to provide instructions on how to prepare them. [Solution] The system according to the embodiment comprises a reception unit, a creation unit, and a generation unit. The reception unit receives input on food preferences, nutritional balance, and health information. The creation unit analyzes the information received by the reception unit and creates a menu for one month. The generation unit generates videos of how to make each dish based on the menu created by the creation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a menu according to individual dietary preferences and health conditions has not been sufficiently created efficiently and the method of making it has not been provided, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently create a menu according to individual dietary preferences and health conditions and provide the method of making it.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a creation unit, and a generation unit. The reception unit receives input regarding food preferences, nutritional balance, and health information. The creation unit analyzes the information received by the reception unit and creates a menu for one month. The generation unit generates videos of how to prepare each dish based on the menu created by the creation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently create menus tailored to individual dietary preferences and health conditions, and provide instructions on how to prepare them. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The dietary improvement system according to an embodiment of the present invention is a system for improving dietary habits using AI. This dietary improvement system allows users to input their food preferences, nutritional balance, and desired health aspects (such as high blood pressure, diabetes, or weight loss). Next, the AI ​​analyzes this information and creates a one-month meal plan. Furthermore, based on the created meal plan, it generates videos demonstrating how to prepare each dish. This mechanism allows users to easily achieve a healthy diet. For example, a user inputs their food preferences, nutritional balance, and desired health aspects. For instance, they might input information such as, "I like vegetables and want to take measures against diabetes." This information is input to the AI. Next, the AI ​​analyzes the input information and creates a one-month meal plan. The AI ​​considers the user's preferences and health needs and proposes a balanced menu. For example, it might suggest a menu with plenty of vegetables for breakfast, a low-carbohydrate menu for lunch, and a high-protein menu for dinner. Furthermore, based on the created meal plan, it generates videos demonstrating how to prepare each dish. The AI ​​analyzes the recipe for each dish and generates the video. For example, it might show how to make stir-fried vegetables in a video, making it easy for the user to cook. This system makes it easy for users to adopt a healthy diet. Users simply input their food preferences and health concerns, and receive a balanced meal plan and cooking videos. This helps improve their eating habits and achieve a healthier body. For example, if a user inputs "I like vegetables and want to manage diabetes," the AI ​​will suggest a low-carb meal plan rich in vegetables and provide cooking videos. This makes it easy for users to achieve a healthy diet. In this way, the diet improvement system automatically improves the user's eating habits and provides a healthy lifestyle.

[0029] The dietary improvement system according to this embodiment comprises a reception unit, a creation unit, and a generation unit. The reception unit receives input from the user regarding food likes and dislikes, nutritional balance, and health information. For example, the reception unit can receive input from the user regarding their preferences for specific ingredients, tastes, and textures. The reception unit can also receive input from the user regarding allergy information, medical history, and current health status. Furthermore, the reception unit can receive input from the user regarding desired health aspects (e.g., high blood pressure, diabetes, diet measures, etc.). The creation unit analyzes the information received by the reception unit and creates a menu plan for one month. For example, the creation unit proposes a balanced menu considering the user's preferences and health needs. For example, the creation unit proposes a menu with plenty of vegetables for breakfast, a low-carbohydrate menu for lunch, and a high-protein menu for dinner. The creation unit uses AI to analyze the user's input information and create an optimal menu. The generation unit generates videos of how to prepare each dish based on the menu plan created by the creation unit. For example, the generation unit analyzes the recipes for each dish and generates videos. For example, the generation unit displays a video demonstrating how to make stir-fried vegetables, making it easy for users to cook. The generation unit uses AI to analyze the recipe for each dish and generate a video. As a result, the dietary improvement system according to this embodiment can automatically improve the user's diet and provide a healthy diet.

[0030] The reception desk allows users to input information about their food preferences, nutritional balance, and health. Specifically, users can access a dedicated application or website using their smartphone or computer to input detailed preferences regarding ingredients, taste, and texture. For example, users can input specific preferences such as "I like spicy food," "I want to limit sweets," or "I like crispy textures." They can also input allergy information, medical history, and current health status. For example, by inputting information such as "I have a nut allergy," "I have had a stomach ulcer in the past," or "I am currently being treated for high blood pressure," the system can suggest menus tailored to the user's health condition. Furthermore, users can input their desired health aspects. For example, by inputting specific goals such as "I'm on a diet and want to reduce calories," "I have diabetes and would like a low-carbohydrate diet," or "I want to increase muscle mass and would like a high-protein diet," the system will provide a meal plan tailored to the user's goals. The reception desk centrally manages this information and builds a customized database for each user. This allows the reception desk to build a foundation for providing individually optimized dietary improvement plans based on detailed user information.

[0031] The creation department analyzes the information received by the reception department and creates a one-month meal plan. Specifically, the creation department uses AI to analyze data in order to propose a balanced meal plan that takes into account the user's preferences and health needs. For example, if a user inputs "I would like a menu with lots of vegetables for breakfast," "I would like a low-carb menu for lunch," and "I would like a high-protein menu for dinner," the creation department will create a meal plan that takes these preferences into account and considers the nutritional balance of each meal. Based on past data and nutritional knowledge, the AI ​​selects the most suitable ingredients and dishes for the user's preferences and health condition. For example, it might suggest a salad or smoothie with lots of vegetables for breakfast, a low-carb salad chicken or tofu dish for lunch, and a high-protein fish or chicken dish for dinner. The creation department can also propose a meal plan that incorporates seasonal ingredients and local specialties. This allows users to enjoy fresh and delicious meals. Furthermore, the creation department can improve the meal plan based on user feedback. For example, if a user provides feedback such as "I didn't like this dish" or "I'd prefer something easier to make," the menu creation team will take this into account and improve the next menu. This allows the team to provide optimal menus tailored to the user's preferences and health condition, and continuously support improvements in their eating habits.

