system

Abstract

The system according to this embodiment aims to comprehensively support the health of all family members and cohabitants and to suggest the most suitable menu. [Solution] The system according to the embodiment comprises a memory unit, an integration unit, a suggestion unit, an optimization unit, and a management unit. The memory unit stores the food preferences and allergy information of all family members and cohabitants. The integration unit integrates the information stored by the memory unit with health data. The suggestion unit suggests menus tailored to individual health goals based on the data integrated by the integration unit. The optimization unit optimizes the nutritional balance of the menus suggested by the suggestion unit. The management unit automatically manages food inventory and generates shopping lists based on the menus optimized by the optimization 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 prior art, there was a problem that it was difficult to comprehensively manage the food preferences, allergy information, and health data of all family members and cohabitants and propose an optimal menu.

[0005] The system according to the embodiment aims to comprehensively support the health of all family members and cohabitants and propose an optimal menu.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a memory unit, an integration unit, a suggestion unit, an optimization unit, and a management unit. The memory unit stores the food preferences and allergy information of all family members and cohabitants. The integration unit integrates the information stored by the memory unit with health data. The suggestion unit proposes menus tailored to individual health goals based on the data integrated by the integration unit. The optimization unit optimizes the nutritional balance of the menus proposed by the suggestion unit. The management unit automatically manages food inventory and generates shopping lists based on the menus optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively support the health of all family members and cohabitants and suggest optimal menus. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 menu agent system according to an embodiment of the present invention is a system for comprehensively supporting the health of all family members and cohabitants and realizing sustainable health management. This system memorizes the food preferences and allergy information of all family members and cohabitants and integrates health data. Next, it proposes menus tailored to individual health goals. Furthermore, it optimizes nutritional balance and automatically manages food inventory and generates shopping lists. It also enables real-time suggestions through chat-style dialogue and menu adjustments that reflect feedback from all family members and cohabitants. This frees users from the daily stress of planning menus. For example, it memorizes the food preferences and allergy information of all family members and cohabitants. In this process, it records each member's likes and dislikes and allergy information in detail and integrates it with health data. For example, if someone is allergic to a specific ingredient, the system can propose a menu that does not include that ingredient. Next, based on the health data, it proposes menus tailored to individual health goals. For example, it proposes a low-calorie menu for a member on a diet, and a high-protein menu for a member aiming to build muscle. In this way, it can provide optimal menus tailored to each member's health goals. Furthermore, it optimizes nutritional balance using an algorithm supervised by a registered dietitian. For example, it suggests balanced menus that take into account vitamin and mineral intake. It also manages food inventory and automatically generates shopping lists. This allows users to purchase necessary ingredients without waste and prepare meals efficiently. It also enables real-time suggestions through chat-style dialogue. For example, if a user asks, "What should we have for dinner tonight?", the AI ​​will suggest the optimal menu based on the day's ingredients and health data. Furthermore, it adjusts menus based on feedback from all family members and household members. For example, it can adjust the next menu based on evaluations of the previous day's menu. This system comprehensively supports the health of all family members and household members, enabling sustainable health management. It also relieves the daily stress of planning menus and provides meal plans that satisfy the whole family. In this way, the menu agent system comprehensively supports the health of all family members and household members, enabling sustainable health management.

[0029] The menu agent system according to the embodiment comprises a memory unit, an integration unit, a suggestion unit, an optimization unit, and a management unit. The memory unit stores the food preferences and allergy information of all family members and cohabitants. The memory unit, for example, records each member's likes and dislikes and allergy information in detail. The memory unit, for example, can suggest a menu that does not include an allergy to a specific ingredient. The memory unit, for example, integrates each member's likes and dislikes and allergy information with health data. The integration unit integrates the information stored by the memory unit with the health data. The integration unit, for example, suggests a menu tailored to each member's health goals based on each member's health data. The integration unit, for example, suggests a low-calorie menu for a member on a diet, and a high-protein menu for a member aiming to build muscle. The integration unit, for example, can provide an optimal menu tailored to each member's health goals. The suggestion unit suggests a menu tailored to individual health goals based on the data integrated by the integration unit. The suggestion unit, for example, suggests a menu tailored to each member's health goals based on health data. The suggestion unit, for example, suggests low-calorie menus for members on a diet and high-protein menus for members aiming to build muscle. The suggestion unit can provide optimal menus tailored to each member's health goals. The optimization unit optimizes the nutritional balance of the menus suggested by the suggestion unit. The optimization unit proposes balanced menus, for example, by considering the intake of vitamins and minerals. The optimization unit optimizes the nutritional balance using an algorithm supervised by a nutritionist. The optimization unit proposes balanced menus, for example, by considering the intake of vitamins and minerals. The management unit automatically generates food inventory management and shopping lists based on the menus optimized by the optimization unit. The management unit purchases necessary ingredients without waste and prepares meals efficiently. The management unit automatically manages food inventory and generates shopping lists. The management unit purchases necessary ingredients without waste and prepares meals efficiently. As a result, the menu agent system according to this embodiment can comprehensively support the health of all family members and cohabitants and realize sustainable health management.

[0030] The memory unit stores the food preferences and allergy information of all family members and cohabitants. Specifically, it meticulously records each member's likes and dislikes and allergy information, and stores this information in a database. For example, if someone is allergic to a particular food, the system can suggest a menu that does not include that food. The memory unit also has the function of integrating each member's likes and dislikes and allergy information with health data. This enables meal suggestions tailored to each individual's health condition and goals. The memory unit also records each member's eating history, health checkup results, and daily physical condition data, and can perform detailed analysis based on this data. Furthermore, the memory unit allows each member to easily update their preferences and allergy information through the user interface. This ensures that menu suggestions are always based on the latest information. In addition, the memory unit can analyze past eating history and understand each member's eating tendencies and patterns to provide more personalized suggestions. For example, if a particular member tends to like a specific food on a specific day of the week, the system can suggest a menu that takes this tendency into account. This allows the memory unit to meticulously record the food preferences and allergy information of all family members and cohabitants, and to support the suggestion of optimal menus tailored to each individual's health condition and goals.

[0031] The integration unit integrates information stored by the memory unit with health data. Specifically, it proposes menus tailored to each member's individual health goals based on their health data. For example, it proposes low-calorie menus for members on a diet and high-protein menus for members aiming to build muscle. The integration unit can provide optimal menus tailored to each member's health goals. For example, the integration unit analyzes each member's health checkup results and daily physical condition data, and conducts a detailed health assessment based on this data. Furthermore, the integration unit uses AI to analyze each member's health data and proposes optimal menus tailored to their individual health status and goals. For example, the AI ​​optimizes nutritional balance and calorie intake based on each member's health data and proposes menus tailored to their individual health goals. In addition, the integration unit can update each member's health data in real time and provide menu suggestions based on the latest information. For example, every time health checkup results or daily physical condition data are updated, the integration unit provides the latest menu suggestions based on this data. In this way, the integration unit can support optimal menu suggestions tailored to each member's health status and goals, enabling sustainable health management.

[0032] The Proposal Department proposes menus tailored to individual health goals based on data integrated by the Integration Department. Specifically, it proposes menus that match each member's health goals based on health data. For example, it proposes low-calorie menus for members on a diet and high-protein menus for members aiming to build muscle. The Proposal Department can provide optimal menus tailored to each member's health goals. The Proposal Department uses AI to analyze each member's health data and proposes optimal menus according to their individual health status and goals. For example, the AI ​​optimizes nutritional balance and calorie intake based on each member's health data and proposes menus tailored to their individual health goals. Furthermore, the Proposal Department can update each member's health data in real time and provide menu suggestions based on the latest information. For example, whenever health checkup results or daily health data are updated, the Proposal Department provides updated menu suggestions based on this data. In addition, the Proposal Department allows each member to easily set their own health goals and preferences through a user interface. This enables menu suggestions to always be based on the latest information. As a result, the Proposal Department can support optimal menu suggestions tailored to each member's health status and goals, enabling sustainable health management.

