system

Abstract

The system according to this embodiment aims to understand the user's ingredients and provide the best recipe, efficient cooking procedures, and menu suggestions. [Solution] The system according to this embodiment comprises a material understanding unit, a recipe provision unit, a procedure provision unit, and a menu support unit. The material understanding unit understands the user's materials. The recipe provision unit provides the best recipe based on the materials understood by the material understanding unit. The procedure provision unit provides efficient cooking procedures based on the recipe provided by the recipe provision unit. The menu support unit proposes a menu based on the procedures provided by the procedure provision unit.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems that it is difficult to select materials, improve the efficiency of cooking procedures, and create a daily menu, especially burdensome for busy businesspersons and housewives.

[0005] The system according to the embodiment aims to understand the user's materials, provide the best recipes and efficient cooking procedures, and further propose a menu.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a material understanding unit, a recipe provision unit, a procedure provision unit, and a menu support unit. The material understanding unit understands the user's materials. The recipe provision unit provides the best recipe based on the materials understood by the material understanding unit. The procedure provision unit provides efficient cooking procedures based on the recipe provided by the recipe provision unit. The menu support unit proposes a menu based on the procedures provided by the procedure provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can understand the user's ingredients and provide the best recipe, efficient cooking procedures, and menu suggestions. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​kitchen assistant according to an embodiment of the present invention is a system that supports business people, housewives, and the general public who feel they are not good at cooking, by helping them to improve the efficiency and variety of their cooking. The AI ​​kitchen assistant understands the ingredients and preferences of the user and provides the best recipe from the available ingredients. For example, if the user inputs the ingredients they have in their refrigerator, the AI ​​will suggest a recipe using those ingredients. Furthermore, even if some ingredients are missing, the AI ​​will provide alternatives, making it possible to cook without going shopping. Next, the AI ​​will provide the most efficient cooking procedure. For example, it will learn the optimal procedure for preparing, cooking, and cleaning up, and instruct the user accordingly. This allows users to cook efficiently even while doing housework or childcare. In addition, the AI ​​will support the creation of daily menus. It will make suggestions that take into account nutritional balance and family preferences, saving time and increasing variety. For example, the AI ​​will inform users about seasonal ingredients and discount information, enabling economical menu planning. This system reduces the effort involved in cooking, making it easy for anyone to prepare delicious meals. It also supports the health of the whole family by providing nutritionally balanced meals. In this way, the AI ​​kitchen assistant can help improve the efficiency and variety of cooking.

[0029] The AI ​​kitchen assistant according to this embodiment comprises an ingredient understanding unit, a recipe provision unit, a procedure provision unit, and a menu support unit. The ingredient understanding unit understands the user's ingredients. For example, the ingredient understanding unit can understand the ingredients the user has by inputting those ingredients. The ingredient understanding unit can use AI to analyze the type, quantity, freshness, etc., of the ingredients. The recipe provision unit provides the best recipe based on the ingredients understood by the ingredient understanding unit. For example, the recipe provision unit uses AI to suggest the optimal recipe based on the ingredients the user has. The recipe provision unit can use AI to select a recipe considering nutritional value, cooking time, user preferences, etc. The procedure provision unit provides efficient cooking procedures based on the recipe provided by the recipe provision unit. For example, the procedure provision unit instructs the user on the optimal procedures for preparing, cooking, and cleaning up. The procedure provision unit can use AI to shorten cooking time and simplify procedures. The menu support unit suggests a menu based on the procedures provided by the procedure provision unit. For example, the menu support unit suggests a menu that considers nutritional balance and family preferences. The menu support unit can use AI to create economical menu plans that take into account seasonal ingredients and discount information. This allows the AI ​​kitchen assistant, according to the embodiment, to understand the user's ingredients and provide the best recipes, efficient cooking procedures, and menus, thereby supporting increased cooking efficiency and variety.

[0030] The ingredient understanding unit understands the user's ingredients. For example, it can understand the ingredients the user possesses by inputting the ingredients they have. Specifically, the user inputs the names and quantities of the ingredients they have using a smartphone or tablet. This allows the ingredient understanding unit to create a list of the ingredients the user possesses. Furthermore, the ingredient understanding unit can use AI to analyze the type, quantity, freshness, etc. of the ingredients. For example, it can analyze the names of the ingredients entered by the user using natural language processing technology to identify the type of ingredient. It can also use image recognition technology to evaluate the freshness and quality from photos of ingredients taken by the user. As a result, the ingredient understanding unit can grasp detailed information about the ingredients the user possesses and provide accurate data to the recipe provision unit in the next step. In addition, the ingredient understanding unit can learn the user's preferences and frequency of use based on the user's past input data and purchase history, enabling more personalized ingredient analysis. For example, it can predict ingredients that the user frequently uses or ingredients that are purchased in specific seasons and automatically add them to the ingredient list. As a result, the ingredient understanding unit can streamline the user's ingredient management and allow for smoother cooking preparation.

[0031] The recipe provision unit provides the best recipes based on the ingredients understood by the ingredient understanding unit. Specifically, the AI ​​suggests the optimal recipe based on the ingredients the user has. The recipe provision unit can use AI to select recipes considering nutritional value, cooking time, and user preferences. For example, if the user inputs a list of ingredients they have, the AI ​​analyzes those ingredients and searches for the optimal recipe. The AI ​​uses a nutritional value calculation algorithm to evaluate the nutritional value of each ingredient and select a well-balanced recipe. It can also suggest recipes that take into account the user's preferred tastes and cooking methods based on the user's past preference data. Furthermore, the recipe provision unit provides recipes that are tailored to the user's schedule and skill level, considering cooking time and difficulty. For example, it suggests recipes that can be made quickly on busy weekday evenings and recipes that can be made with more time on weekends. This allows the recipe provision unit to provide flexible recipe suggestions that match the user's lifestyle. In addition, the recipe provision unit can collect user feedback and continuously improve the accuracy and satisfaction of the suggested recipes. For example, based on evaluations and comments on recipes created by users, the AI ​​can improve the recipe selection algorithm and suggest recipes that are more suitable for the user. This allows the recipe provider to improve the user's cooking experience and increase their satisfaction.

[0032] The procedure provider unit provides efficient cooking instructions based on recipes provided by the recipe provider unit. Specifically, it instructs the user on the optimal steps for preparing, cooking, and cleaning up. The procedure provider unit can use AI to shorten cooking time and simplify procedures. For example, the AI ​​analyzes the steps of each dish and identifies tasks that can be done simultaneously or tasks that can be made more efficient by rearranging their order. This allows the user to eliminate wasted time and cook efficiently. Furthermore, the procedure provider unit can provide procedures tailored to the user's skill level and cooking environment. For example, it provides detailed instructions and video guides for beginners and a concise procedure list for experienced cooks. The procedure provider unit can also suggest the optimal procedure considering the user's cooking equipment and kitchen layout. This allows the user to execute efficient cooking procedures suited to their kitchen environment. In addition, the procedure provider unit can monitor the user's progress in real time and adjust the procedure as needed. For example, if an unexpected problem occurs during cooking, the AI ​​will immediately suggest an alternative procedure to support the user in continuing to cook smoothly. This allows the procedure provider unit to improve the user's cooking experience and reduce stress.

[0033] The Menu Support Department proposes menus based on procedures provided by the Procedure Provision Department. Specifically, it proposes menus that take into account nutritional balance and family preferences. The Menu Support Department can use AI to create economical menu plans that take into account seasonal ingredients and discount information. For example, the AI ​​can identify seasonal ingredients and propose menus that utilize them. It can also plan economical menus by considering supermarket discount information and special offers. This allows users to enjoy nutritionally balanced meals while keeping costs down. Furthermore, the Menu Support Department can propose menus tailored to individual needs by taking into account family preferences and allergy information. For example, if a family member has an allergy to a specific ingredient, it will propose a menu that avoids that ingredient. It can also learn family preferences and dietary tastes to provide more personalized menus. In addition, the Menu Support Department can propose menus according to the user's schedule and events. For example, it can propose easy-to-prepare menus for busy weekdays and elaborate menus for weekends and special events. This allows the Menu Support Department to provide flexible menu suggestions that match the user's lifestyle. Furthermore, the menu support department can collect user feedback and continuously improve the accuracy and satisfaction level of the suggested menus. This allows the menu support department to enhance the user's dining experience and increase their satisfaction.

[0034] The alternative solution provision unit can provide alternatives when ingredients are insufficient. For example, if a user lacks certain ingredients, the AI ​​will suggest alternative ingredients. The alternative solution provision unit can use AI to analyze the types and methods of alternative ingredients and provide the optimal alternative. For example, if a user lacks certain ingredients, the AI ​​will suggest alternative ingredients. The alternative solution provision unit can use AI to analyze the types and methods of alternative ingredients and provide the optimal alternative. This makes it possible to cook without going shopping, even when ingredients are insufficient, by providing alternatives.

