System

Abstract

An object of a system according to an embodiment is to automatically generate a nutritionally balanced menu and provide the menu to a user.SOLUTION: A system includes a user information input part, an analysis part, a menu generation part, and a recipe provision part. The user information input unit inputs user information. The analysis unit analyzes the information input by the user information input unit. The menu generation unit generates a nutritionally balanced menu on the basis of the information analyzed by the analysis unit. A recipe providing part provides a concrete recipe and a cooking procedure based on the menu generated by the menu generating part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it takes time to plan daily menus, making it difficult to provide nutritionally balanced meals.

[0005] The system according to the embodiment aims to automatically generate nutritionally balanced menus and provide them to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information input unit, an analysis unit, a menu generation unit, and a recipe provision unit. The user information input unit inputs user information. The analysis unit analyzes the information input by the user information input unit. The menu generation unit generates a nutritionally balanced menu based on the information analyzed by the analysis unit. The recipe provision unit provides specific recipes and cooking procedures based on the menu generated by the menu generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate nutritionally balanced menus and provide them to users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The menu creation system according to an embodiment of the present invention is a system that automatically creates nutritionally balanced menus based on user information and provides specific recipes and cooking procedures, thereby reducing the burden on users and enabling them to easily prepare healthy meals.

[0029] A menu creation system according to an embodiment includes a user information input unit, an analysis unit, a menu creation unit, and a recipe provision unit. The user information input unit inputs user information, such as the number of family members, age, allergy information, food preferences, and specific nutritional requirements. The analysis unit analyzes the information input by the user information input unit. For example, the analysis unit analyzes the user's preferences and nutritional balance to create basic data for generating an optimal menu. The menu creation unit generates a nutritionally balanced menu based on the information analyzed by the analysis unit. For example, the system may suggest specific menus such as oatmeal and fruit for breakfast, chicken and vegetable salad for lunch, and grilled fish and vegetables for dinner. The recipe provision unit provides specific recipes and cooking instructions based on the menu created by the menu creation unit. For example, the recipe provides detailed information about the ingredients needed for a chicken and vegetable salad, their quantities, and cooking instructions. This reduces the burden on the user by automatically creating a nutritionally balanced menu based on the user's information and providing specific recipes and cooking instructions.

[0030] The analysis unit can analyze the user's past meal history and predict and automatically input their preferences and nutritional balance. For example, the analysis unit collects the user's past meal history and uses AI to analyze the data to predict their meal preferences and nutritional balance. For example, based on the past month's meal data, the analysis unit automatically identifies the user's favorite ingredients and ingredients to avoid. The analysis unit also analyzes the meal history to identify the nutrients the user frequently consumes and the nutrients they tend to lack. For example, it automatically calculates the amount of protein the user has consumed in large amounts in the past and the amount of vitamins they are lacking. The analysis unit also predicts the user's meal preferences and nutritional balance based on their past eating patterns and reflects this in meal suggestions for the next meal. For example, it prioritizes suggestions based on dishes the user has enjoyed in the past. In this way, by analyzing the user's past meal history, preferences and nutritional balance can be predicted and reflected in meal suggestions for the next meal.

[0031] The analysis unit can link the user's health data to consider more precise nutritional balance. For example, the analysis unit collects the user's exercise data and weight fluctuation data from a fitness tracker or health app, and AI analyzes that data to optimize nutritional balance. For example, it may suggest increasing calorie intake on days when the user exercises a lot. The analysis unit also suggests nutritional balance based on the user's health condition and goals based on data from the health app. For example, it may suggest low-calorie menus for users who are trying to lose weight. The analysis unit also analyzes data from the fitness tracker to consider nutritional balance that matches the user's exercise habits. For example, it may suggest high-protein menus for users who are trying to build muscle. In this way, by linking the user's health data, it is possible to generate menus that take into account more precise nutritional balance.

[0032] The user information input unit can input information using voice recognition technology, reducing the effort required. For example, the user information input unit uses voice recognition technology to automatically convert information input orally by the user into text, which is then analyzed by AI. For example, information such as "Family of four, two children, no allergies, likes vegetables" is input by voice. The user information input unit also analyzes the information input by voice in real time and automatically extracts the necessary data. For example, a request such as "I want to eat more protein" is input by voice. The user information input unit also uses voice recognition technology to reduce the effort required for the user to input information and collect information smoothly. For example, information for all family members is input all at once by voice input. In this way, the use of voice recognition technology can reduce the effort required for the user to input information.

[0033] The user information input unit provides an interface that allows information about all family members to be entered at once, and can generate a menu that takes individual nutritional needs into consideration. For example, the user information input unit provides an interface that allows information about all family members to be entered at once, and AI analyzes the data to generate a menu that takes individual nutritional needs into consideration. For example, the ages and allergy information of all family members are entered at once. The user information input unit also inputs information about all family members at once, and builds a system that suggests menus according to individual nutritional needs. For example, it suggests menus that include a lot of nutrients necessary for children's growth. The user information input unit also provides an interface that allows information about all family members to be entered at once, and generates a menu that takes individual nutritional needs into consideration. For example, the preferences and allergy information of all family members are entered at once. In this way, by entering information about all family members at once, a menu that takes individual nutritional needs into consideration can be generated.

[0034] The menu generation unit can generate a seasonal menu by taking into account ingredients that correspond to the season and weather. The menu generation unit, for example, collects seasonal and weather data, and AI generates a seasonal menu based on that data. For example, in summer, it proposes a menu using cold dishes and seasonal vegetables. The menu generation unit also builds a system that generates a seasonal menu by taking into account ingredients for each season. For example, in autumn, it proposes dishes using mushrooms and pumpkins. The menu generation unit also generates a seasonal menu based on weather data. For example, on cold days, it proposes hot soups and hot pot dishes. In this way, it is possible to generate a seasonal menu by taking into account ingredients that correspond to the season and weather.

