system

Abstract

The system according to the embodiment aims to propose optimal menus and recipes based on the user's dietary content, preferences, and allergy information. [Solution] The system according to the embodiment includes a registration unit, a suggestion unit, and a guide unit. The registration unit registers the user's dietary details, preferences, and allergy information. The suggestion unit analyzes the information registered by the registration unit and suggests today's menu. The guide unit guides the user to time-saving recipes or specialty recipes based on the menu suggested by the suggestion unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it takes time for users to plan their daily menus and it is difficult for them to find suitable recipes.

[0005] The system according to the embodiment aims to propose optimal menus and recipes based on the user's dietary content, preferences, and allergy information. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a suggestion unit, and a guide unit. The registration unit registers the user's dietary details, preferences, and allergy information. The suggestion unit analyzes the information registered by the registration unit and suggests today's menu. The guide unit guides the user to time-saving recipes or specialty recipes based on the menu suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal menus and recipes based on the user's dietary content, preferences, and allergy information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A menu suggestion system according to an embodiment of the present invention registers a user's dietary information, preferences, and allergy information, and then uses a generation AI to propose a daily menu and guide users on cooking methods. The menu suggestion system registers a user's dietary information, preferences, and allergy information, and then uses a generation AI to propose a daily menu. For example, if a user registers that they like Japanese food and would like to incorporate a lot of fish dishes, the generation AI will propose a Japanese menu using fish. The menu suggestion system then provides guidance on cooking methods for both those who want to save time and those who want to make more elaborate meals. For example, if a user wants to save time, the generation AI will suggest simple and quick recipes and guide the user through the cooking steps. On the other hand, if a user wants to make more elaborate meals, the generation AI will provide detailed recipes and cooking tips to help the user create a satisfying meal. This saves users the trouble of planning their daily menu, and with the added cooking support, they can efficiently prepare delicious meals. For example, they can use time-saving recipes on busy weekdays and enjoy elaborate meals on weekends. Furthermore, the generation AI also makes suggestions that take seasonal ingredients and nutritional balance into account. For example, in spring, the system suggests menus using seasonal vegetables and provides recipes that take nutritional balance into consideration. This allows users to enjoy healthy and delicious meals. For example, the system allows users to quickly and accurately decide on their daily menus, reducing the effort required for cooking. In addition, users' satisfaction is improved because menus tailored to their preferences and health condition are suggested.

[0029] A menu suggestion system according to an embodiment includes a registration unit, a suggestion unit, and a guide unit. The registration unit registers a user's meal details, preferences, and allergy information. The meal details include, but are not limited to, ingredients, dish names, and nutritional components. For example, the registration unit allows the user to input the meal details in text format. The registration unit can also register the meal details using voice input or image input. For example, the registration unit converts the user's voice into text using voice recognition technology and registers the text as the meal details. The suggestion unit uses a generation AI to analyze the information registered by the registration unit and suggest today's menu. The suggestion unit analyzes, for example, the user's preferences and allergy information and suggests an appropriate menu. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's preferences and allergy information and generate a menu. The suggestion unit can also suggest menus that take seasonal ingredients and nutritional balance into consideration. For example, the generation AI suggests menus using seasonal vegetables in spring and provides recipes that take nutritional balance into consideration. The guide unit provides guidance on time-saving recipes and special recipes based on the menu proposed by the suggestion unit. For example, if a user wants to cook something quickly, the guide unit suggests simple and quick recipes and guides the user through the cooking steps. Furthermore, if a user wants to cook something with special care, the guide unit provides detailed recipes and cooking tips to help the user cook a dish that satisfies them. As a result, the menu suggestion system according to the embodiment can efficiently suggest menus and guide the user through cooking methods based on the user's dietary content, preferences, and allergy information.

[0030] The suggestion unit can analyze the user's preferences and allergy information using the generation AI. For example, the generation AI analyzes the user's preferences and allergy information and suggests appropriate menus. For example, if the user registers that they like Japanese food and would like to include many fish dishes, the generation AI will suggest Japanese menus that use fish. The generation AI can also take the user's allergy information into consideration and suggest menus that do not contain ingredients that cause allergies. For example, if the user registers that they have a nut allergy, the generation AI will suggest menus that do not contain nuts. This makes it possible to accurately analyze the user's preferences and allergy information and suggest appropriate menus.

[0031] The suggestion unit can use the generation AI to suggest menus that take into account seasonal ingredients. For example, the generation AI considers seasonal ingredients and suggests menus that utilize seasonal ingredients. For example, in spring the generation AI suggests menus that use seasonal vegetables and provides recipes that take nutritional balance into consideration. The generation AI can also consider ingredients specific to a region and suggest menus that use ingredients that are available in that region. For example, if the user is in a specific region, the generation AI will suggest menus that use local specialties. In this way, by considering seasonal ingredients, it is possible to suggest menus that utilize seasonal ingredients.

[0032] The suggestion unit can use the generation AI to suggest menus that take nutritional balance into consideration. The generation AI, for example, considers nutritional balance and suggests healthy menus. For example, the generation AI suggests balanced menus based on the recommended intake of each nutrient. The generation AI can also consider the user's health condition and suggest menus that are high in specific nutrients. For example, if the user registers that they are "iron deficient," the generation AI will suggest menus that use ingredients that are high in iron. This makes it possible to suggest healthy meals by considering nutritional balance.