[0032] The generation unit generates cooking videos for each dish based on the menu created by the creation unit. Specifically, the generation unit analyzes the recipe for each dish and generates videos to make cooking easy for users. Using AI, it analyzes the steps of the recipe, taking into account the necessary ingredients, cooking utensils, and cooking time, to create easy-to-understand videos. For example, when showing how to make stir-fried vegetables in a video, it explains in detail everything from preparing the ingredients to the cooking procedure, adjusting the heat, and plating. The generation unit can also customize the content of the videos according to the user's cooking skills and the cooking utensils they use. For example, it carefully explains basic cooking procedures for beginners and introduces advanced techniques and variations for advanced users. Furthermore, the generation unit can improve the content of the videos based on user feedback. For example, it incorporates feedback such as "This part was difficult to understand" or "I would like a more detailed explanation" to improve the next video. In this way, the generation unit can support users in enjoying cooking and promote improvements in their eating habits. In addition, the generation unit can also suggest related dishes and new recipes based on the user's video viewing history and preferences. This allows users to constantly try new dishes and broaden the variety of their diet.

[0033] The reception unit includes an analysis unit that considers the user's preferences and health needs. For example, the reception unit analyzes information about the user's preferences for specific foods, tastes, and textures. For instance, if the reception unit finds that the user likes vegetables, it analyzes this information and suggests an appropriate menu. The reception unit can also analyze the user's allergy information, medical history, and current health status. For example, if the reception unit finds that the user has a specific allergy, it analyzes this information and suggests a menu that avoids the allergen. Furthermore, the reception unit can also analyze the user's desired health aspects (e.g., hypertension, diabetes, dieting, etc.). For example, if the reception unit finds that the user wants diabetes management, it analyzes this information and suggests a low-carbohydrate menu. This allows the reception unit to receive information that takes into account the user's preferences and health needs. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's input information into the AI, which can then perform the analysis.

[0034] The menu creation unit proposes a balanced menu that takes into account the user's preferences and health needs. For example, the menu creation unit might suggest a menu rich in vegetables for breakfast, a low-carbohydrate menu for lunch, and a high-protein menu for dinner. The menu creation unit can also propose a menu that takes into account the user's allergy information, medical history, and current health condition. For example, if the user has a specific allergy, the menu creation unit will consider that information and propose a menu that avoids allergens. Furthermore, the menu creation unit can also propose a menu that takes into account the user's desired health aspects (e.g., hypertension, diabetes, weight loss). For example, if the user wants to manage diabetes, the menu creation unit will consider that information and propose a low-carbohydrate menu. In this way, the menu creation unit can propose a balanced menu that takes into account the user's preferences and health needs. Some or all of the above processing in the menu creation unit may be performed using AI or not. For example, the menu creation unit can input user information into AI, and the AI ​​can perform analysis.

[0035] The generation unit analyzes the recipe for each dish and generates a video. For example, the generation unit can analyze the recipe for each dish and generate a video. For example, the generation unit can show how to make stir-fried vegetables in a video, making it easy for users to cook. The generation unit can also use AI to analyze the recipe for each dish and generate a video. For example, the generation unit can have AI analyze the recipe and generate a video. This allows the generation unit to show how to make each dish in a video. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input a recipe into a generation AI, and the generation AI can generate a video.

[0036] The generation unit provides a video demonstrating how to make stir-fried vegetables, making it easy for users to cook. The generation unit can also use AI to demonstrate how to make stir-fried vegetables in a video. For example, the generation unit uses AI to analyze a recipe and generate a video. This allows the generation unit to make it easy for users to cook. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input a recipe into AI, and the AI ​​can generate a video.

[0037] The reception desk analyzes the user's past meal history and suggests the optimal input method. For example, the reception desk automatically displays suggestions for food preferences and health information that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information to be entered at specific times based on the user's past meal history. This allows the reception desk to suggest the optimal input method based on the user's past meal history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past meal history into an AI, which can then perform the analysis.

[0038] The reception desk monitors the user's current health status in real time and automatically completes the input content. For example, the reception desk monitors the user's current blood pressure and blood glucose levels and automatically completes appropriate dietary information based on that. It can also monitor the user's current weight and BMI and automatically complete dietary information suitable for dieting based on that. Furthermore, the reception desk can monitor the user's current activity level and automatically complete dietary information that takes nutritional balance into consideration based on that. This allows the reception desk to automatically complete the input content based on the user's current health status. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's health data into AI, which can then perform analysis.

[0039] The reception desk suggests regionally specific ingredients, taking into account the user's geographical location. For example, the reception desk might suggest local specialties from the area where the user lives. It can also suggest local specialties from the area the user is traveling to. Furthermore, if the user is interested in a particular region, the reception desk can suggest local specialties from that region. This allows the reception desk to suggest regionally specific ingredients based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into an AI, which can then perform analysis.

