System

Abstract

An object of the system according to the embodiment is to monitor a stress level and a mental state and provide psychological support in addition to monitoring a personalized meal plan and health data for pregnant women.SOLUTION: A system according to an embodiment includes a reception unit, a proposal unit, a monitoring unit, an alert unit, a surveillance unit, and a support unit. The reception unit inputs basic information of a user. The suggestion unit personalizes the meal plan based on the information input by the reception unit. The monitoring unit monitors health data of a user. The alert unit issues an alert when the abnormality is detected by the monitoring unit. The monitoring unit monitors a stress level and a mental state of the user. The support unit provides psychological support based on the data monitored by the monitoring unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] While existing technologies provide personalized meal plans and health data monitoring for pregnant women, there is room for improvement in terms of monitoring stress levels and mental state and providing psychological support.

[0005] The system of the embodiment aims to provide personalized meal plans and health data monitoring for pregnant women, as well as monitor stress levels and mental state and provide psychological support. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a monitoring unit, an alert unit, a monitoring unit, and a support unit. The reception unit inputs basic information about the user. The proposal unit personalizes the meal plan based on the information input by the reception unit. The monitoring unit monitors the user's health data. The alert unit issues an alert when an abnormality is detected by the monitoring unit. The monitoring unit monitors the user's stress level and mental state. The support unit provides psychological support based on the data monitored by the monitoring unit. [Effects of the Invention]

[0007] In addition to providing personalized meal plans and health data monitoring for pregnant women, the system according to the embodiment can monitor stress levels and mental state and provide psychological support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention comprehensively manages a user's basic information, meal plan, health data, stress level, and mental state, and provides alerts and support as needed. In this health management system, a user inputs their basic information (e.g., age, number of weeks pregnant, medical history) into an application. The application then personalizes the user's meal plan and suggests nutritionally balanced meal menus. For example, for pregnant women prone to iron deficiency, recipes using iron-rich ingredients are suggested. Furthermore, the system periodically monitors the user's health data (e.g., blood pressure, weight, heart rate) and issues alerts if an abnormality is detected. For example, if blood pressure suddenly rises, a notification is sent urging the user to consult a doctor. The system also has a function to monitor the user's stress level and mental state. When a user inputs their daily mood and stress level into the application, AI analyzes the data and provides psychological support as needed. For example, if stress levels are high, the system suggests relaxation techniques or counseling. By supporting comprehensive health management for pregnant women in this way, health risks during pregnancy are reduced, providing a safe and secure environment. This allows the health management system to comprehensively manage the user's health status and provide alerts and support as needed. For example, users can understand their own health status in real time and take appropriate measures. In addition, users can know specific areas for improvement in their health status, improving the effectiveness of their health management.

[0029] A health management system according to an embodiment includes a reception unit, a proposal unit, a monitoring unit, an alert unit, a monitoring unit, and a support unit. The reception unit inputs basic information about a user. The basic information includes, but is not limited to, for example, name, age, gender, height, and weight. The reception unit provides, for example, an interface for the user to input the basic information into an application. The reception unit can also automatically acquire the basic information using voice input or image recognition. The proposal unit personalizes a meal plan based on the information input by the reception unit. The meal plan includes, for example, but is not limited to, calorie count, nutritional balance, and meal frequency. The proposal unit proposes, for example, a meal menu that takes nutritional balance into consideration. The monitoring unit regularly monitors the user's health data. The health data includes, for example, but is not limited to, blood pressure, weight, heart rate, and blood sugar level. The monitoring unit, for example, measures the user's blood pressure daily and records the data. The monitoring unit can also measure the user's weight weekly and record the data. The alert unit issues an alert when an abnormality is detected by the monitoring unit. Examples of alerts include, but are not limited to, a notification urging the user to consult a doctor if the user's blood pressure suddenly rises. For example, the alert unit sends a notification urging the user to consult a doctor if the user's blood pressure exceeds a certain threshold. The monitoring unit monitors the user's stress level and mental state. Examples of stress level and mental state include, but are not limited to, daily mood, stress level, and psychological test results. The monitoring unit collects data, for example, by the user inputting their daily mood into an application. The monitoring unit can also measure the user's stress level using a sensor and collect data. The support unit provides psychological support based on the data monitored by the monitoring unit. Examples of support include, but are not limited to, suggestions for relaxation methods and counseling. For example, the support unit suggests relaxation methods when the user's stress is increasing. The support unit can also suggest counseling depending on the user's mental state.As a result, the health management system according to the embodiment can comprehensively manage the user's health condition and provide alerts and support as needed. For example, the user can grasp their own health condition in real time and take appropriate measures. Furthermore, the user can specifically know areas for improvement in their health condition, improving the effectiveness of health management.

[0030] The suggestion unit can suggest a meal menu based on nutritional balance. Nutritional balance includes, for example, the recommended intake amount of each nutrient and the definition of a balanced diet, but is not limited to these examples. For example, the suggestion unit can suggest recipes using ingredients that are high in iron, taking into account the user's nutritional balance. The suggestion unit can also suggest an appropriate meal menu based on the user's calorie intake. Furthermore, the suggestion unit can also suggest a balanced meal menu taking into account the user's meal frequency. This makes it possible to provide the user with a nutritionally balanced meal menu. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's nutritional balance data into the generation AI and cause the generation AI to suggest a meal menu.

[0031] The monitoring unit can periodically monitor the user's blood pressure, weight, heart rate, and other health data. Periodically includes, but is not limited to, daily, weekly, monthly, and other frequencies. For example, the monitoring unit can measure the user's blood pressure daily and record the data. The monitoring unit can also measure the user's weight weekly and record the data. Furthermore, the monitoring unit can measure the user's heart rate monthly and record the data. This allows the user's health data to be periodically monitored and the user's health condition to be understood. Some or all of the above-described processing in the monitoring unit can be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's health data into the generation AI and have the generation AI analyze the data.