[0033] The Optimization Department optimizes the nutritional balance of the menus proposed by the Proposal Department. Specifically, it proposes balanced menus considering the intake of vitamins and minerals. The Optimization Department optimizes nutritional balance using an algorithm supervised by a registered dietitian. For example, it proposes balanced menus considering the intake of vitamins and minerals. The Optimization Department uses AI to analyze each member's health data and proposes optimal menus tailored to their individual health status and goals. For example, the AI ​​optimizes nutritional balance and calorie intake based on each member's health data and proposes menus that match their individual health goals. Furthermore, the Optimization Department can update each member's health data in real time and provide menu suggestions based on the latest information. For example, whenever health checkup results or daily health data are updated, the Optimization Department provides updated menu suggestions based on this data. In addition, the Optimization Department allows each member to easily set their own health goals and preferences through a user interface. This enables menu suggestions to always be based on the latest information. As a result, the Optimization Department can support optimal menu suggestions tailored to each member's health status and goals, enabling sustainable health management.

[0034] The Management Department automatically manages food inventory and generates shopping lists based on menus optimized by the Optimization Department. Specifically, it purchases necessary ingredients without waste and prepares meals efficiently. The Management Department automatically manages food inventory and generates shopping lists. For example, it purchases necessary ingredients without waste and prepares meals efficiently. The Management Department uses AI to analyze each member's health data and proposes optimal menus tailored to each individual's health condition and goals. For example, the AI ​​optimizes nutritional balance and calorie intake based on each member's health data and proposes menus that match each individual's health goals. In addition, the Management Department can update each member's health data in real time and make menu suggestions based on the latest information. For example, whenever health checkup results or daily health data are updated, the Management Department makes the latest menu suggestions based on this data. Furthermore, the Management Department allows each member to easily set their own health goals and preferences through a user interface. This enables menu suggestions to always be based on the latest information. As a result, the Management Department can support optimal menu suggestions tailored to each member's health condition and goals, realizing sustainable health management.

[0035] The suggestion function can provide real-time suggestions through chat-style dialogue. For example, if a user asks, "What should I have for dinner tonight?", the AI ​​will suggest the optimal menu based on the day's ingredients and health data. The suggestion function can provide real-time suggestions through chat-style dialogue. For example, if a user asks, "What should I have for dinner tonight?", the AI ​​will suggest the optimal menu based on the day's ingredients and health data. The suggestion function can provide real-time suggestions through chat-style dialogue. This allows users to receive menu suggestions in real time.

[0036] The suggestion department can adjust menus to reflect feedback from all family members and household members. For example, the suggestion department adjusts the next menu based on the evaluation of the previous day's menu. The suggestion department adjusts menus to reflect feedback from all family members and household members. The suggestion department adjusts menus to reflect feedback from all family members and household members. For example, the suggestion department adjusts the next menu based on the evaluation of the previous day's menu. The suggestion department adjusts menus to reflect feedback from all family members and household members. This makes it possible to adjust menus to reflect user feedback.

[0037] The memory unit can record detailed information about each member's likes, dislikes, and allergies. For example, the memory unit can record detailed information about each member's likes, dislikes, and allergies. For example, if a member has an allergy to a specific food, the memory unit can suggest a menu that does not include that food. For example, the memory unit can integrate each member's likes, dislikes, and allergies with health data. For example, the memory unit can record detailed information about each member's likes, dislikes, and allergies. This allows for more appropriate menu suggestions by recording detailed information about each member's likes, dislikes, and allergies. Some or all of the above processing in the memory unit may be performed using AI, for example, or without AI.

[0038] The integration unit can integrate with health data. For example, the integration unit can propose menus tailored to each member's individual health goals based on each member's health data. For example, the integration unit can propose low-calorie menus for members on a diet and high-protein menus for members aiming to build muscle. For example, the integration unit can provide optimal menus tailored to each member's health goals. For example, the integration unit proposes menus tailored to each member's health goals based on health data. By integrating with health data, more accurate menu suggestions become possible. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI.

[0039] The optimization unit can propose a balanced menu that takes into account the intake of vitamins and minerals. The optimization unit, for example, proposes a balanced menu that takes into account the intake of vitamins and minerals. The optimization unit, for example, optimizes nutritional balance using an algorithm supervised by a nutritionist. The optimization unit, for example, proposes a balanced menu that takes into account the intake of vitamins and minerals. The optimization unit, for example, proposes a balanced menu that takes into account the intake of vitamins and minerals. This improves health management by proposing a balanced menu that takes into account the intake of vitamins and minerals. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0040] The management department can purchase necessary ingredients without waste and prepare meals efficiently. The management department, for example, purchases necessary ingredients without waste and prepares meals efficiently. The management department, for example, manages ingredient inventory and automatically generates shopping lists. The management department, for example, purchases necessary ingredients without waste and prepares meals efficiently. The management department, for example, purchases necessary ingredients without waste and prepares meals efficiently. This reduces food waste by purchasing necessary ingredients without waste and preparing meals efficiently. Some or all of the above processes in the management department may be performed using AI, for example, or without AI.

[0041] The memory unit can improve the accuracy of its memory by referring to past meal history when storing each member's food preferences and allergy information. For example, the memory unit analyzes frequently eaten and avoided dishes from past meal history to accurately store preferences and allergy information. For example, the memory unit records reactions to specific ingredients based on past meal history and updates allergy information. For example, the memory unit refers to past meal history to understand seasonal food preferences and improve the accuracy of its memory. In this way, the accuracy of memory is improved by referring to past meal history. Some or all of the above processing in the memory unit may be performed using AI, for example, or without using AI.

[0042] The memory unit can store information related to the season and weather when it stores food preferences and allergy information. For example, the memory unit can store seasonal food preferences and reflect them in menu suggestions. For example, the memory unit can store food preferences based on the weather and suggest menus suitable for rainy or hot days. For example, the memory unit can record allergy reactions based on the season and weather and suggest appropriate menus. In this way, by storing information related to the season and weather, more appropriate menu suggestions become possible. Some or all of the above processing in the memory unit may be performed using AI, for example, or without using AI.

[0043] The memory unit can prioritize storing highly relevant information when storing food preferences and allergy information, taking into account the user's geographical location. For example, the memory unit stores food preferences based on local specialties and food culture in the area where the user lives. For example, the memory unit stores allergy information considering the food culture of places the user frequently visits. For example, the memory unit prioritizes storing region-specific allergy information based on the user's geographical location. This allows the memory unit to prioritize storing highly relevant information by taking into account the user's geographical location. Some or all of the above processing in the memory unit may be performed using AI, for example, or without using AI.

[0044] The memory unit can analyze the user's social media activity and store relevant information when storing food preferences and allergy information. For example, the memory unit analyzes photos and comments of meals shared by the user on social media to store food preferences. For example, the memory unit stores allergy information mentioned by the user on social media. For example, the memory unit updates food preferences and allergy information based on the user's social media activity. In this way, relevant information can be stored by analyzing the user's social media activity. Some or all of the above processing in the memory unit may be performed using AI, for example, or without using AI.

[0045] The integration unit can improve the accuracy of the integration by referring to past health history when integrating health data. For example, the integration unit can extract important data from past health history to improve the accuracy of the integration. For example, the integration unit can prioritize the integration of specific health indicators based on past health history. For example, the integration unit can integrate seasonal health data by referring to past health history. This improves the accuracy of the integration by referring to past health history. Some or all of the above processes in the integration unit may be performed using AI, for example, or without using AI.