[0040] The reception desk analyzes the user's social media activity and automatically inputs relevant information. For example, the reception desk can analyze photos of meals shared by the user on social media and automatically input their preferred ingredients. It can also analyze health-related accounts that the user follows on social media and automatically input relevant health information. Furthermore, the reception desk can analyze information about diet groups that the user participates in on social media and automatically input relevant diet information. This allows the reception desk to automatically input relevant information based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, which can then perform the analysis.

[0041] The menu creation unit proposes the optimal menu by referring to the user's past meal history. For example, the menu creation unit proposes the optimal menu based on the dishes the user has enjoyed eating in the past. The menu creation unit can also propose a menu that takes nutritional balance into consideration based on the user's past meal history. Furthermore, the menu creation unit can analyze the user's past meal history and propose a menu that takes health into consideration. In this way, the menu creation unit can propose the optimal menu based on the user's past meal history. Some or all of the above processing in the menu creation unit may be performed using AI or not. For example, the menu creation unit can input the user's past meal history into AI, and the AI ​​can perform the analysis.

[0042] The menu creation unit proposes different menus depending on the season and weather. For example, in the summer, it may suggest cold or refreshing dishes. In the winter, it may also suggest warm or nutritious dishes. Furthermore, on rainy days, it may suggest dishes that can be enjoyed at home. In this way, the menu creation unit can propose menus that are appropriate for the season and weather. Some or all of the above processing in the menu creation unit may be performed using AI or not. For example, the menu creation unit can input seasonal and weather data into the AI, which can then perform the analysis.

[0043] The creation unit proposes menus using regionally specific ingredients, taking into account the user's geographical location. For example, the creation unit might propose a menu using local specialties from the area where the user lives. It can also propose a menu using local specialties from the region the user is traveling in. Furthermore, if the user is interested in a particular region, the creation unit can propose a menu using local specialties from that region. This allows the creation unit to propose menus using regionally specific ingredients based on the user's geographical location. Some or all of the above processing in the creation unit may be performed using AI, or not. For example, the creation unit can input the user's geographical location information into an AI, which can then analyze it.

[0044] The creation unit analyzes the user's social media activity and suggests relevant meal plans. For example, it can analyze photos of meals shared by the user on social media and suggest meal plans using their preferred ingredients. It can also analyze health-related accounts followed by the user on social media and suggest relevant healthy meal plans. Furthermore, it can analyze information about diet groups the user participates in on social media and suggest relevant diet meal plans. This allows the creation unit to suggest relevant meal plans based on the user's social media activity. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input the user's social media data into an AI, which can then perform the analysis.

[0045] The generation unit suggests the optimal cooking method by referring to the user's past cooking history. For example, the generation unit suggests the optimal cooking method based on dishes the user has enjoyed making in the past. The generation unit can also suggest efficient cooking methods based on the user's past cooking history. Furthermore, the generation unit can analyze the user's past cooking history and suggest cooking methods that take health into consideration. In this way, the generation unit can suggest the optimal cooking method based on the user's past cooking history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past cooking history into AI, which can then perform the analysis.

[0046] The generation unit applies different video generation algorithms depending on the difficulty of the cooking. For example, for easy dishes, the generation unit applies a simple video generation algorithm. For more difficult dishes, the generation unit can also apply a video generation algorithm that includes detailed instructions. Furthermore, for dishes of moderate difficulty, the generation unit can apply a video generation algorithm with a moderate level of detail. In this way, the generation unit can apply a video generation algorithm appropriate to the difficulty of the cooking. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input cooking difficulty data into AI, and the AI ​​can perform analysis.

[0047] The generation unit proposes region-specific cooking methods, taking into account the user's geographical location information. For example, the generation unit proposes cooking methods using local specialties from the area where the user lives. Furthermore, if the user is traveling, the generation unit can propose cooking methods using local specialties from that region. In this way, the generation unit can propose region-specific cooking methods based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input the user's geographical location information into an AI, which can then perform the analysis.

[0048] The generation unit analyzes the user's social media activity and suggests relevant cooking methods. For example, the generation unit analyzes photos of food shared by the user on social media and suggests preferred cooking methods. It can also analyze cooking-related accounts that the user follows on social media and suggest relevant cooking methods. Furthermore, the generation unit can analyze information about cooking groups that the user participates in on social media and suggest relevant cooking methods. In this way, the generation unit can suggest relevant cooking methods based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media data into AI, which can then perform the analysis.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The reception desk can also suggest regionally specific ingredients, taking into account the user's geographical location. For example, it can suggest local specialties from the area where the user lives. If the user is traveling, it can also suggest local specialties from that region. Furthermore, if the reception desk has an interest in a particular region, it can suggest local specialties from that region. This allows the reception desk to suggest regionally specific ingredients based on the user's geographical location.

[0051] The menu creation department can also propose different menus depending on the season and weather. For example, in the summer, they can suggest cold or refreshing dishes. In the winter, they can suggest warm or nutritious dishes. Furthermore, on rainy days, they can suggest dishes that can be enjoyed at home. In this way, the menu creation department can propose menus that are appropriate for the season and weather.