[0032] The alert unit can send a notification urging the user to consult a doctor if the user's blood pressure suddenly rises. A sudden rise includes, but is not limited to, the magnitude or rate of increase in blood pressure within a certain period of time. For example, the alert unit can send a notification urging the user to consult a doctor if the user's blood pressure exceeds a certain standard. The alert unit can also send an emergency notification if the user's blood pressure suddenly rises. Furthermore, the alert unit can also send a notification to the user's family or a medical institution if the user's blood pressure suddenly rises. This allows the user to promptly consult a doctor if the user's blood pressure suddenly rises. Some or all of the above-described processing in the alert unit can be performed, for example, using AI or without AI. For example, the alert unit can input the user's blood pressure data into a generation AI and cause the generation AI to detect abnormalities and issue a notification.

[0033] The monitoring unit can monitor the user's daily mood and stress level. Examples of daily mood include, but are not limited to, self-reporting, questionnaires, and sensor measurements. The monitoring unit collects data, for example, by the user inputting their daily mood into an application. The monitoring unit can also measure the user's stress level using a sensor and collect data. Furthermore, the monitoring unit can evaluate the user's mood and stress level using a psychological test and collect data. This makes it possible to monitor the user's daily mood and stress level and understand their psychological state. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's mood data into a generation AI and have the generation AI analyze the data.

[0034] The support unit can suggest relaxation methods or counseling when stress levels are high. Examples of relaxation methods include, but are not limited to, deep breathing, meditation, yoga, etc. Examples of counseling include, but are not limited to, online counseling and face-to-face counseling. For example, the support unit can suggest relaxation methods when the user's stress levels are high. The support unit can also suggest counseling based on the user's mental state. Furthermore, the support unit can suggest appropriate support methods based on the user's stress level. This makes it possible to suggest appropriate relaxation methods or counseling when the user's stress levels are high. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or without AI. For example, the support unit can input the user's stress data into the generation AI and have the generation AI execute the relaxation method or counseling suggestions.

[0035] The reception unit can analyze the user's past input history and provide an auto-completion function to improve input efficiency. Examples of input efficiency include, but are not limited to, shortening input time and reducing input errors. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest information that the user will use during a specific time period based on the user's past input history. This can provide an auto-completion function based on the user's past input history and improve input efficiency. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can input the user's input history data to a generation AI and cause the generation AI to provide the auto-completion function.

[0036] The reception unit can automatically acquire basic information using voice input or image recognition when a user inputs information. Voice input includes, but is not limited to, a voice recognition algorithm, a microphone type, etc. Image recognition includes, but is not limited to, face recognition, object recognition, OCR, etc. For example, when a user inputs basic information by voice, the reception unit automatically converts the information into text using voice recognition technology. The reception unit can also extract basic information using image recognition technology by having the user upload an image. Furthermore, the reception unit can combine voice input and image recognition to more efficiently acquire basic information. This allows the basic information to be automatically acquired using voice input or image recognition, thereby streamlining the input process. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice data or image data to a generation AI and have the generation AI acquire the basic information.

[0037] The reception unit can check the consistency of the input content when the user enters it and display a warning if there is a discrepancy. Methods for checking consistency include, but are not limited to, checking against a database or a rule-based check. For example, the reception unit can automatically display a warning if there is a discrepancy in the information entered by the user. The reception unit can also check the information entered by the user in real time to maintain consistency. Furthermore, the reception unit can highlight discrepancies when the user confirms the input content. This allows the consistency of the input content to be checked and a warning to be displayed if there is a discrepancy, thereby encouraging the user to enter accurate information. Some or all of the above-mentioned processing by the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI check the consistency and display a warning.

[0038] The reception unit may provide an option to input region-specific health information taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS, IP address, and manual user input. The reception unit may provide region-specific health risk information based on the user's current location. The reception unit may also suggest region-specific ingredients and recipes based on the user's geographical location information. The reception unit may also provide information on local medical institutions taking into account the user's location information. This allows region-specific health information to be provided based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's geographical location data into the generation AI and cause the generation AI to provide region-specific health information.

[0039] The reception unit can analyze the user's social media activity and automatically input related basic information. Social media activity includes, but is not limited to, analysis of post content and follower count. For example, the reception unit automatically acquires basic information that the user has made public on social media. The reception unit can also analyze the user's social media activity and input related information. Furthermore, the reception unit can complement the basic information by referring to information about the user's social media friends. This makes it possible to automatically input related basic information based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to input the basic information.

[0040] The reception unit can customize the input interface by reflecting the user's past feedback. Examples of feedback include, but are not limited to, questionnaires, user comments, and usage history. The reception unit can improve the input interface, for example, based on feedback previously provided by the user. The reception unit can also simplify the input procedure by reflecting the user's feedback. Furthermore, the reception unit can adjust the design of the input interface based on the user's feedback. This allows the input interface to be customized and improve usability based on the user's past feedback. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI customize the input interface.

[0041] The suggestion unit can customize a meal menu by taking into consideration the user's allergy information and ingredient preferences. Examples of allergy information include, but are not limited to, the user's self-reporting and reference to medical data. Examples of ingredient preferences include, but are not limited to, questionnaires and past meal history. For example, the suggestion unit can suggest a menu that avoids ingredients to which the user is allergic. The suggestion unit can also suggest a menu that uses the user's favorite ingredients to suit the user's preferences. Furthermore, the suggestion unit can combine the user's allergy information and preferences to suggest an optimal menu. This makes it possible to suggest an optimal meal menu based on the user's allergy information and preferences. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's allergy information and preference data into the generation AI and cause the generation AI to customize the meal menu.

[0042] When proposing a meal menu, the suggestion unit can adjust the proposed content taking into account the season and local specialties. Seasons include, but are not limited to, spring, summer, autumn, and winter. Regional specialties include, but are not limited to, a list of regional specialties and seasonal specialties. The suggestion unit, for example, proposes a menu using seasonal ingredients. The suggestion unit can also propose a menu using regional specialties. Furthermore, the suggestion unit can propose a menu that combines seasonal and regional specialties. This allows for proposing a more appropriate meal menu taking into account the season and regional specialties. Some or all of the above-described processing by the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input seasonal and regional specialty data into the generation AI and cause the generation AI to adjust the proposed meal menu.