[0033] The guide unit can suggest time-saving recipes and provide real-time support for cooking procedures. For example, if you want to cook something in a short amount of time, the guide unit can suggest simple and quick recipes and provide real-time support for cooking procedures. For example, the guide unit can provide advice and real-time feedback at appropriate times during cooking. The guide unit can also provide support for immediately dealing with problems that occur during cooking. For example, the guide unit can suggest solutions in real-time for problems that occur during cooking. This allows for efficient cooking by suggesting time-saving recipes and providing real-time support for cooking procedures.

[0034] The guide unit can suggest special recipes and provide cooking tips. For example, if a user wants to make something special, the guide unit can provide detailed recipes and cooking tips to help the user make a dish that satisfies them. For example, the guide unit can provide tips on specific cooking techniques, how to handle ingredients, how to use seasonings, etc. The guide unit can also provide support for immediately dealing with problems that occur during cooking. For example, the guide unit can suggest solutions in real time to problems that arise during cooking. In this way, the guide unit can help the user make a dish that satisfies them by suggesting special recipes and providing cooking tips.

[0035] The registration unit can analyze the user's past meal history and select the optimal registration method. For example, the registration unit can automatically display meal contents that the user has frequently registered in the past as candidates. The registration unit can also predict and suggest meal contents to be registered for a specific time period based on the user's past meal history. For example, the registration unit can analyze the user's past meal history and suggest the most efficient registration method. In this way, the analysis of the past meal history can suggest the optimal registration method for the user.

[0036] When registering meal contents, the registration unit can filter the meal contents based on the user's current health and nutritional status. For example, the registration unit can propose appropriate meal contents taking into account the user's current health status. The registration unit can also propose balanced meal contents based on the user's nutritional status. For example, the registration unit filters appropriate meal contents according to the user's health goals. This makes it possible to propose appropriate meal contents based on the user's health and nutritional status.

[0037] When registering meal details, the registration unit can select the optimal registration means depending on the user's input method. For example, when the user registers meal details by voice, the registration unit automatically converts the voice into text using voice recognition technology. In addition, when the user registers meal details using images, the registration unit can also automatically analyze the meal details using image recognition technology. For example, when the user registers meal details using text, the registration unit provides an input completion function to enable efficient registration. This allows meal details to be registered efficiently by selecting the optimal registration means depending on the user's input method.

[0038] When registering meal contents, the registration unit can prioritize the registration of highly relevant meal contents by taking into account the user's geographical location information. For example, if the user is in a specific area, the registration unit can suggest meal contents using local specialties. Furthermore, if the user is traveling, the registration unit can also suggest local specialties of the travel destination. For example, if the user is at home, the registration unit can suggest meal contents using ingredients available at a nearby supermarket. In this way, highly relevant meal contents can be suggested by taking into account the user's geographical location information.

[0039] When registering meal details, the registration unit can analyze the user's social media activity and register related meal details. For example, the registration unit automatically registers meal details shared by the user on social media. The registration unit can also analyze the content posted by the user on social media and suggest related meal details. For example, the registration unit can suggest related meal details by referring to the activity of the user's friends on social media. In this way, related meal details can be suggested by analyzing the user's social media activity.

[0040] The registration unit can customize the registration method by reflecting the user's past feedback when registering meal details. For example, the registration unit can suggest the optimal registration method based on feedback provided by the user in the past. The registration unit can also preferentially suggest specific ingredients or recipes based on the user's past feedback. For example, the registration unit analyzes the user's past feedback and customizes the registration interface. This makes it possible to suggest the optimal registration method by reflecting the user's past feedback.

[0041] When proposing a menu, the suggestion unit can adjust the level of detail of the suggestion based on the user's meal history. For example, the suggestion unit can suggest detailed recipes based on dishes that the user has frequently eaten in the past. The suggestion unit can also suggest menus that use specific ingredients based on the user's meal history. For example, the suggestion unit can analyze the user's meal history and suggest an optimal menu. In this way, by adjusting the level of detail of the suggestion based on the user's meal history, it is possible to suggest a more appropriate menu.

[0042] When proposing a menu, the suggestion unit can apply different suggestion algorithms depending on the user's health and nutritional status. For example, the suggestion unit considers the user's health status to suggest an appropriate menu. The suggestion unit can also suggest a balanced menu based on the user's nutritional status. For example, the suggestion unit applies different suggestion algorithms depending on the user's health goals. This makes it possible to suggest an appropriate menu depending on the user's health and nutritional status.

[0043] When proposing a menu, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit suggests an optimal menu based on suggestions that the user has accepted in the past. The suggestion unit can also preferentially suggest specific ingredients or recipes based on the user's past suggestion results. For example, the suggestion unit analyzes the user's past suggestion results and improves the suggestion algorithm. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0044] When proposing a menu, the suggestion unit can determine the priority of suggestions based on the time of submission of the user's meals. For example, if the user submits meals for a specific time period, the suggestion unit can suggest a menu suitable for that time period. Furthermore, if the user submits meals on a specific day of the week, the suggestion unit can also suggest a menu suitable for that day of the week. For example, the suggestion unit can analyze the time of submission of the user's meals and suggest an optimal menu. In this way, by determining the priority of suggestions based on the time of submission of the user's meals, it is possible to suggest a more appropriate menu.