[0035] The discount information provision department can provide seasonal ingredients and discount information. For example, the discount information provision department uses AI to collect seasonal ingredients and discount information and provide it to users. The discount information provision department can use AI to analyze the types of seasonal ingredients and the content of discount information, and provide the most suitable information. This enables economical menu planning by providing seasonal ingredients and discount information.

[0036] The ingredient understanding unit can analyze the user's past cooking history and provide a method for selecting the optimal ingredients. For example, the ingredient understanding unit can prioritize selecting ingredients that the user has frequently used in the past. The ingredient understanding unit can use AI to analyze the user's past cooking history and provide a method for selecting the optimal ingredients. For example, the ingredient understanding unit can suggest ingredients suitable for a specific dish based on the user's past cooking history. The ingredient understanding unit can use AI to analyze the user's past cooking history and provide a method for selecting the optimal ingredients. For example, the ingredient understanding unit can analyze the user's past cooking history and suggest ingredients for trying a new dish. The ingredient understanding unit can use AI to analyze the user's past cooking history and provide a method for selecting the optimal ingredients. This allows the system to select the most suitable ingredients for the user by analyzing their past cooking history.

[0037] The material understanding unit can evaluate the freshness and quality of materials and select the optimal material. For example, the material understanding unit can evaluate the freshness of materials and select the freshest material. The material understanding unit can use AI to evaluate the freshness and quality of materials and select the optimal material. For example, the material understanding unit can evaluate the quality of materials and select the highest quality material. The material understanding unit can use AI to evaluate the freshness and quality of materials and select the optimal material. For example, the material understanding unit can evaluate the storage condition of materials and select the optimal material. The material understanding unit can use AI to evaluate the freshness and quality of materials and select the optimal material. This allows for the selection of the optimal material by evaluating the freshness and quality of the materials.

[0038] The material understanding unit can prioritize the selection of region-specific materials by considering the user's geographical location. For example, the material understanding unit can prioritize the selection of local specialties from the area where the user lives. The material understanding unit can use AI to prioritize the selection of region-specific materials by considering the user's geographical location. For example, the material understanding unit can select materials available in the local market based on the user's geographical location. The material understanding unit can use AI to prioritize the selection of region-specific materials by considering the user's geographical location. For example, the material understanding unit can select ingredients suitable for traditional regional cuisine by considering the user's geographical location. The material understanding unit can use AI to prioritize the selection of region-specific materials by considering the user's geographical location. This allows for the selection of region-specific materials by considering the user's geographical location.

[0039] The ingredient understanding unit can analyze a user's social media activity and select ingredients based on trends. For example, the ingredient understanding unit can select ingredients that a user frequently mentions on social media. The ingredient understanding unit can use AI to analyze a user's social media activity and select ingredients based on trends. For example, the ingredient understanding unit can select ingredients used in popular dishes on social media. The ingredient understanding unit can use AI to analyze a user's social media activity and select ingredients based on trends. For example, the ingredient understanding unit can analyze a user's social media activity and select ingredients based on the latest cooking trends. The ingredient understanding unit can use AI to analyze a user's social media activity and select ingredients based on trends. This allows for the selection of trend-based ingredients by analyzing social media activity.

[0040] The recipe provider can adjust the level of detail in a recipe based on the importance of the ingredients. For example, it can provide detailed explanations of key ingredients and brief explanations of auxiliary ingredients. The recipe provider can use AI to adjust the level of detail in a recipe based on the importance of the ingredients. For example, it can adjust the level of detail in cooking steps based on the importance of the ingredients. The recipe provider can use AI to adjust the level of detail in a recipe based on the importance of the ingredients. For example, it can provide detailed explanations of precautions and tips regarding important ingredients. The recipe provider can use AI to adjust the level of detail in a recipe based on the importance of the ingredients. This allows for the provision of more appropriate recipes by adjusting the level of detail in a recipe based on the importance of the ingredients.

[0041] The recipe provider can apply different recipe algorithms depending on the category of the dish when providing a recipe. For example, the recipe provider can apply the optimal recipe algorithm depending on the category, such as Japanese food, Western food, or Chinese food. The recipe provider can use AI to apply different recipe algorithms depending on the category of the dish. For example, the recipe provider can apply different recipe algorithms depending on the category, such as dessert, main dish, or side dish. The recipe provider can use AI to apply different recipe algorithms depending on the category of the dish. For example, the recipe provider can apply a customized recipe algorithm for each category according to the user's preferences. The recipe provider can use AI to apply different recipe algorithms depending on the category of the dish. This allows for the provision of more appropriate recipes by applying different recipe algorithms depending on the category of the dish.

[0042] The recipe provisioning unit can prioritize recipes based on the availability of ingredients when providing them. For example, the recipe provisioning unit prioritizes recipes using seasonal ingredients. The recipe provisioning unit can use AI to prioritize recipes based on the availability of ingredients. For example, the recipe provisioning unit suggests the most suitable recipe based on the availability of ingredients. The recipe provisioning unit can use AI to prioritize recipes based on the availability of ingredients. For example, the recipe provisioning unit adjusts the priority of recipes considering the availability of ingredients. The recipe provisioning unit can use AI to prioritize recipes based on the availability of ingredients. This allows for the provision of more appropriate recipes by prioritizing recipes based on the availability of ingredients.

[0043] The recipe provider can adjust the order of recipes based on the relationships between ingredients when providing recipes. For example, the recipe provider can suggest the optimal order of recipes based on the relationships between ingredients. The recipe provider can use AI to adjust the order of recipes based on the relationships between ingredients. For example, the recipe provider adjusts the order of recipes considering the relationships between ingredients. The recipe provider can use AI to adjust the order of recipes based on the relationships between ingredients. For example, the recipe provider determines the priority of recipes based on the relationships between ingredients. The recipe provider can use AI to adjust the order of recipes based on the relationships between ingredients. By adjusting the order of recipes based on the relationships between ingredients, it is possible to provide more appropriate recipes.

[0044] The procedure provider can adjust the level of detail in the instructions based on the difficulty of the dish when providing instructions. For example, for easy dishes, the procedure provider provides simple and easy-to-understand instructions. The procedure provider can use AI to adjust the level of detail in the instructions based on the difficulty of the dish. For example, for difficult dishes, the procedure provider provides instructions that include detailed explanations. The procedure provider can use AI to adjust the level of detail in the instructions based on the difficulty of the dish. For example, the procedure provider adjusts the level of detail in the instructions according to the difficulty of the dish. The procedure provider can use AI to adjust the level of detail in the instructions based on the difficulty of the dish. This allows for the provision of more appropriate instructions by adjusting the level of detail in the instructions based on the difficulty of the dish.

[0045] The procedure provider can apply different procedure algorithms depending on the category of the dish when providing instructions. For example, the procedure provider can apply the optimal procedure algorithm depending on the category, such as Japanese food, Western food, or Chinese food. The procedure provider can use AI to apply different procedure algorithms depending on the category of the dish. For example, the procedure provider can apply different procedure algorithms depending on the category, such as dessert, main dish, or side dish. The procedure provider can use AI to apply different procedure algorithms depending on the category of the dish. For example, the procedure provider can apply a customized procedure algorithm for each category according to the user's preferences. The procedure provider can use AI to apply different procedure algorithms depending on the category of the dish. This allows for the provision of more appropriate instructions by applying different procedure algorithms depending on the category of the dish.

[0046] The procedure provider can determine the priority of procedures based on the preparation time of the dish when providing procedures. For example, the procedure provider can prioritize procedures that can be cooked in a short amount of time. The procedure provider can use AI to determine the priority of procedures based on the preparation time of the dish. For example, the procedure provider can determine the optimal priority of procedures based on the preparation time of the dish. The procedure provider can use AI to determine the priority of procedures based on the preparation time of the dish. For example, the procedure provider can adjust the priority of procedures taking the preparation time of the dish into consideration. The procedure provider can use AI to determine the priority of procedures based on the preparation time of the dish. This allows for the provision of more appropriate procedures by determining the priority of procedures based on the preparation time of the dish.

[0047] The procedure provider can adjust the order of steps based on the relationships between dishes when providing instructions. For example, the procedure provider can suggest the optimal order of steps based on the relationships between dishes. The procedure provider can use AI to adjust the order of steps based on the relationships between dishes. For example, the procedure provider adjusts the order of steps considering the relationships between dishes. The procedure provider can use AI to adjust the order of steps based on the relationships between dishes. For example, the procedure provider determines the priority of steps based on the relationships between dishes. The procedure provider can use AI to adjust the order of steps based on the relationships between dishes. By adjusting the order of steps based on the relationships between dishes, it is possible to provide more appropriate instructions.

[0048] The menu support department can adjust the level of detail in menu suggestions based on nutritional balance. For example, the menu support department can provide menus that include detailed explanations while considering nutritional balance. The menu support department can use AI to adjust the level of detail in menus based on nutritional balance. For example, the menu support department can provide concise and easy-to-understand menus based on nutritional balance. The menu support department can use AI to adjust the level of detail in menus based on nutritional balance. For example, the menu support department can prioritize suggesting menus that emphasize nutritional balance. The menu support department can use AI to adjust the level of detail in menus based on nutritional balance. This allows for the provision of more appropriate menus by adjusting the level of detail in menus based on nutritional balance.