[0043] When proposing a meal menu, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past meal history. Past meal history includes, but is not limited to, the user's self-reporting and app usage history. For example, the suggestion unit can suggest a menu that takes into account the user's preferences and nutritional balance based on the user's past meal history. The suggestion unit can also re-suggest menus that the user has previously enjoyed. Furthermore, the suggestion unit can analyze the user's past meal history and suggest nutritionally balanced menus. This allows for more accurate meal menu suggestions based on the user's past meal history. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past meal history data into the generation AI and cause the generation AI to suggest meal menus.

[0044] When proposing a meal menu, the suggestion unit can suggest meal timings that match the user's lifestyle rhythm. Life rhythms include, but are not limited to, for example, sleep patterns, activity times, and meal times. For example, the suggestion unit can suggest the timings of breakfast, lunch, and dinner in accordance with the user's lifestyle rhythm. The suggestion unit can also suggest the timing of snacks taking the user's lifestyle rhythm into consideration. Furthermore, the suggestion unit can also suggest optimal meal timings based on the user's lifestyle rhythm. This makes it possible to suggest optimal meal timings based on the user's lifestyle rhythm. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to suggest meal timings.

[0045] When proposing a meal menu, the suggestion unit can suggest a menu that the whole family can enjoy, taking into account the user's family composition. Family composition includes, for example, the number of family members, ages, and food preferences, but is not limited to these examples. For example, the suggestion unit can suggest a menu that the whole family can enjoy, based on the user's family composition. The suggestion unit can also suggest a menu that will satisfy everyone, taking into account the user's family preferences. Furthermore, the suggestion unit can propose an optimal menu by combining the user's family composition and preferences. This makes it possible to suggest a menu that the whole family can enjoy, based on the user's family composition. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's family composition data into the generation AI and cause the generation AI to suggest a menu that the whole family can enjoy.

[0046] When proposing a meal menu, the suggestion unit can calculate calories taking into account the user's amount of exercise. Examples of the amount of exercise include, but are not limited to, a pedometer, an exercise app, or self-reporting. The suggestion unit can, for example, suggest a menu with an appropriate calorie amount based on the user's amount of exercise. The suggestion unit can also suggest a nutritionally balanced menu taking into account the user's amount of exercise. Furthermore, the suggestion unit can suggest an optimal menu based on the user's amount of exercise and calorie consumption. This makes it possible to suggest a menu with an appropriate calorie amount based on the user's amount of exercise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's exercise amount data into the generation AI and cause the generation AI to calculate calories.

[0047] During monitoring, the monitoring unit can improve the accuracy of detecting abnormal values ​​by referring to the user's past health data. Examples of past health data include, but are not limited to, medical records, self-reports, and app usage history. For example, the monitoring unit can improve the accuracy of detecting abnormal values ​​based on the user's past health data. The monitoring unit can also analyze patterns of abnormal values ​​by referring to the user's past health data. Furthermore, the monitoring unit can improve the accuracy of predicting abnormal values ​​based on the user's past health data. This improves the accuracy of detecting abnormal values ​​based on the user's past health data. Some or all of the above-described processing in the monitoring unit can be performed using, or without, AI. For example, the monitoring unit can input the user's past health data into the generation AI and cause the generation AI to detect abnormal values.

[0048] During monitoring, the monitoring unit can improve the accuracy of detecting abnormal values ​​by referring to the user's past health data. Examples of past health data include, but are not limited to, medical records, self-reports, and app usage history. For example, the monitoring unit can improve the accuracy of detecting abnormal values ​​based on the user's past health data. The monitoring unit can also analyze patterns of abnormal values ​​by referring to the user's past health data. Furthermore, the monitoring unit can improve the accuracy of predicting abnormal values ​​based on the user's past health data. This improves the accuracy of detecting abnormal values ​​based on the user's past health data. Some or all of the above-described processing in the monitoring unit can be performed using, or without, AI. For example, the monitoring unit can input the user's past health data into the generation AI and cause the generation AI to detect abnormal values.

[0049] During monitoring, the monitoring unit can correct data based on the user's living environment (temperature and humidity). Living environment includes, but is not limited to, for example, temperature, humidity, and residential environment. The monitoring unit corrects the monitoring data based on, for example, the user's living environment. The monitoring unit can also improve the accuracy of the data by taking into account the user's living environment (temperature, humidity, etc.). Furthermore, the monitoring unit can adjust the method of correcting the monitoring data based on the user's living environment. This allows the monitoring data to be corrected and the accuracy to be improved based on the user's living environment. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's living environment data into a generation AI and have the generation AI correct the data.

[0050] During monitoring, the monitoring unit may interpret the data taking into account the user's activity level. Examples of activity levels include, but are not limited to, those obtained from a pedometer, an exercise app, or self-reporting. The monitoring unit may interpret the monitoring data based on, for example, the user's activity level. The monitoring unit may also improve the accuracy of detecting abnormal values ​​by taking the user's activity level into account. Furthermore, the monitoring unit may adjust the method of interpreting the monitoring data based on the user's activity level. This allows the monitoring data to be interpreted based on the user's activity level, thereby improving the accuracy of detecting abnormal values. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the user's activity level data into a generation AI and have the generation AI interpret the data.

[0051] During monitoring, the monitoring unit can monitor region-specific health risks by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS, IP address, and manual user input. The monitoring unit monitors region-specific health risks, for example, based on the user's geographical location information. The monitoring unit can also provide region-specific health risk information based on the user's location information. Furthermore, the monitoring unit can correct the monitoring data by taking into account the user's geographical location information. This allows region-specific health risks to be monitored based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's geographical location data into the generation AI and cause the generation AI to monitor region-specific health risks.

[0052] During monitoring, the monitoring unit can analyze the user's social media activity and monitor related health data. Social media activity includes, but is not limited to, analysis of posted content and the number of followers. For example, the monitoring unit analyzes the user's social media activity and monitors related health data. The monitoring unit can also predict health risks based on the user's social media posts. Furthermore, the monitoring unit can monitor related health data with reference to the activities of the user's friends on social media. This makes it possible to monitor related health data based on the user's social media activity. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media data into a generation AI and cause the generation AI to monitor the health data.