[0045] When proposing a menu, the suggestion unit can adjust the order of suggestions based on the relevance of the user's meals. For example, the suggestion unit prioritizes suggesting menus that are highly relevant to dishes the user has eaten in the past. The suggestion unit can also suggest menus that use highly relevant ingredients based on the user's meal history. For example, the suggestion unit analyzes the relevance of the user's meals and determines the optimal suggestion order. In this way, by adjusting the order of suggestions based on the relevance of the user's meals, a more appropriate menu can be proposed.

[0046] When proposing a menu, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, if the user is a beginner at cooking, the suggestion unit can suggest a menu using simple terms. Also, if the user has expertise in cooking, the suggestion unit can suggest a menu using detailed technical terms. For example, the suggestion unit analyzes the user's level of expertise and suggests using optimal terms. In this way, by adjusting the use of technical terms in the suggestion according to the user's level of expertise, it is possible to suggest a more appropriate menu.

[0047] When providing cooking guidance, the guide unit can analyze the user's past cooking history and select the optimal guide method. For example, the guide unit can suggest the optimal guide method based on dishes the user has cooked in the past. The guide unit can also preferentially suggest specific cooking methods based on the user's past cooking history. For example, the guide unit can analyze the user's past cooking history and suggest the most efficient guide method. In this way, the optimal guide method can be suggested by analyzing the user's past cooking history.

[0048] The guide unit can customize the guide means based on the user's current cooking skills when providing cooking guidance. For example, if the user is a beginner at cooking, the guide unit can provide detailed guidance on basic cooking procedures. Furthermore, if the user has specialized cooking knowledge, the guide unit can also provide guidance on more advanced cooking procedures. For example, the guide unit can analyze the user's cooking skills and provide the optimal guide means. This allows the provision of more appropriate cooking guidance by customizing the guide means according to the user's cooking skills.

[0049] The guide unit can improve the method of guidance by reflecting user feedback when providing cooking guidance. For example, the guide unit improves the content of the cooking guidance based on feedback previously provided by the user. The guide unit can also provide detailed guidance on specific cooking procedures based on the user's feedback. For example, the guide unit analyzes the user's feedback and customizes the method of guidance. This allows the user's feedback to be reflected to provide a more appropriate cooking guide.

[0050] When providing cooking guidance, the guide unit can select the optimal guidance method by taking into account the user's geographical location information. For example, if the user is in a specific area, the guide unit can provide a cooking guide using local specialties. Furthermore, if the user is traveling, the guide unit can also provide a cooking guide for local specialties of the travel destination. For example, if the user is at home, the guide unit can provide a cooking guide using ingredients available at a nearby supermarket. In this way, the optimal cooking guide can be provided by taking into account the user's geographical location information.

[0051] When providing cooking guidance, the guide unit can analyze the user's social media activity and suggest guidance methods. For example, the guide unit provides cooking guidance for dishes shared by the user on social media. The guide unit can also analyze the content posted by the user on social media and suggest related cooking guidance. For example, the guide unit can suggest related cooking guidance by referring to the activities of the user's friends on social media. In this way, related cooking guidance can be suggested by analyzing the user's social media activity.

[0052] When providing cooking guidance, the guide unit can customize the guidance method by reflecting the user's past feedback. For example, the guide unit can suggest the optimal guidance method based on feedback provided by the user in the past. The guide unit can also prioritize guidance on specific cooking procedures based on the user's past feedback. For example, the guide unit analyzes the user's past feedback and customizes the guidance method. In this way, more appropriate cooking guidance can be provided by reflecting the user's past feedback.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The suggestion unit not only analyzes the user's dietary content, preferences, and allergy information, but also analyzes the user's past meal history to suggest optimal menus. For example, it can suggest similar dishes based on dishes the user has frequently eaten in the past. It can also suggest menus using ingredients that the user has previously enjoyed. It can also suggest menus using specific ingredients based on the user's past meal history, reducing food waste. This makes it possible to suggest menus that take the user's past meal history into consideration, thereby providing more efficient meal plans.

[0055] The suggestion unit not only analyzes the user's dietary content, preferences, and allergy information, but also proposes menus taking into account the user's health and nutritional status. For example, if the user needs to consume a lot of a specific nutrient, it will propose a menu using ingredients that contain a lot of that nutrient. Also, if the user has a specific health goal, it can propose a menu that matches that goal. Furthermore, it can propose a balanced menu based on the user's health status to support healthy eating. This makes it possible to propose menus that take into account the user's health and nutritional status, allowing for the provision of healthier meals.

[0056] The suggestion unit not only analyzes the user's dietary content, preferences, and allergy information, but can also suggest menus taking the user's geographical location information into consideration. For example, if the user is in a specific area, it can suggest menus using local specialties. If the user is traveling, it can also suggest local specialties from the travel destination. Furthermore, if the user is at home, it can suggest menus using ingredients available at a nearby supermarket. This makes it possible to suggest menus that take the user's geographical location information into consideration, allowing for the provision of meals that are more locally rooted.

[0057] The suggestion unit not only analyzes the user's dietary content, preferences, and allergy information, but can also analyze the user's social media activity to suggest related menus. For example, it can suggest similar menus based on the meal content the user has shared on social media. It can also analyze the user's social media posts to suggest related ingredients and recipes. It can also suggest related menus based on the activity of the user's friends on social media. This makes it possible to suggest menus that take the user's social media activity into consideration, allowing for more personalized meals to be provided.