[0052] The generation unit can also apply different video generation algorithms depending on the difficulty of the cooking. For example, a simple video generation algorithm can be applied to easy dishes. For more difficult dishes, a video generation algorithm with detailed instructions can be applied. Furthermore, for dishes of moderate difficulty, a video generation algorithm with a moderate level of detail can be applied. In this way, the generation unit can apply a video generation algorithm appropriate to the difficulty of the cooking.

[0053] The reception desk can analyze users' social media activity and automatically input relevant information. For example, it can analyze photos of meals shared by users on social media and automatically input their preferred ingredients. It can also analyze health-related accounts that users follow on social media and automatically input relevant health information. Furthermore, it can analyze information about diet groups that users participate in on social media and automatically input relevant diet information. This allows the reception desk to automatically input relevant information based on users' social media activity.

[0054] The generation unit can also suggest the optimal cooking method by referring to the user's past cooking history. For example, it can suggest the optimal cooking method based on dishes the user has enjoyed making in the past. It can also suggest efficient cooking methods based on the user's past cooking history. Furthermore, it can analyze the user's past cooking history and suggest cooking methods that take health into consideration. In this way, the generation unit can suggest the optimal cooking method based on the user's past cooking history.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk allows users to input information about their food preferences, nutritional balance, and health status. For example, users can input information about their preferences for specific ingredients, tastes, and textures, allergy information, medical history, current health status, and desired health aspects (such as high blood pressure, diabetes, or weight loss). Step 2: The creation department analyzes the information received by the reception department and creates a one-month meal plan. The creation department proposes a balanced menu that takes into account the user's preferences and health needs. For example, it might suggest a menu with plenty of vegetables for breakfast, a low-carbohydrate menu for lunch, and a high-protein menu for dinner. The creation department uses AI to analyze the user's input information and create the optimal menu. Step 3: The generation unit generates videos showing how to make each dish based on the menu created by the creation unit. The generation unit analyzes the recipe for each dish and generates a video. For example, it might create a video showing how to make stir-fried vegetables so that users can easily cook them. The generation unit uses AI to analyze the recipe for each dish and generate a video.

[0057] (Example of form 2) The dietary improvement system according to an embodiment of the present invention is a system for improving dietary habits using AI. This dietary improvement system allows users to input their food preferences, nutritional balance, and desired health aspects (such as high blood pressure, diabetes, or weight loss). Next, the AI ​​analyzes this information and creates a one-month meal plan. Furthermore, based on the created meal plan, it generates videos demonstrating how to prepare each dish. This mechanism allows users to easily achieve a healthy diet. For example, a user inputs their food preferences, nutritional balance, and desired health aspects. For instance, they might input information such as, "I like vegetables and want to take measures against diabetes." This information is input to the AI. Next, the AI ​​analyzes the input information and creates a one-month meal plan. The AI ​​considers the user's preferences and health needs and proposes a balanced menu. For example, it might suggest a menu with plenty of vegetables for breakfast, a low-carbohydrate menu for lunch, and a high-protein menu for dinner. Furthermore, based on the created meal plan, it generates videos demonstrating how to prepare each dish. The AI ​​analyzes the recipe for each dish and generates the video. For example, it might show how to make stir-fried vegetables in a video, making it easy for the user to cook. This system makes it easy for users to adopt a healthy diet. Users simply input their food preferences and health concerns, and receive a balanced meal plan and cooking videos. This helps improve their eating habits and achieve a healthier body. For example, if a user inputs "I like vegetables and want to manage diabetes," the AI ​​will suggest a low-carb meal plan rich in vegetables and provide cooking videos. This makes it easy for users to achieve a healthy diet. In this way, the diet improvement system automatically improves the user's eating habits and provides a healthy lifestyle.

[0058] The dietary improvement system according to this embodiment comprises a reception unit, a creation unit, and a generation unit. The reception unit receives input from the user regarding food likes and dislikes, nutritional balance, and health information. For example, the reception unit can receive input from the user regarding their preferences for specific ingredients, tastes, and textures. The reception unit can also receive input from the user regarding allergy information, medical history, and current health status. Furthermore, the reception unit can receive input from the user regarding desired health aspects (e.g., high blood pressure, diabetes, diet measures, etc.). The creation unit analyzes the information received by the reception unit and creates a menu plan for one month. For example, the creation unit proposes a balanced menu considering the user's preferences and health needs. For example, the creation unit proposes a menu with plenty of vegetables for breakfast, a low-carbohydrate menu for lunch, and a high-protein menu for dinner. The creation unit uses AI to analyze the user's input information and create an optimal menu. The generation unit generates videos of how to prepare each dish based on the menu plan created by the creation unit. For example, the generation unit analyzes the recipes for each dish and generates videos. For example, the generation unit displays a video demonstrating how to make stir-fried vegetables, making it easy for users to cook. The generation unit uses AI to analyze the recipe for each dish and generate a video. As a result, the dietary improvement system according to this embodiment can automatically improve the user's diet and provide a healthy diet.

[0059] The reception desk allows users to input information about their food preferences, nutritional balance, and health. Specifically, users can access a dedicated application or website using their smartphone or computer to input detailed preferences regarding ingredients, taste, and texture. For example, users can input specific preferences such as "I like spicy food," "I want to limit sweets," or "I like crispy textures." They can also input allergy information, medical history, and current health status. For example, by inputting information such as "I have a nut allergy," "I have had a stomach ulcer in the past," or "I am currently being treated for high blood pressure," the system can suggest menus tailored to the user's health condition. Furthermore, users can input their desired health aspects. For example, by inputting specific goals such as "I'm on a diet and want to reduce calories," "I have diabetes and would like a low-carbohydrate diet," or "I want to increase muscle mass and would like a high-protein diet," the system will provide a meal plan tailored to the user's goals. The reception desk centrally manages this information and builds a customized database for each user. This allows the reception desk to build a foundation for providing individually optimized dietary improvement plans based on detailed user information.

