system

Abstract

The system according to this embodiment aims to propose individual meal plans based on the user's preferences, restrictions, and nutritional needs, and to monitor their progress. [Solution] The system according to the embodiment comprises a recognition unit, a management unit, a proposal unit, and a monitoring unit. The recognition unit recognizes the user's preferences, restrictions, and nutritional needs. The management unit manages the meal records based on the information recognized by the recognition unit. The proposal unit analyzes the meal records managed by the management unit and proposes an individual meal plan. The monitoring unit monitors the progress based on the meal plan proposed by the proposal 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, the method including 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 conventional technology, it is difficult to provide an individual diet plan based on a user's preferences, restrictions, and nutritional needs, and there is room for improvement.

[0005] The system according to an embodiment aims to propose an individual diet plan based on a user's preferences, restrictions, and nutritional needs and monitor the progress.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recognition unit, a management unit, a proposal unit, and a monitoring unit. The recognition unit recognizes the user's preferences, restrictions, and nutritional needs. The management unit manages the meal records based on the information recognized by the recognition unit. The proposal unit analyzes the meal records managed by the management unit and proposes an individual meal plan. The monitoring unit monitors the progress based on the meal plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose individual meal plans based on the user's preferences, restrictions, and nutritional needs, and monitor their progress. [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 a plurality of 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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The meal suggestion system according to an embodiment of the present invention is a system that aims to maximize the enjoyment of an individual's meal experience using an AI agent. This meal suggestion system interacts with the user to understand their preferences, restrictions, and nutritional needs. Next, by taking a photo of the menu each time and recording the meal, the AI ​​agent customizes the optimal meal menu based on this information. This allows users to achieve their weight loss and health management goals while enjoying nutritionally balanced meals of their choice. Furthermore, for people with illnesses or dietary restrictions, the system accurately understands their individual restrictions and nutritional needs through interaction with the AI ​​agent and provides a meal plan that takes these constraints into consideration. For example, it suggests low-carbohydrate and low-sodium menus for people with diabetes or high blood pressure, and allergy-friendly menus for people with allergies. In addition, it suggests recipes that utilize seasonal and in-season ingredients. Users can enjoy a variety of dishes using fresh ingredients, maintaining meal diversity and staying engaged without getting bored. The system also manages the user's meal records and supports monitoring of progress. The AI ​​agent analyzes the user's data and provides personalized advice and feedback to help improve motivation. Users can visualize their own successes and progress, and feel the results of their diet and other goals. For people with illnesses or dietary restrictions, this service helps them enjoy healthy meals with peace of mind. By offering personalized meal plans that consider individual restrictions and needs, and providing medical support, it reduces the stress and anxiety associated with dietary restrictions, supporting a healthy and fulfilling life from a "food" perspective. The meal suggestion system proposes individual meal plans based on the user's preferences, restrictions, and nutritional needs, and monitors their progress, allowing them to maximize their personalized dining experience.

[0029] The meal suggestion system according to this embodiment comprises a comprehension unit, a management unit, a suggestion unit, and a monitoring unit. The comprehension unit comprehends the user's preferences, restrictions, and nutritional needs. For example, the comprehension unit interacts with the user to collect information such as favorite and disliked foods and taste preferences. The comprehension unit can also comprehend restrictions such as allergies, religious restrictions, and dietary restrictions. Furthermore, the comprehension unit can also comprehend the intake of specific nutrients and nutritional balance according to the user's health condition. For example, the comprehension unit can comprehend nutritional needs based on the user's meal history and health checkup results. The management unit manages meal records based on the information comprehend by the comprehension unit. For example, the management unit saves images of menus taken by the user and manages them as meal records. The management unit can also save and manage the meal details entered by the user in a database. Furthermore, the management unit can analyze meal records to understand the user's eating patterns. For example, the management unit organizes the user's meal records chronologically to understand eating trends. The suggestion unit analyzes the meal records managed by the management unit and proposes individual meal plans. The suggestion unit proposes optimal meal menus based on the user's preferences, restrictions, and nutritional needs. The suggestion unit can also propose recipes utilizing seasonal or in-season ingredients. Furthermore, it can provide meal plans that take into account restrictions on specific nutrients or ingredients for individuals with illnesses or dietary restrictions. For example, it might suggest low-carbohydrate or low-sodium menus for people with diabetes or high blood pressure. The monitoring unit monitors progress based on the meal plan proposed by the suggestion unit. For example, the monitoring unit periodically checks the user's meal records to understand progress. It can also monitor changes in the user's weight and health status to evaluate the effectiveness of the meal plan. Additionally, the monitoring unit can provide users with personalized advice and feedback to help improve motivation. For example, it analyzes the user's meal records and provides feedback on areas for improvement and success stories. As a result, the meal suggestion system according to this embodiment allows users to maximize their individual meal experience by proposing meal plans based on their preferences, restrictions, and nutritional needs and monitoring their progress.

[0030] The information gathering unit understands the user's preferences, restrictions, and nutritional needs. Specifically, it collects information such as favorite and disliked foods and taste preferences through dialogue with the user. For example, if the user lists tomatoes and cheese as favorite foods, it will prioritize suggesting menus that include these foods. Similarly, if the user lists bell peppers and celery as disliked foods, it will suggest menus that do not include these foods. Furthermore, the information gathering unit also understands restrictions such as allergies, religious restrictions, and dietary restrictions. For example, if the user has a nut allergy, it will suggest menus that do not include nuts. If the user needs to avoid certain foods for religious reasons, it will suggest menus that take that restriction into consideration. For dietary restrictions, it will suggest, for example, low-calorie or low-sugar menus. In addition, the information gathering unit also understands the intake of specific nutrients and nutritional balance according to the user's health condition. For example, based on the user's eating history and health checkup results, it will understand the intake of vitamins and minerals and suggest menus that supplement the necessary nutrients. In this way, the information gathering unit can understand the user's detailed dietary needs and collect basic information to provide individualized meal plans.

[0031] The Management Department manages meal records based on information gathered by the Data Gathering Department. Specifically, the Management Department saves images of menus taken by users and manages them as meal records. For example, when a user takes a picture of the food they ate and uploads it to the application, the image is saved in the database. The Management Department can also save and manage meal details entered by users in the database. For example, when a user enters the name of the dish they ate, the ingredients, and the amount consumed, that information is saved in the database. Furthermore, the Management Department can analyze meal records to understand users' eating patterns. For example, the Management Department can organize users' meal records chronologically to understand their eating trends. This allows them to understand whether users are frequently consuming certain ingredients or whether they are deficient in certain nutrients. In addition, the Management Department can provide the analysis results of the meal records to the Data Gathering Department and the Proposal Department to help create individual meal plans. This allows the Management Department to efficiently manage users' meal records and improve the overall performance of the system.

[0032] The Proposal Department analyzes meal records managed by the Management Department and proposes individual meal plans. Specifically, the Proposal Department suggests optimal meal menus based on the user's preferences, restrictions, and nutritional needs. For example, it may suggest menus that include ingredients the user likes or menus that do not include ingredients the user dislikes. The Proposal Department can also suggest recipes that utilize seasonal or in-season ingredients. For example, it may suggest salads using fresh vegetables in the spring or desserts using seasonal fruits in the fall. Furthermore, the Proposal Department can provide meal plans that take into account restrictions on specific nutrients or ingredients for people with illnesses or dietary restrictions. For example, it may suggest low-carbohydrate or low-sodium menus for people with diabetes or high blood pressure. The Proposal Department uses AI to analyze the user's meal records and nutritional needs and generate optimal meal plans. The AI ​​learns the user's past eating history and health status and provides meal plans tailored to individual needs. This allows the Proposal Department to provide users with healthy and balanced meal plans and improve the quality of their meals.

[0033] The Monitoring Department monitors progress based on the meal plans proposed by the Proposal Department. Specifically, the Monitoring Department regularly checks users' meal records to understand their progress. For example, it verifies whether users are eating according to the proposed meal plan and whether the meals are appropriate. The Monitoring Department can also monitor changes in users' weight and health status to evaluate the effectiveness of the meal plan. For example, it checks whether users are losing weight or whether their blood pressure and blood sugar levels are improving. Furthermore, the Monitoring Department can provide users with personalized advice and feedback to help improve their motivation. For example, it analyzes users' meal records and provides feedback on areas for improvement and success stories. This allows users to understand their progress and maintain their motivation. In addition, the Monitoring Department can improve the overall system performance by collecting user feedback and providing it to the Proposal Department and Management Department. This enables the Monitoring Department to efficiently monitor users' progress and maximize the effectiveness of individual meal plans.

[0034] The proposal unit includes a constraint-response unit that provides meal plans that take into account restrictions on specific nutrients and ingredients for people with illnesses or dietary restrictions. The constraint-response unit provides meal plans that take into account restrictions such as carbohydrate restriction, gluten-free diets, and low sodium diets. For example, the constraint-response unit suggests a low-carbohydrate menu for people with diabetes. It can also suggest a low-sodium menu for people with hypertension. Furthermore, the constraint-response unit can suggest allergy-friendly menus for people with allergies. For example, the constraint-response unit suggests a gluten-free menu for people with gluten allergies. This makes it possible to provide meal plans that take into account restrictions on specific nutrients and ingredients even for people with illnesses or dietary restrictions. Some or all of the above processing in the constraint-response unit may be performed using AI, for example, or without AI. For example, the constraint-response unit can suggest meal plans using an AI model that takes the user's health status and constraints as input and outputs an optimal meal plan.