[0046] The integration unit can integrate seasonal and weather-related data when integrating health data. For example, the integration unit can integrate seasonal health data and propose health management appropriate for each season. For example, the integration unit can integrate weather-related health data and propose health management suitable for rainy or hot days. For example, the integration unit can integrate seasonal and weather-related health data to help with allergy and physical condition management. By integrating seasonal and weather-related data, more appropriate health management becomes possible. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0047] The integration unit can prioritize the integration of highly relevant data by considering the user's geographical location when integrating health data. For example, the integration unit may prioritize the integration of health data for the area where the user lives. For example, the integration unit may consider and integrate health data for places the user frequently visits. For example, the integration unit may prioritize the integration of region-specific health data based on the user's geographical location. This allows for the priority integration of highly relevant data by considering the user's geographical location. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0048] The integration unit can analyze users' social media activity and integrate relevant data when integrating health data. For example, the integration unit integrates health information shared by users on social media. For example, the integration unit integrates health data mentioned by users on social media. For example, the integration unit updates health data based on users' social media activity. This allows for the integration of relevant data by analyzing users' social media activity. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI.

[0049] The suggestion unit can improve the accuracy of its suggestions by referring to past suggestion history when proposing menus. For example, the suggestion unit can improve the accuracy of its suggestions based on menus that were frequently suggested from past suggestion history. For example, the suggestion unit can prioritize suggesting menus that include specific ingredients based on past suggestion history. For example, the suggestion unit can suggest seasonal menus by referring to past suggestion history. In this way, the accuracy of suggestions is improved by referring to past suggestion history. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without using AI.

[0050] The suggestion department can also suggest menus that are appropriate for the season and weather when proposing menus. For example, the suggestion department can suggest menus that use seasonal ingredients. For example, the suggestion department can suggest menus that are appropriate for the weather. For example, the suggestion department can suggest menus that are appropriate for the season and weather to help with health management. This makes it possible to manage health more appropriately by suggesting menus that are appropriate for the season and weather. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without using AI.

[0051] The suggestion unit can prioritize suggesting menus that are highly relevant to the user, taking into account the user's geographical location. For example, the suggestion unit may suggest menus that use local specialties from the area where the user lives. For example, the suggestion unit may suggest menus that take into account the food culture of places the user frequently visits. For example, the suggestion unit may prioritize suggesting menus specific to a region based on the user's geographical location. In this way, by taking into account the user's geographical location, it is possible to prioritize suggesting menus that are highly relevant. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0052] The suggestion unit can analyze the user's social media activity and suggest relevant menus when proposing menus. For example, the suggestion unit can analyze photos and comments of meals shared by the user on social media and suggest relevant menus. For example, the suggestion unit can suggest menus that include ingredients mentioned by the user on social media. For example, the suggestion unit can suggest menus that reflect the user's food preferences and trends based on the user's social media activity. In this way, relevant menus can be suggested by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0053] The optimization unit can improve the accuracy of optimization by referring to past nutritional data when optimizing nutritional balance. For example, the optimization unit can extract important nutrients from past nutritional data to improve the accuracy of optimization. For example, the optimization unit can prioritize the optimization of specific nutrients based on past nutritional data. For example, the optimization unit can optimize the nutritional balance for each season by referring to past nutritional data. In this way, the accuracy of optimization is improved by referring to past nutritional data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without using AI.

[0054] The optimization unit can optimize nutritional balance in accordance with the season and weather. For example, the optimization unit can optimize nutritional balance for each season and propose health management appropriate for that season. For example, the optimization unit can optimize nutritional balance according to the weather and propose health management suitable for rainy or hot days. For example, the optimization unit can optimize nutritional balance according to the season and weather and use it to help with allergy and physical condition management. As a result, more appropriate health management becomes possible by optimizing nutritional balance according to the season and weather. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0055] The optimization unit can prioritize optimizing nutritional balance by considering the user's geographical location information when optimizing nutritional balance. For example, the optimization unit can optimize nutritional balance using local specialties from the area where the user lives. For example, the optimization unit can optimize nutritional balance by considering the food culture of places the user frequently visits. For example, the optimization unit can prioritize optimizing region-specific nutritional balance based on the user's geographical location information. In this way, by considering the user's geographical location information, it is possible to prioritize optimizing nutritional balance with high relevance. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0056] The optimization unit can analyze the user's social media activity and optimize the relevant nutritional balance when optimizing nutritional balance. For example, the optimization unit analyzes photos and comments of meals shared by the user on social media and optimizes the relevant nutritional balance. For example, the optimization unit optimizes menus that include nutrients mentioned by the user on social media. For example, the optimization unit optimizes nutritional balance that reflects the user's food preferences and trends based on the user's social media activity. In this way, the relevant nutritional balance can be optimized by analyzing the user's social media activity. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0057] The management department can improve the accuracy of its inventory management and shopping list generation by referring to past inventory data. For example, the management department can improve the accuracy of its management by analyzing frequently purchased ingredients from past inventory data. For example, the management department can optimize shopping lists by understanding consumption patterns of specific ingredients based on past inventory data. For example, the management department can improve the accuracy of its management by understanding seasonal ingredient consumption patterns by referring to past inventory data. In this way, the accuracy of management is improved by referring to past inventory data. Some or all of the above processes in the management department may be performed using AI, for example, or without using AI.

[0058] The management department can manage food inventory and generate shopping lists while also incorporating seasonal and weather-based inventory management. For example, the management department can understand seasonal food consumption patterns and manage inventory accordingly. For example, the management department can understand food consumption patterns based on weather and manage inventory appropriately for rainy or hot days. For example, the management department can manage inventory according to season and weather and generate efficient shopping lists. This allows for more appropriate management by incorporating seasonal and weather-based inventory management. Some or all of the above processes in the management department may be performed using AI, for example, or without AI.

[0059] The management department can prioritize the management of highly relevant ingredients by considering the user's geographical location when managing ingredient inventory and generating shopping lists. For example, the management department can prioritize the management of local specialties in the area where the user lives. For example, the management department can manage ingredients by considering the food culture of places the user frequently visits. For example, the management department can prioritize the management of region-specific ingredients based on the user's geographical location. This allows for the prioritization of highly relevant ingredients by considering the user's geographical location. Some or all of the above processes in the management department may be performed using AI, for example, or without AI.

[0060] The management department can manage relevant ingredients by analyzing users' social media activity when managing ingredient inventory and generating shopping lists. For example, the management department can analyze photos and comments of meals shared by users on social media and manage relevant ingredients. For example, the management department can prioritize the management of ingredients mentioned by users on social media. For example, the management department can manage ingredients that reflect food preferences and trends based on users' social media activity. In this way, relevant ingredients can be managed by analyzing users' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The management department can prioritize the management of highly relevant ingredients by considering the user's geographical location when managing ingredient inventory and generating shopping lists. For example, it can prioritize the management of local specialties in the user's area of ​​residence. It can also manage ingredients considering the food culture of places the user frequently visits. Based on the user's geographical location, it can prioritize the management of region-specific ingredients. In this way, by considering the user's geographical location, it can prioritize the management of highly relevant ingredients.

[0063] The integration unit can improve the accuracy of health data integration by referring to past health history. For example, it can extract important data from past health history to improve integration accuracy. Based on past health history, specific health indicators can be prioritized for integration. Seasonal health data can be integrated by referring to past health history. In this way, referencing past health history improves the accuracy of integration.

[0064] The proposal department can improve the accuracy of its menu suggestions by referring to past suggestion history. For example, it can improve the accuracy of suggestions based on menus that were frequently suggested in the past suggestion history. It can also prioritize suggesting menus that include specific ingredients based on past suggestion history. It can suggest seasonal menus by referring to past suggestion history. In this way, the accuracy of suggestions is improved by referring to past suggestion history.