[0060] The creation department analyzes the information received by the reception department and creates a one-month meal plan. Specifically, the creation department uses AI to analyze data in order to propose a balanced meal plan that takes into account the user's preferences and health needs. For example, if a user inputs "I would like a menu with lots of vegetables for breakfast," "I would like a low-carb menu for lunch," and "I would like a high-protein menu for dinner," the creation department will create a meal plan that takes these preferences into account and considers the nutritional balance of each meal. Based on past data and nutritional knowledge, the AI ​​selects the most suitable ingredients and dishes for the user's preferences and health condition. For example, it might suggest a salad or smoothie with lots of vegetables for breakfast, a low-carb salad chicken or tofu dish for lunch, and a high-protein fish or chicken dish for dinner. The creation department can also propose a meal plan that incorporates seasonal ingredients and local specialties. This allows users to enjoy fresh and delicious meals. Furthermore, the creation department can improve the meal plan based on user feedback. For example, if a user provides feedback such as "I didn't like this dish" or "I'd prefer something easier to make," the menu creation team will take this into account and improve the next menu. This allows the team to provide optimal menus tailored to the user's preferences and health condition, and continuously support improvements in their eating habits.

[0061] The generation unit generates cooking videos for each dish based on the menu created by the creation unit. Specifically, the generation unit analyzes the recipe for each dish and generates videos to make cooking easy for users. Using AI, it analyzes the steps of the recipe, taking into account the necessary ingredients, cooking utensils, and cooking time, to create easy-to-understand videos. For example, when showing how to make stir-fried vegetables in a video, it explains in detail everything from preparing the ingredients to the cooking procedure, adjusting the heat, and plating. The generation unit can also customize the content of the videos according to the user's cooking skills and the cooking utensils they use. For example, it carefully explains basic cooking procedures for beginners and introduces advanced techniques and variations for advanced users. Furthermore, the generation unit can improve the content of the videos based on user feedback. For example, it incorporates feedback such as "This part was difficult to understand" or "I would like a more detailed explanation" to improve the next video. In this way, the generation unit can support users in enjoying cooking and promote improvements in their eating habits. In addition, the generation unit can also suggest related dishes and new recipes based on the user's video viewing history and preferences. This allows users to constantly try new dishes and broaden the variety of their diet.

[0062] The reception unit includes an analysis unit that considers the user's preferences and health needs. For example, the reception unit analyzes information about the user's preferences for specific foods, tastes, and textures. For instance, if the reception unit finds that the user likes vegetables, it analyzes this information and suggests an appropriate menu. The reception unit can also analyze the user's allergy information, medical history, and current health status. For example, if the reception unit finds that the user has a specific allergy, it analyzes this information and suggests a menu that avoids the allergen. Furthermore, the reception unit can also analyze the user's desired health aspects (e.g., hypertension, diabetes, dieting, etc.). For example, if the reception unit finds that the user wants diabetes management, it analyzes this information and suggests a low-carbohydrate menu. This allows the reception unit to receive information that takes into account the user's preferences and health needs. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's input information into the AI, which can then perform the analysis.

[0063] The menu creation unit proposes a balanced menu that takes into account the user's preferences and health needs. For example, the menu creation unit might suggest a menu rich in vegetables for breakfast, a low-carbohydrate menu for lunch, and a high-protein menu for dinner. The menu creation unit can also propose a menu that takes into account the user's allergy information, medical history, and current health condition. For example, if the user has a specific allergy, the menu creation unit will consider that information and propose a menu that avoids allergens. Furthermore, the menu creation unit can also propose a menu that takes into account the user's desired health aspects (e.g., hypertension, diabetes, weight loss). For example, if the user wants to manage diabetes, the menu creation unit will consider that information and propose a low-carbohydrate menu. In this way, the menu creation unit can propose a balanced menu that takes into account the user's preferences and health needs. Some or all of the above processing in the menu creation unit may be performed using AI or not. For example, the menu creation unit can input user information into AI, and the AI ​​can perform analysis.

[0064] The generation unit analyzes the recipe for each dish and generates a video. For example, the generation unit can analyze the recipe for each dish and generate a video. For example, the generation unit can show how to make stir-fried vegetables in a video, making it easy for users to cook. The generation unit can also use AI to analyze the recipe for each dish and generate a video. For example, the generation unit can have AI analyze the recipe and generate a video. This allows the generation unit to show how to make each dish in a video. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input a recipe into a generation AI, and the generation AI can generate a video.

[0065] The generation unit provides a video demonstrating how to make stir-fried vegetables, making it easy for users to cook. The generation unit can also use AI to demonstrate how to make stir-fried vegetables in a video. For example, the generation unit uses AI to analyze a recipe and generate a video. This allows the generation unit to make it easy for users to cook. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input a recipe into AI, and the AI ​​can generate a video.