[0035] The menu generation unit can link the user's ingredient inventory information and generate menus that prioritize the use of stock ingredients to reduce waste. The menu generation unit, for example, links inventory information from the user's refrigerator or pantry, and AI generates menus that prioritize the use of stock ingredients based on that data. For example, it suggests dishes that use vegetables in the refrigerator. The menu generation unit also builds a system that generates menus to reduce waste based on inventory information. For example, it suggests menus that prioritize the use of ingredients that are close to their expiration date. The menu generation unit also analyzes the user's inventory information and generates menus that prioritize the use of stock ingredients to reduce waste. For example, it suggests dishes that use up ingredients in the refrigerator. In this way, by linking the user's ingredient inventory information, it is possible to generate menus that prioritize the use of stock ingredients to reduce waste.

[0036] The menu generation unit generates a multinational menu that incorporates cuisine from different cultures and countries, thereby increasing the variety of meals. The menu generation unit, for example, builds a system that generates a multinational menu that incorporates cuisine from different cultures and countries. For example, it proposes a menu that incorporates Italian cuisine and Japanese cuisine. The menu generation unit also utilizes a multinational cuisine recipe database to generate a menu that incorporates cuisine from different cultures. For example, it proposes Mexican cuisine and Indian cuisine. The menu generation unit also generates a multinational menu that incorporates cuisine from different countries, thereby increasing the variety of meals. For example, it proposes French cuisine and Thai cuisine. In this way, it is possible to increase the variety of meals by incorporating cuisine from different cultures and countries.

[0037] The menu generation unit can generate menus that correspond to specific diets or dietary restrictions. The menu generation unit, for example, builds a system that generates menus that correspond to specific diets or dietary restrictions. For example, it proposes low-carbohydrate menus that correspond to ketogenic diets. The menu generation unit also generates menus that correspond to dietary restrictions such as vegans and vegetarians. For example, it proposes vegan dishes that do not use animal products. The menu generation unit also generates menus that correspond to specific diets or dietary restrictions to meet the needs of users. For example, it proposes gluten-free or low-carbohydrate menus. In this way, it is possible to provide menus that meet the needs of users by accommodating specific diets or dietary restrictions.

[0038] The recipe providing unit can propose recipes according to cooking time and difficulty, and provide cooking procedures that match the user's skill level. The recipe providing unit proposes recipes that match the user's skill level, taking into account, for example, cooking time and difficulty. For example, it provides simple recipes for beginners and complex recipes for advanced cooks. The recipe providing unit also analyzes the user's cooking skills and builds a system that provides cooking procedures that match the skill level. For example, it provides detailed procedures for beginners and simple procedures for advanced cooks. The recipe providing unit also provides cooking procedures that match the user's skill level based on cooking time and difficulty. For example, it proposes recipes with short cooking times or recipes with low difficulty. In this way, by proposing recipes according to cooking time and difficulty, it is possible to provide cooking procedures that match the user's skill level.

[0039] The recipe providing unit can generate cooking videos and provide visually easy-to-understand steps. For example, the recipe providing unit uses AI to automatically generate cooking videos to make cooking steps visually easy to understand. For example, it displays separate videos for each step. The recipe providing unit also generates cooking videos and builds a system that makes it easy for users to visually understand the steps. For example, it shows how to cut ingredients and cooking tips in videos. The recipe providing unit also provides cooking steps in videos so that users can visually confirm them. For example, it explains cooking tips and points to note in videos. In this way, by generating cooking videos, it is possible to provide visually easy-to-understand steps.

[0040] The recipe providing unit can provide recipes in multiple languages ​​to accommodate international users. For example, the recipe providing unit translates recipes into multiple languages ​​to build a system that accommodates international users. For example, recipes are provided in English, French, Chinese, etc. The recipe providing unit also makes recipes multilingual to make them easy to use for users who speak different languages. For example, the recipe is displayed according to the user's language setting. The recipe providing unit can also provide recipes in multiple languages ​​to accommodate international users. For example, cooking steps are explained in detail in the language of each country. In this way, by providing recipes in multiple languages, international users can be accommodated.

[0041] The recipe providing unit can collect user feedback on recipes and preferentially suggest popular recipes. For example, the recipe providing unit collects user feedback on recipes and builds a system that preferentially suggests popular recipes based on that data. For example, highly rated recipes are preferentially displayed. The recipe providing unit also analyzes user feedback and suggests popular recipes. For example, recipe rankings are created based on user comments and ratings. The recipe providing unit also collects feedback on recipes and preferentially suggests popular recipes. For example, recipes that are highly rated by users are displayed as recommendations. In this way, popular recipes can be preferentially suggested by collecting user feedback.

[0042] The ingredient purchasing list generation unit can analyze the user's past purchase history and add frequently purchased ingredients to the list with priority. The ingredient purchasing list generation unit, for example, builds a system that analyzes the user's past purchase history and adds frequently purchased ingredients to the list with priority. For example, frequently purchased ingredients are automatically added to the list. The ingredient purchasing list generation unit also adds ingredients that the user frequently purchases to the list based on the purchase history. For example, it analyzes past data and displays ingredients that are always purchased with priority. The ingredient purchasing list generation unit also analyzes the user's purchase history and adds frequently purchased ingredients to the list. For example, ingredients that the user frequently purchases are automatically included in the list. In this way, by analyzing the user's past purchase history, frequently purchased ingredients can be added to the list with priority.

[0043] The ingredient purchasing list generation unit can link ingredient price information to generate an optimal purchasing list within a budget. The ingredient purchasing list generation unit, for example, builds a system that links ingredient price information to generate an optimal purchasing list within a budget. For example, it selects ingredients according to a budget and creates a list. The ingredient purchasing list generation unit also generates an optimal purchasing list within a budget based on price information. For example, it prioritizes adding inexpensive ingredients to the list. The ingredient purchasing list generation unit also analyzes ingredient price information to generate an optimal purchasing list within a budget. For example, it selects ingredients so that the budget is not exceeded. In this way, by linking ingredient price information, an optimal purchasing list within a budget can be generated.