[0053] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, questionnaires, user comments, and usage history. The monitoring unit can improve the monitoring method, for example, based on the user's past feedback. The monitoring unit can also adjust the display method of monitoring data by reflecting the user's feedback. Furthermore, the monitoring unit can improve the accuracy of monitoring based on the user's feedback. This allows the monitoring method to be customized and the accuracy to be improved based on the user's past feedback. Some or all of the above-described processing in the monitoring unit can be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's feedback data into the generation AI and have the generation AI customize the monitoring method.

[0054] When issuing an alert, the alert unit can improve the accuracy of the alert by referring to the user's past health data. Past health data includes, but is not limited to, medical records, self-reports, and app usage history. The alert unit can improve the accuracy of issuing an alert, for example, based on the user's past health data. The alert unit can also analyze abnormal value patterns by referring to the user's past health data. Furthermore, the alert unit can improve the accuracy of alert predictions based on the user's past health data. This improves the accuracy of issuing an alert based on the user's past health data. Some or all of the above-described processing in the alert unit can be performed, for example, using AI or without AI. For example, the alert unit can input the user's past health data into the generation AI and cause the generation AI to improve the accuracy of issuing an alert.

[0055] When issuing an alert, the alert unit can select the optimal notification timing taking into account the user's lifestyle rhythm. Lifestyle rhythms include, but are not limited to, sleep patterns, activity times, meal times, etc. The alert unit selects the optimal alert notification timing, for example, based on the user's lifestyle rhythm. The alert unit can also adjust the alert notification method taking into account the user's lifestyle rhythm. Furthermore, the alert unit can also optimize the timing of issuing an alert based on the user's lifestyle rhythm. This makes it possible to select the optimal alert notification timing based on the user's lifestyle rhythm. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input the user's lifestyle rhythm data into a generation AI and cause the generation AI to select the notification timing.

[0056] When issuing an alert, the alert unit can suggest an appropriate medical institution taking into account the user's medical history. Medical history includes, but is not limited to, medical records, self-reports, and app usage history. The alert unit can, for example, suggest an appropriate medical institution based on the user's medical history. The alert unit can also select an optimal medical institution taking into account the user's medical history. Furthermore, the alert unit can adjust the method of suggesting medical institutions based on the user's medical history. This makes it possible to suggest an appropriate medical institution based on the user's medical history. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input the user's medical history data into the generation AI and cause the generation AI to suggest medical institutions.

[0057] When issuing an alert, the alert unit can suggest regional medical institutions taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS, IP address, and manual user input. The alert unit can suggest regional medical institutions based on, for example, the user's geographical location information. The alert unit can also provide regional medical institution information based on the user's location information. Furthermore, the alert unit can suggest the most appropriate medical institution taking into account the user's geographical location information. This makes it possible to suggest regional medical institutions based on the user's geographical location information. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input the user's geographical location data into the generation AI and cause the generation AI to suggest regional medical institutions.

[0058] When issuing an alert, the alert unit can analyze the user's social media activity and issue a related alert. Social media activity includes, but is not limited to, analysis of post content and follower count. The alert unit, for example, analyzes the user's social media activity and issues a related alert. The alert unit can also predict health risks based on the user's social media posts. Furthermore, the alert unit can also issue a related alert based on the activity of the user's friends on social media. This makes it possible to issue a related alert based on the user's social media activity. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input the user's social media data into a generation AI and cause the generation AI to issue an alert.

[0059] When issuing an alert, the alert unit can customize the alert method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, surveys, user comments, and usage history. The alert unit can improve the alert method, for example, based on the user's past feedback. The alert unit can also adjust the alert notification method by reflecting the user's feedback. Furthermore, the alert unit can also improve the accuracy of issuing alerts based on the user's feedback. This allows the alert method to be customized and the accuracy to be improved based on the user's past feedback. Some or all of the above-described processing in the alert unit may be performed, for example, using AI, or may be performed without using AI. For example, the alert unit can input user feedback data into the generation AI and have the generation AI customize the alert method.

[0060] During monitoring, the monitoring unit can improve the accuracy of the monitoring by referring to the user's past mood and stress level. Examples of past mood and stress levels include, but are not limited to, questionnaires, self-reports, and sensor measurements. The monitoring unit can improve the accuracy of the monitoring based on, for example, the user's past mood and stress level. The monitoring unit can also analyze abnormal value patterns by referring to the user's past mood and stress level. Furthermore, the monitoring unit can improve the accuracy of predicting abnormal values ​​based on the user's past mood and stress level. This allows the accuracy of monitoring to be improved based on the user's past mood and stress level. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past mood and stress level data into the generation AI and cause the generation AI to improve the accuracy of the monitoring.

[0061] During monitoring, the monitoring unit can correct data based on the user's living environment (work and home). Living environments include, but are not limited to, work environment, home environment, and residential environment. The monitoring unit corrects the monitoring data based on, for example, the user's living environment. The monitoring unit can also improve the accuracy of the data by taking into account the user's living environment (work, home, etc.). Furthermore, the monitoring unit can adjust the method of correcting the monitoring data based on the user's living environment. This allows the monitoring data to be corrected and its accuracy improved based on the user's living environment. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's living environment data into a generation AI and have the generation AI correct the data.

[0062] During monitoring, the monitoring unit may interpret the data taking into account the user's activity level. Examples of activity levels include, but are not limited to, pedometers, exercise apps, and self-reports. The monitoring unit may interpret the monitoring data based on, for example, the user's activity level. The monitoring unit may also improve the accuracy of detecting abnormal values ​​by taking the user's activity level into account. Furthermore, the monitoring unit may adjust the method of interpreting the monitoring data based on the user's activity level. This allows the monitoring data to be interpreted based on the user's activity level, thereby improving the accuracy of detecting abnormal values. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the user's activity level data to a generation AI and have the generation AI interpret the data.