[0049] The menu support unit can apply different menu algorithms depending on the family's preferences when suggesting menus. For example, the menu support unit applies the optimal menu algorithm according to the family's preferences. The menu support unit can use AI to apply different menu algorithms according to the family's preferences. For example, the menu support unit proposes a customized menu considering the family's preferences. The menu support unit can use AI to apply different menu algorithms according to the family's preferences. For example, the menu support unit applies a different menu algorithm based on the family's preferences. The menu support unit can use AI to apply different menu algorithms according to the family's preferences. This allows for the provision of more appropriate menus by applying different menu algorithms according to the family's preferences.

[0050] The menu support department can prioritize menus based on seasonal ingredients when proposing menus. For example, the menu support department will prioritize menus that use seasonal ingredients. The menu support department can use AI to determine menu priorities based on seasonal ingredients. For example, the menu support department will determine the optimal menu priorities based on seasonal ingredients. The menu support department can use AI to determine menu priorities based on seasonal ingredients. For example, the menu support department will adjust menu priorities considering seasonal ingredients. The menu support department can use AI to determine menu priorities based on seasonal ingredients. This allows for the provision of more appropriate menus by prioritizing menus based on seasonal ingredients.

[0051] The menu support unit can adjust the order of menu items based on discount information when proposing menus. For example, the menu support unit will propose the optimal menu order by considering discount information. The menu support unit can use AI to adjust the order of menu items based on discount information. For example, the menu support unit will adjust the order of menu items based on discount information. The menu support unit can use AI to adjust the order of menu items based on discount information. For example, the menu support unit will determine the priority of menu items based on discount information. The menu support unit can use AI to adjust the order of menu items based on discount information. This allows for the provision of more economical menus by adjusting the order of menu items based on discount information.

[0052] The alternative solution provider can adjust the level of detail of alternative solutions based on the substitutability of materials when providing them. For example, the alternative solution provider can provide alternative solutions that include detailed explanations, taking into account the substitutability of materials. The alternative solution provider can use AI to adjust the level of detail of alternative solutions based on the substitutability of materials. For example, the alternative solution provider can provide concise and easy-to-understand alternative solutions based on the substitutability of materials. The alternative solution provider can use AI to adjust the level of detail of alternative solutions based on the substitutability of materials. For example, the alternative solution provider can prioritize proposing alternative solutions that emphasize the substitutability of materials. The alternative solution provider can use AI to adjust the level of detail of alternative solutions based on the substitutability of materials. This allows for the provision of more appropriate alternative solutions by adjusting the level of detail of alternative solutions based on the substitutability of materials.

[0053] The alternative solution provider can determine the priority of alternatives based on the availability of materials when providing alternatives. For example, the alternative solution provider proposes the optimal alternative, taking into account the availability of materials. The alternative solution provider can use AI to determine the priority of alternatives based on the availability of materials. For example, the alternative solution provider can determine the priority of alternatives based on the availability of materials. The alternative solution provider can use AI to determine the priority of alternatives based on the availability of materials. For example, the alternative solution provider adjusts the order of alternatives, taking into account the availability of materials. The alternative solution provider can use AI to determine the priority of alternatives based on the availability of materials. This allows for the provision of more appropriate alternatives by determining the priority of alternatives based on the availability of materials.

[0054] The discount information provision unit can adjust the level of detail of discount information based on the material's discount rate when providing discount information. For example, the discount information provision unit can provide discount information that includes detailed explanations, taking into account the material's discount rate. The discount information provision unit can use AI to adjust the level of detail of discount information based on the material's discount rate. For example, the discount information provision unit can provide concise and easy-to-understand discount information based on the material's discount rate. The discount information provision unit can use AI to adjust the level of detail of discount information based on the material's discount rate. For example, the discount information provision unit can prioritize suggesting discount information that emphasizes the material's discount rate. The discount information provision unit can use AI to adjust the level of detail of discount information based on the material's discount rate. This allows for the provision of more appropriate discount information by adjusting the level of detail of discount information based on the material's discount rate.

[0055] The discount information provision unit can determine the priority of discount information based on the availability of materials when providing discount information. For example, the discount information provision unit proposes the most suitable discount information considering the availability of materials. The discount information provision unit can use AI to determine the priority of discount information based on the availability of materials. For example, the discount information provision unit determines the priority of discount information based on the availability of materials. The discount information provision unit can use AI to determine the priority of discount information based on the availability of materials. For example, the discount information provision unit adjusts the order of discount information considering the availability of materials. The discount information provision unit can use AI to determine the priority of discount information based on the availability of materials. This allows for the provision of more appropriate discount information by determining the priority of discount information based on the availability of materials.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The ingredient understanding unit can monitor the user's health condition and select the optimal ingredients. For example, if the user has diabetes, it will prioritize selecting low-carbohydrate ingredients. The ingredient understanding unit can use AI to analyze the user's health condition and select the optimal ingredients. For example, if the user has high blood pressure, it will select low-sodium ingredients. The ingredient understanding unit can use AI to monitor the user's health condition and select the optimal ingredients. For example, if the user has allergies, it will select ingredients that do not contain allergens. In this way, by selecting the optimal ingredients according to the user's health condition, healthier meals can be provided.

[0058] The recipe provider can offer recipes that take into account the user's dietary restrictions. For example, if the user is vegetarian, it will provide meat-free recipes. The recipe provider can use AI to analyze the user's dietary restrictions and provide the most suitable recipes. For example, if the user requires a gluten-free diet, it will provide gluten-free recipes. The recipe provider can use AI to offer recipes that take into account the user's dietary restrictions. For example, if the user desires a low-calorie diet, it will provide low-calorie recipes. In this way, by providing the most suitable recipes according to the user's dietary restrictions, it is possible to provide more appropriate meals.

[0059] The procedure provider can provide instructions considering the user's cooking skill level. For example, it can provide detailed instructions to beginner users and concise instructions to advanced users. The procedure provider can use AI to analyze the user's cooking skill level and provide the optimal instructions. For example, it can provide step-by-step instructions to beginner users and only the key points to advanced users. The procedure provider can use AI to provide instructions considering the user's cooking skill level. For example, it can provide video tutorials to beginner users and text-only instructions to advanced users. This allows for more appropriate cooking support by providing the optimal instructions according to the user's cooking skill level.

[0060] The menu support department can analyze a user's eating history and suggest a variety of menus. For example, it can suggest dishes different from those previously offered. The menu support department uses AI to analyze a user's eating history and suggest a variety of menus. For example, it can suggest dishes the user has never eaten before. The menu support department uses AI to analyze a user's eating history and suggest a variety of menus. For example, it can suggest dishes using ingredients the user has never eaten before. By suggesting a variety of menus based on the user's eating history, the enjoyment of meals can be increased.

[0061] The discount information provision unit can analyze a user's purchase history and provide the most suitable discount information. For example, it can prioritize providing discount information on materials the user has purchased in the past. The discount information provision unit can use AI to analyze a user's purchase history and provide the most suitable discount information. For example, it can provide discount information on materials the user frequently purchases. The discount information provision unit can use AI to analyze a user's purchase history and provide the most suitable discount information. For example, it can provide discount information on materials the user has never purchased in the past. In this way, by providing the most suitable discount information based on the user's purchase history, it can support more economical shopping.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The material understanding unit understands the user's materials. By inputting the materials the user possesses, the unit can understand those materials. The material understanding unit uses AI to analyze the type, quantity, freshness, etc. of the materials. Step 2: The recipe provider unit provides the best recipe based on the ingredients understood by the ingredient understanding unit. The recipe provider unit uses AI to suggest the optimal recipe based on the ingredients the user has. Recipes are selected considering nutritional value, cooking time, user preferences, etc. Step 3: The procedure provider unit provides efficient cooking instructions based on the recipe provided by the recipe provider unit. It instructs the user on the optimal steps for preparing, cooking, and cleaning up. The procedure provider unit uses AI to shorten cooking time and simplify procedures. Step 4: The Menu Support Department proposes menus based on the procedures provided by the Procedure Provision Department. The menus are designed to be nutritionally balanced and to suit the family's preferences. The Menu Support Department uses AI to create economical menu plans that take into account seasonal ingredients and discount information.

[0064] (Example of form 2) The AI ​​kitchen assistant according to an embodiment of the present invention is a system that supports business people, housewives, and the general public who feel they are not good at cooking, by helping them to improve the efficiency and variety of their cooking. The AI ​​kitchen assistant understands the ingredients and preferences of the user and provides the best recipe from the available ingredients. For example, if the user inputs the ingredients they have in their refrigerator, the AI ​​will suggest a recipe using those ingredients. Furthermore, even if some ingredients are missing, the AI ​​will provide alternatives, making it possible to cook without going shopping. Next, the AI ​​will provide the most efficient cooking procedure. For example, it will learn the optimal procedure for preparing, cooking, and cleaning up, and instruct the user accordingly. This allows users to cook efficiently even while doing housework or childcare. In addition, the AI ​​will support the creation of daily menus. It will make suggestions that take into account nutritional balance and family preferences, saving time and increasing variety. For example, the AI ​​will inform users about seasonal ingredients and discount information, enabling economical menu planning. This system reduces the effort involved in cooking, making it easy for anyone to prepare delicious meals. It also supports the health of the whole family by providing nutritionally balanced meals. In this way, the AI ​​kitchen assistant can help improve the efficiency and variety of cooking.