[0058] The guide unit can analyze the user's past cooking history and select the optimal guide method. For example, it can provide cooking guides for similar dishes based on dishes the user has cooked in the past. It can also preferentially suggest specific cooking methods based on the user's past cooking history. It can also analyze the user's past cooking history and suggest the most efficient guide method. This makes it possible to provide cooking guides that take the user's past cooking history into consideration, supporting more efficient cooking.

[0059] The guide unit can customize the guide means based on the user's current cooking skills. For example, if the user is a beginner at cooking, it can provide detailed guidance on basic cooking procedures. If the user has specialized cooking knowledge, it can also provide guidance on more advanced cooking procedures. It can also analyze the user's cooking skills and provide the optimal guide means. This makes it possible to provide guidance tailored to the user's cooking skills, thereby providing more appropriate cooking support.

[0060] The guide unit can improve the guidance method by reflecting user feedback. For example, the content of the cooking guide can be improved based on feedback provided by the user in the past. The guide unit can also provide detailed guidance on specific cooking steps based on the user's feedback. Furthermore, the guide unit can analyze the user's feedback and customize the guidance method. This enables guidance that reflects the user's feedback, making it possible to provide more appropriate cooking support.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The registration unit registers the user's meal details, preferences, and allergy information. Meal details include, for example, ingredients, dish names, and nutritional information. The user can input the meal details in text format, or can register the meal details using voice input or image input. For example, the user's voice can be converted into text using voice recognition technology and registered as the meal details. Step 2: The suggestion unit uses the generation AI to analyze the information registered by the registration unit and propose today's menu. The suggestion unit analyzes the user's preferences and allergy information and proposes an appropriate menu. The generation AI uses text generation AI (e.g., LLM) to analyze the user's preferences and allergy information and generate a menu. The suggestion unit can also propose menus that take into account seasonal ingredients and nutritional balance. For example, in spring, the generation AI proposes menus using seasonal vegetables and provides recipes that take nutritional balance into consideration. Step 3: The guide section provides guidance on time-saving recipes and special recipes based on the menus proposed by the suggestion section. If the user wants to save time, the guide section suggests simple and quick recipes and guides the user through the cooking steps. If the user wants to make special dishes, the guide section provides detailed recipes and cooking tips to help the user create dishes that satisfy them.

[0063] (Example 2) A menu suggestion system according to an embodiment of the present invention registers a user's dietary information, preferences, and allergy information, and then uses a generation AI to propose a daily menu and guide users on cooking methods. The menu suggestion system registers a user's dietary information, preferences, and allergy information, and then uses a generation AI to propose a daily menu. For example, if a user registers that they like Japanese food and would like to incorporate a lot of fish dishes, the generation AI will propose a Japanese menu using fish. The menu suggestion system then provides guidance on cooking methods for both those who want to save time and those who want to make more elaborate meals. For example, if a user wants to save time, the generation AI will suggest simple and quick recipes and guide the user through the cooking steps. On the other hand, if a user wants to make more elaborate meals, the generation AI will provide detailed recipes and cooking tips to help the user create a satisfying meal. This saves users the trouble of planning their daily menu, and with the added cooking support, they can efficiently prepare delicious meals. For example, they can use time-saving recipes on busy weekdays and enjoy elaborate meals on weekends. Furthermore, the generation AI also makes suggestions that take seasonal ingredients and nutritional balance into account. For example, in spring, the system suggests menus using seasonal vegetables and provides recipes that take nutritional balance into consideration. This allows users to enjoy healthy and delicious meals. For example, the system allows users to quickly and accurately decide on their daily menus, reducing the effort required for cooking. In addition, users' satisfaction is improved because menus tailored to their preferences and health condition are suggested.

[0064] A menu suggestion system according to an embodiment includes a registration unit, a suggestion unit, and a guide unit. The registration unit registers a user's meal details, preferences, and allergy information. The meal details include, but are not limited to, ingredients, dish names, and nutritional components. For example, the registration unit allows the user to input the meal details in text format. The registration unit can also register the meal details using voice input or image input. For example, the registration unit converts the user's voice into text using voice recognition technology and registers the text as the meal details. The suggestion unit uses a generation AI to analyze the information registered by the registration unit and suggest today's menu. The suggestion unit analyzes, for example, the user's preferences and allergy information and suggests an appropriate menu. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's preferences and allergy information and generate a menu. The suggestion unit can also suggest menus that take seasonal ingredients and nutritional balance into consideration. For example, the generation AI suggests menus using seasonal vegetables in spring and provides recipes that take nutritional balance into consideration. The guide unit provides guidance on time-saving recipes and special recipes based on the menu proposed by the suggestion unit. For example, if a user wants to cook something quickly, the guide unit suggests simple and quick recipes and guides the user through the cooking steps. Furthermore, if a user wants to cook something with special care, the guide unit provides detailed recipes and cooking tips to help the user cook a dish that satisfies them. As a result, the menu suggestion system according to the embodiment can efficiently suggest menus and guide the user through cooking methods based on the user's dietary content, preferences, and allergy information.