[0063] During monitoring, the monitoring unit can monitor region-specific stress factors by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS, IP address, and manual user input. The monitoring unit can monitor region-specific stress factors, for example, based on the user's geographical location information. The monitoring unit can also provide region-specific stress factor information based on the user's location information. Furthermore, the monitoring unit can correct the monitoring data by taking into account the user's geographical location information. This allows region-specific stress factors to be monitored based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's geographical location data to the generation AI and cause the generation AI to monitor region-specific stress factors.

[0064] During monitoring, the monitoring unit can analyze the user's social media activity and monitor related stress factors. Social media activity includes, but is not limited to, analysis of posted content and the number of followers. The monitoring unit, for example, analyzes the user's social media activity and monitors related stress factors. The monitoring unit can also predict stress factors based on the user's social media posts. Furthermore, the monitoring unit can monitor related stress factors with reference to the activities of the user's friends on social media. This makes it possible to monitor related stress factors based on the user's social media activity. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media data into a generation AI and cause the generation AI to monitor stress factors.

[0065] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, questionnaires, user comments, and usage history. The monitoring unit can improve the monitoring method, for example, based on the user's past feedback. The monitoring unit can also adjust the display method of the monitoring data by reflecting the user's feedback. Furthermore, the monitoring unit can improve the accuracy of monitoring based on the user's feedback. This allows the monitoring method to be customized and the accuracy to be improved based on the user's past feedback. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's feedback data into the generation AI and have the generation AI customize the monitoring method.

[0066] When providing support, the support unit can select an optimal support method by referring to the user's past stress level. Examples of past stress levels include, but are not limited to, questionnaires, self-reports, and sensor measurements. The support unit can, for example, suggest an optimal relaxation method based on the user's past stress level. The support unit can also determine the need for counseling by referring to the user's past stress level. Furthermore, the support unit can predict the effectiveness of a support method based on the user's past stress level. This allows the optimal support method to be selected based on the user's past stress level. Some or all of the above-described processing in the support unit may be performed using, or without, AI. For example, the support unit can input the user's past stress level data into the generation AI and have the generation AI select a support method.

[0067] The support unit can customize the support content taking into account the user's living environment (work, home, etc.) when providing support. Examples of living environment include, but are not limited to, a work environment, a home environment, and a residential environment. The support unit can, for example, propose an optimal support method based on the user's living environment. The support unit can also adjust the support content taking into account the user's living environment (work, home, etc.). Furthermore, the support unit can predict the effectiveness of the support method based on the user's living environment. This makes it possible to customize the support content and provide appropriate support based on the user's living environment. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without using AI. For example, the support unit can input the user's living environment data into a generation AI and cause the generation AI to customize the support content.

[0068] The support unit can improve the support method by reflecting user feedback when providing support. Feedback includes, but is not limited to, for example, questionnaires, user comments, and usage history. The support unit can improve the support method, for example, based on user feedback. The support unit can also adjust the support content by reflecting user feedback. Furthermore, the support unit can predict the effectiveness of the support method based on user feedback. This allows the support method to be improved and its effectiveness to be enhanced based on user feedback. Some or all of the above-described processing in the support unit can be performed, for example, using AI or without AI. For example, the support unit can input user feedback data into a generation AI and have the generation AI improve the support method.

[0069] When providing assistance, the assistance unit can propose a region-specific assistance method by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS, IP address, and manual user input. For example, the assistance unit can propose a region-specific relaxation method based on the user's geographical location information. The assistance unit can also propose local counseling services based on the user's location information. Furthermore, the assistance unit can propose an optimal assistance method by taking into account the user's geographical location information. This allows the region-specific assistance method to be proposed based on the user's geographical location information. Some or all of the above-described processing in the assistance unit may be performed using, for example, AI, or may be performed without using AI. For example, the assistance unit can input the user's geographical location data into a generation AI and cause the generation AI to propose a region-specific assistance method.

[0070] During support, the support unit can analyze the user's social media activity and suggest relevant support methods. Examples of social media activity include, but are not limited to, analyzing the content of posts and the number of followers. For example, the support unit can analyze the user's social media activity and suggest relevant relaxation methods. The support unit can also determine the need for counseling based on the content of the user's social media posts. Furthermore, the support unit can suggest relevant support methods by referring to the activities of the user's friends on social media. This allows relevant support methods to be suggested based on the user's social media activity. Some or all of the above-described processing in the support unit can be performed, for example, using AI or without AI. For example, the support unit can input the user's social media data into a generation AI and have the generation AI suggest support methods.

[0071] When providing assistance, the assistance unit can customize the assistance method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, questionnaires, user comments, and usage history. The assistance unit can improve the assistance method, for example, based on the user's past feedback. The assistance unit can also adjust the assistance content by reflecting the user's feedback. Furthermore, the assistance unit can predict the effectiveness of the assistance method based on the user's feedback. This allows the assistance method to be customized and its effectiveness improved based on the user's past feedback. Some or all of the above-described processing in the assistance unit may be performed using, for example, AI, or may be performed without using AI. For example, the assistance unit can input the user's feedback data into a generation AI and have the generation AI customize the assistance method.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] When inputting the user's basic information, the reception unit can improve input efficiency by referring to the user's past input history. For example, input time can be reduced by automatically displaying information previously input by the user as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information that will be used in a specific time period based on the user's past input history. This provides an auto-complete function based on the user's past input history, improving input efficiency.

[0074] The monitoring unit can correct data based on the user's living environment (temperature and humidity). For example, the monitoring data is corrected based on the user's living environment. The accuracy of the data can also be improved by taking the user's living environment (temperature, humidity, etc.) into consideration. Furthermore, the method of correcting the monitoring data can also be adjusted based on the user's living environment. This allows the monitoring data to be corrected based on the user's living environment, improving accuracy.

[0075] The monitoring unit can analyze the user's social media activities and monitor related stress factors. For example, the monitoring unit can analyze the user's social media activities and monitor related stress factors. It can also predict stress factors based on the content of the user's social media posts. It can also monitor related stress factors by referring to the activities of the user's friends on social media. In this way, it is possible to monitor related stress factors based on the user's social media activities.