[0066] The reception desk estimates the user's emotions and adjusts the display of the input interface based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input, allowing for quick input of food preferences and health information. In this way, the reception desk can provide an input interface that responds to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI, which can then estimate the emotions.

[0067] The reception desk analyzes the user's past meal history and suggests the optimal input method. For example, the reception desk automatically displays suggestions for food preferences and health information that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information to be entered at specific times based on the user's past meal history. This allows the reception desk to suggest the optimal input method based on the user's past meal history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past meal history into an AI, which can then perform the analysis.

[0068] The reception desk monitors the user's current health status in real time and automatically completes the input content. For example, the reception desk monitors the user's current blood pressure and blood glucose levels and automatically completes appropriate dietary information based on that. It can also monitor the user's current weight and BMI and automatically complete dietary information suitable for dieting based on that. Furthermore, the reception desk can monitor the user's current activity level and automatically complete dietary information that takes nutritional balance into consideration based on that. This allows the reception desk to automatically complete the input content based on the user's current health status. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's health data into AI, which can then perform analysis.

[0069] The reception desk estimates the user's emotions and prioritizes the input content based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize inputting only important information. If the user is relaxed, the reception desk may also prioritize inputting detailed information. Furthermore, if the user is in a hurry, the reception desk may prioritize inputting the most important information. In this way, the reception desk can determine the priority of input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI, which can estimate the emotions.

[0070] The reception desk suggests regionally specific ingredients, taking into account the user's geographical location. For example, the reception desk might suggest local specialties from the area where the user lives. It can also suggest local specialties from the area the user is traveling to. Furthermore, if the user is interested in a particular region, the reception desk can suggest local specialties from that region. This allows the reception desk to suggest regionally specific ingredients based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into an AI, which can then perform analysis.

[0071] The reception desk analyzes the user's social media activity and automatically inputs relevant information. For example, the reception desk can analyze photos of meals shared by the user on social media and automatically input their preferred ingredients. It can also analyze health-related accounts that the user follows on social media and automatically input relevant health information. Furthermore, the reception desk can analyze information about diet groups that the user participates in on social media and automatically input relevant diet information. This allows the reception desk to automatically input relevant information based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, which can then perform the analysis.

[0072] The creation unit estimates the user's emotions and adjusts the menu suggestion method based on the estimated emotions. For example, if the user is stressed, the creation unit suggests a simple and easy menu. If the user is relaxed, the creation unit can also suggest a menu that can be enjoyed at a leisurely pace. Furthermore, if the user is in a hurry, the creation unit can suggest a menu that can be prepared quickly. In this way, the creation unit can provide a menu suggestion method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input user emotion data into an AI, which can then estimate the emotions.

[0073] The menu creation unit proposes the optimal menu by referring to the user's past meal history. For example, the menu creation unit proposes the optimal menu based on the dishes the user has enjoyed eating in the past. The menu creation unit can also propose a menu that takes nutritional balance into consideration based on the user's past meal history. Furthermore, the menu creation unit can analyze the user's past meal history and propose a menu that takes health into consideration. In this way, the menu creation unit can propose the optimal menu based on the user's past meal history. Some or all of the above processing in the menu creation unit may be performed using AI or not. For example, the menu creation unit can input the user's past meal history into AI, and the AI ​​can perform the analysis.

[0074] The menu creation unit proposes different menus depending on the season and weather. For example, in the summer, it may suggest cold or refreshing dishes. In the winter, it may also suggest warm or nutritious dishes. Furthermore, on rainy days, it may suggest dishes that can be enjoyed at home. In this way, the menu creation unit can propose menus that are appropriate for the season and weather. Some or all of the above processing in the menu creation unit may be performed using AI or not. For example, the menu creation unit can input seasonal and weather data into the AI, which can then perform the analysis.

[0075] The creation unit estimates the user's emotions and determines the priority of the menu based on the estimated emotions. For example, if the user is stressed, the creation unit will prioritize easy and quick menus. If the user is relaxed, the creation unit can also prioritize menus that can be enjoyed at a leisurely pace. Furthermore, if the user is in a hurry, the creation unit can prioritize menus that can be prepared quickly. In this way, the creation unit can determine the priority of the menu according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input user emotion data into an AI, which can estimate the emotions.

[0076] The creation unit proposes menus using regionally specific ingredients, taking into account the user's geographical location. For example, the creation unit might propose a menu using local specialties from the area where the user lives. It can also propose a menu using local specialties from the region the user is traveling in. Furthermore, if the user is interested in a particular region, the creation unit can propose a menu using local specialties from that region. This allows the creation unit to propose menus using regionally specific ingredients based on the user's geographical location. Some or all of the above processing in the creation unit may be performed using AI, or not. For example, the creation unit can input the user's geographical location information into an AI, which can then analyze it.

[0077] The creation unit analyzes the user's social media activity and suggests relevant meal plans. For example, it can analyze photos of meals shared by the user on social media and suggest meal plans using their preferred ingredients. It can also analyze health-related accounts followed by the user on social media and suggest relevant healthy meal plans. Furthermore, it can analyze information about diet groups the user participates in on social media and suggest relevant diet meal plans. This allows the creation unit to suggest relevant meal plans based on the user's social media activity. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input the user's social media data into an AI, which can then perform the analysis.

[0078] The generation unit estimates the user's emotions and adjusts the video's presentation based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a video that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a video that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a video with visually stimulating effects. In this way, the generation unit can provide a video presentation that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI, which can then estimate the emotions.