[0065] The suggestion unit can analyze the user's preferences and allergy information using the generation AI. For example, the generation AI analyzes the user's preferences and allergy information and suggests appropriate menus. For example, if the user registers that they like Japanese food and would like to include many fish dishes, the generation AI will suggest Japanese menus that use fish. The generation AI can also take the user's allergy information into consideration and suggest menus that do not contain ingredients that cause allergies. For example, if the user registers that they have a nut allergy, the generation AI will suggest menus that do not contain nuts. This makes it possible to accurately analyze the user's preferences and allergy information and suggest appropriate menus.

[0066] The suggestion unit can use the generation AI to suggest menus that take into account seasonal ingredients. For example, the generation AI considers seasonal ingredients and suggests menus that utilize seasonal ingredients. For example, in spring the generation AI suggests menus that use seasonal vegetables and provides recipes that take nutritional balance into consideration. The generation AI can also consider ingredients specific to a region and suggest menus that use ingredients that are available in that region. For example, if the user is in a specific region, the generation AI will suggest menus that use local specialties. In this way, by considering seasonal ingredients, it is possible to suggest menus that utilize seasonal ingredients.

[0067] The suggestion unit can use the generation AI to suggest menus that take nutritional balance into consideration. The generation AI, for example, considers nutritional balance and suggests healthy menus. For example, the generation AI suggests balanced menus based on the recommended intake of each nutrient. The generation AI can also consider the user's health condition and suggest menus that are high in specific nutrients. For example, if the user registers that they are "iron deficient," the generation AI will suggest menus that use ingredients that are high in iron. This makes it possible to suggest healthy meals by considering nutritional balance.

[0068] The guide unit can suggest time-saving recipes and provide real-time support for cooking procedures. For example, if you want to cook something in a short amount of time, the guide unit can suggest simple and quick recipes and provide real-time support for cooking procedures. For example, the guide unit can provide advice and real-time feedback at appropriate times during cooking. The guide unit can also provide support for immediately dealing with problems that occur during cooking. For example, the guide unit can suggest solutions in real-time for problems that occur during cooking. This allows for efficient cooking by suggesting time-saving recipes and providing real-time support for cooking procedures.

[0069] The guide unit can suggest special recipes and provide cooking tips. For example, if a user wants to make something special, the guide unit can provide detailed recipes and cooking tips to help the user make a dish that satisfies them. For example, the guide unit can provide tips on specific cooking techniques, how to handle ingredients, how to use seasonings, etc. The guide unit can also provide support for immediately dealing with problems that occur during cooking. For example, the guide unit can suggest solutions in real time to problems that arise during cooking. In this way, the guide unit can help the user make a dish that satisfies them by suggesting special recipes and providing cooking tips.

[0070] The registration unit can estimate the user's emotions and adjust the timing of meal content registration based on the estimated user emotions. For example, if the user is feeling stressed, the registration unit prompts the user to register meal content during a time when the user is able to relax. The registration unit can also adjust the registration timing by providing an interface that allows the user to register in a short time when the user is busy. For example, if the user is relaxed, the registration unit adjusts the registration timing so that detailed information can be entered. This allows the timing of meal content registration to be adjusted according to the user's emotions, thereby enabling registration at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0071] The registration unit can analyze the user's past meal history and select the optimal registration method. For example, the registration unit can automatically display meal contents that the user has frequently registered in the past as candidates. The registration unit can also predict and suggest meal contents to be registered for a specific time period based on the user's past meal history. For example, the registration unit can analyze the user's past meal history and suggest the most efficient registration method. In this way, the analysis of the past meal history can suggest the optimal registration method for the user.

[0072] When registering meal contents, the registration unit can filter the meal contents based on the user's current health and nutritional status. For example, the registration unit can propose appropriate meal contents taking into account the user's current health status. The registration unit can also propose balanced meal contents based on the user's nutritional status. For example, the registration unit filters appropriate meal contents according to the user's health goals. This makes it possible to propose appropriate meal contents based on the user's health and nutritional status.

[0073] When registering meal details, the registration unit can select the optimal registration means depending on the user's input method. For example, when the user registers meal details by voice, the registration unit automatically converts the voice into text using voice recognition technology. In addition, when the user registers meal details using images, the registration unit can also automatically analyze the meal details using image recognition technology. For example, when the user registers meal details using text, the registration unit provides an input completion function to enable efficient registration. This allows meal details to be registered efficiently by selecting the optimal registration means depending on the user's input method.

[0074] The registration unit can estimate the user's emotions and determine the priority of meal contents to be registered based on the estimated user emotions. For example, if the user is feeling stressed, the registration unit can prioritize suggesting meal contents that have a relaxing effect. Furthermore, if the user is tired, the registration unit can prioritize suggesting nutritious meal contents. For example, if the user is feeling energetic, the registration unit can prioritize suggesting new ingredients or recipes. This allows more appropriate meal contents to be suggested by prioritizing meal contents according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] When registering meal contents, the registration unit can prioritize the registration of highly relevant meal contents by taking into account the user's geographical location information. For example, if the user is in a specific area, the registration unit can suggest meal contents using local specialties. Furthermore, if the user is traveling, the registration unit can also suggest local specialties of the travel destination. For example, if the user is at home, the registration unit can suggest meal contents using ingredients available at a nearby supermarket. In this way, highly relevant meal contents can be suggested by taking into account the user's geographical location information.