[0065] The AI ​​kitchen assistant according to this embodiment comprises an ingredient understanding unit, a recipe provision unit, a procedure provision unit, and a menu support unit. The ingredient understanding unit understands the user's ingredients. For example, the ingredient understanding unit can understand the ingredients the user has by inputting those ingredients. The ingredient understanding unit can use AI to analyze the type, quantity, freshness, etc., of the ingredients. The recipe provision unit provides the best recipe based on the ingredients understood by the ingredient understanding unit. For example, the recipe provision unit uses AI to suggest the optimal recipe based on the ingredients the user has. The recipe provision unit can use AI to select a recipe considering nutritional value, cooking time, user preferences, etc. The procedure provision unit provides efficient cooking procedures based on the recipe provided by the recipe provision unit. For example, the procedure provision unit instructs the user on the optimal procedures for preparing, cooking, and cleaning up. The procedure provision unit can use AI to shorten cooking time and simplify procedures. The menu support unit suggests a menu based on the procedures provided by the procedure provision unit. For example, the menu support unit suggests a menu that considers nutritional balance and family preferences. The menu support unit can use AI to create economical menu plans that take into account seasonal ingredients and discount information. This allows the AI ​​kitchen assistant, according to the embodiment, to understand the user's ingredients and provide the best recipes, efficient cooking procedures, and menus, thereby supporting increased cooking efficiency and variety.

[0066] The ingredient understanding unit understands the user's ingredients. For example, it can understand the ingredients the user possesses by inputting the ingredients they have. Specifically, the user inputs the names and quantities of the ingredients they have using a smartphone or tablet. This allows the ingredient understanding unit to create a list of the ingredients the user possesses. Furthermore, the ingredient understanding unit can use AI to analyze the type, quantity, freshness, etc. of the ingredients. For example, it can analyze the names of the ingredients entered by the user using natural language processing technology to identify the type of ingredient. It can also use image recognition technology to evaluate the freshness and quality from photos of ingredients taken by the user. As a result, the ingredient understanding unit can grasp detailed information about the ingredients the user possesses and provide accurate data to the recipe provision unit in the next step. In addition, the ingredient understanding unit can learn the user's preferences and frequency of use based on the user's past input data and purchase history, enabling more personalized ingredient analysis. For example, it can predict ingredients that the user frequently uses or ingredients that are purchased in specific seasons and automatically add them to the ingredient list. As a result, the ingredient understanding unit can streamline the user's ingredient management and allow for smoother cooking preparation.

[0067] The recipe provision unit provides the best recipes based on the ingredients understood by the ingredient understanding unit. Specifically, the AI ​​suggests the optimal recipe based on the ingredients the user has. The recipe provision unit can use AI to select recipes considering nutritional value, cooking time, and user preferences. For example, if the user inputs a list of ingredients they have, the AI ​​analyzes those ingredients and searches for the optimal recipe. The AI ​​uses a nutritional value calculation algorithm to evaluate the nutritional value of each ingredient and select a well-balanced recipe. It can also suggest recipes that take into account the user's preferred tastes and cooking methods based on the user's past preference data. Furthermore, the recipe provision unit provides recipes that are tailored to the user's schedule and skill level, considering cooking time and difficulty. For example, it suggests recipes that can be made quickly on busy weekday evenings and recipes that can be made with more time on weekends. This allows the recipe provision unit to provide flexible recipe suggestions that match the user's lifestyle. In addition, the recipe provision unit can collect user feedback and continuously improve the accuracy and satisfaction of the suggested recipes. For example, based on evaluations and comments on recipes created by users, the AI ​​can improve the recipe selection algorithm and suggest recipes that are more suitable for the user. This allows the recipe provider to improve the user's cooking experience and increase their satisfaction.

[0068] The procedure provider unit provides efficient cooking instructions based on recipes provided by the recipe provider unit. Specifically, it instructs the user on the optimal steps for preparing, cooking, and cleaning up. The procedure provider unit can use AI to shorten cooking time and simplify procedures. For example, the AI ​​analyzes the steps of each dish and identifies tasks that can be done simultaneously or tasks that can be made more efficient by rearranging their order. This allows the user to eliminate wasted time and cook efficiently. Furthermore, the procedure provider unit can provide procedures tailored to the user's skill level and cooking environment. For example, it provides detailed instructions and video guides for beginners and a concise procedure list for experienced cooks. The procedure provider unit can also suggest the optimal procedure considering the user's cooking equipment and kitchen layout. This allows the user to execute efficient cooking procedures suited to their kitchen environment. In addition, the procedure provider unit can monitor the user's progress in real time and adjust the procedure as needed. For example, if an unexpected problem occurs during cooking, the AI ​​will immediately suggest an alternative procedure to support the user in continuing to cook smoothly. This allows the procedure provider unit to improve the user's cooking experience and reduce stress.

[0069] The Menu Support Department proposes menus based on procedures provided by the Procedure Provision Department. Specifically, it proposes menus that take into account nutritional balance and family preferences. The Menu Support Department can use AI to create economical menu plans that take into account seasonal ingredients and discount information. For example, the AI ​​can identify seasonal ingredients and propose menus that utilize them. It can also plan economical menus by considering supermarket discount information and special offers. This allows users to enjoy nutritionally balanced meals while keeping costs down. Furthermore, the Menu Support Department can propose menus tailored to individual needs by taking into account family preferences and allergy information. For example, if a family member has an allergy to a specific ingredient, it will propose a menu that avoids that ingredient. It can also learn family preferences and dietary tastes to provide more personalized menus. In addition, the Menu Support Department can propose menus according to the user's schedule and events. For example, it can propose easy-to-prepare menus for busy weekdays and elaborate menus for weekends and special events. This allows the Menu Support Department to provide flexible menu suggestions that match the user's lifestyle. Furthermore, the menu support department can collect user feedback and continuously improve the accuracy and satisfaction level of the suggested menus. This allows the menu support department to enhance the user's dining experience and increase their satisfaction.

[0070] The alternative solution provision unit can provide alternatives when ingredients are insufficient. For example, if a user lacks certain ingredients, the AI ​​will suggest alternative ingredients. The alternative solution provision unit can use AI to analyze the types and methods of alternative ingredients and provide the optimal alternative. For example, if a user lacks certain ingredients, the AI ​​will suggest alternative ingredients. The alternative solution provision unit can use AI to analyze the types and methods of alternative ingredients and provide the optimal alternative. This makes it possible to cook without going shopping, even when ingredients are insufficient, by providing alternatives.

[0071] The discount information provision department can provide seasonal ingredients and discount information. For example, the discount information provision department uses AI to collect seasonal ingredients and discount information and provide it to users. The discount information provision department can use AI to analyze the types of seasonal ingredients and the content of discount information, and provide the most suitable information. This enables economical menu planning by providing seasonal ingredients and discount information.

[0072] The ingredient understanding unit can estimate the user's emotions and adjust the selection of ingredients based on those emotions. For example, if the user is stressed, the ingredient understanding unit will prioritize ingredients that are easy and quick to prepare. The ingredient understanding unit can use AI to estimate the user's emotions and adjust the selection of ingredients based on those emotions. For example, if the user is relaxed, the ingredient understanding unit will select ingredients suitable for a slightly more time-consuming but delicious dish. The ingredient understanding unit can use AI to estimate the user's emotions and adjust the selection of ingredients based on those emotions. For example, if the user is in a hurry, the ingredient understanding unit will prioritize ingredients that require a short cooking time. The ingredient understanding unit can use AI to estimate the user's emotions and adjust the selection of ingredients based on those emotions. This allows for the selection of more appropriate ingredients by adjusting the selection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The ingredient understanding unit can analyze the user's past cooking history and provide a method for selecting the optimal ingredients. For example, the ingredient understanding unit can prioritize selecting ingredients that the user has frequently used in the past. The ingredient understanding unit can use AI to analyze the user's past cooking history and provide a method for selecting the optimal ingredients. For example, the ingredient understanding unit can suggest ingredients suitable for a specific dish based on the user's past cooking history. The ingredient understanding unit can use AI to analyze the user's past cooking history and provide a method for selecting the optimal ingredients. For example, the ingredient understanding unit can analyze the user's past cooking history and suggest ingredients for trying a new dish. The ingredient understanding unit can use AI to analyze the user's past cooking history and provide a method for selecting the optimal ingredients. This allows the system to select the most suitable ingredients for the user by analyzing their past cooking history.