[0079] The generation unit suggests the optimal cooking method by referring to the user's past cooking history. For example, the generation unit suggests the optimal cooking method based on dishes the user has enjoyed making in the past. The generation unit can also suggest efficient cooking methods based on the user's past cooking history. Furthermore, the generation unit can analyze the user's past cooking history and suggest cooking methods that take health into consideration. In this way, the generation unit can suggest the optimal cooking method based on the user's past cooking history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past cooking history into AI, which can then perform the analysis.

[0080] The generation unit applies different video generation algorithms depending on the difficulty of the cooking. For example, for easy dishes, the generation unit applies a simple video generation algorithm. For more difficult dishes, the generation unit can also apply a video generation algorithm that includes detailed instructions. Furthermore, for dishes of moderate difficulty, the generation unit can apply a video generation algorithm with a moderate level of detail. In this way, the generation unit can apply a video generation algorithm appropriate to the difficulty of the cooking. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input cooking difficulty data into AI, and the AI ​​can perform analysis.

[0081] The generation unit estimates the user's emotions and adjusts the video length based on the estimated emotions. For example, if the user is in a hurry, the generation unit generates a short, concise video. If the user is relaxed, the generation unit can also generate a longer video with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a video with visually stimulating effects. This allows the generation unit to provide video lengths that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI, which can then estimate the emotions.

[0082] The generation unit proposes region-specific cooking methods, taking into account the user's geographical location information. For example, the generation unit proposes cooking methods using local specialties from the area where the user lives. Furthermore, if the user is traveling, the generation unit can propose cooking methods using local specialties from that region. In this way, the generation unit can propose region-specific cooking methods based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input the user's geographical location information into an AI, which can then perform the analysis.

[0083] The generation unit analyzes the user's social media activity and suggests relevant cooking methods. For example, the generation unit analyzes photos of food shared by the user on social media and suggests preferred cooking methods. It can also analyze cooking-related accounts that the user follows on social media and suggest relevant cooking methods. Furthermore, the generation unit can analyze information about cooking groups that the user participates in on social media and suggest relevant cooking methods. In this way, the generation unit can suggest relevant cooking methods based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media data into AI, which can then perform the analysis.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The reception desk can also analyze the user's past emotional data regarding meals and suggest the most suitable menu. For example, if a user has expressed positive emotions when eating a particular dish in the past, the system will prioritize suggesting menus that include that dish. Conversely, if a user has expressed negative emotions when eating a particular dish in the past, the system can suggest menus that avoid that dish. Furthermore, based on the user's emotional data, the reception desk can suggest menus that include ingredients with relaxing effects to reduce stress. This allows the reception desk to suggest more personalized menus based on the user's past emotional data.

[0086] The menu creation function can also analyze the user's past emotional data regarding meals and suggest the most suitable menu. For example, if a user has expressed positive emotions when eating a particular dish in the past, the menu will prioritize suggesting a menu that includes that dish. Conversely, if a user has expressed negative emotions when eating a particular dish in the past, the menu can suggest a menu that avoids that dish. Furthermore, based on the user's emotional data, the menu creation function can also suggest a menu that includes ingredients with relaxing effects to reduce stress. This allows the menu creation function to suggest a more personalized menu based on the user's past emotional data.

[0087] The generation unit can also estimate the user's emotions and adjust the video's presentation based on those emotions. For example, if the user is relaxed, it can generate a video that progresses at a leisurely pace. If the user is in a hurry, it can generate a video that emphasizes the shortest route. Furthermore, if the user is excited, it can generate a video with visually stimulating effects. In this way, the generation unit can provide a video presentation that responds to the user's emotions.

[0088] The reception desk can also suggest regionally specific ingredients, taking into account the user's geographical location. For example, it can suggest local specialties from the area where the user lives. If the user is traveling, it can also suggest local specialties from that region. Furthermore, if the reception desk has an interest in a particular region, it can suggest local specialties from that region. This allows the reception desk to suggest regionally specific ingredients based on the user's geographical location.

[0089] The menu creation department can also propose different menus depending on the season and weather. For example, in the summer, they can suggest cold or refreshing dishes. In the winter, they can suggest warm or nutritious dishes. Furthermore, on rainy days, they can suggest dishes that can be enjoyed at home. In this way, the menu creation department can propose menus that are appropriate for the season and weather.

[0090] The generation unit can also apply different video generation algorithms depending on the difficulty of the cooking. For example, a simple video generation algorithm can be applied to easy dishes. For more difficult dishes, a video generation algorithm with detailed instructions can be applied. Furthermore, for dishes of moderate difficulty, a video generation algorithm with a moderate level of detail can be applied. In this way, the generation unit can apply a video generation algorithm appropriate to the difficulty of the cooking.

[0091] The reception desk can analyze users' social media activity and automatically input relevant information. For example, it can analyze photos of meals shared by users on social media and automatically input their preferred ingredients. It can also analyze health-related accounts that users follow on social media and automatically input relevant health information. Furthermore, it can analyze information about diet groups that users participate in on social media and automatically input relevant diet information. This allows the reception desk to automatically input relevant information based on users' social media activity.