[0044] The ingredient purchasing list generation unit can link the ingredient purchasing list with an online shopping site to enable one-click purchases. For example, the ingredient purchasing list generation unit links the ingredient purchasing list with an online shopping site to build a system that enables one-click purchases. For example, all ingredients on the list are added to a cart at once. The ingredient purchasing list generation unit also links with the online shopping site to enable easy purchases of ingredients based on the purchase list. For example, ingredients on the list are automatically ordered. The ingredient purchasing list generation unit also links with the online shopping site to enable one-click purchases. For example, all ingredients on the list are purchased at once. In this way, by linking the ingredient purchasing list with the online shopping site, one-click purchases can be made.

[0045] The ingredient purchasing list generation unit can give priority to adding fresh ingredients from local farms and markets to the list. The ingredient purchasing list generation unit, for example, builds a system that gives priority to adding fresh ingredients from local farms and markets to the list. For example, ingredients that can be purchased directly from local farmers are added to the list. The ingredient purchasing list generation unit also adds fresh ingredients from local markets to the list and provides it to the user. For example, seasonal ingredients sold at local markets are included in the list. The ingredient purchasing list generation unit also gives priority to adding fresh ingredients from local farms and markets to the list. For example, organic vegetables provided by local farmers are included in the list. In this way, fresh ingredients from local farms and markets are given priority to being added to the list, thereby providing fresh ingredients to the user.

[0046] The nutritional value calculation unit can optimize nutritional value according to the user's health goals. The nutritional value calculation unit, for example, builds a system that optimizes nutritional value according to the user's health goals. For example, it suggests a low-calorie menu to a user who is trying to lose weight. The nutritional value calculation unit also optimizes nutritional value according to the health goals and suggests the optimal menu for the user. For example, it suggests a high-protein menu to a user who is trying to build muscle. The nutritional value calculation unit also optimizes nutritional value based on the user's health goals. For example, it suggests a balanced menu to a user who is trying to maintain health. In this way, by optimizing nutritional value according to the user's health goals, it is possible to provide the optimal menu for the user.

[0047] The nutritional value calculation unit periodically updates the nutritional value database of ingredients to provide the latest nutritional information. The nutritional value calculation unit, for example, periodically updates the nutritional value database of ingredients to build a system that provides the latest nutritional information. For example, the database is updated based on new research results. The nutritional value calculation unit also periodically updates the nutritional value database to provide the latest nutritional information to the user. For example, new nutritional value information of ingredients is added. The nutritional value calculation unit also periodically updates the nutritional value database of ingredients to provide the latest nutritional information. For example, the database is updated in accordance with fluctuations in nutritional value. In this way, by periodically updating the nutritional value database of ingredients, the latest nutritional information can be provided.

[0048] The nutritional value calculation unit can visually display nutritional value information in graphs or charts to make it easier to understand. The nutritional value calculation unit, for example, visually displays nutritional value information in graphs or charts to build a system that makes it easier for users to understand. For example, it displays nutrient intake amounts in bar graphs or pie charts. The nutritional value calculation unit also visually displays nutritional value information to make it easier for users to understand. For example, it displays nutrient balances in radar charts. The nutritional value calculation unit also visually displays nutritional value information in graphs or charts to make it easier for users to understand. For example, it displays daily nutrient intake amounts in a line graph. In this way, visually displaying nutritional value information in graphs or charts makes it easier for users to understand.

[0049] The nutritional value calculation unit can link nutritional value information with other health apps and fitness trackers to support comprehensive health management. For example, the nutritional value calculation unit links nutritional value information with other health apps and fitness trackers to build a system that supports comprehensive health management. For example, it integrates exercise data and nutritional data from a fitness tracker. The nutritional value calculation unit also links with other health apps to comprehensively manage nutritional value information. For example, it automatically sends nutritional data for meals to a health app. The nutritional value calculation unit also links nutritional value information with a fitness tracker to support comprehensive health management. For example, it provides advice on nutritional intake based on the amount of exercise. In this way, by linking nutritional value information with other health apps and fitness trackers, comprehensive health management can be supported.

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

[0051] The menu creation system can further include a storage method analysis unit that takes into account the user's method of storing ingredients. The storage method analysis unit analyzes the storage period and appropriate storage method of ingredients based on information such as refrigeration, freezing, and room temperature storage. For example, it can propose a menu that prioritizes the use of ingredients that can be frozen. The storage method analysis unit can also provide cooking procedures according to the storage method of ingredients. For example, it can provide detailed instructions for thawing frozen ingredients. By taking into account the user's method of storing ingredients, it is possible to reduce food waste and support efficient cooking.

[0052] The menu generation system can further include a time zone analysis unit that takes into account the user's meal times. The time zone analysis unit, for example, proposes an optimal menu according to the time zones of breakfast, lunch, and dinner. For example, it proposes high-calorie dishes for breakfast to replenish energy, and light dishes that are easy to digest for dinner. The time zone analysis unit can also propose meals that match the user's lifestyle. For example, it proposes meals that are suitable for nighttime for a user who works the night shift. In this way, by taking into account the user's meal times, it is possible to provide a more appropriate menu.

[0053] The menu generation system can further include a frequency analysis unit that takes into account the frequency of a user's meals. The frequency analysis unit proposes menus according to the frequency of meals, such as three meals a day, two meals a day, or one meal a day. For example, it proposes nutritious dishes to a user who eats one meal a day, and proposes a balanced menu to a user who eats three meals a day. The frequency analysis unit can also consider nutritional balance according to the user's frequency of meals. For example, it proposes a menu that supplements the nutrients needed at each meal to a user who eats two meals a day. In this way, by taking into account the user's frequency of meals, it is possible to provide a more appropriate nutritional balance.

[0054] The menu generation system can further include a location analysis unit that takes into account the location where the user is eating. The location analysis unit proposes a menu according to the location of the meal, such as home, work, or away from home. For example, it proposes dishes that require cooking at home and suggests easy-to-carry lunch boxes at work. The location analysis unit can also provide cooking instructions according to the location where the user is eating. For example, it proposes a recipe for a sandwich that can be easily eaten away from home. In this way, by taking into account the location where the user is eating, it is possible to provide a more appropriate menu.