[0076] When registering meal details, the registration unit can analyze the user's social media activity and register related meal details. For example, the registration unit automatically registers meal details shared by the user on social media. The registration unit can also analyze the content posted by the user on social media and suggest related meal details. For example, the registration unit can suggest related meal details by referring to the activity of the user's friends on social media. In this way, related meal details can be suggested by analyzing the user's social media activity.

[0077] The registration unit can customize the registration method by reflecting the user's past feedback when registering meal details. For example, the registration unit can suggest the optimal registration method based on feedback provided by the user in the past. The registration unit can also preferentially suggest specific ingredients or recipes based on the user's past feedback. For example, the registration unit analyzes the user's past feedback and customizes the registration interface. This makes it possible to suggest the optimal registration method by reflecting the user's past feedback.

[0078] The suggestion unit can estimate the user's emotions and adjust the menu suggestion method based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a menu with a relaxing effect. Furthermore, if the user is tired, the suggestion unit can also suggest a nutritious menu. For example, if the user is feeling energetic, the suggestion unit can suggest new ingredients or recipes. In this way, by adjusting the menu suggestion method according to the user's emotions, it is possible to suggest a more appropriate menu. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] When proposing a menu, the suggestion unit can adjust the level of detail of the suggestion based on the user's meal history. For example, the suggestion unit can suggest detailed recipes based on dishes that the user has frequently eaten in the past. The suggestion unit can also suggest menus that use specific ingredients based on the user's meal history. For example, the suggestion unit can analyze the user's meal history and suggest an optimal menu. In this way, by adjusting the level of detail of the suggestion based on the user's meal history, it is possible to suggest a more appropriate menu.

[0080] When proposing a menu, the suggestion unit can apply different suggestion algorithms depending on the user's health and nutritional status. For example, the suggestion unit considers the user's health status to suggest an appropriate menu. The suggestion unit can also suggest a balanced menu based on the user's nutritional status. For example, the suggestion unit applies different suggestion algorithms depending on the user's health goals. This makes it possible to suggest an appropriate menu depending on the user's health and nutritional status.

[0081] When proposing a menu, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit suggests an optimal menu based on suggestions that the user has accepted in the past. The suggestion unit can also preferentially suggest specific ingredients or recipes based on the user's past suggestion results. For example, the suggestion unit analyzes the user's past suggestion results and improves the suggestion algorithm. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0082] The suggestion unit can estimate the user's emotions and adjust the length of the menu suggestion based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit can suggest a menu that can be made in a short time. The suggestion unit can also suggest a menu that includes detailed recipes if the user is relaxed. For example, if the user is excited, the suggestion unit can suggest a visually stimulating menu. This allows for adjusting the length of the menu suggestion according to the user's emotions, thereby suggesting a more appropriate menu. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0083] When proposing a menu, the suggestion unit can determine the priority of suggestions based on the time of submission of the user's meals. For example, if the user submits meals for a specific time period, the suggestion unit can suggest a menu suitable for that time period. Furthermore, if the user submits meals on a specific day of the week, the suggestion unit can also suggest a menu suitable for that day of the week. For example, the suggestion unit can analyze the time of submission of the user's meals and suggest an optimal menu. In this way, by determining the priority of suggestions based on the time of submission of the user's meals, it is possible to suggest a more appropriate menu.

[0084] When proposing a menu, the suggestion unit can adjust the order of suggestions based on the relevance of the user's meals. For example, the suggestion unit prioritizes suggesting menus that are highly relevant to dishes the user has eaten in the past. The suggestion unit can also suggest menus that use highly relevant ingredients based on the user's meal history. For example, the suggestion unit analyzes the relevance of the user's meals and determines the optimal suggestion order. In this way, by adjusting the order of suggestions based on the relevance of the user's meals, a more appropriate menu can be proposed.

[0085] When proposing a menu, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, if the user is a beginner at cooking, the suggestion unit can suggest a menu using simple terms. Also, if the user has expertise in cooking, the suggestion unit can suggest a menu using detailed technical terms. For example, the suggestion unit analyzes the user's level of expertise and suggests using optimal terms. In this way, by adjusting the use of technical terms in the suggestion according to the user's level of expertise, it is possible to suggest a more appropriate menu.

[0086] The guide unit can estimate the user's emotions and adjust the cooking guide method based on the estimated user's emotions. For example, if the user is feeling stressed, the guide unit can provide a cooking guide that helps the user relax. Furthermore, if the user is tired, the guide unit can provide a cooking guide that helps the user make simple and quick meals. For example, if the user is feeling energetic, the guide unit can provide new cooking methods and tips. This allows the cooking guide method to be adjusted according to the user's emotions, making it possible to provide a more appropriate cooking guide. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] When providing cooking guidance, the guide unit can analyze the user's past cooking history and select the optimal guide method. For example, the guide unit can suggest the optimal guide method based on dishes the user has cooked in the past. The guide unit can also preferentially suggest specific cooking methods based on the user's past cooking history. For example, the guide unit can analyze the user's past cooking history and suggest the most efficient guide method. In this way, the optimal guide method can be suggested by analyzing the user's past cooking history.

[0088] The guide unit can customize the guide means based on the user's current cooking skills when providing cooking guidance. For example, if the user is a beginner at cooking, the guide unit can provide detailed guidance on basic cooking procedures. Furthermore, if the user has specialized cooking knowledge, the guide unit can also provide guidance on more advanced cooking procedures. For example, the guide unit can analyze the user's cooking skills and provide the optimal guide means. This allows the provision of more appropriate cooking guidance by customizing the guide means according to the user's cooking skills.