[0035] The suggestion section includes a seasonal suggestion section that proposes recipes utilizing seasonal ingredients and ingredients in season. The seasonal suggestion section proposes recipes that utilize seasonal ingredients such as spring vegetables, summer fruits, and autumn mushrooms. For example, in spring, the seasonal suggestion section proposes a salad using fresh spring vegetables. In summer, the seasonal suggestion section can also propose a dessert using cold fruits. Furthermore, in autumn, the seasonal suggestion section can propose a warm soup using mushrooms. For example, the seasonal suggestion section proposes a dish using seasonal fish. In this way, by proposing recipes that utilize seasonal ingredients and ingredients in season, it is possible to maintain meal diversity and prevent boredom. Some or all of the above processing in the seasonal suggestion section may be performed using AI, for example, or without AI. For example, the seasonal suggestion section can propose recipes using an AI model that takes information on seasonal ingredients and ingredients in season as input and outputs the optimal recipe.

[0036] The monitoring unit monitors the user's progress and provides personalized advice and feedback to help improve motivation. For example, the monitoring unit monitors the user's progress, such as changes in weight, dietary adherence, and exercise performance. For instance, it regularly records changes in the user's weight to track progress. It can also evaluate the user's dietary adherence and provide feedback. Furthermore, it can monitor the user's exercise performance and provide advice. For example, it analyzes the user's dietary records and provides feedback on areas for improvement and successes. This allows the monitoring unit to visualize the user's successes and progress, enabling them to experience tangible results such as weight loss, by monitoring their progress and providing personalized advice and feedback to improve motivation. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can provide feedback using an AI model that takes user progress data as input and outputs feedback.

[0037] The understanding unit analyzes the user's past eating history to improve the accuracy of understanding preferences, restrictions, and nutritional needs. For example, the understanding unit analyzes the menus the user has frequently eaten in the past to understand their preferences. For example, the understanding unit understands the intake trends of specific nutrients from the user's past eating history. The understanding unit can also understand allergies and dietary restrictions based on the user's past eating history. For example, the understanding unit analyzes the user's eating history to understand the intake of specific nutrients. This allows for an improvement in the accuracy of understanding preferences, restrictions, and nutritional needs by analyzing the user's past eating history. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can improve its accuracy by using an AI model that takes the user's past eating history data as input and outputs preferences, restrictions, and nutritional needs.

[0038] The understanding unit customizes the understanding of preferences, restrictions, and nutritional needs based on the user's lifestyle and health status. For example, the understanding unit understands nutritional needs by considering the user's exercise habits. For example, the understanding unit understands dietary restrictions based on the user's health checkup results. The understanding unit can also understand nutritional needs by considering the user's sleep patterns. For example, the understanding unit analyzes the user's lifestyle data to understand nutritional needs. This allows for the understanding of preferences, restrictions, and nutritional needs to be customized based on the user's lifestyle and health status, thereby providing more appropriate information. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can customize the understanding using an AI model that takes the user's lifestyle and health status data as input and outputs preferences, restrictions, and nutritional needs.

[0039] The information gathering unit considers the user's geographical location information to understand their preferences, restrictions, and nutritional needs based on regional food culture and ingredients. For example, the information gathering unit considers the food culture of the area where the user lives to understand their preferences. For example, the information gathering unit considers regional ingredients based on the user's geographical location information to understand their nutritional needs. The information gathering unit can also understand regional dietary restrictions based on the user's geographical location information. For example, the information gathering unit analyzes the user's geographical location information to understand their preferences, restrictions, and nutritional needs based on regional food culture and ingredients. This allows for the gathering of more appropriate information by considering the user's geographical location information to understand their preferences, restrictions, and nutritional needs based on regional food culture and ingredients. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can gather information using an AI model that takes the user's geographical location information as input and outputs preferences, restrictions, and nutritional needs.

[0040] The information gathering unit analyzes the user's social media activity to understand their preferences, restrictions, and nutritional needs. For example, the information gathering unit analyzes photos of meals shared by the user on social media to understand their preferences. For example, the information gathering unit understands dietary restrictions based on the content of the user's social media posts. The information gathering unit can also analyze the accounts the user follows on social media to understand their nutritional needs. For example, the information gathering unit analyzes the user's social media activity to understand their preferences, restrictions, and nutritional needs. This allows the information gathering unit to understand the user's preferences, restrictions, and nutritional needs by analyzing the user's social media activity. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can gather information using an AI model that takes the user's social media data as input and outputs preferences, restrictions, and nutritional needs.

[0041] The management department analyzes users' past meal records to improve the accuracy of management methods. For example, the management department analyzes meal data recorded by users in the past to optimize management methods. For example, the management department can understand the intake trends of specific nutrients from users' past meal records and adjust management methods accordingly. The management department can also propose management methods that take allergies and dietary restrictions into account based on users' past meal records. For example, the management department analyzes users' meal records to understand the intake of specific nutrients. This allows the accuracy of management methods to be improved by analyzing users' past meal records. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can improve the accuracy of management methods by using an AI model that takes users' past meal record data as input and outputs management methods.

[0042] The management department customizes the management of meal records based on the user's lifestyle and health status. For example, the management department adjusts the meal record management method considering the user's exercise habits. For example, the management department optimizes the meal record management method based on the user's health checkup results. The management department can also customize the meal record management method considering the user's sleep patterns. For example, the management department analyzes the user's lifestyle data and adjusts the meal record management method. This allows for more appropriate management by customizing the meal record management based on the user's lifestyle and health status. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can customize management using an AI model that takes data on the user's lifestyle and health status as input and outputs a management method.

[0043] The management department manages meal records based on regional food culture and ingredients, taking into account the user's geographical location. For example, the management department manages meal records considering the food culture of the area where the user lives. For example, the management department manages meal records considering regional ingredients based on the user's geographical location. The management department can also manage meal records considering regional dietary restrictions based on the user's geographical location. For example, the management department analyzes the user's geographical location and manages meal records based on regional food culture and ingredients. This allows for more appropriate management by considering the user's geographical location and managing meal records based on regional food culture and ingredients. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can perform management using an AI model that takes the user's geographical location as input and outputs meal records.

[0044] The management department analyzes users' social media activity and manages their meal records. For example, the management department analyzes photos of meals shared by users on social media and manages their meal records. For example, the management department manages meal records based on the content of users' social media posts. The management department can also analyze the accounts that users follow on social media and manage their meal records. For example, the management department analyzes users' social media activity and manages their meal records. This allows for the management of meal records 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. For example, the management department can perform management using an AI model that takes users' social media data as input and outputs meal records.

[0045] The suggestion unit analyzes the user's past eating history to improve the accuracy of its suggestions. For example, the suggestion unit analyzes the menus the user has eaten in the past and suggests a meal plan that suits their preferences. For example, the suggestion unit can understand the intake trends of specific nutrients from the user's past eating history and suggest a nutritionally balanced meal plan. The suggestion unit can also suggest a meal plan that takes allergies and dietary restrictions into account based on the user's past eating history. For example, the suggestion unit analyzes the user's eating history to understand the intake of specific nutrients. By analyzing the user's past eating history, the accuracy of the suggestion unit can be improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can improve the accuracy of its suggestions by using an AI model that takes the user's past eating history data as input and outputs a suggestion method.

[0046] The suggestion unit customizes meal plan suggestions based on the user's lifestyle and health condition. For example, the suggestion unit customizes meal plans by considering the user's exercise habits. For example, the suggestion unit optimizes meal plans based on the user's health checkup results. The suggestion unit can also customize meal plans by considering the user's sleep patterns. For example, the suggestion unit analyzes the user's lifestyle data and customizes meal plans. This allows for more appropriate suggestions by customizing meal plan suggestions based on the user's lifestyle and health condition. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can customize suggestions using an AI model that takes the user's lifestyle and health condition data as input and outputs meal plans.

[0047] The suggestion unit proposes meal plans based on the food culture and ingredients specific to the region, taking into account the user's geographical location. For example, the suggestion unit proposes meal plans considering the food culture of the area where the user lives. For example, the suggestion unit proposes meal plans considering ingredients specific to the region, based on the user's geographical location. The suggestion unit can also propose meal plans considering dietary restrictions specific to the region, based on the user's geographical location. For example, the suggestion unit analyzes the user's geographical location and proposes meal plans based on the food culture and ingredients specific to the region. This allows for more appropriate suggestions by considering the user's geographical location and proposing meal plans based on the food culture and ingredients specific to the region. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can make suggestions using an AI model that takes the user's geographical location as input and outputs meal plans.

[0048] The suggestion unit analyzes the user's social media activity and proposes meal plans. For example, the suggestion unit analyzes photos of meals shared by the user on social media and proposes meal plans that suit their preferences. For example, the suggestion unit proposes meal plans that take dietary restrictions into account based on the content of the user's social media posts. The suggestion unit can also analyze the accounts the user follows on social media and propose meal plans that take nutritional needs into account. For example, the suggestion unit analyzes the user's social media activity to understand their preferences, restrictions, and nutritional needs and proposes meal plans based on that. In this way, meal plans can be proposed 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 not using AI. For example, the suggestion unit can make suggestions using an AI model that takes the user's social media data as input and outputs meal plans.

[0049] The monitoring unit analyzes the user's past progress data to improve the accuracy of the monitoring method. For example, the monitoring unit analyzes progress data previously recorded by the user to optimize the monitoring method. For example, the monitoring unit can understand the intake trends of specific nutrients from the user's past progress data and adjust the monitoring method. The monitoring unit can also propose a monitoring method that takes allergies and dietary restrictions into account based on the user's past progress data. For example, the monitoring unit analyzes the user's progress data to understand the intake of specific nutrients. This allows the accuracy of the monitoring method to be improved by analyzing the user's past progress data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can improve the accuracy of the monitoring method by using an AI model that takes the user's past progress data as input and outputs a monitoring method.