[0055] The menu generation system can further include a purpose analysis unit that considers the user's purpose for eating. The purpose analysis unit proposes a menu according to the purpose, such as dieting, muscle building, or maintaining health. For example, it proposes low-calorie dishes to a user on a diet, and high-protein dishes to a user aiming to build muscle. The purpose analysis unit can also consider nutritional balance according to the user's purpose for eating. For example, it proposes a balanced menu to a user aiming to maintain health. In this way, by considering the user's purpose for eating, it is possible to provide a more appropriate menu.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The user information input unit inputs user information, such as the number of family members, age, allergy information, food preferences, and specific nutritional requirements. Step 2: The analysis unit analyzes the information input by the user information input unit. For example, it analyzes the user's preferences and nutritional balance, and creates basic data for generating an optimal menu. Step 3: The menu generator generates a nutritionally balanced menu based on the information analyzed by the analyzer. For example, it suggests specific menus such as oatmeal and fruit for breakfast, chicken and vegetable salad for lunch, and grilled fish and vegetables for dinner. Step 4: The recipe provider provides a specific recipe and cooking instructions based on the menu generated by the menu generator. For example, for a chicken and vegetable salad recipe, it provides the necessary ingredients, their amounts, and cooking instructions in detail.

[0058] (Example 2) The menu creation system according to an embodiment of the present invention is a system that automatically creates nutritionally balanced menus based on user information and provides specific recipes and cooking procedures, thereby reducing the burden on users and enabling them to easily prepare healthy meals.

[0059] A menu creation system according to an embodiment includes a user information input unit, an analysis unit, a menu creation unit, and a recipe provision unit. The user information input unit inputs user information, such as the number of family members, age, allergy information, food preferences, and specific nutritional requirements. The analysis unit analyzes the information input by the user information input unit. For example, the analysis unit analyzes the user's preferences and nutritional balance to create basic data for generating an optimal menu. The menu creation unit generates a nutritionally balanced menu based on the information analyzed by the analysis unit. For example, the system may suggest specific menus such as oatmeal and fruit for breakfast, chicken and vegetable salad for lunch, and grilled fish and vegetables for dinner. The recipe provision unit provides specific recipes and cooking instructions based on the menu created by the menu creation unit. For example, the recipe provides detailed information about the ingredients needed for a chicken and vegetable salad, their quantities, and cooking instructions. This reduces the burden on the user by automatically creating a nutritionally balanced menu based on the user's information and providing specific recipes and cooking instructions.

[0060] The analysis unit can analyze the user's past meal history and predict and automatically input their preferences and nutritional balance. For example, the analysis unit collects the user's past meal history and uses AI to analyze the data to predict their meal preferences and nutritional balance. For example, based on the past month's meal data, the analysis unit automatically identifies the user's favorite ingredients and ingredients to avoid. The analysis unit also analyzes the meal history to identify the nutrients the user frequently consumes and the nutrients they tend to lack. For example, it automatically calculates the amount of protein the user has consumed in large amounts in the past and the amount of vitamins they are lacking. The analysis unit also predicts the user's meal preferences and nutritional balance based on their past eating patterns and reflects this in meal suggestions for the next meal. For example, it prioritizes suggestions based on dishes the user has enjoyed in the past. In this way, by analyzing the user's past meal history, preferences and nutritional balance can be predicted and reflected in meal suggestions for the next meal.

[0061] The analysis unit can link the user's health data to consider more precise nutritional balance. For example, the analysis unit collects the user's exercise data and weight fluctuation data from a fitness tracker or health app, and AI analyzes that data to optimize nutritional balance. For example, it may suggest increasing calorie intake on days when the user exercises a lot. The analysis unit also suggests nutritional balance based on the user's health condition and goals based on data from the health app. For example, it may suggest low-calorie menus for users who are trying to lose weight. The analysis unit also analyzes data from the fitness tracker to consider nutritional balance that matches the user's exercise habits. For example, it may suggest high-protein menus for users who are trying to build muscle. In this way, by linking the user's health data, it is possible to generate menus that take into account more precise nutritional balance.

[0062] The analysis unit can use the emotion estimation function to analyze the user's current mood and stress level and adjust meal suggestions based on that. The analysis unit, for example, analyzes the user's facial expressions and voice to estimate the user's current mood and stress level. For example, it can analyze the user's emotions in real time using a camera or microphone and suggest relaxing meals. The analysis unit can also use the emotion estimation function to analyze the user's stress level and suggest ingredients and dishes that help reduce stress. For example, on a stressful day, it can suggest herbal tea that has a relaxing effect. The analysis unit can also suggest meals that match the user's mood based on the user's emotion data. For example, on a day when you're feeling down, it can suggest colorful dishes to lift your spirits. This makes it possible to improve meal satisfaction by suggesting meals that match the user's mood and stress level.

[0063] The user information input unit can input information using voice recognition technology, reducing the effort required. For example, the user information input unit uses voice recognition technology to automatically convert information input orally by the user into text, which is then analyzed by AI. For example, information such as "Family of four, two children, no allergies, likes vegetables" is input by voice. The user information input unit also analyzes the information input by voice in real time and automatically extracts the necessary data. For example, a request such as "I want to eat more protein" is input by voice. The user information input unit also uses voice recognition technology to reduce the effort required for the user to input information and collect information smoothly. For example, information for all family members is input all at once by voice input. In this way, the use of voice recognition technology can reduce the effort required for the user to input information.

[0064] The user information input unit provides an interface that allows information about all family members to be entered at once, and can generate a menu that takes individual nutritional needs into consideration. For example, the user information input unit provides an interface that allows information about all family members to be entered at once, and AI analyzes the data to generate a menu that takes individual nutritional needs into consideration. For example, the ages and allergy information of all family members are entered at once. The user information input unit also inputs information about all family members at once, and builds a system that suggests menus according to individual nutritional needs. For example, it suggests menus that include a lot of nutrients necessary for children's growth. The user information input unit also provides an interface that allows information about all family members to be entered at once, and generates a menu that takes individual nutritional needs into consideration. For example, the preferences and allergy information of all family members are entered at once. In this way, by entering information about all family members at once, a menu that takes individual nutritional needs into consideration can be generated.