[0065] The management department can manage food inventory and generate shopping lists while also incorporating seasonal and weather-based inventory management. For example, it can understand seasonal food consumption patterns and manage inventory accordingly. It can also understand food consumption patterns based on weather and manage inventory appropriately for rainy or hot days. By managing inventory according to season and weather, it can generate efficient shopping lists. This allows for more effective inventory management by incorporating seasonal and weather-based inventory management.

[0066] The memory unit can prioritize storing highly relevant information, taking into account the user's geographical location, when memorizing food preferences and allergy information. For example, it can store food preferences based on local specialties and food culture in the area where the user lives. It can also store allergy information considering the food culture of places the user frequently visits. Based on the user's geographical location, it can prioritize storing region-specific allergy information. In this way, by considering the user's geographical location, it can prioritize storing highly relevant information.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The memory unit stores the food preferences and allergy information of all family members and housemates. For example, it can record each member's likes and dislikes and allergy information in detail, and if someone is allergic to a specific ingredient, it can suggest a menu that does not include that ingredient. Step 2: The integration unit integrates the information stored by the memory unit with health data. For example, based on each member's health data, it proposes a menu tailored to their individual health goals. For example, it might suggest a low-calorie menu for a member on a diet, or a high-protein menu for a member aiming to build muscle. Step 3: The proposal department proposes menus tailored to individual health goals based on the data integrated by the integration department. For example, based on health data, they propose menus tailored to each member's health goals. For members on a diet, they propose low-calorie menus, and for members aiming to build muscle, they propose high-protein menus. Step 4: The optimization unit optimizes the nutritional balance of the menu proposed by the proposal unit. For example, it considers the intake of vitamins and minerals and proposes a balanced menu. The nutritional balance is optimized using an algorithm supervised by a registered dietitian. Step 5: The management department automatically generates food inventory management and shopping lists based on the menu optimized by the optimization department. For example, it purchases necessary ingredients without waste and prepares meals efficiently.

[0069] (Example of form 2) The menu agent system according to an embodiment of the present invention is a system for comprehensively supporting the health of all family members and cohabitants and realizing sustainable health management. This system memorizes the food preferences and allergy information of all family members and cohabitants and integrates health data. Next, it proposes menus tailored to individual health goals. Furthermore, it optimizes nutritional balance and automatically manages food inventory and generates shopping lists. It also enables real-time suggestions through chat-style dialogue and menu adjustments that reflect feedback from all family members and cohabitants. This frees users from the daily stress of planning menus. For example, it memorizes the food preferences and allergy information of all family members and cohabitants. In this process, it records each member's likes and dislikes and allergy information in detail and integrates it with health data. For example, if someone is allergic to a specific ingredient, the system can propose a menu that does not include that ingredient. Next, based on the health data, it proposes menus tailored to individual health goals. For example, it proposes a low-calorie menu for a member on a diet, and a high-protein menu for a member aiming to build muscle. In this way, it can provide optimal menus tailored to each member's health goals. Furthermore, it optimizes nutritional balance using an algorithm supervised by a registered dietitian. For example, it suggests balanced menus that take into account vitamin and mineral intake. It also manages food inventory and automatically generates shopping lists. This allows users to purchase necessary ingredients without waste and prepare meals efficiently. It also enables real-time suggestions through chat-style dialogue. For example, if a user asks, "What should we have for dinner tonight?", the AI ​​will suggest the optimal menu based on the day's ingredients and health data. Furthermore, it adjusts menus based on feedback from all family members and household members. For example, it can adjust the next menu based on evaluations of the previous day's menu. This system comprehensively supports the health of all family members and household members, enabling sustainable health management. It also relieves the daily stress of planning menus and provides meal plans that satisfy the whole family. In this way, the menu agent system comprehensively supports the health of all family members and household members, enabling sustainable health management.

[0070] The menu agent system according to the embodiment comprises a memory unit, an integration unit, a suggestion unit, an optimization unit, and a management unit. The memory unit stores the food preferences and allergy information of all family members and cohabitants. The memory unit, for example, records each member's likes and dislikes and allergy information in detail. The memory unit, for example, can suggest a menu that does not include an allergy to a specific ingredient. The memory unit, for example, integrates each member's likes and dislikes and allergy information with health data. The integration unit integrates the information stored by the memory unit with the health data. The integration unit, for example, suggests a menu tailored to each member's health goals based on each member's health data. The integration unit, for example, suggests a low-calorie menu for a member on a diet, and a high-protein menu for a member aiming to build muscle. The integration unit, for example, can provide an optimal menu tailored to each member's health goals. The suggestion unit suggests a menu tailored to individual health goals based on the data integrated by the integration unit. The suggestion unit, for example, suggests a menu tailored to each member's health goals based on health data. The suggestion unit, for example, suggests low-calorie menus for members on a diet and high-protein menus for members aiming to build muscle. The suggestion unit can provide optimal menus tailored to each member's health goals. The optimization unit optimizes the nutritional balance of the menus suggested by the suggestion unit. The optimization unit proposes balanced menus, for example, by considering the intake of vitamins and minerals. The optimization unit optimizes the nutritional balance using an algorithm supervised by a nutritionist. The optimization unit proposes balanced menus, for example, by considering the intake of vitamins and minerals. The management unit automatically generates food inventory management and shopping lists based on the menus optimized by the optimization unit. The management unit purchases necessary ingredients without waste and prepares meals efficiently. The management unit automatically manages food inventory and generates shopping lists. The management unit purchases necessary ingredients without waste and prepares meals efficiently. As a result, the menu agent system according to this embodiment can comprehensively support the health of all family members and cohabitants and realize sustainable health management.

[0071] The memory unit stores the food preferences and allergy information of all family members and cohabitants. Specifically, it meticulously records each member's likes and dislikes and allergy information, and stores this information in a database. For example, if someone is allergic to a particular food, the system can suggest a menu that does not include that food. The memory unit also has the function of integrating each member's likes and dislikes and allergy information with health data. This enables meal suggestions tailored to each individual's health condition and goals. The memory unit also records each member's eating history, health checkup results, and daily physical condition data, and can perform detailed analysis based on this data. Furthermore, the memory unit allows each member to easily update their preferences and allergy information through the user interface. This ensures that menu suggestions are always based on the latest information. In addition, the memory unit can analyze past eating history and understand each member's eating tendencies and patterns to provide more personalized suggestions. For example, if a particular member tends to like a specific food on a specific day of the week, the system can suggest a menu that takes this tendency into account. This allows the memory unit to meticulously record the food preferences and allergy information of all family members and cohabitants, and to support the suggestion of optimal menus tailored to each individual's health condition and goals.

[0072] The integration unit integrates information stored by the memory unit with health data. Specifically, it proposes menus tailored to each member's individual health goals based on their health data. For example, it proposes low-calorie menus for members on a diet and high-protein menus for members aiming to build muscle. The integration unit can provide optimal menus tailored to each member's health goals. For example, the integration unit analyzes each member's health checkup results and daily physical condition data, and conducts a detailed health assessment based on this data. Furthermore, the integration unit uses AI to analyze each member's health data and proposes optimal menus tailored to their individual health status and goals. For example, the AI ​​optimizes nutritional balance and calorie intake based on each member's health data and proposes menus tailored to their individual health goals. In addition, the integration unit can update each member's health data in real time and provide menu suggestions based on the latest information. For example, every time health checkup results or daily physical condition data are updated, the integration unit provides the latest menu suggestions based on this data. In this way, the integration unit can support optimal menu suggestions tailored to each member's health status and goals, enabling sustainable health management.