[0076] When proposing a meal menu, the suggestion unit can adjust the suggestion content by taking into account the season and local specialties. For example, it can suggest a menu using seasonal ingredients. It can also suggest a menu using local specialties. It can also suggest a menu that combines seasonal ingredients and local specialties. This makes it possible to suggest a more appropriate meal menu by taking into account the season and local specialties.

[0077] When issuing an alert, the alert unit can select the optimal notification timing taking into account the user's lifestyle. For example, the optimal alert notification timing is selected based on the user's lifestyle. The alert notification method can also be adjusted taking into account the user's lifestyle. Furthermore, the alert notification timing can be optimized based on the user's lifestyle. This makes it possible to select the optimal alert notification timing based on the user's lifestyle.

[0078] When providing support, the support unit can select the optimal support method by referring to the user's past stress level. For example, the support unit can suggest the optimal relaxation method based on the user's past stress level. The support unit can also refer to the user's past stress level to determine the need for counseling. Furthermore, the support unit can predict the effectiveness of the support method based on the user's past stress level. This makes it possible to select the optimal support method based on the user's past stress level.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The reception unit inputs the user's basic information. This basic information includes name, age, gender, height, weight, etc. The reception unit provides an interface for the user to input the basic information into the application. The basic information can also be acquired automatically using voice input or image recognition. Step 2: The suggestion unit personalizes the meal plan based on the information input by the reception unit. The meal plan includes the number of calories, nutritional balance, meal frequency, etc. The suggestion unit proposes a meal menu that takes nutritional balance into consideration. Step 3: The monitoring unit periodically monitors the user's health data. The health data includes blood pressure, weight, heart rate, blood sugar level, etc. The monitoring unit measures the user's blood pressure every day and records the data. It can also measure the user's weight every week and record the data. Step 4: The alert unit issues an alert if an abnormality is detected by the monitoring unit. The alert includes a notification urging the user to consult a doctor if blood pressure rises sharply. The alert unit sends a notification urging the user to consult a doctor if the user's blood pressure exceeds a certain standard. Step 5: The monitoring unit monitors the user's stress level and mental state. The stress level and mental state include daily mood, stress level, psychological test results, etc. The monitoring unit collects data by having the user input their daily mood into the application. The monitoring unit can also collect data by measuring the user's stress level with a sensor. Step 6: The support unit provides psychological support based on the data monitored by the monitoring unit. The support includes suggestions for relaxation methods and counseling. The support unit suggests relaxation methods if the user's stress level is high. It can also suggest counseling depending on the user's mental state.

[0081] (Example 2) A health management system according to an embodiment of the present invention comprehensively manages a user's basic information, meal plan, health data, stress level, and mental state, and provides alerts and support as needed. In this health management system, a user inputs their basic information (e.g., age, number of weeks pregnant, medical history) into an application. The application then personalizes the user's meal plan and suggests nutritionally balanced meal menus. For example, for pregnant women prone to iron deficiency, recipes using iron-rich ingredients are suggested. Furthermore, the system periodically monitors the user's health data (e.g., blood pressure, weight, heart rate) and issues alerts if an abnormality is detected. For example, if blood pressure suddenly rises, a notification is sent urging the user to consult a doctor. The system also has a function to monitor the user's stress level and mental state. When a user inputs their daily mood and stress level into the application, AI analyzes the data and provides psychological support as needed. For example, if stress levels are high, the system suggests relaxation techniques or counseling. By supporting comprehensive health management for pregnant women in this way, health risks during pregnancy are reduced, providing a safe and secure environment. This allows the health management system to comprehensively manage the user's health status and provide alerts and support as needed. For example, users can understand their own health status in real time and take appropriate measures. In addition, users can know specific areas for improvement in their health status, improving the effectiveness of their health management.

[0082] A health management system according to an embodiment includes a reception unit, a proposal unit, a monitoring unit, an alert unit, a monitoring unit, and a support unit. The reception unit inputs basic information about a user. The basic information includes, but is not limited to, for example, name, age, gender, height, and weight. The reception unit provides, for example, an interface for the user to input the basic information into an application. The reception unit can also automatically acquire the basic information using voice input or image recognition. The proposal unit personalizes a meal plan based on the information input by the reception unit. The meal plan includes, for example, but is not limited to, calorie count, nutritional balance, and meal frequency. The proposal unit proposes, for example, a meal menu that takes nutritional balance into consideration. The monitoring unit regularly monitors the user's health data. The health data includes, for example, but is not limited to, blood pressure, weight, heart rate, and blood sugar level. The monitoring unit, for example, measures the user's blood pressure daily and records the data. The monitoring unit can also measure the user's weight weekly and record the data. The alert unit issues an alert when an abnormality is detected by the monitoring unit. Examples of alerts include, but are not limited to, a notification urging the user to consult a doctor if the user's blood pressure suddenly rises. For example, the alert unit sends a notification urging the user to consult a doctor if the user's blood pressure exceeds a certain threshold. The monitoring unit monitors the user's stress level and mental state. Examples of stress level and mental state include, but are not limited to, daily mood, stress level, and psychological test results. The monitoring unit collects data, for example, by the user inputting their daily mood into an application. The monitoring unit can also measure the user's stress level using a sensor and collect data. The support unit provides psychological support based on the data monitored by the monitoring unit. Examples of support include, but are not limited to, suggestions for relaxation methods and counseling. For example, the support unit suggests relaxation methods when the user's stress is increasing. The support unit can also suggest counseling depending on the user's mental state.As a result, the health management system according to the embodiment can comprehensively manage the user's health condition and provide alerts and support as needed. For example, the user can grasp their own health condition in real time and take appropriate measures. Furthermore, the user can specifically know areas for improvement in their health condition, improving the effectiveness of health management.

[0083] The suggestion unit can suggest a meal menu based on nutritional balance. Nutritional balance includes, for example, the recommended intake amount of each nutrient and the definition of a balanced diet, but is not limited to these examples. For example, the suggestion unit can suggest recipes using ingredients that are high in iron, taking into account the user's nutritional balance. The suggestion unit can also suggest an appropriate meal menu based on the user's calorie intake. Furthermore, the suggestion unit can also suggest a balanced meal menu taking into account the user's meal frequency. This makes it possible to provide the user with a nutritionally balanced meal menu. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's nutritional balance data into the generation AI and cause the generation AI to suggest a meal menu.