[0065] The user information input unit can use the emotion estimation function to analyze the emotion of the user when entering information in real time and provide positive feedback. The user information input unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering information in real time and provide positive feedback. For example, if the user is smiling while entering information, an encouraging message is displayed. The user information input unit also builds a system that analyzes the user's emotion and provides positive feedback while entering information. For example, if the entry is proceeding smoothly, it displays "Going well!". The user information input unit also uses the emotion estimation function to analyze the emotion of the user when entering information and provides positive feedback. For example, it displays "Good job!" when the entry is completed. In this way, the user's entry experience can be improved by analyzing the emotion of the user when entering information and providing positive feedback.

[0066] The menu generation unit can generate a seasonal menu by taking into account ingredients that correspond to the season and weather. The menu generation unit, for example, collects seasonal and weather data, and AI generates a seasonal menu based on that data. For example, in summer, it proposes a menu using cold dishes and seasonal vegetables. The menu generation unit also builds a system that generates a seasonal menu by taking into account ingredients for each season. For example, in autumn, it proposes dishes using mushrooms and pumpkins. The menu generation unit also generates a seasonal menu based on weather data. For example, on cold days, it proposes hot soups and hot pot dishes. In this way, it is possible to generate a seasonal menu by taking into account ingredients that correspond to the season and weather.

[0067] The menu generation unit can link the user's ingredient inventory information and generate menus that prioritize the use of stock ingredients to reduce waste. The menu generation unit, for example, links inventory information from the user's refrigerator or pantry, and AI generates menus that prioritize the use of stock ingredients based on that data. For example, it suggests dishes that use vegetables in the refrigerator. The menu generation unit also builds a system that generates menus to reduce waste based on inventory information. For example, it suggests menus that prioritize the use of ingredients that are close to their expiration date. The menu generation unit also analyzes the user's inventory information and generates menus that prioritize the use of stock ingredients to reduce waste. For example, it suggests dishes that use up ingredients in the refrigerator. In this way, by linking the user's ingredient inventory information, it is possible to generate menus that prioritize the use of stock ingredients to reduce waste.

[0068] The menu generation unit uses the emotion estimation function to suggest a menu that matches the user's mood, thereby improving meal satisfaction. The menu generation unit, for example, uses the emotion estimation function to analyze the user's mood and suggest a menu that matches that mood. For example, if the user is tired, it suggests dishes that are easy to make. The menu generation unit also builds a system that suggests menus that match the user's mood based on the user's emotion data. For example, if the user wants to relax, it suggests dishes that use ingredients that have a relaxing effect. The menu generation unit also uses the emotion estimation function to suggest a menu that matches the user's mood, thereby improving meal satisfaction. For example, if the user wants to feel energized, it suggests dishes that will replenish energy. In this way, meal satisfaction can be improved by suggesting a menu that matches the user's mood.

[0069] The menu generation unit generates a multinational menu that incorporates cuisine from different cultures and countries, thereby increasing the variety of meals. The menu generation unit, for example, builds a system that generates a multinational menu that incorporates cuisine from different cultures and countries. For example, it proposes a menu that incorporates Italian cuisine and Japanese cuisine. The menu generation unit also utilizes a multinational cuisine recipe database to generate a menu that incorporates cuisine from different cultures. For example, it proposes Mexican cuisine and Indian cuisine. The menu generation unit also generates a multinational menu that incorporates cuisine from different countries, thereby increasing the variety of meals. For example, it proposes French cuisine and Thai cuisine. In this way, it is possible to increase the variety of meals by incorporating cuisine from different cultures and countries.

[0070] The menu generation unit can generate menus that correspond to specific diets or dietary restrictions. The menu generation unit, for example, builds a system that generates menus that correspond to specific diets or dietary restrictions. For example, it proposes low-carbohydrate menus that correspond to ketogenic diets. The menu generation unit also generates menus that correspond to dietary restrictions such as vegans and vegetarians. For example, it proposes vegan dishes that do not use animal products. The menu generation unit also generates menus that correspond to specific diets or dietary restrictions to meet the needs of users. For example, it proposes gluten-free or low-carbohydrate menus. In this way, it is possible to provide menus that meet the needs of users by accommodating specific diets or dietary restrictions.

[0071] The menu generation unit can use the emotion estimation function to consider the emotions of all family members and generate a menu that will satisfy everyone. The menu generation unit, for example, uses the emotion estimation function to analyze the emotions of all family members and generate a menu that will satisfy everyone. For example, it suggests dishes that will relax the whole family. The menu generation unit also builds a system that suggests a menu that will satisfy everyone based on the emotion data of all family members. For example, it suggests party dishes that the whole family can enjoy. The menu generation unit also uses the emotion estimation function to consider the emotions of all family members and generate a menu that will satisfy everyone. For example, it suggests dishes that will cheer up the whole family. In this way, it is possible to generate a menu that will satisfy everyone by considering the emotions of all family members.

[0072] The recipe providing unit can propose recipes according to cooking time and difficulty, and provide cooking procedures that match the user's skill level. The recipe providing unit proposes recipes that match the user's skill level, taking into account, for example, cooking time and difficulty. For example, it provides simple recipes for beginners and complex recipes for advanced cooks. The recipe providing unit also analyzes the user's cooking skills and builds a system that provides cooking procedures that match the skill level. For example, it provides detailed procedures for beginners and simple procedures for advanced cooks. The recipe providing unit also provides cooking procedures that match the user's skill level based on cooking time and difficulty. For example, it proposes recipes with short cooking times or recipes with low difficulty. In this way, by proposing recipes according to cooking time and difficulty, it is possible to provide cooking procedures that match the user's skill level.

[0073] The recipe providing unit can generate cooking videos and provide visually easy-to-understand steps. For example, the recipe providing unit uses AI to automatically generate cooking videos to make cooking steps visually easy to understand. For example, it displays separate videos for each step. The recipe providing unit also generates cooking videos and builds a system that makes it easy for users to visually understand the steps. For example, it shows how to cut ingredients and cooking tips in videos. The recipe providing unit also provides cooking steps in videos so that users can visually confirm them. For example, it explains cooking tips and points to note in videos. In this way, by generating cooking videos, it is possible to provide visually easy-to-understand steps.