[0074] The material understanding unit can evaluate the freshness and quality of materials and select the optimal material. For example, the material understanding unit can evaluate the freshness of materials and select the freshest material. The material understanding unit can use AI to evaluate the freshness and quality of materials and select the optimal material. For example, the material understanding unit can evaluate the quality of materials and select the highest quality material. The material understanding unit can use AI to evaluate the freshness and quality of materials and select the optimal material. For example, the material understanding unit can evaluate the storage condition of materials and select the optimal material. The material understanding unit can use AI to evaluate the freshness and quality of materials and select the optimal material. This allows for the selection of the optimal material by evaluating the freshness and quality of the materials.

[0075] The ingredient understanding unit can estimate the user's emotions and determine the priority of ingredients based on those emotions. For example, if the user is stressed, the ingredient understanding unit will prioritize ingredients that are easy and quick to prepare. The ingredient understanding unit can use AI to estimate the user's emotions and determine the priority of ingredients based on those emotions. For example, if the user is relaxed, the ingredient understanding unit will select ingredients suitable for a slightly more time-consuming but delicious dish. The ingredient understanding unit can use AI to estimate the user's emotions and determine the priority of ingredients based on those emotions. For example, if the user is in a hurry, the ingredient understanding unit will prioritize ingredients that require a short cooking time. The ingredient understanding unit can use AI to estimate the user's emotions and determine the priority of ingredients based on those emotions. This allows for the selection of more appropriate ingredients by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The material understanding unit can prioritize the selection of region-specific materials by considering the user's geographical location. For example, the material understanding unit can prioritize the selection of local specialties from the area where the user lives. The material understanding unit can use AI to prioritize the selection of region-specific materials by considering the user's geographical location. For example, the material understanding unit can select materials available in the local market based on the user's geographical location. The material understanding unit can use AI to prioritize the selection of region-specific materials by considering the user's geographical location. For example, the material understanding unit can select ingredients suitable for traditional regional cuisine by considering the user's geographical location. The material understanding unit can use AI to prioritize the selection of region-specific materials by considering the user's geographical location. This allows for the selection of region-specific materials by considering the user's geographical location.

[0077] The ingredient understanding unit can analyze a user's social media activity and select ingredients based on trends. For example, the ingredient understanding unit can select ingredients that a user frequently mentions on social media. The ingredient understanding unit can use AI to analyze a user's social media activity and select ingredients based on trends. For example, the ingredient understanding unit can select ingredients used in popular dishes on social media. The ingredient understanding unit can use AI to analyze a user's social media activity and select ingredients based on trends. For example, the ingredient understanding unit can analyze a user's social media activity and select ingredients based on the latest cooking trends. The ingredient understanding unit can use AI to analyze a user's social media activity and select ingredients based on trends. This allows for the selection of trend-based ingredients by analyzing social media activity.

[0078] The recipe provider can estimate the user's emotions and adjust the way the recipe is presented based on those emotions. For example, if the user is stressed, the recipe provider will provide a simple and easy-to-understand recipe. The recipe provider can use AI to estimate the user's emotions and adjust the way the recipe is presented based on those emotions. For example, if the user is relaxed, the recipe provider will provide a recipe with detailed instructions. The recipe provider can use AI to estimate the user's emotions and adjust the way the recipe is presented based on those emotions. For example, if the user is in a hurry, the recipe provider will provide a recipe that can be cooked in a short time. The recipe provider can use AI to estimate the user's emotions and adjust the way the recipe is presented based on those emotions. This allows for the provision of more appropriate recipes by adjusting the way the recipe is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The recipe provider can adjust the level of detail in a recipe based on the importance of the ingredients. For example, it can provide detailed explanations of key ingredients and brief explanations of auxiliary ingredients. The recipe provider can use AI to adjust the level of detail in a recipe based on the importance of the ingredients. For example, it can adjust the level of detail in cooking steps based on the importance of the ingredients. The recipe provider can use AI to adjust the level of detail in a recipe based on the importance of the ingredients. For example, it can provide detailed explanations of precautions and tips regarding important ingredients. The recipe provider can use AI to adjust the level of detail in a recipe based on the importance of the ingredients. This allows for the provision of more appropriate recipes by adjusting the level of detail in a recipe based on the importance of the ingredients.

[0080] The recipe provider can apply different recipe algorithms depending on the category of the dish when providing a recipe. For example, the recipe provider can apply the optimal recipe algorithm depending on the category, such as Japanese food, Western food, or Chinese food. The recipe provider can use AI to apply different recipe algorithms depending on the category of the dish. For example, the recipe provider can apply different recipe algorithms depending on the category, such as dessert, main dish, or side dish. The recipe provider can use AI to apply different recipe algorithms depending on the category of the dish. For example, the recipe provider can apply a customized recipe algorithm for each category according to the user's preferences. The recipe provider can use AI to apply different recipe algorithms depending on the category of the dish. This allows for the provision of more appropriate recipes by applying different recipe algorithms depending on the category of the dish.

[0081] The recipe provider can estimate the user's emotions and adjust the recipe length based on those emotions. For example, if the user is stressed, the recipe provider will provide a short and concise recipe. The recipe provider can use AI to estimate the user's emotions and adjust the recipe length based on those emotions. For example, if the user is relaxed, the recipe provider will provide a longer recipe with detailed explanations. The recipe provider can use AI to estimate the user's emotions and adjust the recipe length based on those emotions. For example, if the user is in a hurry, the recipe provider will provide a recipe that can be cooked quickly. The recipe provider can use AI to estimate the user's emotions and adjust the recipe length based on those emotions. This allows for the provision of more appropriate recipes by adjusting the recipe length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The recipe provisioning unit can prioritize recipes based on the availability of ingredients when providing them. For example, the recipe provisioning unit prioritizes recipes using seasonal ingredients. The recipe provisioning unit can use AI to prioritize recipes based on the availability of ingredients. For example, the recipe provisioning unit suggests the most suitable recipe based on the availability of ingredients. The recipe provisioning unit can use AI to prioritize recipes based on the availability of ingredients. For example, the recipe provisioning unit adjusts the priority of recipes considering the availability of ingredients. The recipe provisioning unit can use AI to prioritize recipes based on the availability of ingredients. This allows for the provision of more appropriate recipes by prioritizing recipes based on the availability of ingredients.

[0083] The recipe provider can adjust the order of recipes based on the relationships between ingredients when providing recipes. For example, the recipe provider can suggest the optimal order of recipes based on the relationships between ingredients. The recipe provider can use AI to adjust the order of recipes based on the relationships between ingredients. For example, the recipe provider adjusts the order of recipes considering the relationships between ingredients. The recipe provider can use AI to adjust the order of recipes based on the relationships between ingredients. For example, the recipe provider determines the priority of recipes based on the relationships between ingredients. The recipe provider can use AI to adjust the order of recipes based on the relationships between ingredients. By adjusting the order of recipes based on the relationships between ingredients, it is possible to provide more appropriate recipes.

[0084] The procedure provider can estimate the user's emotions and adjust the way the procedure is displayed based on the estimated emotions. For example, if the user is stressed, the procedure provider will provide a simple and easy-to-understand procedure. The procedure provider can use AI to estimate the user's emotions and adjust the way the procedure is displayed based on the estimated emotions. For example, if the user is relaxed, the procedure provider will provide a procedure that includes detailed explanations. The procedure provider can use AI to estimate the user's emotions and adjust the way the procedure is displayed based on the estimated emotions. For example, if the user is in a hurry, the procedure provider will provide a procedure that can be prepared in a short amount of time. The procedure provider can use AI to estimate the user's emotions and adjust the way the procedure is displayed based on the estimated emotions. This allows for the provision of more appropriate procedures by adjusting the way the procedure is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The procedure provider can adjust the level of detail in the instructions based on the difficulty of the dish when providing instructions. For example, for easy dishes, the procedure provider provides simple and easy-to-understand instructions. The procedure provider can use AI to adjust the level of detail in the instructions based on the difficulty of the dish. For example, for difficult dishes, the procedure provider provides instructions that include detailed explanations. The procedure provider can use AI to adjust the level of detail in the instructions based on the difficulty of the dish. For example, the procedure provider adjusts the level of detail in the instructions according to the difficulty of the dish. The procedure provider can use AI to adjust the level of detail in the instructions based on the difficulty of the dish. This allows for the provision of more appropriate instructions by adjusting the level of detail in the instructions based on the difficulty of the dish.

[0086] The procedure provider can apply different procedure algorithms depending on the category of the dish when providing instructions. For example, the procedure provider can apply the optimal procedure algorithm depending on the category, such as Japanese food, Western food, or Chinese food. The procedure provider can use AI to apply different procedure algorithms depending on the category of the dish. For example, the procedure provider can apply different procedure algorithms depending on the category, such as dessert, main dish, or side dish. The procedure provider can use AI to apply different procedure algorithms depending on the category of the dish. For example, the procedure provider can apply a customized procedure algorithm for each category according to the user's preferences. The procedure provider can use AI to apply different procedure algorithms depending on the category of the dish. This allows for the provision of more appropriate instructions by applying different procedure algorithms depending on the category of the dish.