[0073] The Proposal Department proposes menus tailored to individual health goals based on data integrated by the Integration Department. Specifically, it proposes menus that match each member's health goals based on health data. For example, it proposes low-calorie menus for members on a diet and high-protein menus for members aiming to build muscle. The Proposal Department can provide optimal menus tailored to each member's health goals. The Proposal Department uses AI to analyze each member's health data and proposes optimal menus according to their individual health status and goals. For example, the AI ​​optimizes nutritional balance and calorie intake based on each member's health data and proposes menus tailored to their individual health goals. Furthermore, the Proposal Department can update each member's health data in real time and provide menu suggestions based on the latest information. For example, whenever health checkup results or daily health data are updated, the Proposal Department provides updated menu suggestions based on this data. In addition, the Proposal Department allows each member to easily set their own health goals and preferences through a user interface. This enables menu suggestions to always be based on the latest information. As a result, the Proposal Department can support optimal menu suggestions tailored to each member's health status and goals, enabling sustainable health management.

[0074] The Optimization Department optimizes the nutritional balance of the menus proposed by the Proposal Department. Specifically, it proposes balanced menus considering the intake of vitamins and minerals. The Optimization Department optimizes nutritional balance using an algorithm supervised by a registered dietitian. For example, it proposes balanced menus considering the intake of vitamins and minerals. The Optimization Department uses AI to analyze each member's health data and proposes optimal menus tailored to their individual health status and goals. For example, the AI ​​optimizes nutritional balance and calorie intake based on each member's health data and proposes menus that match their individual health goals. Furthermore, the Optimization Department can update each member's health data in real time and provide menu suggestions based on the latest information. For example, whenever health checkup results or daily health data are updated, the Optimization Department provides updated menu suggestions based on this data. In addition, the Optimization Department allows each member to easily set their own health goals and preferences through a user interface. This enables menu suggestions to always be based on the latest information. As a result, the Optimization Department can support optimal menu suggestions tailored to each member's health status and goals, enabling sustainable health management.

[0075] The Management Department automatically manages food inventory and generates shopping lists based on menus optimized by the Optimization Department. Specifically, it purchases necessary ingredients without waste and prepares meals efficiently. The Management Department automatically manages food inventory and generates shopping lists. For example, it purchases necessary ingredients without waste and prepares meals efficiently. The Management Department uses AI to analyze each member's health data and proposes optimal menus tailored to each individual's health condition and goals. For example, the AI ​​optimizes nutritional balance and calorie intake based on each member's health data and proposes menus that match each individual's health goals. In addition, the Management Department can update each member's health data in real time and make menu suggestions based on the latest information. For example, whenever health checkup results or daily health data are updated, the Management Department makes the latest menu suggestions based on this data. Furthermore, the Management Department allows each member to easily set their own health goals and preferences through a user interface. This enables menu suggestions to always be based on the latest information. As a result, the Management Department can support optimal menu suggestions tailored to each member's health condition and goals, realizing sustainable health management.

[0076] The suggestion function can provide real-time suggestions through chat-style dialogue. For example, if a user asks, "What should I have for dinner tonight?", the AI ​​will suggest the optimal menu based on the day's ingredients and health data. The suggestion function can provide real-time suggestions through chat-style dialogue. For example, if a user asks, "What should I have for dinner tonight?", the AI ​​will suggest the optimal menu based on the day's ingredients and health data. The suggestion function can provide real-time suggestions through chat-style dialogue. This allows users to receive menu suggestions in real time.

[0077] The suggestion department can adjust menus to reflect feedback from all family members and household members. For example, the suggestion department adjusts the next menu based on the evaluation of the previous day's menu. The suggestion department adjusts menus to reflect feedback from all family members and household members. The suggestion department adjusts menus to reflect feedback from all family members and household members. For example, the suggestion department adjusts the next menu based on the evaluation of the previous day's menu. The suggestion department adjusts menus to reflect feedback from all family members and household members. This makes it possible to adjust menus to reflect user feedback.

[0078] The memory unit can record detailed information about each member's likes, dislikes, and allergies. For example, the memory unit can record detailed information about each member's likes, dislikes, and allergies. For example, if a member has an allergy to a specific food, the memory unit can suggest a menu that does not include that food. For example, the memory unit can integrate each member's likes, dislikes, and allergies with health data. For example, the memory unit can record detailed information about each member's likes, dislikes, and allergies. This allows for more appropriate menu suggestions by recording detailed information about each member's likes, dislikes, and allergies. Some or all of the above processing in the memory unit may be performed using AI, for example, or without AI.

[0079] The integration unit can integrate with health data. For example, the integration unit can propose menus tailored to each member's individual health goals based on each member's health data. For example, the integration unit can propose low-calorie menus for members on a diet and high-protein menus for members aiming to build muscle. For example, the integration unit can provide optimal menus tailored to each member's health goals. For example, the integration unit proposes menus tailored to each member's health goals based on health data. By integrating with health data, more accurate menu suggestions become possible. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI.

[0080] The optimization unit can propose a balanced menu that takes into account the intake of vitamins and minerals. The optimization unit, for example, proposes a balanced menu that takes into account the intake of vitamins and minerals. The optimization unit, for example, optimizes nutritional balance using an algorithm supervised by a nutritionist. The optimization unit, for example, proposes a balanced menu that takes into account the intake of vitamins and minerals. The optimization unit, for example, proposes a balanced menu that takes into account the intake of vitamins and minerals. This improves health management by proposing a balanced menu that takes into account the intake of vitamins and minerals. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0081] The management department can purchase necessary ingredients without waste and prepare meals efficiently. The management department, for example, purchases necessary ingredients without waste and prepares meals efficiently. The management department, for example, manages ingredient inventory and automatically generates shopping lists. The management department, for example, purchases necessary ingredients without waste and prepares meals efficiently. The management department, for example, purchases necessary ingredients without waste and prepares meals efficiently. This reduces food waste by purchasing necessary ingredients without waste and preparing meals efficiently. Some or all of the above processes in the management department may be performed using AI, for example, or without AI.

[0082] The memory unit can estimate the user's emotions and adjust how food preferences and allergy information are stored based on the estimated emotions. For example, if the user is stressed, the memory unit provides a simple interface and minimizes the input steps. For example, if the user is relaxed, the memory unit provides detailed input options and suggests a customizable input method. For example, if the user is in a hurry, the memory unit prioritizes voice input to allow for quick input of food preferences and allergy information. This allows for the storage of more appropriate information by adjusting the storage method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the memory unit may be performed using AI, for example, or without AI.

[0083] The memory unit can improve the accuracy of its memory by referring to past meal history when storing each member's food preferences and allergy information. For example, the memory unit analyzes frequently eaten and avoided dishes from past meal history to accurately store preferences and allergy information. For example, the memory unit records reactions to specific ingredients based on past meal history and updates allergy information. For example, the memory unit refers to past meal history to understand seasonal food preferences and improve the accuracy of its memory. In this way, the accuracy of memory is improved by referring to past meal history. Some or all of the above processing in the memory unit may be performed using AI, for example, or without using AI.

[0084] The memory unit can store information related to the season and weather when it stores food preferences and allergy information. For example, the memory unit can store seasonal food preferences and reflect them in menu suggestions. For example, the memory unit can store food preferences based on the weather and suggest menus suitable for rainy or hot days. For example, the memory unit can record allergy reactions based on the season and weather and suggest appropriate menus. In this way, by storing information related to the season and weather, more appropriate menu suggestions become possible. Some or all of the above processing in the memory unit may be performed using AI, for example, or without using AI.

[0085] The memory unit can estimate the user's emotions and determine the priority of information to store based on the estimated emotions. For example, if the user is stressed, the memory unit will prioritize storing important allergy information. If the user is relaxed, the memory unit will prioritize storing detailed food preference information. If the user is in a hurry, the memory unit will prioritize storing important information with simple input. This allows important information to be stored preferentially by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the memory unit may be performed using AI, for example, or without AI.