[0092] The creation unit can also estimate the user's emotions and prioritize menu items based on those emotions. For example, if the user is stressed, it will prioritize easy and quick menus. If the user is relaxed, it can prioritize menus that can be enjoyed at a leisurely pace. Furthermore, if the user is in a hurry, it can prioritize menus that can be prepared quickly. In this way, the creation unit can determine the priority of menu items according to the user's emotions.

[0093] The generation unit can also suggest the optimal cooking method by referring to the user's past cooking history. For example, it can suggest the optimal cooking method based on dishes the user has enjoyed making in the past. It can also suggest efficient cooking methods based on the user's past cooking history. Furthermore, it can analyze the user's past cooking history and suggest cooking methods that take health into consideration. In this way, the generation unit can suggest the optimal cooking method based on the user's past cooking history.

[0094] The generation unit can also estimate the user's emotions and adjust the video length based on those emotions. For example, if the user is in a hurry, it can generate a short, concise video. If the user is relaxed, it can generate a longer video with detailed explanations. Furthermore, if the user is excited, it can generate a video with visually stimulating effects. In this way, the generation unit can provide video lengths that match the user's emotions.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The reception desk allows users to input information about their food preferences, nutritional balance, and health status. For example, users can input information about their preferences for specific ingredients, tastes, and textures, allergy information, medical history, current health status, and desired health aspects (such as high blood pressure, diabetes, or weight loss). Step 2: The creation department analyzes the information received by the reception department and creates a one-month meal plan. The creation department proposes a balanced menu that takes into account the user's preferences and health needs. For example, it might suggest a menu with plenty of vegetables for breakfast, a low-carbohydrate menu for lunch, and a high-protein menu for dinner. The creation department uses AI to analyze the user's input information and create the optimal menu. Step 3: The generation unit generates videos showing how to make each dish based on the menu created by the creation unit. The generation unit analyzes the recipe for each dish and generates a video. For example, it might create a video showing how to make stir-fried vegetables so that users can easily cook them. The generation unit uses AI to analyze the recipe for each dish and generate a video.

[0097] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0100] Each of the multiple elements described above, including the reception unit, creation unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing the user to input information about food preferences, nutritional balance, and health. The creation unit is implemented by the specific processing unit 290 of the data processing device 12, analyzing the user's input information and creating a menu for one month. The generation unit is implemented by the control unit 46A of the smart device 14, generating videos of how to prepare each dish based on the created menu. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the reception unit, creation unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to input information about food preferences, nutritional balance, and health. The creation unit is implemented by the specific processing unit 290 of the data processing device 12, analyzing the user's input information and creating a menu for one month. The generation unit is implemented by the control unit 46A of the smart glasses 214, generating videos of how to prepare each dish based on the created menu. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, creation unit, and generation unit, is implemented in at least one of the following: the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to input information about food preferences, nutritional balance, and health. The creation unit is implemented by the specific processing unit 290 of the data processing device 12, analyzing the user's input information and creating a menu for one month. The generation unit is implemented by the control unit 46A of the headset terminal 314, generating videos of how to prepare each dish based on the created menu. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, creation unit, and generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing the user to input information about food preferences, nutritional balance, and health. The creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the user's input information and creates a menu for one month. The generation unit is implemented by, for example, the control unit 46A of the robot 414, which generates videos of how to make each dish based on the created menu. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) The reception area is where you input information about your food preferences, nutritional balance, and health. The creation unit analyzes the information received by the reception unit and creates a menu plan for one month, The system includes a generation unit that generates videos showing how to prepare each dish based on the menu created by the creation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It includes an analysis unit that takes into account user preferences and health considerations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned creation unit, We propose balanced menus that take into account the user's preferences and health needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Analyze the recipe for each dish and generate a video. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The video demonstrates how to make stir-fried vegetables, making it easy for users to cook. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past meal history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It monitors the user's current health status in real time and automatically completes the input content. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is We suggest local ingredients based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Analyze users' social media activity and automatically populate with relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned creation unit, The system estimates the user's emotions and adjusts the menu suggestion method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned creation unit, The system suggests the optimal menu based on the user's past meal history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned creation unit, We offer different menus depending on the season and weather. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned creation unit, The system estimates the user's emotions and determines the priority of the menu based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned creation unit, The system suggests menus using local ingredients, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned creation unit, Analyze users' social media activity and suggest relevant menus. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The system estimates the user's emotions and adjusts the video's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system suggests the optimal cooking method by referencing the user's past cooking history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is Apply different video generation algorithms depending on the difficulty level of the cooking. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the video length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It suggests region-specific cooking methods, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is Analyze users' social media activity and suggest relevant cooking methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area is where you input information about your food preferences, nutritional balance, and health. The creation unit analyzes the information received by the reception unit and creates a menu plan for one month, The system includes a generation unit that generates videos showing how to prepare each dish based on the menu created by the creation unit. A system characterized by the following features.

2. The aforementioned reception unit is It includes an analysis unit that takes into account user preferences and health considerations. The system according to feature 1.

3. The aforementioned creation unit, We propose balanced menus that take into account the user's preferences and health needs. The system according to feature 1.

4. The generating unit is Analyze the recipe for each dish and generate a video. The system according to feature 1.

5. The generating unit is The video demonstrates how to make stir-fried vegetables, making it easy for users to cook. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past meal history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is It monitors the user's current health status in real time and automatically completes the input content. The system according to feature 1.

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