[0074] The recipe providing unit can use the emotion estimation function to consider the user's stress level and suggest relaxing cooking procedures. The recipe providing unit, for example, uses the emotion estimation function to analyze the user's stress level and suggest relaxing cooking procedures. For example, on days when stress is high, it suggests simple cooking procedures. The recipe providing unit also builds a system that suggests cooking procedures that consider the user's stress level based on the user's emotion data. For example, it suggests a procedure for cooking while playing music that has a relaxing effect. The recipe providing unit also uses the emotion estimation function to consider the user's stress level and suggest relaxing cooking procedures. For example, on days when stress is low, it suggests a procedure for trying a new dish. In this way, it is possible to suggest relaxing cooking procedures by considering the user's stress level.

[0075] The recipe providing unit can provide recipes in multiple languages ​​to accommodate international users. For example, the recipe providing unit translates recipes into multiple languages ​​to build a system that accommodates international users. For example, recipes are provided in English, French, Chinese, etc. The recipe providing unit also makes recipes multilingual to make them easy to use for users who speak different languages. For example, the recipe is displayed according to the user's language setting. The recipe providing unit can also provide recipes in multiple languages ​​to accommodate international users. For example, cooking steps are explained in detail in the language of each country. In this way, by providing recipes in multiple languages, international users can be accommodated.

[0076] The recipe providing unit can collect user feedback on recipes and preferentially suggest popular recipes. For example, the recipe providing unit collects user feedback on recipes and builds a system that preferentially suggests popular recipes based on that data. For example, highly rated recipes are preferentially displayed. The recipe providing unit also analyzes user feedback and suggests popular recipes. For example, recipe rankings are created based on user comments and ratings. The recipe providing unit also collects feedback on recipes and preferentially suggests popular recipes. For example, recipes that are highly rated by users are displayed as recommendations. In this way, popular recipes can be preferentially suggested by collecting user feedback.

[0077] The recipe providing unit can use the emotion estimation function to monitor the user's emotions while cooking and provide encouragement or advice as needed. The recipe providing unit, for example, uses the emotion estimation function to monitor the user's emotions while cooking and provide encouragement or advice as needed. For example, if the user is having trouble, it displays "Good luck!". The recipe providing unit also analyzes the user's emotions in real time and builds a system that provides appropriate advice while cooking. For example, if cooking is going well, it displays "Good luck!". The recipe providing unit also uses the emotion estimation function to monitor the user's emotions while cooking and provide encouragement or advice as needed. For example, if the user is feeling stressed, it suggests ways to relax. In this way, by monitoring the user's emotions while cooking, it is possible to provide encouragement or advice as needed.

[0078] The ingredient purchasing list generation unit can analyze the user's past purchase history and add frequently purchased ingredients to the list with priority. The ingredient purchasing list generation unit, for example, builds a system that analyzes the user's past purchase history and adds frequently purchased ingredients to the list with priority. For example, frequently purchased ingredients are automatically added to the list. The ingredient purchasing list generation unit also adds ingredients that the user frequently purchases to the list based on the purchase history. For example, it analyzes past data and displays ingredients that are always purchased with priority. The ingredient purchasing list generation unit also analyzes the user's purchase history and adds frequently purchased ingredients to the list. For example, ingredients that the user frequently purchases are automatically included in the list. In this way, by analyzing the user's past purchase history, frequently purchased ingredients can be added to the list with priority.

[0079] The ingredient purchasing list generation unit can link ingredient price information to generate an optimal purchasing list within a budget. The ingredient purchasing list generation unit, for example, builds a system that links ingredient price information to generate an optimal purchasing list within a budget. For example, it selects ingredients according to a budget and creates a list. The ingredient purchasing list generation unit also generates an optimal purchasing list within a budget based on price information. For example, it prioritizes adding inexpensive ingredients to the list. The ingredient purchasing list generation unit also analyzes ingredient price information to generate an optimal purchasing list within a budget. For example, it selects ingredients so that the budget is not exceeded. In this way, by linking ingredient price information, an optimal purchasing list within a budget can be generated.

[0080] The grocery shopping list generation unit can use the emotion estimation function to make suggestions to reduce the user's shopping stress. The grocery shopping list generation unit, for example, uses the emotion estimation function to analyze the user's shopping stress and make suggestions to reduce stress. For example, it makes a suggestion to simplify the shopping list. The grocery shopping list generation unit also builds a system that makes suggestions to reduce shopping stress based on the user's emotion data. For example, it makes a suggestion to optimize the shopping order. The grocery shopping list generation unit also uses the emotion estimation function to make suggestions to reduce the user's shopping stress. For example, it suggests online shopping on days when stress is high. In this way, the emotion estimation function can be used to make suggestions to reduce the user's shopping stress.

[0081] The ingredient purchasing list generation unit can link the ingredient purchasing list with an online shopping site to enable one-click purchases. For example, the ingredient purchasing list generation unit links the ingredient purchasing list with an online shopping site to build a system that enables one-click purchases. For example, all ingredients on the list are added to a cart at once. The ingredient purchasing list generation unit also links with the online shopping site to enable easy purchases of ingredients based on the purchase list. For example, ingredients on the list are automatically ordered. The ingredient purchasing list generation unit also links with the online shopping site to enable one-click purchases. For example, all ingredients on the list are purchased at once. In this way, by linking the ingredient purchasing list with the online shopping site, one-click purchases can be made.

[0082] The ingredient purchasing list generation unit can give priority to adding fresh ingredients from local farms and markets to the list. The ingredient purchasing list generation unit, for example, builds a system that gives priority to adding fresh ingredients from local farms and markets to the list. For example, ingredients that can be purchased directly from local farmers are added to the list. The ingredient purchasing list generation unit also adds fresh ingredients from local markets to the list and provides it to the user. For example, seasonal ingredients sold at local markets are included in the list. The ingredient purchasing list generation unit also gives priority to adding fresh ingredients from local farms and markets to the list. For example, organic vegetables provided by local farmers are included in the list. In this way, fresh ingredients from local farms and markets are given priority to being added to the list, thereby providing fresh ingredients to the user.