[0089] The guide unit can improve the method of guidance by reflecting user feedback when providing cooking guidance. For example, the guide unit improves the content of the cooking guidance based on feedback previously provided by the user. The guide unit can also provide detailed guidance on specific cooking procedures based on the user's feedback. For example, the guide unit analyzes the user's feedback and customizes the method of guidance. This allows the user's feedback to be reflected to provide a more appropriate cooking guide.

[0090] The guide unit can estimate the user's emotions and determine the priority of cooking guides based on the estimated user emotions. For example, if the user is feeling stressed, the guide unit can prioritize providing cooking guides that have a relaxing effect. Also, if the user is tired, the guide unit can prioritize providing cooking guides that are easy and quick to make. For example, if the user is feeling energetic, the guide unit can prioritize providing new cooking methods and tips. In this way, by determining the priority of cooking guides according to the user's emotions, it is possible to provide more appropriate cooking guides. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] When providing cooking guidance, the guide unit can select the optimal guidance method by taking into account the user's geographical location information. For example, if the user is in a specific area, the guide unit can provide a cooking guide using local specialties. Furthermore, if the user is traveling, the guide unit can also provide a cooking guide for local specialties of the travel destination. For example, if the user is at home, the guide unit can provide a cooking guide using ingredients available at a nearby supermarket. In this way, the optimal cooking guide can be provided by taking into account the user's geographical location information.

[0092] When providing cooking guidance, the guide unit can analyze the user's social media activity and suggest guidance methods. For example, the guide unit provides cooking guidance for dishes shared by the user on social media. The guide unit can also analyze the content posted by the user on social media and suggest related cooking guidance. For example, the guide unit can suggest related cooking guidance by referring to the activities of the user's friends on social media. In this way, related cooking guidance can be suggested by analyzing the user's social media activity.

[0093] When providing cooking guidance, the guide unit can customize the guidance method by reflecting the user's past feedback. For example, the guide unit can suggest the optimal guidance method based on feedback provided by the user in the past. The guide unit can also prioritize guidance on specific cooking procedures based on the user's past feedback. For example, the guide unit analyzes the user's past feedback and customizes the guidance method. In this way, more appropriate cooking guidance can be provided by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the registration unit, suggestion unit, and guide unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart device 14 and registers the user's dietary content, preferences, and allergy information in text format, voice input, or image input. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's preferences and allergy information using a generation AI to suggest today's menu. The guide unit is realized, for example, by the control unit 46A of the smart device 14 and guides the user to time-saving recipes and specialty recipes based on the suggested menu. === Hard Collateral 1-2 === Each of the multiple elements, including the registration unit, suggestion unit, and guide unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214 and registers the user's dietary content, preferences, and allergy information in text format, voice input, or image input. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's preferences and allergy information using a generation AI to suggest today's menu. The guide unit is realized, for example, by the control unit 46A of the smart glasses 214 and guides the user to time-saving recipes and specialty recipes based on the suggested menu. === Hard Collateral 1-3 === Each of the multiple elements, including the registration unit, suggestion unit, and guide unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the headset terminal 314 and registers the user's dietary details, preferences, and allergy information in text format, voice input, or image input. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's preferences and allergy information using a generation AI to suggest today's menu. The guide unit is realized, for example, by the control unit 46A of the headset terminal 314 and guides the user to time-saving recipes and specialty recipes based on the suggested menu. === Hard Collateral 1-4 === Each of the multiple elements, including the registration unit, suggestion unit, and guide unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the robot 414 and registers the user's dietary details, preferences, and allergy information in text format, voice input, or image input. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's preferences and allergy information using a generation AI to suggest today's menu. The guide unit is realized, for example, by the control unit 46A of the robot 414 and guides the user to time-saving recipes and specialty recipes based on the suggested menu.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The suggestion unit not only analyzes the user's dietary content, preferences, and allergy information, but also estimates the user's feelings about food and can suggest menus based on the estimated feelings. For example, if the user is feeling stressed, it can suggest menus using ingredients with a relaxing effect. Also, if the user is tired, it can suggest menus using nutritious ingredients. Furthermore, if the user is feeling energetic, it can suggest new ingredients and recipes, making cooking more enjoyable. This makes it possible to suggest menus that reflect the user's feelings, providing a more satisfying dining experience.

[0096] The suggestion unit not only analyzes the user's dietary content, preferences, and allergy information, but also analyzes the user's past meal history to suggest optimal menus. For example, it can suggest similar dishes based on dishes the user has frequently eaten in the past. It can also suggest menus using ingredients that the user has previously enjoyed. It can also suggest menus using specific ingredients based on the user's past meal history, reducing food waste. This makes it possible to suggest menus that take the user's past meal history into consideration, thereby providing more efficient meal plans.

[0097] The suggestion unit not only analyzes the user's dietary content, preferences, and allergy information, but also proposes menus taking into account the user's health and nutritional status. For example, if the user needs to consume a lot of a specific nutrient, it will propose a menu using ingredients that contain a lot of that nutrient. Also, if the user has a specific health goal, it can propose a menu that matches that goal. Furthermore, it can propose a balanced menu based on the user's health status to support healthy eating. This makes it possible to propose menus that take into account the user's health and nutritional status, allowing for the provision of healthier meals.