[0050] The monitoring unit customizes progress monitoring based on the user's lifestyle and health status. For example, the monitoring unit adjusts the progress monitoring method considering the user's exercise habits. For example, the monitoring unit optimizes the progress monitoring method based on the user's health checkup results. The monitoring unit can also customize the progress monitoring method considering the user's sleep patterns. For example, the monitoring unit analyzes the user's lifestyle data and adjusts the monitoring method. This allows for more appropriate monitoring by customizing progress monitoring based on the user's lifestyle and health status. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can customize monitoring using an AI model that takes user lifestyle and health data as input and outputs a monitoring method.

[0051] The monitoring unit monitors progress based on regional food culture and ingredients, taking into account the user's geographical location. For example, the monitoring unit monitors progress considering the food culture of the area where the user lives. For example, the monitoring unit monitors progress considering regional ingredients based on the user's geographical location. The monitoring unit can also monitor progress considering regional dietary restrictions based on the user's geographical location. For example, the monitoring unit analyzes the user's geographical location and monitors progress based on regional food culture and ingredients. This allows for more appropriate monitoring by considering the user's geographical location and monitoring progress based on regional food culture and ingredients. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can perform monitoring using an AI model that takes the user's geographical location as input and outputs progress.

[0052] The monitoring unit analyzes the user's social media activity and monitors its progress. For example, the monitoring unit analyzes photos of meals shared by the user on social media and monitors its progress. For example, the monitoring unit monitors its progress based on the content of the user's social media posts. The monitoring unit can also analyze the accounts the user follows on social media and monitor its progress. For example, the monitoring unit analyzes the user's social media activity and monitors its progress. This allows for monitoring of progress by analyzing the user's social media activity. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can perform monitoring using an AI model that takes the user's social media data as input and outputs progress.

[0053] The constraint handling unit analyzes the user's past meal history to improve the accuracy of the constraint handling method. For example, the constraint handling unit analyzes the menus the user has eaten in the past and optimizes the constraint handling method. For example, the constraint handling unit understands the intake trends of specific nutrients from the user's past meal history and adjusts the constraint handling method. The constraint handling unit can also propose constraint handling methods that take into account allergies and dietary restrictions based on the user's past meal history. For example, the constraint handling unit analyzes the user's meal history to understand the intake of specific nutrients. This allows the accuracy of the constraint handling method to be improved by analyzing the user's past meal history. Some or all of the above processing in the constraint handling unit may be performed using AI, for example, or without AI. For example, the constraint handling unit can improve the accuracy of the method by using an AI model that takes the user's past meal history data as input and outputs a constraint handling method.

[0054] The constraint response unit customizes the means of constraint response based on the user's lifestyle and health status. For example, the constraint response unit adjusts the constraint response method by considering the user's exercise habits. For example, the constraint response unit optimizes the constraint response method based on the user's health checkup results. The constraint response unit can also customize the constraint response method by considering the user's sleep patterns. For example, the constraint response unit analyzes the user's lifestyle data and adjusts the constraint response method. This allows for more appropriate responses by customizing the means of constraint response based on the user's lifestyle and health status. Some or all of the above processing in the constraint response unit may be performed using AI, for example, or without AI. For example, the constraint response unit can customize the means using an AI model that takes user lifestyle and health status data as input and outputs constraint response methods.

[0055] The constraint handling unit considers the user's geographical location information and handles constraints based on regionally specific food culture and ingredients. For example, the constraint handling unit considers the food culture of the area where the user lives and handles constraints accordingly. For example, the constraint handling unit considers regionally specific ingredients based on the user's geographical location information and handles constraints accordingly. The constraint handling unit can also consider regionally specific dietary restrictions based on the user's geographical location information and handles constraints accordingly. For example, the constraint handling unit analyzes the user's geographical location information and handles constraints based on regionally specific food culture and ingredients. This allows for more appropriate responses by considering the user's geographical location information and handling constraints based on regionally specific food culture and ingredients. Some or all of the above processing in the constraint handling unit may be performed using AI, for example, or without AI. For example, the constraint handling unit can use an AI model that takes the user's geographical location information as input and outputs constraint responses.

[0056] The constraint response unit analyzes the user's social media activity and proposes means of responding to constraints. For example, the constraint response unit analyzes photos of meals shared by the user on social media and proposes methods for responding to constraints. For example, the constraint response unit proposes methods for responding to constraints based on the content of the user's social media posts. The constraint response unit can also analyze the accounts the user follows on social media and propose methods for responding to constraints. For example, the constraint response unit analyzes the user's social media activity and proposes methods for responding to constraints. In this way, means of responding to constraints can be proposed by analyzing the user's social media activity. Some or all of the above processing in the constraint response unit may be performed using AI, for example, or without AI. For example, the constraint response unit can propose means using an AI model that takes the user's social media data as input and outputs methods for responding to constraints.

[0057] The seasonal suggestion unit analyzes the user's past eating history to improve the accuracy of seasonal suggestion methods. For example, the seasonal suggestion unit analyzes the menus the user has eaten in the past and optimizes the seasonal suggestion method. For example, the seasonal suggestion unit understands the intake trends of specific nutrients from the user's past eating history and adjusts the seasonal suggestion method accordingly. The seasonal suggestion unit can also propose seasonal suggestion methods that take into account allergies and dietary restrictions based on the user's past eating history. For example, the seasonal suggestion unit analyzes the user's eating history to understand the intake of specific nutrients. This allows the accuracy of seasonal suggestion methods to be improved by analyzing the user's past eating history. Some or all of the above processing in the seasonal suggestion unit may be performed using AI, for example, or without AI. For example, the seasonal suggestion unit can improve the accuracy of its methods by using an AI model that takes the user's past eating history data as input and outputs seasonal suggestion methods.

[0058] The seasonal suggestion unit customizes the means of seasonal suggestion based on the user's lifestyle and health condition. For example, the seasonal suggestion unit adjusts the seasonal suggestion method considering the user's exercise habits. For example, the seasonal suggestion unit optimizes the seasonal suggestion method based on the user's health checkup results. The seasonal suggestion unit can also customize the seasonal suggestion method considering the user's sleep patterns. For example, the seasonal suggestion unit analyzes the user's lifestyle data and adjusts the seasonal suggestion method. This allows for more appropriate suggestions by customizing the means of seasonal suggestion based on the user's lifestyle and health condition. Some or all of the above processing in the seasonal suggestion unit may be performed using AI, for example, or without AI. For example, the seasonal suggestion unit can customize the means using an AI model that takes user lifestyle and health condition data as input and outputs a seasonal suggestion method.

[0059] The seasonal suggestion unit makes seasonal suggestions based on regional food culture and ingredients, taking into account the user's geographical location. For example, the seasonal suggestion unit makes seasonal suggestions considering the food culture of the area where the user lives. For example, the seasonal suggestion unit makes seasonal suggestions considering regional ingredients based on the user's geographical location. The seasonal suggestion unit can also make seasonal suggestions considering regional dietary restrictions based on the user's geographical location. For example, the seasonal suggestion unit analyzes the user's geographical location and makes seasonal suggestions based on regional food culture and ingredients. This allows for more appropriate suggestions by considering the user's geographical location and making seasonal suggestions based on regional food culture and ingredients. Some or all of the above processing in the seasonal suggestion unit may be performed using AI, for example, or without AI. For example, the seasonal suggestion unit can make suggestions using an AI model that takes the user's geographical location as input and outputs seasonal suggestions.

[0060] The seasonal suggestion unit analyzes the user's social media activity and proposes methods for seasonal suggestions. For example, the seasonal suggestion unit analyzes photos of meals shared by the user on social media and proposes seasonal suggestion methods. For example, the seasonal suggestion unit proposes seasonal suggestion methods based on the content of the user's social media posts. The seasonal suggestion unit can also analyze the accounts the user follows on social media and propose seasonal suggestion methods. For example, the seasonal suggestion unit analyzes the user's social media activity and proposes seasonal suggestion methods. In this way, by analyzing the user's social media activity, it is possible to propose methods for seasonal suggestions. Some or all of the above processing in the seasonal suggestion unit may be performed using AI, for example, or without AI. For example, the seasonal suggestion unit can propose methods using an AI model that takes the user's social media data as input and outputs seasonal suggestion methods.

[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 suggestion function can also analyze the user's past eating history to improve the accuracy of its suggestions. For example, it can analyze the menus the user has eaten in the past and suggest a meal plan that suits their preferences. It can also identify the user's intake trends for specific nutrients from their past eating history and suggest a nutritionally balanced meal plan. Furthermore, it can suggest a meal plan that takes into account allergies and dietary restrictions based on the user's past eating history. In this way, analyzing the user's past eating history can improve the accuracy of the suggestion method.

[0063] The suggestion department can also customize meal plan suggestions based on the user's lifestyle and health condition. For example, it can customize meal plans considering the user's exercise habits. It can also optimize meal plans based on the user's health checkup results. Furthermore, it can customize meal plans considering the user's sleep patterns. By customizing meal plan suggestions based on the user's lifestyle and health condition, more appropriate suggestions can be made.

[0064] The proposal function can also suggest meal plans based on the user's geographical location and the region's unique food culture and ingredients. For example, it can suggest meal plans considering the food culture of the area where the user lives. It can also suggest meal plans considering the region's unique ingredients based on the user's geographical location. Furthermore, it can suggest meal plans considering the region's unique dietary restrictions based on the user's geographical location. By considering the user's geographical location and suggesting meal plans based on the region's unique food culture and ingredients, more appropriate suggestions can be made.