[0086] The memory unit can prioritize storing highly relevant information when storing food preferences and allergy information, taking into account the user's geographical location. For example, the memory unit stores food preferences based on local specialties and food culture in the area where the user lives. For example, the memory unit stores allergy information considering the food culture of places the user frequently visits. For example, the memory unit prioritizes storing region-specific allergy information based on the user's geographical location. This allows the memory unit to prioritize storing highly relevant information by taking into account the user's geographical location. Some or all of the above processing in the memory unit may be performed using AI, for example, or without using AI.

[0087] The memory unit can analyze the user's social media activity and store relevant information when storing food preferences and allergy information. For example, the memory unit analyzes photos and comments of meals shared by the user on social media to store food preferences. For example, the memory unit stores allergy information mentioned by the user on social media. For example, the memory unit updates food preferences and allergy information based on the user's social media activity. In this way, relevant information can be stored by analyzing the user's social media activity. Some or all of the above processing in the memory unit may be performed using AI, for example, or without using AI.

[0088] The integration unit can estimate the user's emotions and adjust the method of integrating health data based on the estimated user emotions. For example, if the user is stressed, the integration unit provides a simple integration method and prioritizes the integration of important health data. For example, if the user is relaxed, the integration unit integrates detailed health data and proposes a customizable integration method. For example, if the user is in a hurry, the integration unit enables the rapid integration of health data. This allows for more appropriate data integration by adjusting the method of integrating health data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not using AI.

[0089] The integration unit can improve the accuracy of the integration by referring to past health history when integrating health data. For example, the integration unit can extract important data from past health history to improve the accuracy of the integration. For example, the integration unit can prioritize the integration of specific health indicators based on past health history. For example, the integration unit can integrate seasonal health data by referring to past health history. This improves the accuracy of the integration by referring to past health history. Some or all of the above processes in the integration unit may be performed using AI, for example, or without using AI.

[0090] The integration unit can integrate seasonal and weather-related data when integrating health data. For example, the integration unit can integrate seasonal health data and propose health management appropriate for each season. For example, the integration unit can integrate weather-related health data and propose health management suitable for rainy or hot days. For example, the integration unit can integrate seasonal and weather-related health data to help with allergy and physical condition management. By integrating seasonal and weather-related data, more appropriate health management becomes possible. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0091] The integration unit can estimate the user's emotions and determine the priority of data to integrate based on the estimated user emotions. For example, if the user is stressed, the integration unit will prioritize integrating important health data. For example, if the user is relaxed, the integration unit will prioritize integrating detailed health data. For example, if the user is in a hurry, the integration unit will prioritize integrating important data with simple input. This allows for the priority integration of important data by determining data priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI.

[0092] The integration unit can prioritize the integration of highly relevant data by considering the user's geographical location when integrating health data. For example, the integration unit may prioritize the integration of health data for the area where the user lives. For example, the integration unit may consider and integrate health data for places the user frequently visits. For example, the integration unit may prioritize the integration of region-specific health data based on the user's geographical location. This allows for the priority integration of highly relevant data by considering the user's geographical location. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0093] The integration unit can analyze users' social media activity and integrate relevant data when integrating health data. For example, the integration unit integrates health information shared by users on social media. For example, the integration unit integrates health data mentioned by users on social media. For example, the integration unit updates health data based on users' social media activity. This allows for the integration of relevant data by analyzing users' social media activity. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI.

[0094] The suggestion unit can estimate the user's emotions and adjust the menu suggestion method based on the estimated emotions. For example, if the user is stressed, the suggestion unit will suggest a simple menu. For example, if the user is relaxed, the suggestion unit will suggest a detailed menu. For example, if the user is in a hurry, the suggestion unit will suggest a menu that can be prepared quickly. By adjusting the menu suggestion method according to the user's emotions, more appropriate menu suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI.

[0095] The suggestion unit can improve the accuracy of its suggestions by referring to past suggestion history when proposing menus. For example, the suggestion unit can improve the accuracy of its suggestions based on menus that were frequently suggested from past suggestion history. For example, the suggestion unit can prioritize suggesting menus that include specific ingredients based on past suggestion history. For example, the suggestion unit can suggest seasonal menus by referring to past suggestion history. In this way, the accuracy of suggestions is improved by referring to past suggestion history. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without using AI.

[0096] The suggestion department can also suggest menus that are appropriate for the season and weather when proposing menus. For example, the suggestion department can suggest menus that use seasonal ingredients. For example, the suggestion department can suggest menus that are appropriate for the weather. For example, the suggestion department can suggest menus that are appropriate for the season and weather to help with health management. This makes it possible to manage health more appropriately by suggesting menus that are appropriate for the season and weather. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without using AI.

[0097] The suggestion unit can estimate the user's emotions and determine the priority of the suggested menus based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting menus containing important nutrients. For example, if the user is relaxed, the suggestion unit will prioritize suggesting detailed menus. For example, if the user is in a hurry, the suggestion unit will prioritize suggesting menus that can be prepared quickly. In this way, by prioritizing menus according to the user's emotions, important menus can be suggested preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI.

[0098] The suggestion unit can prioritize suggesting menus that are highly relevant to the user, taking into account the user's geographical location. For example, the suggestion unit may suggest menus that use local specialties from the area where the user lives. For example, the suggestion unit may suggest menus that take into account the food culture of places the user frequently visits. For example, the suggestion unit may prioritize suggesting menus specific to a region based on the user's geographical location. In this way, by taking into account the user's geographical location, it is possible to prioritize suggesting menus that are highly relevant. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0099] The suggestion unit can analyze the user's social media activity and suggest relevant menus when proposing menus. For example, the suggestion unit can analyze photos and comments of meals shared by the user on social media and suggest relevant menus. For example, the suggestion unit can suggest menus that include ingredients mentioned by the user on social media. For example, the suggestion unit can suggest menus that reflect the user's food preferences and trends based on the user's social media activity. In this way, relevant menus can be suggested by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0100] The optimization unit can estimate the user's emotions and adjust the method of optimizing nutritional balance based on the estimated user emotions. For example, if the user is feeling stressed, the optimization unit optimizes a menu that includes nutrients that help reduce stress. For example, if the user is relaxed, the optimization unit optimizes a menu that includes balanced nutrients. For example, if the user is in a hurry, the optimization unit optimizes a menu that includes easily ingestible nutrients. This allows for more appropriate nutritional management by adjusting the method of optimizing nutritional balance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI.

[0101] The optimization unit can improve the accuracy of optimization by referring to past nutritional data when optimizing nutritional balance. For example, the optimization unit can extract important nutrients from past nutritional data to improve the accuracy of optimization. For example, the optimization unit can prioritize the optimization of specific nutrients based on past nutritional data. For example, the optimization unit can optimize the nutritional balance for each season by referring to past nutritional data. In this way, the accuracy of optimization is improved by referring to past nutritional data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without using AI.