[0083] The grocery shopping list generation unit can use the emotion estimation function to analyze the emotions felt by the user while shopping and make suggestions to improve the shopping experience. The grocery shopping list generation unit can, for example, use the emotion estimation function to analyze the emotions felt by the user while shopping and make suggestions to improve the shopping experience. For example, it can suggest music that is relaxing while shopping. The grocery shopping list generation unit also builds a system that makes suggestions to improve the shopping experience based on the user's emotion data. For example, it can suggest methods to reduce stress while shopping. The grocery shopping list generation unit can also use the emotion estimation function to analyze the emotions felt by the user while shopping and make suggestions to improve the shopping experience. For example, it can suggest rest points where the user can refresh themselves while shopping. In this way, the emotion estimation function can be used to analyze the emotions felt by the user while shopping and make suggestions to improve the shopping experience.

[0084] The nutritional value calculation unit can optimize nutritional value according to the user's health goals. The nutritional value calculation unit, for example, builds a system that optimizes nutritional value according to the user's health goals. For example, it suggests a low-calorie menu to a user who is trying to lose weight. The nutritional value calculation unit also optimizes nutritional value according to the health goals and suggests the optimal menu for the user. For example, it suggests a high-protein menu to a user who is trying to build muscle. The nutritional value calculation unit also optimizes nutritional value based on the user's health goals. For example, it suggests a balanced menu to a user who is trying to maintain health. In this way, by optimizing nutritional value according to the user's health goals, it is possible to provide the optimal menu for the user.

[0085] The nutritional value calculation unit periodically updates the nutritional value database of ingredients to provide the latest nutritional information. The nutritional value calculation unit, for example, periodically updates the nutritional value database of ingredients to build a system that provides the latest nutritional information. For example, the database is updated based on new research results. The nutritional value calculation unit also periodically updates the nutritional value database to provide the latest nutritional information to the user. For example, new nutritional value information of ingredients is added. The nutritional value calculation unit also periodically updates the nutritional value database of ingredients to provide the latest nutritional information. For example, the database is updated in accordance with fluctuations in nutritional value. In this way, by periodically updating the nutritional value database of ingredients, the latest nutritional information can be provided.

[0086] The nutritional value calculation unit can use the emotion estimation function to analyze a user's emotions regarding health and provide nutritional information to increase motivation. The nutritional value calculation unit, for example, uses the emotion estimation function to analyze a user's emotions regarding health and provide nutritional information to increase motivation. For example, it displays an encouraging message to encourage the user to have positive emotions regarding health. The nutritional value calculation unit also builds a system that provides nutritional information to increase motivation regarding health based on the user's emotion data. For example, it displays a message of praise when the user achieves a goal. The nutritional value calculation unit also uses the emotion estimation function to analyze a user's emotions regarding health and provide nutritional information to increase motivation. For example, it displays an encouraging message when the user has negative emotions regarding health. In this way, by using the emotion estimation function, it is possible to analyze a user's emotions regarding health and provide nutritional information to increase motivation.

[0087] The nutritional value calculation unit can visually display nutritional value information in graphs or charts to make it easier to understand. The nutritional value calculation unit, for example, visually displays nutritional value information in graphs or charts to build a system that makes it easier for users to understand. For example, it displays nutrient intake amounts in bar graphs or pie charts. The nutritional value calculation unit also visually displays nutritional value information to make it easier for users to understand. For example, it displays nutrient balances in radar charts. The nutritional value calculation unit also visually displays nutritional value information in graphs or charts to make it easier for users to understand. For example, it displays daily nutrient intake amounts in a line graph. In this way, visually displaying nutritional value information in graphs or charts makes it easier for users to understand.

[0088] The nutritional value calculation unit can link nutritional value information with other health apps and fitness trackers to support comprehensive health management. For example, the nutritional value calculation unit links nutritional value information with other health apps and fitness trackers to build a system that supports comprehensive health management. For example, it integrates exercise data and nutritional data from a fitness tracker. The nutritional value calculation unit also links with other health apps to comprehensively manage nutritional value information. For example, it automatically sends nutritional data for meals to a health app. The nutritional value calculation unit also links nutritional value information with a fitness tracker to support comprehensive health management. For example, it provides advice on nutritional intake based on the amount of exercise. In this way, by linking nutritional value information with other health apps and fitness trackers, comprehensive health management can be supported.

[0089] The nutritional value calculation unit can use the emotion estimation function to analyze the emotion a user feels when viewing nutritional value information and provide positive feedback. The nutritional value calculation unit, for example, uses the emotion estimation function to analyze the emotion a user feels when viewing nutritional value information and provide positive feedback. For example, a message praising the user is displayed when the user achieves a balanced nutritional intake. The nutritional value calculation unit also builds a system that analyzes the emotion a user feels when viewing nutritional value information based on the user's emotion data and provides positive feedback. For example, a message of encouragement is displayed when the user achieves a goal. The nutritional value calculation unit also uses the emotion estimation function to analyze the emotion a user feels when viewing nutritional value information and provide positive feedback. For example, "Amazing!" is displayed when the user achieves a balanced nutritional intake. In this way, the emotion estimation function can be used to analyze the emotion a user feels when viewing nutritional value information and provide positive feedback.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The menu creation system can further include a storage method analysis unit that takes into account the user's method of storing ingredients. The storage method analysis unit analyzes the storage period and appropriate storage method of ingredients based on information such as refrigeration, freezing, and room temperature storage. For example, it can propose a menu that prioritizes the use of ingredients that can be frozen. The storage method analysis unit can also provide cooking procedures according to the storage method of ingredients. For example, it can provide detailed instructions for thawing frozen ingredients. By taking into account the user's method of storing ingredients, it is possible to reduce food waste and support efficient cooking.

[0092] The menu generation system can further include a time zone analysis unit that takes into account the user's meal times. The time zone analysis unit, for example, proposes an optimal menu according to the time zones of breakfast, lunch, and dinner. For example, it proposes high-calorie dishes for breakfast to replenish energy, and light dishes that are easy to digest for dinner. The time zone analysis unit can also propose meals that match the user's lifestyle. For example, it proposes meals that are suitable for nighttime for a user who works the night shift. In this way, by taking into account the user's meal times, it is possible to provide a more appropriate menu.