[0087] The procedure provider can estimate the user's emotions and adjust the length of the procedure based on the estimated emotions. For example, if the user is stressed, the procedure provider will provide a short and concise procedure. The procedure provider can use AI to estimate the user's emotions and adjust the length of the procedure based on the estimated emotions. For example, if the user is relaxed, the procedure provider will provide a longer procedure with detailed explanations. The procedure provider can use AI to estimate the user's emotions and adjust the length of the procedure based on the estimated emotions. For example, if the user is in a hurry, the procedure provider will provide a procedure that can be prepared in a short time. The procedure provider can use AI to estimate the user's emotions and adjust the length of the procedure based on the estimated emotions. This allows for the provision of more appropriate procedures by adjusting the length of the procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The procedure provider can determine the priority of procedures based on the preparation time of the dish when providing procedures. For example, the procedure provider can prioritize procedures that can be cooked in a short amount of time. The procedure provider can use AI to determine the priority of procedures based on the preparation time of the dish. For example, the procedure provider can determine the optimal priority of procedures based on the preparation time of the dish. The procedure provider can use AI to determine the priority of procedures based on the preparation time of the dish. For example, the procedure provider can adjust the priority of procedures taking the preparation time of the dish into consideration. The procedure provider can use AI to determine the priority of procedures based on the preparation time of the dish. This allows for the provision of more appropriate procedures by determining the priority of procedures based on the preparation time of the dish.

[0089] The procedure provider can adjust the order of steps based on the relationships between dishes when providing instructions. For example, the procedure provider can suggest the optimal order of steps based on the relationships between dishes. The procedure provider can use AI to adjust the order of steps based on the relationships between dishes. For example, the procedure provider adjusts the order of steps considering the relationships between dishes. The procedure provider can use AI to adjust the order of steps based on the relationships between dishes. For example, the procedure provider determines the priority of steps based on the relationships between dishes. The procedure provider can use AI to adjust the order of steps based on the relationships between dishes. By adjusting the order of steps based on the relationships between dishes, it is possible to provide more appropriate instructions.

[0090] The menu support unit can estimate the user's emotions and adjust the menu display method based on the estimated emotions. For example, if the user is stressed, the menu support unit will provide a simple and easy-to-understand menu. The menu support unit can use AI to estimate the user's emotions and adjust the menu display method based on the estimated emotions. For example, if the user is relaxed, the menu support unit will provide a menu with detailed explanations. The menu support unit can use AI to estimate the user's emotions and adjust the menu display method based on the estimated emotions. For example, if the user is in a hurry, the menu support unit will provide a menu that can be prepared in a short time. The menu support unit can use AI to estimate the user's emotions and adjust the menu display method based on the estimated emotions. This allows for the provision of more appropriate menus by adjusting the menu display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The menu support department can adjust the level of detail in menu suggestions based on nutritional balance. For example, the menu support department can provide menus that include detailed explanations while considering nutritional balance. The menu support department can use AI to adjust the level of detail in menus based on nutritional balance. For example, the menu support department can provide concise and easy-to-understand menus based on nutritional balance. The menu support department can use AI to adjust the level of detail in menus based on nutritional balance. For example, the menu support department can prioritize suggesting menus that emphasize nutritional balance. The menu support department can use AI to adjust the level of detail in menus based on nutritional balance. This allows for the provision of more appropriate menus by adjusting the level of detail in menus based on nutritional balance.

[0092] The menu support unit can apply different menu algorithms depending on the family's preferences when suggesting menus. For example, the menu support unit applies the optimal menu algorithm according to the family's preferences. The menu support unit can use AI to apply different menu algorithms according to the family's preferences. For example, the menu support unit proposes a customized menu considering the family's preferences. The menu support unit can use AI to apply different menu algorithms according to the family's preferences. For example, the menu support unit applies a different menu algorithm based on the family's preferences. The menu support unit can use AI to apply different menu algorithms according to the family's preferences. This allows for the provision of more appropriate menus by applying different menu algorithms according to the family's preferences.

[0093] The menu support system can estimate the user's emotions and prioritize menus based on those emotions. For example, if the user is stressed, the menu support system will prioritize menus that are easy and quick to prepare. The menu support system can use AI to estimate the user's emotions and prioritize menus based on those emotions. For example, if the user is relaxed, the menu support system will suggest menus that are a little more time-consuming but delicious. The menu support system can use AI to estimate the user's emotions and prioritize menus based on those emotions. For example, if the user is in a hurry, the menu support system will prioritize menus that can be prepared in a short amount of time. The menu support system can use AI to estimate the user's emotions and prioritize menus based on those emotions. This allows the system to provide more appropriate menus by prioritizing menus according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0094] The menu support department can prioritize menus based on seasonal ingredients when proposing menus. For example, the menu support department will prioritize menus that use seasonal ingredients. The menu support department can use AI to determine menu priorities based on seasonal ingredients. For example, the menu support department will determine the optimal menu priorities based on seasonal ingredients. The menu support department can use AI to determine menu priorities based on seasonal ingredients. For example, the menu support department will adjust menu priorities considering seasonal ingredients. The menu support department can use AI to determine menu priorities based on seasonal ingredients. This allows for the provision of more appropriate menus by prioritizing menus based on seasonal ingredients.

[0095] The menu support unit can adjust the order of menu items based on discount information when proposing menus. For example, the menu support unit will propose the optimal menu order by considering discount information. The menu support unit can use AI to adjust the order of menu items based on discount information. For example, the menu support unit will adjust the order of menu items based on discount information. The menu support unit can use AI to adjust the order of menu items based on discount information. For example, the menu support unit will determine the priority of menu items based on discount information. The menu support unit can use AI to adjust the order of menu items based on discount information. This allows for the provision of more economical menus by adjusting the order of menu items based on discount information.

[0096] The alternative solution provider can estimate the user's emotions and adjust how alternative solutions are displayed based on those emotions. For example, if the user is stressed, the alternative solution provider will provide a simple and easy-to-understand alternative solution. The alternative solution provider can use AI to estimate the user's emotions and adjust how alternative solutions are displayed based on those emotions. For example, if the user is relaxed, the alternative solution provider will provide an alternative solution with a detailed explanation. The alternative solution provider can use AI to estimate the user's emotions and adjust how alternative solutions are displayed based on those emotions. For example, if the user is in a hurry, the alternative solution provider will provide an alternative solution that can be prepared in a short time. The alternative solution provider can use AI to estimate the user's emotions and adjust how alternative solutions are displayed based on those emotions. This allows for the provision of more appropriate alternative solutions by adjusting how alternative solutions are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The alternative solution provider can adjust the level of detail of alternative solutions based on the substitutability of materials when providing them. For example, the alternative solution provider can provide alternative solutions that include detailed explanations, taking into account the substitutability of materials. The alternative solution provider can use AI to adjust the level of detail of alternative solutions based on the substitutability of materials. For example, the alternative solution provider can provide concise and easy-to-understand alternative solutions based on the substitutability of materials. The alternative solution provider can use AI to adjust the level of detail of alternative solutions based on the substitutability of materials. For example, the alternative solution provider can prioritize proposing alternative solutions that emphasize the substitutability of materials. The alternative solution provider can use AI to adjust the level of detail of alternative solutions based on the substitutability of materials. This allows for the provision of more appropriate alternative solutions by adjusting the level of detail of alternative solutions based on the substitutability of materials.

[0098] The alternative solution provider can estimate the user's emotions and determine the priority of alternative solutions based on those emotions. For example, if the user is stressed, the alternative solution provider will prioritize suggesting easy and convenient alternatives. The alternative solution provider can use AI to estimate the user's emotions and determine the priority of alternative solutions based on those emotions. For example, if the user is relaxed, the alternative solution provider will suggest alternatives that are a little more time-consuming but delicious. The alternative solution provider can use AI to estimate the user's emotions and determine the priority of alternative solutions based on those emotions. For example, if the user is in a hurry, the alternative solution provider will prioritize suggesting alternatives that can be prepared in a short amount of time. The alternative solution provider can use AI to estimate the user's emotions and determine the priority of alternative solutions based on those emotions. This allows for the provision of more appropriate alternatives by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0099] The alternative solution provider can determine the priority of alternatives based on the availability of materials when providing alternatives. For example, the alternative solution provider proposes the optimal alternative, taking into account the availability of materials. The alternative solution provider can use AI to determine the priority of alternatives based on the availability of materials. For example, the alternative solution provider can determine the priority of alternatives based on the availability of materials. The alternative solution provider can use AI to determine the priority of alternatives based on the availability of materials. For example, the alternative solution provider adjusts the order of alternatives, taking into account the availability of materials. The alternative solution provider can use AI to determine the priority of alternatives based on the availability of materials. This allows for the provision of more appropriate alternatives by determining the priority of alternatives based on the availability of materials.