[0102] The optimization unit can optimize nutritional balance in accordance with the season and weather. For example, the optimization unit can optimize nutritional balance for each season and propose health management appropriate for that season. For example, the optimization unit can optimize nutritional balance according to the weather and propose health management suitable for rainy or hot days. For example, the optimization unit can optimize nutritional balance according to the season and weather and use it to help with allergy and physical condition management. As a result, more appropriate health management becomes possible by optimizing nutritional balance according to the season and weather. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0103] The optimization unit can estimate the user's emotions and determine the priority of the nutritional balance to optimize based on the estimated user emotions. For example, if the user is stressed, the optimization unit will prioritize optimizing nutrients that help reduce stress. For example, if the user is relaxed, the optimization unit will prioritize optimizing balanced nutrients. For example, if the user is in a hurry, the optimization unit will prioritize optimizing easily ingestible nutrients. In this way, by determining the priority of the nutritional balance according to the user's emotions, important nutrients can be prioritized and optimized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0104] The optimization unit can prioritize optimizing nutritional balance by considering the user's geographical location information when optimizing nutritional balance. For example, the optimization unit can optimize nutritional balance using local specialties from the area where the user lives. For example, the optimization unit can optimize nutritional balance by considering the food culture of places the user frequently visits. For example, the optimization unit can prioritize optimizing region-specific nutritional balance based on the user's geographical location information. In this way, by considering the user's geographical location information, it is possible to prioritize optimizing nutritional balance with high relevance. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0105] The optimization unit can analyze the user's social media activity and optimize the relevant nutritional balance when optimizing nutritional balance. For example, the optimization unit analyzes photos and comments of meals shared by the user on social media and optimizes the relevant nutritional balance. For example, the optimization unit optimizes menus that include nutrients mentioned by the user on social media. For example, the optimization unit optimizes nutritional balance that reflects the user's food preferences and trends based on the user's social media activity. In this way, the relevant nutritional balance can be optimized by analyzing the user's social media activity. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0106] The management unit can estimate the user's emotions and adjust the methods for managing food inventory and generating shopping lists based on the estimated emotions. For example, if the user is stressed, the management unit can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the management unit can provide detailed input options and suggest customizable input methods. For example, if the user is in a hurry, the management unit can prioritize voice input to quickly manage food inventory and generate shopping lists. This allows for more appropriate management by adjusting the methods for inventory management and shopping list generation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not using AI.

[0107] The management department can improve the accuracy of its inventory management and shopping list generation by referring to past inventory data. For example, the management department can improve the accuracy of its management by analyzing frequently purchased ingredients from past inventory data. For example, the management department can optimize shopping lists by understanding consumption patterns of specific ingredients based on past inventory data. For example, the management department can improve the accuracy of its management by understanding seasonal ingredient consumption patterns by referring to past inventory data. In this way, the accuracy of management is improved by referring to past inventory data. Some or all of the above processes in the management department may be performed using AI, for example, or without using AI.

[0108] The management department can manage food inventory and generate shopping lists while also incorporating seasonal and weather-based inventory management. For example, the management department can understand seasonal food consumption patterns and manage inventory accordingly. For example, the management department can understand food consumption patterns based on weather and manage inventory appropriately for rainy or hot days. For example, the management department can manage inventory according to season and weather and generate efficient shopping lists. This allows for more appropriate management by incorporating seasonal and weather-based inventory management. Some or all of the above processes in the management department may be performed using AI, for example, or without AI.

[0109] The management unit can estimate the user's emotions and determine the priority of ingredients to manage based on the estimated user emotions. For example, if the user is stressed, the management unit will prioritize managing important ingredients. For example, if the user is relaxed, the management unit will prioritize managing detailed ingredient information. For example, if the user is in a hurry, the management unit will prioritize managing important ingredients with simple input. In this way, important ingredients can be prioritized by determining the priority of ingredients according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI.

[0110] The management department can prioritize the management of highly relevant ingredients by considering the user's geographical location when managing ingredient inventory and generating shopping lists. For example, the management department can prioritize the management of local specialties in the area where the user lives. For example, the management department can manage ingredients by considering the food culture of places the user frequently visits. For example, the management department can prioritize the management of region-specific ingredients based on the user's geographical location. This allows for the prioritization of highly relevant ingredients by considering the user's geographical location. Some or all of the above processes in the management department may be performed using AI, for example, or without AI.

[0111] The management department can manage relevant ingredients by analyzing users' social media activity when managing ingredient inventory and generating shopping lists. For example, the management department can analyze photos and comments of meals shared by users on social media and manage relevant ingredients. For example, the management department can prioritize the management of ingredients mentioned by users on social media. For example, the management department can manage ingredients that reflect food preferences and trends based on users' social media activity. In this way, relevant ingredients can be managed by analyzing users' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The suggestion function can estimate the user's emotions and adjust the menu suggestion method based on those emotions. For example, if the user is stressed, it can suggest a simple and easy-to-prepare menu. If the user is relaxed, it can suggest an elaborate menu that can be prepared over time. If the user is in a hurry, it can suggest a menu that can be prepared in a short time. This makes it possible to suggest menus that match the user's emotions, thereby improving user satisfaction.

[0114] The management department can prioritize the management of highly relevant ingredients by considering the user's geographical location when managing ingredient inventory and generating shopping lists. For example, it can prioritize the management of local specialties in the user's area of ​​residence. It can also manage ingredients considering the food culture of places the user frequently visits. Based on the user's geographical location, it can prioritize the management of region-specific ingredients. In this way, by considering the user's geographical location, it can prioritize the management of highly relevant ingredients.

[0115] The memory unit can estimate the user's emotions and adjust how it stores food preferences and allergy information based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. If the user is in a hurry, it can prioritize voice input to allow for quick input of food preferences and allergy information. This allows for more accurate information storage by adjusting the memory method according to the user's emotions.

[0116] The integration unit can improve the accuracy of health data integration by referring to past health history. For example, it can extract important data from past health history to improve integration accuracy. Based on past health history, specific health indicators can be prioritized for integration. Seasonal health data can be integrated by referring to past health history. In this way, referencing past health history improves the accuracy of integration.

[0117] The optimization unit can estimate the user's emotions and adjust the method of optimizing nutritional balance based on those emotions. For example, if the user is stressed, it can optimize a menu that includes nutrients that help reduce stress. If the user is relaxed, it can optimize a menu that includes balanced nutrients. If the user is in a hurry, it can optimize a menu that includes easily consumed nutrients. By adjusting the method of optimizing nutritional balance according to the user's emotions, more appropriate nutritional management becomes possible.

[0118] The proposal department can improve the accuracy of its menu suggestions by referring to past suggestion history. For example, it can improve the accuracy of suggestions based on menus that were frequently suggested in the past suggestion history. It can also prioritize suggesting menus that include specific ingredients based on past suggestion history. It can suggest seasonal menus by referring to past suggestion history. In this way, the accuracy of suggestions is improved by referring to past suggestion history.

[0119] The management department can manage food inventory and generate shopping lists while also incorporating seasonal and weather-based inventory management. For example, it can understand seasonal food consumption patterns and manage inventory accordingly. It can also understand food consumption patterns based on weather and manage inventory appropriately for rainy or hot days. By managing inventory according to season and weather, it can generate efficient shopping lists. This allows for more effective inventory management by incorporating seasonal and weather-based inventory management.

[0120] The integration unit can estimate the user's emotions and adjust the method of integrating health data based on those emotions. For example, if the user is stressed, it can provide a simple integration method and prioritize the integration of important health data. If the user is relaxed, it can integrate detailed health data and suggest a customizable integration method. If the user is in a hurry, it can enable rapid integration of health data. This allows for more appropriate data integration by adjusting the integration method according to the user's emotions.

[0121] The memory unit can prioritize storing highly relevant information, taking into account the user's geographical location, when memorizing food preferences and allergy information. For example, it can store food preferences based on local specialties and food culture in the area where the user lives. It can also store allergy information considering the food culture of places the user frequently visits. Based on the user's geographical location, it can prioritize storing region-specific allergy information. In this way, by considering the user's geographical location, it can prioritize storing highly relevant information.