[0065] The proposal department can also analyze users' social media activity and suggest meal plans. For example, it can analyze photos of meals shared by users on social media and suggest meal plans that suit their preferences. It can also suggest meal plans that take dietary restrictions into account based on the content of users' social media posts. Furthermore, it can analyze the accounts that users follow on social media and suggest meal plans that take nutritional needs into consideration. In this way, meal plans can be suggested by analyzing users' social media activity.

[0066] The monitoring unit can also analyze the user's past progress data to improve the accuracy of the monitoring method. For example, it can analyze progress data previously recorded by the user and optimize the monitoring method. It can also identify the user's intake trends for specific nutrients from their past progress data and adjust the monitoring method accordingly. Furthermore, it can suggest monitoring methods that take allergies and dietary restrictions into account based on the user's past progress data. In this way, the accuracy of the monitoring method can be improved by analyzing the user's past progress data.

[0067] The monitoring unit can also customize progress monitoring based on the user's lifestyle and health status. For example, it can adjust the progress monitoring method considering the user's exercise habits. It can also optimize the progress monitoring method based on the user's health checkup results. Furthermore, it can customize the progress monitoring method considering the user's sleep patterns. This allows for more appropriate monitoring by customizing progress monitoring based on the user's lifestyle and health status.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The understanding unit grasps the user's preferences, restrictions, and nutritional needs. For example, it interacts with the user to collect information on favorite and disliked foods, taste preferences, allergies, religious restrictions, dietary restrictions, and other restrictions. It can also understand the intake of specific nutrients and nutritional balance according to the user's health condition based on their eating history and health check results. Step 2: The management department manages meal records based on the information gathered by the data collection department. For example, it saves images of menus taken by users and manages them as meal records. It can also save and manage the meal details entered by users in a database. Furthermore, the management department analyzes the meal records to understand the users' eating patterns. For example, it organizes the users' meal records chronologically to understand their eating trends. Step 3: The proposal department analyzes the meal records managed by the management department and proposes individual meal plans. For example, it proposes optimal meal menus based on the user's preferences, restrictions, and nutritional needs. It can also propose recipes that utilize seasonal or in-season ingredients. Furthermore, it can provide meal plans that take into account restrictions on specific nutrients or ingredients for people with illnesses or dietary restrictions. Step 4: The monitoring unit monitors progress based on the meal plan proposed by the proposal unit. For example, it regularly checks the user's meal log to understand their progress. It can also monitor changes in the user's weight and health status to evaluate the effectiveness of the meal plan. Furthermore, it can provide the user with personalized advice and feedback to help improve their motivation.

[0070] (Example of form 2) The meal suggestion system according to an embodiment of the present invention is a system that aims to maximize the enjoyment of an individual's meal experience using an AI agent. This meal suggestion system interacts with the user to understand their preferences, restrictions, and nutritional needs. Next, by taking a photo of the menu each time and recording the meal, the AI ​​agent customizes the optimal meal menu based on this information. This allows users to achieve their weight loss and health management goals while enjoying nutritionally balanced meals of their choice. Furthermore, for people with illnesses or dietary restrictions, the system accurately understands their individual restrictions and nutritional needs through interaction with the AI ​​agent and provides a meal plan that takes these constraints into consideration. For example, it suggests low-carbohydrate and low-sodium menus for people with diabetes or high blood pressure, and allergy-friendly menus for people with allergies. In addition, it suggests recipes that utilize seasonal and in-season ingredients. Users can enjoy a variety of dishes using fresh ingredients, maintaining meal diversity and staying engaged without getting bored. The system also manages the user's meal records and supports monitoring of progress. The AI ​​agent analyzes the user's data and provides personalized advice and feedback to help improve motivation. Users can visualize their own successes and progress, and feel the results of their diet and other goals. For people with illnesses or dietary restrictions, this service helps them enjoy healthy meals with peace of mind. By offering personalized meal plans that consider individual restrictions and needs, and providing medical support, it reduces the stress and anxiety associated with dietary restrictions, supporting a healthy and fulfilling life from a "food" perspective. The meal suggestion system proposes individual meal plans based on the user's preferences, restrictions, and nutritional needs, and monitors their progress, allowing them to maximize their personalized dining experience.

[0071] The meal suggestion system according to this embodiment comprises a comprehension unit, a management unit, a suggestion unit, and a monitoring unit. The comprehension unit comprehends the user's preferences, restrictions, and nutritional needs. For example, the comprehension unit interacts with the user to collect information such as favorite and disliked foods and taste preferences. The comprehension unit can also comprehend restrictions such as allergies, religious restrictions, and dietary restrictions. Furthermore, the comprehension unit can also comprehend the intake of specific nutrients and nutritional balance according to the user's health condition. For example, the comprehension unit can comprehend nutritional needs based on the user's meal history and health checkup results. The management unit manages meal records based on the information comprehend by the comprehension unit. For example, the management unit saves images of menus taken by the user and manages them as meal records. The management unit can also save and manage the meal details entered by the user in a database. Furthermore, the management unit can analyze meal records to understand the user's eating patterns. For example, the management unit organizes the user's meal records chronologically to understand eating trends. The suggestion unit analyzes the meal records managed by the management unit and proposes individual meal plans. The suggestion unit proposes optimal meal menus based on the user's preferences, restrictions, and nutritional needs. The suggestion unit can also propose recipes utilizing seasonal or in-season ingredients. Furthermore, it can provide meal plans that take into account restrictions on specific nutrients or ingredients for individuals with illnesses or dietary restrictions. For example, it might suggest low-carbohydrate or low-sodium menus for people with diabetes or high blood pressure. The monitoring unit monitors progress based on the meal plan proposed by the suggestion unit. For example, the monitoring unit periodically checks the user's meal records to understand progress. It can also monitor changes in the user's weight and health status to evaluate the effectiveness of the meal plan. Additionally, the monitoring unit can provide users with personalized advice and feedback to help improve motivation. For example, it analyzes the user's meal records and provides feedback on areas for improvement and success stories. As a result, the meal suggestion system according to this embodiment allows users to maximize their individual meal experience by proposing meal plans based on their preferences, restrictions, and nutritional needs and monitoring their progress.

[0072] The information gathering unit understands the user's preferences, restrictions, and nutritional needs. Specifically, it collects information such as favorite and disliked foods and taste preferences through dialogue with the user. For example, if the user lists tomatoes and cheese as favorite foods, it will prioritize suggesting menus that include these foods. Similarly, if the user lists bell peppers and celery as disliked foods, it will suggest menus that do not include these foods. Furthermore, the information gathering unit also understands restrictions such as allergies, religious restrictions, and dietary restrictions. For example, if the user has a nut allergy, it will suggest menus that do not include nuts. If the user needs to avoid certain foods for religious reasons, it will suggest menus that take that restriction into consideration. For dietary restrictions, it will suggest, for example, low-calorie or low-sugar menus. In addition, the information gathering unit also understands the intake of specific nutrients and nutritional balance according to the user's health condition. For example, based on the user's eating history and health checkup results, it will understand the intake of vitamins and minerals and suggest menus that supplement the necessary nutrients. In this way, the information gathering unit can understand the user's detailed dietary needs and collect basic information to provide individualized meal plans.

[0073] The Management Department manages meal records based on information gathered by the Data Gathering Department. Specifically, the Management Department saves images of menus taken by users and manages them as meal records. For example, when a user takes a picture of the food they ate and uploads it to the application, the image is saved in the database. The Management Department can also save and manage meal details entered by users in the database. For example, when a user enters the name of the dish they ate, the ingredients, and the amount consumed, that information is saved in the database. Furthermore, the Management Department can analyze meal records to understand users' eating patterns. For example, the Management Department can organize users' meal records chronologically to understand their eating trends. This allows them to understand whether users are frequently consuming certain ingredients or whether they are deficient in certain nutrients. In addition, the Management Department can provide the analysis results of the meal records to the Data Gathering Department and the Proposal Department to help create individual meal plans. This allows the Management Department to efficiently manage users' meal records and improve the overall performance of the system.

[0074] The Proposal Department analyzes meal records managed by the Management Department and proposes individual meal plans. Specifically, the Proposal Department suggests optimal meal menus based on the user's preferences, restrictions, and nutritional needs. For example, it may suggest menus that include ingredients the user likes or menus that do not include ingredients the user dislikes. The Proposal Department can also suggest recipes that utilize seasonal or in-season ingredients. For example, it may suggest salads using fresh vegetables in the spring or desserts using seasonal fruits in the fall. Furthermore, the Proposal Department can provide meal plans that take into account restrictions on specific nutrients or ingredients for people with illnesses or dietary restrictions. For example, it may suggest low-carbohydrate or low-sodium menus for people with diabetes or high blood pressure. The Proposal Department uses AI to analyze the user's meal records and nutritional needs and generate optimal meal plans. The AI ​​learns the user's past eating history and health status and provides meal plans tailored to individual needs. This allows the Proposal Department to provide users with healthy and balanced meal plans and improve the quality of their meals.

[0075] The Monitoring Department monitors progress based on the meal plans proposed by the Proposal Department. Specifically, the Monitoring Department regularly checks users' meal records to understand their progress. For example, it verifies whether users are eating according to the proposed meal plan and whether the meals are appropriate. The Monitoring Department can also monitor changes in users' weight and health status to evaluate the effectiveness of the meal plan. For example, it checks whether users are losing weight or whether their blood pressure and blood sugar levels are improving. Furthermore, the Monitoring Department can provide users with personalized advice and feedback to help improve their motivation. For example, it analyzes users' meal records and provides feedback on areas for improvement and success stories. This allows users to understand their progress and maintain their motivation. In addition, the Monitoring Department can improve the overall system performance by collecting user feedback and providing it to the Proposal Department and Management Department. This enables the Monitoring Department to efficiently monitor users' progress and maximize the effectiveness of individual meal plans.