[0093] The menu generation system can further include a frequency analysis unit that takes into account the frequency of a user's meals. The frequency analysis unit proposes menus according to the frequency of meals, such as three meals a day, two meals a day, or one meal a day. For example, it proposes nutritious dishes to a user who eats one meal a day, and proposes a balanced menu to a user who eats three meals a day. The frequency analysis unit can also consider nutritional balance according to the user's frequency of meals. For example, it proposes a menu that supplements the nutrients needed at each meal to a user who eats two meals a day. In this way, by taking into account the user's frequency of meals, it is possible to provide a more appropriate nutritional balance.

[0094] The menu generation system can further include a location analysis unit that takes into account the location where the user is eating. The location analysis unit proposes a menu according to the location of the meal, such as home, work, or away from home. For example, it proposes dishes that require cooking at home and suggests easy-to-carry lunch boxes at work. The location analysis unit can also provide cooking instructions according to the location where the user is eating. For example, it proposes a recipe for a sandwich that can be easily eaten away from home. In this way, by taking into account the location where the user is eating, it is possible to provide a more appropriate menu.

[0095] The menu generation system can further include a purpose analysis unit that considers the user's purpose for eating. The purpose analysis unit proposes a menu according to the purpose, such as dieting, muscle building, or maintaining health. For example, it proposes low-calorie dishes to a user on a diet, and high-protein dishes to a user aiming to build muscle. The purpose analysis unit can also consider nutritional balance according to the user's purpose for eating. For example, it proposes a balanced menu to a user aiming to maintain health. In this way, by considering the user's purpose for eating, it is possible to provide a more appropriate menu.

[0096] The menu generation system can further include an emotion analysis unit that takes the user's emotions into consideration. The emotion analysis unit, for example, analyzes the user's facial expressions and voice to estimate their current mood and stress level. For example, if the user wants to relax, it will suggest dishes with a relaxing effect, and if the user wants to cheer up, it will suggest dishes that will replenish energy. The emotion analysis unit can also suggest ingredients that correspond to the user's emotions. For example, on a stressful day, it will suggest herbal tea that has a relaxing effect. In this way, by taking the user's emotions into consideration, it is possible to provide a menu that is more satisfying.

[0097] The menu generation system can further include an emotion analysis unit that takes the user's emotions into consideration. The emotion analysis unit, for example, analyzes the user's emotions in real time and provides positive feedback. For example, if the user is smiling, an encouraging message is displayed, and if the user is feeling stressed, a method for relaxing is suggested. The emotion analysis unit can also provide cooking instructions according to the user's emotions. For example, on a stressful day, simple cooking instructions are suggested, and instructions for cooking while playing relaxing music are suggested. In this way, by taking the user's emotions into consideration, a more comfortable cooking experience can be provided.

[0098] The menu creation system can further include an emotion analysis unit that takes the user's emotions into consideration. The emotion analysis unit, for example, analyzes the user's emotions and provides appropriate advice while cooking. For example, if the user is having trouble, it may display "Good luck!", and if the cooking is going well, it may display "Good luck!". The emotion analysis unit can also provide cooking instructions that correspond to the user's emotions. For example, on a stressful day, it may suggest simple cooking instructions and instructions for cooking while playing relaxing music. In this way, by taking the user's emotions into consideration, it is possible to provide a more comfortable cooking experience.

[0099] The menu generation system can further include an emotion analysis unit that takes the user's emotions into consideration. The emotion analysis unit, for example, analyzes the user's emotions and makes suggestions to improve meal satisfaction. For example, if the user wants to relax, it will suggest dishes with a relaxing effect, and if the user wants to cheer up, it will suggest dishes that will replenish energy. The emotion analysis unit can also suggest ingredients that correspond to the user's emotions. For example, on a stressful day, it will suggest herbal tea that has a relaxing effect. In this way, by taking the user's emotions into consideration, it is possible to provide a menu that will provide a higher level of satisfaction.

[0100] The menu generation system can further include an emotion analysis unit that takes the user's emotions into consideration. The emotion analysis unit, for example, analyzes the user's emotions and suggests a menu that will satisfy the whole family. For example, it can suggest dishes that will relax the whole family, or dishes that will replenish energy if the family wants to cheer up. The emotion analysis unit can also suggest ingredients that correspond to the emotions of each family member. For example, it can suggest herbal tea that has a relaxing effect on a stressful day. In this way, by taking the emotions of each family member into consideration, it is possible to provide a menu that will satisfy everyone.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The user information input unit inputs user information, such as the number of family members, age, allergy information, food preferences, and specific nutritional requirements. Step 2: The analysis unit analyzes the information input by the user information input unit. For example, it analyzes the user's preferences and nutritional balance, and creates basic data for generating an optimal menu. Step 3: The menu generator generates a nutritionally balanced menu based on the information analyzed by the analyzer. For example, it suggests specific menus such as oatmeal and fruit for breakfast, chicken and vegetable salad for lunch, and grilled fish and vegetables for dinner. Step 4: The recipe provider provides a specific recipe and cooking instructions based on the menu generated by the menu generator. For example, for a chicken and vegetable salad recipe, it provides the necessary ingredients, their amounts, and cooking instructions in detail.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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 user information input unit for inputting user information; an analysis unit that analyzes the information input by the user information input unit; a menu creation unit that creates a nutritionally balanced menu based on the information analyzed by the analysis unit; a recipe providing unit that provides specific recipes and cooking procedures based on the menu generated by the menu generating unit. A system characterized by:

2. The analysis unit Analyzes the user's past eating history, predicts preferences and nutritional balance, and automatically inputs them 2. The system of claim 1.

3. The analysis unit Integrate user health data to consider more precise nutritional balance 2. The system of claim 1.

4. The analysis unit Analyzes the user's current mood and stress level and tailors meal suggestions accordingly 2. The system of claim 1.

5. The user information input unit Use voice recognition technology to input information and save time 2. The system of claim 1.

6. The user information input unit Provides an interface that allows users to input information for all family members at once, and generates menus that take into account individual nutritional needs 2. The system of claim 1.

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