[0100] The discount information provider can estimate the user's emotions and adjust how discount information is displayed based on those emotions. For example, if the user is stressed, the discount information provider will provide simple and easy-to-understand discount information. The discount information provider can use AI to estimate the user's emotions and adjust how discount information is displayed based on those emotions. For example, if the user is relaxed, the discount information provider will provide discount information that includes detailed explanations. The discount information provider can use AI to estimate the user's emotions and adjust how discount information is displayed based on those emotions. For example, if the user is in a hurry, the discount information provider will provide discount information that can be understood in a short amount of time. The discount information provider can use AI to estimate the user's emotions and adjust how discount information is displayed based on those emotions. This allows for the provision of more appropriate discount information by adjusting how discount information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The discount information provision unit can adjust the level of detail of discount information based on the material's discount rate when providing discount information. For example, the discount information provision unit can provide discount information that includes detailed explanations, taking into account the material's discount rate. The discount information provision unit can use AI to adjust the level of detail of discount information based on the material's discount rate. For example, the discount information provision unit can provide concise and easy-to-understand discount information based on the material's discount rate. The discount information provision unit can use AI to adjust the level of detail of discount information based on the material's discount rate. For example, the discount information provision unit can prioritize suggesting discount information that emphasizes the material's discount rate. The discount information provision unit can use AI to adjust the level of detail of discount information based on the material's discount rate. This allows for the provision of more appropriate discount information by adjusting the level of detail of discount information based on the material's discount rate.

[0102] The discount information provider can estimate the user's emotions and prioritize discount information based on those emotions. For example, if the user is stressed, the discount information provider will prioritize discount information that is easy and quick to understand. The discount information provider can use AI to estimate the user's emotions and prioritize discount information based on those emotions. For example, if the user is relaxed, the discount information provider will suggest discount information that includes detailed explanations. The discount information provider can use AI to estimate the user's emotions and prioritize discount information based on those emotions. For example, if the user is in a hurry, the discount information provider will prioritize discount information that can be understood in a short amount of time. The discount information provider can use AI to estimate the user's emotions and prioritize discount information based on those emotions. This allows for the provision of more appropriate discount information by prioritizing discount information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The discount information provision unit can determine the priority of discount information based on the availability of materials when providing discount information. For example, the discount information provision unit proposes the most suitable discount information considering the availability of materials. The discount information provision unit can use AI to determine the priority of discount information based on the availability of materials. For example, the discount information provision unit determines the priority of discount information based on the availability of materials. The discount information provision unit can use AI to determine the priority of discount information based on the availability of materials. For example, the discount information provision unit adjusts the order of discount information considering the availability of materials. The discount information provision unit can use AI to determine the priority of discount information based on the availability of materials. This allows for the provision of more appropriate discount information by determining the priority of discount information based on the availability of materials.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The ingredient understanding unit can monitor the user's health condition and select the optimal ingredients. For example, if the user has diabetes, it will prioritize selecting low-carbohydrate ingredients. The ingredient understanding unit can use AI to analyze the user's health condition and select the optimal ingredients. For example, if the user has high blood pressure, it will select low-sodium ingredients. The ingredient understanding unit can use AI to monitor the user's health condition and select the optimal ingredients. For example, if the user has allergies, it will select ingredients that do not contain allergens. In this way, by selecting the optimal ingredients according to the user's health condition, healthier meals can be provided.

[0106] The recipe provider can offer recipes that take into account the user's dietary restrictions. For example, if the user is vegetarian, it will provide meat-free recipes. The recipe provider can use AI to analyze the user's dietary restrictions and provide the most suitable recipes. For example, if the user requires a gluten-free diet, it will provide gluten-free recipes. The recipe provider can use AI to offer recipes that take into account the user's dietary restrictions. For example, if the user desires a low-calorie diet, it will provide low-calorie recipes. In this way, by providing the most suitable recipes according to the user's dietary restrictions, it is possible to provide more appropriate meals.

[0107] The procedure provider can provide instructions considering the user's cooking skill level. For example, it can provide detailed instructions to beginner users and concise instructions to advanced users. The procedure provider can use AI to analyze the user's cooking skill level and provide the optimal instructions. For example, it can provide step-by-step instructions to beginner users and only the key points to advanced users. The procedure provider can use AI to provide instructions considering the user's cooking skill level. For example, it can provide video tutorials to beginner users and text-only instructions to advanced users. This allows for more appropriate cooking support by providing the optimal instructions according to the user's cooking skill level.

[0108] The menu support department can analyze a user's eating history and suggest a variety of menus. For example, it can suggest dishes different from those previously offered. The menu support department uses AI to analyze a user's eating history and suggest a variety of menus. For example, it can suggest dishes the user has never eaten before. The menu support department uses AI to analyze a user's eating history and suggest a variety of menus. For example, it can suggest dishes using ingredients the user has never eaten before. By suggesting a variety of menus based on the user's eating history, the enjoyment of meals can be increased.

[0109] The material understanding unit can estimate the user's emotions and suggest a method of storing the material based on those emotions. For example, if the user is stressed, it will suggest a method that allows for easy storage. The material understanding unit can use AI to estimate the user's emotions and suggest a method of storing the material based on those emotions. For example, if the user is relaxed, it will suggest a method that allows for long-term storage. The material understanding unit can use AI to estimate the user's emotions and suggest a method of storing the material based on those emotions. For example, if the user is in a hurry, it will suggest a method that allows for quick storage. In this way, by suggesting a method of storing the material according to the user's emotions, a more appropriate storage method can be provided.

[0110] The recipe provider can estimate the user's emotions and adjust the difficulty of the recipe based on those emotions. For example, if the user is stressed, it will provide an easy recipe. The recipe provider can use AI to estimate the user's emotions and adjust the difficulty of the recipe based on those emotions. For example, if the user is relaxed, it will provide a slightly more difficult recipe. The recipe provider can use AI to estimate the user's emotions and adjust the difficulty of the recipe based on those emotions. For example, if the user is in a hurry, it will provide a recipe that can be cooked in a short time. In this way, by adjusting the difficulty of the recipe according to the user's emotions, it is possible to provide more appropriate recipes.

[0111] The procedure provider can estimate the user's emotions and adjust the order of the procedures based on those emotions. For example, if the user is stressed, it will start with simpler procedures. The procedure provider can use AI to estimate the user's emotions and adjust the order of the procedures based on those emotions. For example, if the user is relaxed, it will start with more complex procedures. The procedure provider can use AI to estimate the user's emotions and adjust the order of the procedures based on those emotions. For example, if the user is in a hurry, it will start with procedures that can be completed quickly. This allows for the provision of more appropriate procedures by adjusting the order of procedures according to the user's emotions.

[0112] The menu support unit can estimate the user's emotions and adjust the menu variations based on those emotions. For example, if the user is stressed, it will provide a simple menu. The menu support unit can use AI to estimate the user's emotions and adjust the menu variations based on those emotions. For example, if the user is relaxed, it will provide a varied menu. The menu support unit can use AI to estimate the user's emotions and adjust the menu variations based on those emotions. For example, if the user is in a hurry, it will provide a menu that can be prepared in a short time. In this way, by adjusting the menu variations according to the user's emotions, a more appropriate menu can be provided.

[0113] The alternative solution provider can estimate the user's emotions and adjust the selection of alternative solutions based on those emotions. For example, if the user is feeling stressed, it can provide an alternative that is easy and quick to prepare. The alternative solution provider can use AI to estimate the user's emotions and adjust the selection of alternative solutions based on those emotions. For example, if the user is relaxed, it can provide a slightly more time-consuming but delicious alternative. The alternative solution provider can use AI to estimate the user's emotions and adjust the selection of alternative solutions based on those emotions. For example, if the user is in a hurry, it can provide an alternative that can be prepared in a short amount of time. In this way, by adjusting the selection of alternative solutions according to the user's emotions, more appropriate alternative solutions can be provided.