[0084] The monitoring unit can periodically monitor the user's blood pressure, weight, heart rate, and other health data. Periodically includes, but is not limited to, daily, weekly, monthly, and other frequencies. For example, the monitoring unit can measure the user's blood pressure daily and record the data. The monitoring unit can also measure the user's weight weekly and record the data. Furthermore, the monitoring unit can measure the user's heart rate monthly and record the data. This allows the user's health data to be periodically monitored and the user's health condition to be understood. Some or all of the above-described processing in the monitoring unit can be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's health data into the generation AI and have the generation AI analyze the data.

[0085] The alert unit can send a notification urging the user to consult a doctor if the user's blood pressure suddenly rises. A sudden rise includes, but is not limited to, the magnitude or rate of increase in blood pressure within a certain period of time. For example, the alert unit can send a notification urging the user to consult a doctor if the user's blood pressure exceeds a certain standard. The alert unit can also send an emergency notification if the user's blood pressure suddenly rises. Furthermore, the alert unit can also send a notification to the user's family or a medical institution if the user's blood pressure suddenly rises. This allows the user to promptly consult a doctor if the user's blood pressure suddenly rises. Some or all of the above-described processing in the alert unit can be performed, for example, using AI or without AI. For example, the alert unit can input the user's blood pressure data into a generation AI and cause the generation AI to detect abnormalities and issue a notification.

[0086] The monitoring unit can monitor the user's daily mood and stress level. Examples of daily mood include, but are not limited to, self-reporting, questionnaires, and sensor measurements. The monitoring unit collects data, for example, by the user inputting their daily mood into an application. The monitoring unit can also measure the user's stress level using a sensor and collect data. Furthermore, the monitoring unit can evaluate the user's mood and stress level using a psychological test and collect data. This makes it possible to monitor the user's daily mood and stress level and understand their psychological state. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's mood data into a generation AI and have the generation AI analyze the data.

[0087] The support unit can suggest relaxation methods or counseling when stress levels are high. Examples of relaxation methods include, but are not limited to, deep breathing, meditation, yoga, etc. Examples of counseling include, but are not limited to, online counseling and face-to-face counseling. For example, the support unit can suggest relaxation methods when the user's stress levels are high. The support unit can also suggest counseling based on the user's mental state. Furthermore, the support unit can suggest appropriate support methods based on the user's stress level. This makes it possible to suggest appropriate relaxation methods or counseling when the user's stress levels are high. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or without AI. For example, the support unit can input the user's stress data into the generation AI and have the generation AI execute the relaxation method or counseling suggestions.

[0088] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is nervous, the reception unit can provide a calming interface to reduce visual stress. If the user is having fun, the reception unit can provide a brightly colored interface to make input tasks more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input tasks easier. This allows the design of the input interface to be adjusted according to the user's emotions and reduce user stress. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the design of the input interface.

[0089] The reception unit can analyze the user's past input history and provide an auto-completion function to improve input efficiency. Examples of input efficiency include, but are not limited to, shortening input time and reducing input errors. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest information that the user will use during a specific time period based on the user's past input history. This can provide an auto-completion function based on the user's past input history and improve input efficiency. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can input the user's input history data to a generation AI and cause the generation AI to provide the auto-completion function.

[0090] The reception unit can automatically acquire basic information using voice input or image recognition when a user inputs information. Voice input includes, but is not limited to, a voice recognition algorithm, a microphone type, etc. Image recognition includes, but is not limited to, face recognition, object recognition, OCR, etc. For example, when a user inputs basic information by voice, the reception unit automatically converts the information into text using voice recognition technology. The reception unit can also extract basic information using image recognition technology by having the user upload an image. Furthermore, the reception unit can combine voice input and image recognition to more efficiently acquire basic information. This allows the basic information to be automatically acquired using voice input or image recognition, thereby streamlining the input process. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice data or image data to a generation AI and have the generation AI acquire the basic information.

[0091] The reception unit can check the consistency of the input content when the user enters it and display a warning if there is a discrepancy. Methods for checking consistency include, but are not limited to, checking against a database or a rule-based check. For example, the reception unit can automatically display a warning if there is a discrepancy in the information entered by the user. The reception unit can also check the information entered by the user in real time to maintain consistency. Furthermore, the reception unit can highlight discrepancies when the user confirms the input content. This allows the consistency of the input content to be checked and a warning to be displayed if there is a discrepancy, thereby encouraging the user to enter accurate information. Some or all of the above-mentioned processing by the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI check the consistency and display a warning.

[0092] The reception unit can estimate the user's emotions and prioritize inputs based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, when the user is stressed, the reception unit prioritizes the input of important information. The reception unit can also prompt the user to input detailed information when the user is relaxed. Furthermore, when the user is in a hurry, the reception unit can minimize the input of information. This allows the input priorities to be determined according to the user's emotions and allows important information to be input preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the input priorities.

[0093] The reception unit may provide an option to input region-specific health information taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS, IP address, and manual user input. The reception unit may provide region-specific health risk information based on the user's current location. The reception unit may also suggest region-specific ingredients and recipes based on the user's geographical location information. The reception unit may also provide information on local medical institutions taking into account the user's location information. This allows region-specific health information to be provided based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's geographical location data into the generation AI and cause the generation AI to provide region-specific health information.

[0094] The reception unit can analyze the user's social media activity and automatically input related basic information. Social media activity includes, but is not limited to, analysis of post content and follower count. For example, the reception unit automatically acquires basic information that the user has made public on social media. The reception unit can also analyze the user's social media activity and input related information. Furthermore, the reception unit can complement the basic information by referring to information about the user's social media friends. This makes it possible to automatically input related basic information based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to input the basic information.