[0122] The suggestion function can estimate the user's emotions and determine the priority of suggested menus based on those emotions. For example, if the user is stressed, it can prioritize suggesting menus containing important nutrients. If the user is relaxed, it can prioritize suggesting detailed menus. If the user is in a hurry, it can prioritize suggesting menus that can be prepared quickly. In this way, by prioritizing menus according to the user's emotions, important menus can be suggested preferentially.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The memory unit stores the food preferences and allergy information of all family members and housemates. For example, it can record each member's likes and dislikes and allergy information in detail, and if someone is allergic to a specific ingredient, it can suggest a menu that does not include that ingredient. Step 2: The integration unit integrates the information stored by the memory unit with health data. For example, based on each member's health data, it proposes a menu tailored to their individual health goals. For example, it might suggest a low-calorie menu for a member on a diet, or a high-protein menu for a member aiming to build muscle. Step 3: The proposal department proposes menus tailored to individual health goals based on the data integrated by the integration department. For example, based on health data, they propose menus tailored to each member's health goals. For members on a diet, they propose low-calorie menus, and for members aiming to build muscle, they propose high-protein menus. Step 4: The optimization unit optimizes the nutritional balance of the menu proposed by the proposal unit. For example, it considers the intake of vitamins and minerals and proposes a balanced menu. The nutritional balance is optimized using an algorithm supervised by a registered dietitian. Step 5: The management department automatically generates food inventory management and shopping lists based on the menu optimized by the optimization department. For example, it purchases necessary ingredients without waste and prepares meals efficiently.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the memory unit, integration unit, suggestion unit, optimization unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the memory unit is implemented by the control unit 46A of the smart device 14 and stores the food preferences and allergy information of all family members and cohabitants. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the information stored by the memory unit with health data. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests menus tailored to individual health goals based on the data integrated by the integration unit. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the nutritional balance of the menus suggested by the suggestion unit. The management unit is implemented by the control unit 46A of the smart device 14 and automatically manages food inventory and generates shopping lists based on the menus optimized by the optimization unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the memory unit, integration unit, suggestion unit, optimization unit, and management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the memory unit is implemented by the control unit 46A of the smart glasses 214 and stores the food preferences and allergy information of all family members and cohabitants. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the information stored by the memory unit with health data. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests menus tailored to individual health goals based on the data integrated by the integration unit. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the nutritional balance of the menus suggested by the suggestion unit. The management unit is implemented by the control unit 46A of the smart glasses 214 and automatically manages food inventory and generates shopping lists based on the menus optimized by the optimization unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the memory unit, integration unit, suggestion unit, optimization unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the memory unit is implemented by the control unit 46A of the headset terminal 314 and stores the food preferences and allergy information of all family members and cohabitants. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the information stored by the memory unit with health data. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests menus tailored to individual health goals based on the data integrated by the integration unit. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the nutritional balance of the menus suggested by the suggestion unit. The management unit is implemented by the control unit 46A of the headset terminal 314 and automatically generates food inventory management and shopping lists based on the menus optimized by the optimization unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the memory unit, integration unit, suggestion unit, optimization unit, and management unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the memory unit is implemented by the control unit 46A of the robot 414 and stores the food preferences and allergy information of all family members and cohabitants. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the information stored by the memory unit with health data. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests menus tailored to individual health goals based on the data integrated by the integration unit. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the nutritional balance of the menus suggested by the suggestion unit. The management unit is implemented by the control unit 46A of the robot 414 and automatically manages food inventory and generates shopping lists based on the menus optimized by the optimization unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A memory unit that stores the food preferences and allergy information of all family members and housemates, An integration unit that integrates the information stored in the aforementioned storage unit with health data, Based on the data integrated by the aforementioned integration unit, a proposal unit proposes menus tailored to individual health goals, An optimization unit that optimizes the nutritional balance of the menu proposed by the aforementioned proposal unit, The system includes a management unit that automatically generates a shopping list and manages the inventory of ingredients based on the menu optimized by the optimization unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We provide real-time suggestions through chat-style dialogue. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We will adjust the menu to reflect feedback from all family members and household members. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned storage unit is Record each member's likes, dislikes, and allergy information in detail. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned integration unit is Integrate with health data The system described in Appendix 1, characterized by the features described herein. (Note 6) The optimization unit, We propose a balanced menu that takes into account the intake of vitamins and minerals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, Purchase necessary ingredients without waste and prepare meals efficiently. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned storage unit is It estimates the user's emotions and adjusts how food preferences and allergy information are stored based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned storage unit is When memorizing each member's food preferences and allergy information, past meal history is referenced to improve the accuracy of the memory. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned storage unit is When memorizing food preferences and allergy information, also memorize information relevant to the season and weather. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned storage unit is It estimates the user's emotions and determines the priority of information to remember based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned storage unit is When remembering food preferences and allergy information, the system prioritizes remembering highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned storage unit is When remembering food preferences and allergy information, the system analyzes the user's social media activity to remember relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned integration unit is We estimate the user's emotions and adjust how health data is integrated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned integration unit is When integrating health data, referencing past health history improves the accuracy of the integration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned integration unit is When integrating health data, we also integrate data that is relevant to the season and weather. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned integration unit is It estimates user sentiment and determines the priority of data to integrate based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned integration unit is When integrating health data, the system prioritizes integrating highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned integration unit is When integrating health data, analyze users' social media activity and integrate relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the menu suggestion method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When proposing menus, we refer to past proposal history to improve the accuracy of the suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When proposing menus, we also suggest menus that are appropriate for the season and weather. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggested menus based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When suggesting menus, the system prioritizes suggesting menus that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When suggesting menus, we analyze the user's social media activity and suggest relevant menus. The system described in Appendix 1, characterized by the features described herein. (Note 26) The optimization unit, It estimates the user's emotions and adjusts the method of optimizing nutritional balance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The optimization unit, When optimizing nutritional balance, we refer to past nutritional data to improve the accuracy of the optimization. The system described in Appendix 1, characterized by the features described herein. (Note 28) The optimization unit, When optimizing nutritional balance, we also optimize it in accordance with the season and weather. The system described in Appendix 1, characterized by the features described herein. (Note 29) The optimization unit, It estimates the user's emotions and determines the priority of the optimal nutritional balance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The optimization unit, When optimizing nutritional balance, the system prioritizes optimizing the most relevant nutritional balance by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The optimization unit, When optimizing nutritional balance, we analyze the user's social media activity to optimize the nutritional balance based on relevant factors. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, The system estimates the user's emotions and adjusts how ingredients are managed and shopping lists are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, When managing food inventory and generating shopping lists, referencing past inventory data improves the accuracy of management. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned management department, When managing food inventory and generating shopping lists, we also incorporate seasonal and weather-based inventory management. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned management department, It estimates the user's emotions and determines the priority of ingredients to manage based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned management department, When managing food inventory and generating shopping lists, the system prioritizes relevant food items by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned management department, When managing food inventory and generating shopping lists, we analyze users' social media activity to manage relevant food items. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 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 memory unit that stores the food preferences and allergy information of all family members and housemates, An integration unit that integrates the information stored in the aforementioned storage unit with health data, Based on the data integrated by the aforementioned integration unit, a proposal unit proposes menus tailored to individual health goals, An optimization unit that optimizes the nutritional balance of the menu proposed by the aforementioned proposal unit, The system includes a management unit that automatically generates a shopping list and manages the inventory of ingredients based on the menu optimized by the optimization unit. A system characterized by the following features.

2. The aforementioned proposal section is, We provide real-time suggestions through chat-style dialogue. The system according to feature 1.

3. The aforementioned proposal section is, We will adjust the menu to reflect feedback from all family members and household members. The system according to feature 1.

4. The aforementioned storage unit is Record each member's likes, dislikes, and allergy information in detail. The system according to feature 1.

5. The aforementioned integration unit is Integrate with health data The system according to feature 1.

6. The optimization unit, We propose a balanced menu that takes into account the intake of vitamins and minerals. The system according to feature 1.

7. The aforementioned management department, Purchase necessary ingredients without waste and prepare meals efficiently. The system according to feature 1.

8. The aforementioned storage unit is It estimates the user's emotions and adjusts how food preferences and allergy information are stored based on those estimated emotions. The system according to feature 1.

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