[0114] The discount information provision unit can analyze a user's purchase history and provide the most suitable discount information. For example, it can prioritize providing discount information on materials the user has purchased in the past. The discount information provision unit can use AI to analyze a user's purchase history and provide the most suitable discount information. For example, it can provide discount information on materials the user frequently purchases. The discount information provision unit can use AI to analyze a user's purchase history and provide the most suitable discount information. For example, it can provide discount information on materials the user has never purchased in the past. In this way, by providing the most suitable discount information based on the user's purchase history, it can support more economical shopping.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The material understanding unit understands the user's materials. By inputting the materials the user possesses, the unit can understand those materials. The material understanding unit uses AI to analyze the type, quantity, freshness, etc. of the materials. Step 2: The recipe provider unit provides the best recipe based on the ingredients understood by the ingredient understanding unit. The recipe provider unit uses AI to suggest the optimal recipe based on the ingredients the user has. Recipes are selected considering nutritional value, cooking time, user preferences, etc. Step 3: The procedure provider unit provides efficient cooking instructions based on the recipe provided by the recipe provider unit. It instructs the user on the optimal steps for preparing, cooking, and cleaning up. The procedure provider unit uses AI to shorten cooking time and simplify procedures. Step 4: The Menu Support Department proposes menus based on the procedures provided by the Procedure Provision Department. The menus are designed to be nutritionally balanced and to suit the family's preferences. The Menu Support Department uses AI to create economical menu plans that take into account seasonal ingredients and discount information.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the ingredient understanding unit, recipe provision unit, procedure provision unit, menu support unit, alternative suggestion provision unit, and discount information provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the ingredient understanding unit inputs the user's ingredients using the camera 42 and reception device 38 of the smart device 14, and the ingredients are analyzed by the specific processing unit 290 of the data processing unit 12. The recipe provision unit generates an optimal recipe using the specific processing unit 290 of the data processing unit 12 and provides it to the user through the output device 40 of the smart device 14. The procedure provision unit generates an efficient cooking procedure using the specific processing unit 290 of the data processing unit 12 and instructs the user through the output device 40 of the smart device 14. The menu support unit generates a menu that takes into account nutritional balance and family preferences using the specific processing unit 290 of the data processing unit 12 and provides it to the user through the output device 40 of the smart device 14. The alternative suggestion provision unit proposes alternative ingredients using the specific processing unit 290 of the data processing unit 12 and provides them to the user through the output device 40 of the smart device 14. The discount information provision unit collects seasonal food and discount information using the specific processing unit 290 of the data processing device 12 and provides it to the user through the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the ingredient understanding unit, recipe provision unit, procedure provision unit, menu support unit, alternative suggestion provision unit, and discount information provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the ingredient understanding unit inputs the user's ingredients using the camera 42 and microphone 238 of the smart glasses 214, and the information is analyzed by the identification processing unit 290 of the data processing unit 12. The recipe provision unit generates an optimal recipe using the identification processing unit 290 of the data processing unit 12 and provides it to the user through the speaker 240 of the smart glasses 214. The procedure provision unit generates an efficient cooking procedure using the identification processing unit 290 of the data processing unit 12 and instructs the user through the speaker 240 of the smart glasses 214. The menu support unit generates a menu that takes into account nutritional balance and family preferences using the identification processing unit 290 of the data processing unit 12 and provides it to the user through the speaker 240 of the smart glasses 214. The alternative solution provision unit proposes alternative materials using the specific processing unit 290 of the data processing device 12 and provides them to the user through the speaker 240 of the smart glasses 214. The discount information provision unit collects seasonal ingredients and discount information using the specific processing unit 290 of the data processing device 12 and provides it to the user through the speaker 240 of the smart glasses 214. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the ingredient understanding unit, recipe provision unit, procedure provision unit, menu support unit, alternative suggestion provision unit, and discount information provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the ingredient understanding unit inputs the user's ingredients using the camera 42 and microphone 238 of the headset terminal 314, and the ingredients are analyzed by the identification processing unit 290 of the data processing unit 12. The recipe provision unit generates an optimal recipe using the identification processing unit 290 of the data processing unit 12 and provides it to the user through the speaker 240 of the headset terminal 314. The procedure provision unit generates an efficient cooking procedure using the identification processing unit 290 of the data processing unit 12 and instructs the user through the speaker 240 of the headset terminal 314. The menu support unit generates a menu that takes into account nutritional balance and family preferences using the identification processing unit 290 of the data processing unit 12 and provides it to the user through the speaker 240 of the headset terminal 314. The alternative proposal unit proposes alternative materials using the specific processing unit 290 of the data processing device 12 and provides them to the user through the speaker 240 of the headset terminal 314. The discount information provision unit collects seasonal ingredients and discount information using the specific processing unit 290 of the data processing device 12 and provides it to the user through the speaker 240 of the headset terminal 314. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the ingredient understanding unit, recipe provision unit, procedure provision unit, menu support unit, alternative suggestion provision unit, and discount information provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the ingredient understanding unit inputs the user's ingredients using the camera 42 and microphone 238 of the robot 414, and the specific processing unit 290 of the data processing unit 12 analyzes them. The recipe provision unit generates an optimal recipe using the specific processing unit 290 of the data processing unit 12 and provides it to the user through the speaker 240 of the robot 414. The procedure provision unit generates an efficient cooking procedure using the specific processing unit 290 of the data processing unit 12 and instructs the user through the speaker 240 of the robot 414. The menu support unit generates a menu that takes into account nutritional balance and family preferences using the specific processing unit 290 of the data processing unit 12 and provides it to the user through the speaker 240 of the robot 414. The alternative material provision unit proposes alternative materials using the specific processing unit 290 of the data processing device 12 and provides them to the user through the speaker 240 of the robot 414. The discount information provision unit collects seasonal ingredients and discount information using the specific processing unit 290 of the data processing device 12 and provides it to the user through the speaker 240 of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) A material understanding unit that understands the user's materials, A recipe providing unit that provides the best recipe based on the materials understood by the material understanding unit, A procedure provision unit provides efficient cooking procedures based on the recipes provided by the aforementioned recipe provision unit, The system includes a menu support unit that proposes a menu based on the procedure provided by the procedure provision unit. A system characterized by the following features. (Note 2) It includes an alternative solution provision unit that provides alternative solutions when materials are insufficient. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a discount information section that provides seasonal ingredients and discount information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned material understanding unit is It estimates the user's emotions and adjusts the selection of materials based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned material understanding unit is It analyzes the user's past cooking history and provides a method for selecting the optimal ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned material understanding unit is Evaluate the freshness and quality of the ingredients and select the most suitable ones. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned material understanding unit is It estimates the user's emotions and determines the priority of materials based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned material understanding unit is The system prioritizes the selection of region-specific materials, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned material understanding unit is Analyze users' social media activity and select trend-based content. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recipe provision section, The system estimates the user's emotions and adjusts the way recipes are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recipe provision section, When providing recipes, adjust the level of detail based on the importance of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recipe provision section, When providing recipes, different recipe algorithms are applied depending on the category of the dish. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recipe provision section, It estimates the user's emotions and adjusts the recipe length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recipe provision section, When providing recipes, prioritize recipes based on when the ingredients are available. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recipe provision section, When providing recipes, adjust the order of ingredients based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The procedure provision unit, It estimates the user's emotions and adjusts how the instructions are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The procedure provision unit, When providing instructions, adjust the level of detail based on the difficulty of the dish. The system described in Appendix 1, characterized by the features described herein. (Note 18) The procedure provision unit, When providing instructions, different instruction algorithms are applied depending on the category of the dish. The system described in Appendix 1, characterized by the features described herein. (Note 19) The procedure provision unit, It estimates the user's emotions and adjusts the length of the procedure based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The procedure provision unit, When providing instructions, prioritize the steps based on the preparation time for each dish. The system described in Appendix 1, characterized by the features described herein. (Note 21) The procedure provision unit, When providing instructions, adjust the order of the steps based on the relevance of the dishes. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned menu support unit is: The system estimates the user's emotions and adjusts how the menu is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned menu support unit is: When suggesting menus, adjust the level of detail in the menus based on nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned menu support unit is: When suggesting menus, different menu algorithms are applied depending on the family's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned menu support unit is: It estimates the user's emotions and determines the priority of the menu based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned menu support unit is: When proposing menus, the priority of the menu items is determined based on seasonal ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned menu support unit is: When suggesting menus, adjust the order of the dishes based on discount information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned alternative solution provision unit, It estimates the user's emotions and adjusts how alternatives are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned alternative solution provision unit, When providing alternatives, adjust the level of detail of the alternatives based on the feasibility of substituting materials. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned alternative solution provision unit, It estimates the user's emotions and prioritizes alternatives based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned alternative solution provision unit, When providing alternatives, prioritize them based on the availability of materials. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned discount information provision unit, The system estimates the user's emotions and adjusts how discount information is displayed based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned discount information provision unit, When providing discount information, adjust the level of detail in the discount information based on the discount rate of the materials. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned discount information provision unit, The system estimates user sentiment and prioritizes discount information based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned discount information provision unit, When providing discount information, we prioritize the discount information based on the availability of materials. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A material understanding unit that understands the user's materials, A recipe providing unit that provides the best recipe based on the materials understood by the material understanding unit, A procedure provision unit provides efficient cooking procedures based on the recipes provided by the aforementioned recipe provision unit, The system includes a menu support unit that proposes a menu based on the procedure provided by the procedure provision unit. A system characterized by the following features.

2. It includes an alternative solution provision unit that provides alternative solutions when materials are insufficient. The system according to feature 1.

3. It features a discount information section that provides seasonal ingredients and discount information. The system according to feature 1.

4. The aforementioned material understanding unit is It estimates the user's emotions and adjusts the selection of materials based on the estimated user emotions. The system according to feature 1.

5. The aforementioned material understanding unit is It analyzes the user's past cooking history and provides a method for selecting the optimal ingredients. The system according to feature 1.

6. The aforementioned material understanding unit is Evaluate the freshness and quality of the ingredients and select the most suitable ones. The system according to feature 1.

7. The aforementioned material understanding unit is It estimates the user's emotions and determines the priority of materials based on the estimated user emotions. The system according to feature 1.

8. The aforementioned material understanding unit is The system prioritizes the selection of region-specific materials, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned material understanding unit is Analyze users' social media activity and select trend-based content. The system according to feature 1.

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