[0076] The proposal unit includes a constraint-response unit that provides meal plans that take into account restrictions on specific nutrients and ingredients for people with illnesses or dietary restrictions. The constraint-response unit provides meal plans that take into account restrictions such as carbohydrate restriction, gluten-free diets, and low sodium diets. For example, the constraint-response unit suggests a low-carbohydrate menu for people with diabetes. It can also suggest a low-sodium menu for people with hypertension. Furthermore, the constraint-response unit can suggest allergy-friendly menus for people with allergies. For example, the constraint-response unit suggests a gluten-free menu for people with gluten allergies. This makes it possible to provide meal plans that take into account restrictions on specific nutrients and ingredients even for people with illnesses or dietary restrictions. Some or all of the above processing in the constraint-response unit may be performed using AI, for example, or without AI. For example, the constraint-response unit can suggest meal plans using an AI model that takes the user's health status and constraints as input and outputs an optimal meal plan.

[0077] The suggestion section includes a seasonal suggestion section that proposes recipes utilizing seasonal ingredients and ingredients in season. The seasonal suggestion section proposes recipes that utilize seasonal ingredients such as spring vegetables, summer fruits, and autumn mushrooms. For example, in spring, the seasonal suggestion section proposes a salad using fresh spring vegetables. In summer, the seasonal suggestion section can also propose a dessert using cold fruits. Furthermore, in autumn, the seasonal suggestion section can propose a warm soup using mushrooms. For example, the seasonal suggestion section proposes a dish using seasonal fish. In this way, by proposing recipes that utilize seasonal ingredients and ingredients in season, it is possible to maintain meal diversity and prevent boredom. Some or all of the above processing in the seasonal suggestion section may be performed using AI, for example, or without AI. For example, the seasonal suggestion section can propose recipes using an AI model that takes information on seasonal ingredients and ingredients in season as input and outputs the optimal recipe.

[0078] The monitoring unit monitors the user's progress and provides personalized advice and feedback to help improve motivation. For example, the monitoring unit monitors the user's progress, such as changes in weight, dietary adherence, and exercise performance. For instance, it regularly records changes in the user's weight to track progress. It can also evaluate the user's dietary adherence and provide feedback. Furthermore, it can monitor the user's exercise performance and provide advice. For example, it analyzes the user's dietary records and provides feedback on areas for improvement and successes. This allows the monitoring unit to visualize the user's successes and progress, enabling them to experience tangible results such as weight loss, by monitoring their progress and providing personalized advice and feedback to improve motivation. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can provide feedback using an AI model that takes user progress data as input and outputs feedback.

[0079] The understanding unit estimates the user's emotions and adjusts how it understands preferences, restrictions, and nutritional needs based on the estimated emotions. For example, if the user is stressed, the understanding unit will understand preferences, restrictions, and nutritional needs through simple questions. For example, if the user is relaxed, the understanding unit will understand preferences, restrictions, and nutritional needs through detailed questions. The understanding unit can also prioritize voice input and quickly understand preferences, restrictions, and nutritional needs if the user is in a hurry. For example, the understanding unit adjusts the content and format of questions according to the user's emotional state. This allows for obtaining more appropriate information by adjusting how preferences, restrictions, and nutritional needs are understood 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 understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can adjust the question content using an AI model that takes user emotion data as input and outputs question content.

[0080] The understanding unit analyzes the user's past eating history to improve the accuracy of understanding preferences, restrictions, and nutritional needs. For example, the understanding unit analyzes the menus the user has frequently eaten in the past to understand their preferences. For example, the understanding unit understands the intake trends of specific nutrients from the user's past eating history. The understanding unit can also understand allergies and dietary restrictions based on the user's past eating history. For example, the understanding unit analyzes the user's eating history to understand the intake of specific nutrients. This allows for an improvement in the accuracy of understanding preferences, restrictions, and nutritional needs by analyzing the user's past eating history. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can improve its accuracy by using an AI model that takes the user's past eating history data as input and outputs preferences, restrictions, and nutritional needs.

[0081] The understanding unit customizes the understanding of preferences, restrictions, and nutritional needs based on the user's lifestyle and health status. For example, the understanding unit understands nutritional needs by considering the user's exercise habits. For example, the understanding unit understands dietary restrictions based on the user's health checkup results. The understanding unit can also understand nutritional needs by considering the user's sleep patterns. For example, the understanding unit analyzes the user's lifestyle data to understand nutritional needs. This allows for the understanding of preferences, restrictions, and nutritional needs to be customized based on the user's lifestyle and health status, thereby providing more appropriate information. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can customize the understanding using an AI model that takes the user's lifestyle and health status data as input and outputs preferences, restrictions, and nutritional needs.

[0082] The understanding unit estimates the user's emotions and determines the priority of information to grasp based on the estimated emotions. For example, if the user is stressed, the understanding unit prioritizes grasping limitations. For example, if the user is relaxed, the understanding unit prioritizes grasping preferences. The understanding unit can also prioritize grasping nutritional needs if the user is in a hurry. For example, the understanding unit determines the priority of information to grasp according to the user's emotional state. This allows for the grasping of more appropriate information 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 understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can determine the priority of information using an AI model that takes user emotion data as input and outputs the priority of information.

[0083] The information gathering unit considers the user's geographical location information to understand their preferences, restrictions, and nutritional needs based on regional food culture and ingredients. For example, the information gathering unit considers the food culture of the area where the user lives to understand their preferences. For example, the information gathering unit considers regional ingredients based on the user's geographical location information to understand their nutritional needs. The information gathering unit can also understand regional dietary restrictions based on the user's geographical location information. For example, the information gathering unit analyzes the user's geographical location information to understand their preferences, restrictions, and nutritional needs based on regional food culture and ingredients. This allows for the gathering of more appropriate information by considering the user's geographical location information to understand their preferences, restrictions, and nutritional needs based on regional food culture and ingredients. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can gather information using an AI model that takes the user's geographical location information as input and outputs preferences, restrictions, and nutritional needs.

[0084] The information gathering unit analyzes the user's social media activity to understand their preferences, restrictions, and nutritional needs. For example, the information gathering unit analyzes photos of meals shared by the user on social media to understand their preferences. For example, the information gathering unit understands dietary restrictions based on the content of the user's social media posts. The information gathering unit can also analyze the accounts the user follows on social media to understand their nutritional needs. For example, the information gathering unit analyzes the user's social media activity to understand their preferences, restrictions, and nutritional needs. This allows the information gathering unit to understand the user's preferences, restrictions, and nutritional needs by analyzing the user's social media activity. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can gather information using an AI model that takes the user's social media data as input and outputs preferences, restrictions, and nutritional needs.

[0085] The management unit estimates the user's emotions and adjusts the meal record management method based on the estimated emotions. For example, if the user is stressed, the management unit manages the meal record with a simple interface. If the user is relaxed, the management unit manages the meal record by providing detailed input options. The management unit can also prioritize voice input when the user is in a hurry. For example, the management unit adjusts the meal record management method according to the user's emotional state. This allows for more appropriate management by adjusting the meal record management method 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 without AI. For example, the management unit can adjust the management method using an AI model that takes user emotion data as input and outputs a management method.

[0086] The management department analyzes users' past meal records to improve the accuracy of management methods. For example, the management department analyzes meal data recorded by users in the past to optimize management methods. For example, the management department can understand the intake trends of specific nutrients from users' past meal records and adjust management methods accordingly. The management department can also propose management methods that take allergies and dietary restrictions into account based on users' past meal records. For example, the management department analyzes users' meal records to understand the intake of specific nutrients. This allows the accuracy of management methods to be improved by analyzing users' past meal records. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can improve the accuracy of management methods by using an AI model that takes users' past meal record data as input and outputs management methods.

[0087] The management department customizes the management of meal records based on the user's lifestyle and health status. For example, the management department adjusts the meal record management method considering the user's exercise habits. For example, the management department optimizes the meal record management method based on the user's health checkup results. The management department can also customize the meal record management method considering the user's sleep patterns. For example, the management department analyzes the user's lifestyle data and adjusts the meal record management method. This allows for more appropriate management by customizing the meal record management based on the user's lifestyle and health status. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can customize management using an AI model that takes data on the user's lifestyle and health status as input and outputs a management method.

[0088] The management unit estimates the user's emotions and determines the priority of meal records to manage based on the estimated emotions. For example, if the user is stressed, the management unit prioritizes managing meal records based on restrictions. For example, if the user is relaxed, the management unit prioritizes managing meal records based on preferences. The management unit can also prioritize managing meal records based on nutritional needs if the user is in a hurry. For example, the management unit determines the priority of meal records to manage according to the user's emotional state. This allows for more appropriate management by determining the priority of meal records to manage 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. For example, the management unit can determine priorities using an AI model that takes user emotion data as input and outputs the priority of meal records to manage.

[0089] The management department manages meal records based on regional food culture and ingredients, taking into account the user's geographical location. For example, the management department manages meal records considering the food culture of the area where the user lives. For example, the management department manages meal records considering regional ingredients based on the user's geographical location. The management department can also manage meal records considering regional dietary restrictions based on the user's geographical location. For example, the management department analyzes the user's geographical location and manages meal records based on regional food culture and ingredients. This allows for more appropriate management by considering the user's geographical location and managing meal records based on regional food culture and ingredients. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can perform management using an AI model that takes the user's geographical location as input and outputs meal records.