[0098] The suggestion unit not only analyzes the user's dietary content, preferences, and allergy information, but can also suggest menus taking the user's geographical location information into consideration. For example, if the user is in a specific area, it can suggest menus using local specialties. If the user is traveling, it can also suggest local specialties from the travel destination. Furthermore, if the user is at home, it can suggest menus using ingredients available at a nearby supermarket. This makes it possible to suggest menus that take the user's geographical location information into consideration, allowing for the provision of meals that are more locally rooted.

[0099] The suggestion unit not only analyzes the user's dietary content, preferences, and allergy information, but can also analyze the user's social media activity to suggest related menus. For example, it can suggest similar menus based on the meal content the user has shared on social media. It can also analyze the user's social media posts to suggest related ingredients and recipes. It can also suggest related menus based on the activity of the user's friends on social media. This makes it possible to suggest menus that take the user's social media activity into consideration, allowing for more personalized meals to be provided.

[0100] The guide unit can estimate the user's emotions and adjust the cooking guidance method based on the estimated emotions. For example, if the user is feeling stressed, it can provide a cooking guide that helps them relax. If the user is tired, it can provide a cooking guide that helps them make simple and quick dishes. Furthermore, if the user is feeling energetic, it can provide new cooking methods and tips to make cooking more enjoyable. This makes it possible to provide cooking guidance that responds to the user's emotions, resulting in a more satisfying cooking experience.

[0101] The guide unit can analyze the user's past cooking history and select the optimal guide method. For example, it can provide cooking guides for similar dishes based on dishes the user has cooked in the past. It can also preferentially suggest specific cooking methods based on the user's past cooking history. It can also analyze the user's past cooking history and suggest the most efficient guide method. This makes it possible to provide cooking guides that take the user's past cooking history into consideration, supporting more efficient cooking.

[0102] The guide unit can customize the guide means based on the user's current cooking skills. For example, if the user is a beginner at cooking, it can provide detailed guidance on basic cooking procedures. If the user has specialized cooking knowledge, it can also provide guidance on more advanced cooking procedures. It can also analyze the user's cooking skills and provide the optimal guide means. This makes it possible to provide guidance tailored to the user's cooking skills, thereby providing more appropriate cooking support.

[0103] The guide unit can improve the guidance method by reflecting user feedback. For example, the content of the cooking guide can be improved based on feedback provided by the user in the past. The guide unit can also provide detailed guidance on specific cooking steps based on the user's feedback. Furthermore, the guide unit can analyze the user's feedback and customize the guidance method. This enables guidance that reflects the user's feedback, making it possible to provide more appropriate cooking support.

[0104] The guide unit can estimate the user's emotions and determine the priority of cooking guides based on the estimated emotions. For example, if the user is feeling stressed, cooking guides with a relaxing effect can be provided preferentially. If the user is tired, cooking guides that are easy and quick to make can be provided preferentially. Furthermore, if the user is feeling energetic, new cooking methods and tips can be provided preferentially. This allows the priority of cooking guides to be determined according to the user's emotions, making it possible to provide more appropriate cooking support.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The registration unit registers the user's meal details, preferences, and allergy information. Meal details include, for example, ingredients, dish names, and nutritional information. The user can input the meal details in text format, or can register the meal details using voice input or image input. For example, the user's voice can be converted into text using voice recognition technology and registered as the meal details. Step 2: The suggestion unit uses the generation AI to analyze the information registered by the registration unit and propose today's menu. The suggestion unit analyzes the user's preferences and allergy information and proposes an appropriate menu. The generation AI uses text generation AI (e.g., LLM) to analyze the user's preferences and allergy information and generate a menu. The suggestion unit can also propose menus that take into account seasonal ingredients and nutritional balance. For example, in spring, the generation AI proposes menus using seasonal vegetables and provides recipes that take nutritional balance into consideration. Step 3: The guide section provides guidance on time-saving recipes and special recipes based on the menus proposed by the suggestion section. If the user wants to save time, the guide section suggests simple and quick recipes and guides the user through the cooking steps. If the user wants to make special dishes, the guide section provides detailed recipes and cooking tips to help the user create dishes that satisfy them.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] 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.

[0170] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0171] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0172] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0178] [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a registration unit for registering the user's dietary preferences and allergy information; A suggestion unit that analyzes the information registered by the registration unit and suggests today's menu; A guide unit that guides users to time-saving recipes or specialty recipes based on the menu proposed by the proposal unit. A system characterized by:

2. The proposal unit Analyze user preferences and allergy information using generative AI 2. The system of claim 1.

3. The proposal unit Generative AI proposes menus that take seasonal ingredients into consideration 2. The system of claim 1.

4. The proposal unit Generative AI proposes menus that take nutritional balance into consideration 2. The system of claim 1.

5. The guide portion is Proposes time-saving recipes and provides real-time cooking instructions 2. The system of claim 1.

6. The guide portion is Propose special recipes and provide cooking tips 2. The system of claim 1.

7. The registration unit Estimates the user's emotions and adjusts the timing of registering meal details based on the estimated user emotions.

2. The system of claim 1.

8. The registration unit Analyze the user's past meal history and select the optimal registration method 2. The system of claim 1.

Images