[0090] The management department analyzes users' social media activity and manages their meal records. For example, the management department analyzes photos of meals shared by users on social media and manages their meal records. For example, the management department manages meal records based on the content of users' social media posts. The management department can also analyze the accounts that users follow on social media and manage their meal records. For example, the management department analyzes users' social media activity and manages their meal records. This allows for the management of meal records 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. For example, the management department can perform management using an AI model that takes users' social media data as input and outputs meal records.

[0091] The suggestion unit estimates the user's emotions and adjusts the way it suggests meal plans based on the estimated emotions. For example, if the user is stressed, the suggestion unit suggests a meal plan using a simple method. For example, if the user is relaxed, the suggestion unit suggests a meal plan using a detailed method. The suggestion unit can also prioritize voice input when the user is in a hurry. For example, the suggestion unit adjusts the suggestion method according to the user's emotional state. This allows for more appropriate suggestions by adjusting the meal plan suggestion method 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can adjust the suggestion method using an AI model that takes user emotion data as input and outputs a suggestion method.

[0092] The suggestion unit analyzes the user's past eating history to improve the accuracy of its suggestions. For example, the suggestion unit analyzes the menus the user has eaten in the past and suggests a meal plan that suits their preferences. For example, the suggestion unit can understand the intake trends of specific nutrients from the user's past eating history and suggest a nutritionally balanced meal plan. The suggestion unit can also suggest a meal plan that takes allergies and dietary restrictions into account based on the user's past eating history. For example, the suggestion unit analyzes the user's eating history to understand the intake of specific nutrients. By analyzing the user's past eating history, the accuracy of the suggestion unit can be improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can improve the accuracy of its suggestions by using an AI model that takes the user's past eating history data as input and outputs a suggestion method.

[0093] The suggestion unit customizes meal plan suggestions based on the user's lifestyle and health condition. For example, the suggestion unit customizes meal plans by considering the user's exercise habits. For example, the suggestion unit optimizes meal plans based on the user's health checkup results. The suggestion unit can also customize meal plans by considering the user's sleep patterns. For example, the suggestion unit analyzes the user's lifestyle data and customizes meal plans. This allows for more appropriate suggestions by customizing meal plan suggestions based on the user's lifestyle and health condition. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can customize suggestions using an AI model that takes the user's lifestyle and health condition data as input and outputs meal plans.

[0094] The suggestion unit estimates the user's emotions and determines the priority of suggested meal plans based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting meal plans that address constraints. For example, if the user is relaxed, the suggestion unit will prioritize suggesting meal plans that address preferences. The suggestion unit can also prioritize suggesting meal plans that address nutritional needs if the user is in a hurry. For example, the suggestion unit determines the priority of suggested meal plans according to the user's emotional state. This allows for more appropriate suggestions by determining the priority of suggested meal plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can determine the priority using an AI model that takes user emotion data as input and outputs the priority of meal plans.

[0095] The suggestion unit proposes meal plans based on the food culture and ingredients specific to the region, taking into account the user's geographical location. For example, the suggestion unit proposes meal plans considering the food culture of the area where the user lives. For example, the suggestion unit proposes meal plans considering ingredients specific to the region, based on the user's geographical location. The suggestion unit can also propose meal plans considering dietary restrictions specific to the region, based on the user's geographical location. For example, the suggestion unit analyzes the user's geographical location and proposes meal plans based on the food culture and ingredients specific to the region. This allows for more appropriate suggestions by considering the user's geographical location and proposing meal plans based on the food culture and ingredients specific to the region. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can make suggestions using an AI model that takes the user's geographical location as input and outputs meal plans.

[0096] The suggestion unit analyzes the user's social media activity and proposes meal plans. For example, the suggestion unit analyzes photos of meals shared by the user on social media and proposes meal plans that suit their preferences. For example, the suggestion unit proposes meal plans that take dietary restrictions into account based on the content of the user's social media posts. The suggestion unit can also analyze the accounts the user follows on social media and propose meal plans that take nutritional needs into account. For example, the suggestion unit analyzes the user's social media activity to understand their preferences, restrictions, and nutritional needs and proposes meal plans based on that. In this way, meal plans can be proposed 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 not using AI. For example, the suggestion unit can make suggestions using an AI model that takes the user's social media data as input and outputs meal plans.

[0097] The monitoring unit estimates the user's emotions and adjusts the progress monitoring method based on the estimated user emotions. For example, if the user is stressed, the monitoring unit monitors the progress with a simple interface. For example, if the user is relaxed, the monitoring unit monitors the progress by providing detailed input options. The monitoring unit can also prioritize voice input when the user is in a hurry. For example, the monitoring unit adjusts the monitoring method according to the user's emotional state. This allows for more appropriate monitoring by adjusting the progress monitoring method 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 monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can adjust the monitoring method using an AI model that takes user emotion data as input and outputs a monitoring method.

[0098] The monitoring unit analyzes the user's past progress data to improve the accuracy of the monitoring method. For example, the monitoring unit analyzes progress data previously recorded by the user to optimize the monitoring method. For example, the monitoring unit can understand the intake trends of specific nutrients from the user's past progress data and adjust the monitoring method. The monitoring unit can also propose a monitoring method that takes allergies and dietary restrictions into account based on the user's past progress data. For example, the monitoring unit analyzes the user's progress data to understand the intake of specific nutrients. This allows the accuracy of the monitoring method to be improved by analyzing the user's past progress data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can improve the accuracy of the monitoring method by using an AI model that takes the user's past progress data as input and outputs a monitoring method.

[0099] The monitoring unit customizes progress monitoring based on the user's lifestyle and health status. For example, the monitoring unit adjusts the progress monitoring method considering the user's exercise habits. For example, the monitoring unit optimizes the progress monitoring method based on the user's health checkup results. The monitoring unit can also customize the progress monitoring method considering the user's sleep patterns. For example, the monitoring unit analyzes the user's lifestyle data and adjusts the monitoring method. This allows for more appropriate monitoring by customizing progress monitoring based on the user's lifestyle and health status. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can customize monitoring using an AI model that takes user lifestyle and health data as input and outputs a monitoring method.

[0100] The monitoring unit estimates the user's emotions and determines the priority of the progress to be monitored based on the estimated user emotions. For example, if the user is stressed, the monitoring unit prioritizes monitoring constraints. For example, if the user is relaxed, the monitoring unit prioritizes monitoring preferences. The monitoring unit can also prioritize monitoring nutritional needs if the user is in a hurry. For example, the monitoring unit determines the priority of the progress to be monitored according to the user's emotional state. This allows for more appropriate monitoring by determining the priority of the progress to be monitored 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 monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can determine priorities using an AI model that takes user emotion data as input and outputs the priority of the progress to be monitored.

[0101] The monitoring unit monitors progress based on regional food culture and ingredients, taking into account the user's geographical location. For example, the monitoring unit monitors progress considering the food culture of the area where the user lives. For example, the monitoring unit monitors progress considering regional ingredients based on the user's geographical location. The monitoring unit can also monitor progress considering regional dietary restrictions based on the user's geographical location. For example, the monitoring unit analyzes the user's geographical location and monitors progress based on regional food culture and ingredients. This allows for more appropriate monitoring by considering the user's geographical location and monitoring progress based on regional food culture and ingredients. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can perform monitoring using an AI model that takes the user's geographical location as input and outputs progress.

[0102] The monitoring unit analyzes the user's social media activity and monitors its progress. For example, the monitoring unit analyzes photos of meals shared by the user on social media and monitors its progress. For example, the monitoring unit monitors its progress based on the content of the user's social media posts. The monitoring unit can also analyze the accounts the user follows on social media and monitor its progress. For example, the monitoring unit analyzes the user's social media activity and monitors its progress. This allows for monitoring of progress by analyzing the user's social media activity. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can perform monitoring using an AI model that takes the user's social media data as input and outputs progress.

[0103] The constraint response unit estimates the user's emotions and adjusts the constraint response method based on the estimated user emotions. For example, if the user is stressed, the constraint response unit provides a simple constraint response method. For example, if the user is relaxed, the constraint response unit provides a detailed constraint response method. The constraint response unit can also prioritize voice input and provide a constraint response method if the user is in a hurry. For example, the constraint response unit adjusts the constraint response method according to the user's emotional state. This allows for a more appropriate response by adjusting the constraint response method 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 constraint response unit may be performed using AI, for example, or without AI. For example, the constraint response unit can adjust the method using an AI model that takes user emotion data as input and outputs a constraint response method.

[0104] The constraint handling unit analyzes the user's past meal history to improve the accuracy of the constraint handling method. For example, the constraint handling unit analyzes the menus the user has eaten in the past and optimizes the constraint handling method. For example, the constraint handling unit understands the intake trends of specific nutrients from the user's past meal history and adjusts the constraint handling method. The constraint handling unit can also propose constraint handling methods that take into account allergies and dietary restrictions based on the user's past meal history. For example, the constraint handling unit analyzes the user's meal history to understand the intake of specific nutrients. This allows the accuracy of the constraint handling method to be improved by analyzing the user's past meal history. Some or all of the above processing in the constraint handling unit may be performed using AI, for example, or without AI. For example, the constraint handling unit can improve the accuracy of the method by using an AI model that takes the user's past meal history data as input and outputs a constraint handling method.

[0105] The constraint response unit customizes the means of constraint response based on the user's lifestyle and health status. For example, the constraint response unit adjusts the constraint response method by considering the user's exercise habits. For example, the constraint response unit optimizes the constraint response method based on the user's health checkup results. The constraint response unit can also customize the constraint response method by considering the user's sleep patterns. For example, the constraint response unit analyzes the user's lifestyle data and adjusts the constraint response method. This allows for more appropriate responses by customizing the means of constraint response based on the user's lifestyle and health status. Some or all of the above processing in the constraint response unit may be performed using AI, for example, or without AI. For example, the constraint response unit can customize the means using an AI model that takes user lifestyle and health status data as input and outputs constraint response methods.

[0106] The constraint response unit estimates the user's emotions and determines the priority of constraint responses based on the estimated user emotions. For example, if the user is stressed, the constraint response unit prioritizes constraint responses based on limitations. For example, if the user is relaxed, the constraint response unit prioritizes constraint responses based on preferences. Furthermore, if the user is in a hurry, the constraint response unit can also prioritize constraint responses based on nutritional needs. For example, the constraint response unit determines the priority of constraint responses based on the user's emotional state. This allows for more appropriate responses by determining the priority of constraint responses based on 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 constraint response unit may be performed using AI, for example, or without AI. For example, the constraint response unit can determine priorities using an AI model that takes user emotion data as input and outputs the priority of constraint responses.

[0107] The constraint handling unit considers the user's geographical location information and handles constraints based on regionally specific food culture and ingredients. For example, the constraint handling unit considers the food culture of the area where the user lives and handles constraints accordingly. For example, the constraint handling unit considers regionally specific ingredients based on the user's geographical location information and handles constraints accordingly. The constraint handling unit can also consider regionally specific dietary restrictions based on the user's geographical location information and handles constraints accordingly. For example, the constraint handling unit analyzes the user's geographical location information and handles constraints based on regionally specific food culture and ingredients. This allows for more appropriate responses by considering the user's geographical location information and handling constraints based on regionally specific food culture and ingredients. Some or all of the above processing in the constraint handling unit may be performed using AI, for example, or without AI. For example, the constraint handling unit can use an AI model that takes the user's geographical location information as input and outputs constraint responses.

[0108] The constraint response unit analyzes the user's social media activity and proposes means of responding to constraints. For example, the constraint response unit analyzes photos of meals shared by the user on social media and proposes methods for responding to constraints. For example, the constraint response unit proposes methods for responding to constraints based on the content of the user's social media posts. The constraint response unit can also analyze the accounts the user follows on social media and propose methods for responding to constraints. For example, the constraint response unit analyzes the user's social media activity and proposes methods for responding to constraints. In this way, means of responding to constraints can be proposed by analyzing the user's social media activity. Some or all of the above processing in the constraint response unit may be performed using AI, for example, or without AI. For example, the constraint response unit can propose means using an AI model that takes the user's social media data as input and outputs methods for responding to constraints.

[0109] The seasonal suggestion unit estimates the user's emotions and adjusts the seasonal suggestion method based on the estimated emotions. For example, if the user is stressed, the seasonal suggestion unit provides a simple seasonal suggestion method. For example, if the user is relaxed, the seasonal suggestion unit provides a detailed seasonal suggestion method. The seasonal suggestion unit can also prioritize voice input and provide seasonal suggestion methods if the user is in a hurry. For example, the seasonal suggestion unit adjusts the seasonal suggestion method according to the user's emotional state. This allows for more appropriate suggestions by adjusting the seasonal suggestion method 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 seasonal suggestion unit may be performed using AI, for example, or without AI. For example, the seasonal suggestion unit can adjust the method using an AI model that takes user emotion data as input and outputs seasonal suggestion methods.

[0110] The seasonal suggestion unit analyzes the user's past eating history to improve the accuracy of seasonal suggestion methods. For example, the seasonal suggestion unit analyzes the menus the user has eaten in the past and optimizes the seasonal suggestion method. For example, the seasonal suggestion unit understands the intake trends of specific nutrients from the user's past eating history and adjusts the seasonal suggestion method accordingly. The seasonal suggestion unit can also propose seasonal suggestion methods that take into account allergies and dietary restrictions based on the user's past eating history. For example, the seasonal suggestion unit analyzes the user's eating history to understand the intake of specific nutrients. This allows the accuracy of seasonal suggestion methods to be improved by analyzing the user's past eating history. Some or all of the above processing in the seasonal suggestion unit may be performed using AI, for example, or without AI. For example, the seasonal suggestion unit can improve the accuracy of its methods by using an AI model that takes the user's past eating history data as input and outputs seasonal suggestion methods.

[0111] The seasonal suggestion unit customizes the means of seasonal suggestion based on the user's lifestyle and health condition. For example, the seasonal suggestion unit adjusts the seasonal suggestion method considering the user's exercise habits. For example, the seasonal suggestion unit optimizes the seasonal suggestion method based on the user's health checkup results. The seasonal suggestion unit can also customize the seasonal suggestion method considering the user's sleep patterns. For example, the seasonal suggestion unit analyzes the user's lifestyle data and adjusts the seasonal suggestion method. This allows for more appropriate suggestions by customizing the means of seasonal suggestion based on the user's lifestyle and health condition. Some or all of the above processing in the seasonal suggestion unit may be performed using AI, for example, or without AI. For example, the seasonal suggestion unit can customize the means using an AI model that takes user lifestyle and health condition data as input and outputs a seasonal suggestion method.

[0112] The seasonal suggestion unit estimates the user's emotions and determines the priority of seasonal suggestions based on the estimated emotions. For example, if the user is stressed, the seasonal suggestion unit will prioritize restrictions in its seasonal suggestions. For example, if the user is relaxed, the seasonal suggestion unit will prioritize preferences in its seasonal suggestions. Furthermore, if the user is in a hurry, the seasonal suggestion unit can also prioritize nutritional needs in its seasonal suggestions. For example, the seasonal suggestion unit determines the priority of seasonal suggestions according to the user's emotional state. This allows for more appropriate suggestions by prioritizing seasonal suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 seasonal suggestion unit may be performed using AI or not. For example, the seasonal suggestion unit can determine the priority using an AI model that takes user emotion data as input and outputs the priority of seasonal suggestions.

[0113] The seasonal suggestion unit makes seasonal suggestions based on regional food culture and ingredients, taking into account the user's geographical location. For example, the seasonal suggestion unit makes seasonal suggestions considering the food culture of the area where the user lives. For example, the seasonal suggestion unit makes seasonal suggestions considering regional ingredients based on the user's geographical location. The seasonal suggestion unit can also make seasonal suggestions considering regional dietary restrictions based on the user's geographical location. For example, the seasonal suggestion unit analyzes the user's geographical location and makes seasonal suggestions based on regional food culture and ingredients. This allows for more appropriate suggestions by considering the user's geographical location and making seasonal suggestions based on regional food culture and ingredients. Some or all of the above processing in the seasonal suggestion unit may be performed using AI, for example, or without AI. For example, the seasonal suggestion unit can make suggestions using an AI model that takes the user's geographical location as input and outputs seasonal suggestions.

[0114] The seasonal suggestion unit analyzes the user's social media activity and proposes methods for seasonal suggestions. For example, the seasonal suggestion unit analyzes photos of meals shared by the user on social media and proposes seasonal suggestion methods. For example, the seasonal suggestion unit proposes seasonal suggestion methods based on the content of the user's social media posts. The seasonal suggestion unit can also analyze the accounts the user follows on social media and propose seasonal suggestion methods. For example, the seasonal suggestion unit analyzes the user's social media activity and proposes seasonal suggestion methods. In this way, by analyzing the user's social media activity, it is possible to propose methods for seasonal suggestions. Some or all of the above processing in the seasonal suggestion unit may be performed using AI, for example, or without AI. For example, the seasonal suggestion unit can propose methods using an AI model that takes the user's social media data as input and outputs seasonal suggestion methods.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The suggestion function can estimate the user's emotions and adjust the way meal plans are suggested based on those emotions. For example, if the user is stressed, a simple suggestion method can be used to propose meal plans. Conversely, if the user is relaxed, a more detailed suggestion method can be used. Furthermore, if the user is in a hurry, voice input can be prioritized when suggesting meal plans. By adjusting the suggestion method according to the user's emotions, more appropriate suggestions can be made.

[0117] The suggestion function can also analyze the user's past eating history to improve the accuracy of its suggestions. For example, it can analyze the menus the user has eaten in the past and suggest a meal plan that suits their preferences. It can also identify the user's intake trends for specific nutrients from their past eating history and suggest a nutritionally balanced meal plan. Furthermore, it can suggest a meal plan that takes into account allergies and dietary restrictions based on the user's past eating history. In this way, analyzing the user's past eating history can improve the accuracy of the suggestion method.

[0118] The suggestion department can also customize meal plan suggestions based on the user's lifestyle and health condition. For example, it can customize meal plans considering the user's exercise habits. It can also optimize meal plans based on the user's health checkup results. Furthermore, it can customize meal plans considering the user's sleep patterns. By customizing meal plan suggestions based on the user's lifestyle and health condition, more appropriate suggestions can be made.

[0119] The suggestion function can also estimate the user's emotions and determine the priority of suggested meal plans based on those emotions. For example, if the user is stressed, it can prioritize suggesting meal plans that address their limitations. If the user is relaxed, it can prioritize suggesting meal plans that address their preferences. Furthermore, if the user is in a hurry, it can prioritize suggesting meal plans that address their nutritional needs. By prioritizing meal plans according to the user's emotions, the system can provide more appropriate suggestions.

[0120] The proposal function can also suggest meal plans based on the user's geographical location and the region's unique food culture and ingredients. For example, it can suggest meal plans considering the food culture of the area where the user lives. It can also suggest meal plans considering the region's unique ingredients based on the user's geographical location. Furthermore, it can suggest meal plans considering the region's unique dietary restrictions based on the user's geographical location. By considering the user's geographical location and suggesting meal plans based on the region's unique food culture and ingredients, more appropriate suggestions can be made.

[0121] The proposal department can also analyze users' social media activity and suggest meal plans. For example, it can analyze photos of meals shared by users on social media and suggest meal plans that suit their preferences. It can also suggest meal plans that take dietary restrictions into account based on the content of users' social media posts. Furthermore, it can analyze the accounts that users follow on social media and suggest meal plans that take nutritional needs into consideration. In this way, meal plans can be suggested by analyzing users' social media activity.

[0122] The monitoring unit can also estimate the user's emotions and adjust the progress monitoring method based on those emotions. For example, if the user is stressed, progress can be monitored using a simple interface. If the user is relaxed, progress can be monitored using detailed input options. Furthermore, if the user is in a hurry, progress can be monitored using voice input as the priority. This allows for more appropriate monitoring by adjusting the progress monitoring method according to the user's emotions.

[0123] The monitoring unit can also analyze the user's past progress data to improve the accuracy of the monitoring method. For example, it can analyze progress data previously recorded by the user and optimize the monitoring method. It can also identify the user's intake trends for specific nutrients from their past progress data and adjust the monitoring method accordingly. Furthermore, it can suggest monitoring methods that take allergies and dietary restrictions into account based on the user's past progress data. In this way, the accuracy of the monitoring method can be improved by analyzing the user's past progress data.

[0124] The monitoring unit can also customize progress monitoring based on the user's lifestyle and health status. For example, it can adjust the progress monitoring method considering the user's exercise habits. It can also optimize the progress monitoring method based on the user's health checkup results. Furthermore, it can customize the progress monitoring method considering the user's sleep patterns. This allows for more appropriate monitoring by customizing progress monitoring based on the user's lifestyle and health status.

[0125] The monitoring unit can also estimate the user's emotions and determine the priority of the progress to be monitored based on those emotions. For example, if the user is stressed, it can prioritize monitoring limitations. If the user is relaxed, it can prioritize monitoring preferences. Furthermore, if the user is in a hurry, it can prioritize monitoring nutritional needs. This allows for more appropriate monitoring by prioritizing the progress to be monitored according to the user's emotions.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The understanding unit grasps the user's preferences, restrictions, and nutritional needs. For example, it interacts with the user to collect information on favorite and disliked foods, taste preferences, allergies, religious restrictions, dietary restrictions, and other restrictions. It can also understand the intake of specific nutrients and nutritional balance according to the user's health condition based on their eating history and health check results. Step 2: The management department manages meal records based on the information gathered by the data collection department. For example, it saves images of menus taken by users and manages them as meal records. It can also save and manage the meal details entered by users in a database. Furthermore, the management department analyzes the meal records to understand the users' eating patterns. For example, it organizes the users' meal records chronologically to understand their eating trends. Step 3: The proposal department analyzes the meal records managed by the management department and proposes individual meal plans. For example, it proposes optimal meal menus based on the user's preferences, restrictions, and nutritional needs. It can also propose recipes that utilize seasonal or in-season ingredients. Furthermore, it can provide meal plans that take into account restrictions on specific nutrients or ingredients for people with illnesses or dietary restrictions. Step 4: The monitoring unit monitors progress based on the meal plan proposed by the proposal unit. For example, it regularly checks the user's meal log to understand their progress. It can also monitor changes in the user's weight and health status to evaluate the effectiveness of the meal plan. Furthermore, it can provide the user with personalized advice and feedback to help improve their motivation.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the understanding unit, management unit, proposal unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the understanding unit is implemented by the control unit 46A of the smart device 14 and understands the user's preferences, restrictions, and nutritional needs. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages meal records. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes individual meal plans. The monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors progress. 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.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the grasping unit, management unit, proposal unit, and monitoring unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the grasping unit is implemented by the control unit 46A of the smart glasses 214 and grasps the user's preferences, restrictions, and nutritional needs. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages meal records. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes individual meal plans. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors progress. 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.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the grasping unit, management unit, proposal unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the grasping unit is implemented by the control unit 46A of the headset terminal 314 and grasps the user's preferences, restrictions, and nutritional needs. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages meal records. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes individual meal plans. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] Each of the multiple elements described above, including the grasping unit, management unit, proposal unit, and monitoring unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the grasping unit is implemented by the control unit 46A of the robot 414 and grasps the user's preferences, restrictions, and nutritional needs. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages meal records. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes individual meal plans. The monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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."

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] (Note 1) A unit that understands the user's preferences, restrictions, and nutritional needs, A management unit manages meal records based on the information obtained by the aforementioned information gathering unit, The proposal department analyzes the meal records managed by the aforementioned management department and proposes individual meal plans. The system includes a monitoring unit that monitors the progress based on the meal plan proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, It includes a restriction-adaptation section that provides meal plans that take into account restrictions on specific nutrients and ingredients for people with illnesses or dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, The company has a seasonal proposal department that suggests recipes utilizing seasonal and in-season ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, Monitor user progress and provide personalized advice and feedback to help improve motivation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The gripping part is, We estimate the user's emotions and adjust how we understand their preferences, restrictions, and nutritional needs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The gripping part is, By analyzing users' past eating history, we can improve the accuracy of understanding their preferences, restrictions, and nutritional needs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The gripping part is, Customize the understanding of preferences, restrictions, and nutritional needs based on the user's lifestyle and health status. The system described in Appendix 1, characterized by the features described herein. (Note 8) The gripping part is, It estimates the user's emotions and determines the priority of information to gather based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The gripping part is, By considering the user's geographical location, we can understand their preferences, restrictions, and nutritional needs based on the region's specific food culture and ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 10) The gripping part is, Analyze users' social media activity to understand their preferences, restrictions, and nutritional needs. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned management department, It estimates the user's emotions and adjusts how meal records are managed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned management department, Analyze users' past meal records to improve the accuracy of management methods. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned management department, Customize meal log management based on the user's lifestyle and health status. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned management department, It estimates the user's emotions and determines the priority of meal records to manage based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned management department, Taking the user's geographical location into consideration, meal records are managed based on regional food culture and ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned management department, Analyze users' social media activity and manage their meal records. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, The system estimates the user's emotions and adjusts how meal plans are suggested based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We analyze users' past meal history to improve the accuracy of our recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, Customize meal plan suggestions based on the user's lifestyle and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggested meal plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Taking the user's geographical location into consideration, the system proposes meal plans based on local food culture and ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Analyze users' social media activity and suggest meal plans. The system described in Appendix 1, characterized by the features described herein. (Note 23) The monitoring unit, We estimate user sentiment and adjust progress monitoring methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The monitoring unit, Analyze users' past progress data to improve the accuracy of monitoring methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, Customize progress monitoring based on the user's lifestyle and health status. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, Estimate user sentiment and prioritize monitoring progress based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, The system monitors progress based on the user's geographical location and local food culture and ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, Analyze users' social media activity and monitor their progress. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned constraint handling unit is: It estimates the user's emotions and adjusts the constraint handling method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned constraint handling unit is: Analyze users' past meal history to improve the accuracy of constraint handling methods. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned constraint handling unit is: Customize constraint management methods based on the user's lifestyle and health status. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned constraint handling unit is: The system estimates the user's emotions and determines the priority of constraint resolution based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned constraint handling unit is: The system takes into account the user's geographical location and implements constraints based on regional food culture and ingredients. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned constraint handling unit is: We analyze users' social media activity and propose ways to address constraints. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned seasonal proposal department We estimate the user's sentiment and adjust the seasonal suggestion method based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned seasonal proposal department We analyze users' past meal history to improve the accuracy of seasonal recommendations. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned seasonal proposal department Customize seasonal suggestions based on the user's lifestyle and health status. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned seasonal proposal department It estimates user sentiment and prioritizes seasonal suggestions based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned seasonal proposal department Taking into account the user's geographical location, seasonal suggestions are made based on the region's unique food culture and ingredients. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned seasonal proposal department We analyze users' social media activity and propose methods for seasonal recommendations. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0200] 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 unit that understands the user's preferences, restrictions, and nutritional needs, A management unit manages meal records based on the information obtained by the aforementioned information gathering unit, The proposal department analyzes the meal records managed by the aforementioned management department and proposes individual meal plans. The system includes a monitoring unit that monitors the progress based on the meal plan proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, It includes a restriction-adaptation section that provides meal plans that take into account restrictions on specific nutrients and ingredients for people with illnesses or dietary restrictions. The system according to feature 1.

3. The aforementioned proposal section is, The company has a seasonal proposal department that suggests recipes utilizing seasonal and in-season ingredients. The system according to feature 1.

4. The monitoring unit, Monitor user progress and provide personalized advice and feedback to help improve motivation. The system according to feature 1.

5. The gripping part is, We estimate the user's emotions and adjust how we understand their preferences, restrictions, and nutritional needs based on those estimated emotions. The system according to feature 1.

6. The gripping part is, By analyzing users' past eating history, we can improve the accuracy of understanding their preferences, restrictions, and nutritional needs. The system according to feature 1.

7. The gripping part is, Customize the understanding of preferences, restrictions, and nutritional needs based on the user's lifestyle and health status. The system according to feature 1.

8. The gripping part is, It estimates the user's emotions and determines the priority of information to gather based on those estimated emotions. The system according to feature 1.

9. The gripping part is, By considering the user's geographical location, we can understand their preferences, restrictions, and nutritional needs based on the region's specific food culture and ingredients. The system according to feature 1.

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