[0095] The reception unit can customize the input interface by reflecting the user's past feedback. Examples of feedback include, but are not limited to, questionnaires, user comments, and usage history. The reception unit can improve the input interface, for example, based on feedback previously provided by the user. The reception unit can also simplify the input procedure by reflecting the user's feedback. Furthermore, the reception unit can adjust the design of the input interface based on the user's feedback. This allows the input interface to be customized and improve usability based on the user's past feedback. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI customize the input interface.

[0096] The suggestion unit can estimate the user's emotions and adjust the meal menu suggestion method based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is feeling stressed, the suggestion unit can suggest a menu using ingredients with a relaxing effect. Furthermore, if the user is relaxed, the suggestion unit can also suggest a nutritionally balanced menu. Furthermore, if the user is in a hurry, the suggestion unit can suggest an easy-to-prepare menu. This allows the meal menu suggestion method to be adjusted according to the user's emotions and provide a more appropriate menu. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the meal menu suggestion method.

[0097] The suggestion unit can customize a meal menu by taking into consideration the user's allergy information and ingredient preferences. Examples of allergy information include, but are not limited to, the user's self-reporting and reference to medical data. Examples of ingredient preferences include, but are not limited to, questionnaires and past meal history. For example, the suggestion unit can suggest a menu that avoids ingredients to which the user is allergic. The suggestion unit can also suggest a menu that uses the user's favorite ingredients to suit the user's preferences. Furthermore, the suggestion unit can combine the user's allergy information and preferences to suggest an optimal menu. This makes it possible to suggest an optimal meal menu based on the user's allergy information and preferences. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's allergy information and preference data into the generation AI and cause the generation AI to customize the meal menu.

[0098] When proposing a meal menu, the suggestion unit can adjust the proposed content taking into account the season and local specialties. Seasons include, but are not limited to, spring, summer, autumn, and winter. Regional specialties include, but are not limited to, a list of regional specialties and seasonal specialties. The suggestion unit, for example, proposes a menu using seasonal ingredients. The suggestion unit can also propose a menu using regional specialties. Furthermore, the suggestion unit can propose a menu that combines seasonal and regional specialties. This allows for proposing a more appropriate meal menu taking into account the season and regional specialties. Some or all of the above-described processing by the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input seasonal and regional specialty data into the generation AI and cause the generation AI to adjust the proposed meal menu.

[0099] When proposing a meal menu, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past meal history. Past meal history includes, but is not limited to, the user's self-reporting and app usage history. For example, the suggestion unit can suggest a menu that takes into account the user's preferences and nutritional balance based on the user's past meal history. The suggestion unit can also re-suggest menus that the user has previously enjoyed. Furthermore, the suggestion unit can analyze the user's past meal history and suggest nutritionally balanced menus. This allows for more accurate meal menu suggestions based on the user's past meal history. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past meal history data into the generation AI and cause the generation AI to suggest meal menus.

[0100] The suggestion unit can estimate the user's emotions and adjust the level of detail of the meal menu based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the suggestion unit can suggest simple recipes when the user is stressed. The suggestion unit can also suggest detailed recipes when the user is relaxed. Furthermore, the suggestion unit can suggest recipes that can be made quickly when the user is in a hurry. This allows the level of detail of the meal menu to be adjusted according to the user's emotions and provide a more appropriate menu. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the level of detail of the meal menu.

[0101] When proposing a meal menu, the suggestion unit can suggest meal timings that match the user's lifestyle rhythm. Life rhythms include, but are not limited to, for example, sleep patterns, activity times, and meal times. For example, the suggestion unit can suggest the timings of breakfast, lunch, and dinner in accordance with the user's lifestyle rhythm. The suggestion unit can also suggest the timing of snacks taking the user's lifestyle rhythm into consideration. Furthermore, the suggestion unit can also suggest optimal meal timings based on the user's lifestyle rhythm. This makes it possible to suggest optimal meal timings based on the user's lifestyle rhythm. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to suggest meal timings.

[0102] When proposing a meal menu, the suggestion unit can suggest a menu that the whole family can enjoy, taking into account the user's family composition. Family composition includes, for example, the number of family members, ages, and food preferences, but is not limited to these examples. For example, the suggestion unit can suggest a menu that the whole family can enjoy, based on the user's family composition. The suggestion unit can also suggest a menu that will satisfy everyone, taking into account the user's family preferences. Furthermore, the suggestion unit can propose an optimal menu by combining the user's family composition and preferences. This makes it possible to suggest a menu that the whole family can enjoy, based on the user's family composition. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's family composition data into the generation AI and cause the generation AI to suggest a menu that the whole family can enjoy.

[0103] When proposing a meal menu, the suggestion unit can calculate calories taking into account the user's amount of exercise. Examples of the amount of exercise include, but are not limited to, a pedometer, an exercise app, or self-reporting. The suggestion unit can, for example, suggest a menu with an appropriate calorie amount based on the user's amount of exercise. The suggestion unit can also suggest a nutritionally balanced menu taking into account the user's amount of exercise. Furthermore, the suggestion unit can suggest an optimal menu based on the user's amount of exercise and calorie consumption. This makes it possible to suggest a menu with an appropriate calorie amount based on the user's amount of exercise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's exercise amount data into the generation AI and cause the generation AI to calculate calories.

[0104] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring data based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is nervous, the monitoring unit can provide a simple, highly visible display method. If the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can also provide a display method that focuses on the main points. This allows the display method of the monitoring data to be adjusted according to the user's emotions and improve visibility. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the monitoring data.

[0105] During monitoring, the monitoring unit can improve the accuracy of detecting abnormal values ​​by referring to the user's past health data. Examples of past health data include, but are not limited to, medical records, self-reports, and app usage history. For example, the monitoring unit can improve the accuracy of detecting abnormal values ​​based on the user's past health data. The monitoring unit can also analyze patterns of abnormal values ​​by referring to the user's past health data. Furthermore, the monitoring unit can improve the accuracy of predicting abnormal values ​​based on the user's past health data. This improves the accuracy of detecting abnormal values ​​based on the user's past health data. Some or all of the above-described processing in the monitoring unit can be performed using, or without, AI. For example, the monitoring unit can input the user's past health data into the generation AI and cause the generation AI to detect abnormal values.

[0106] During monitoring, the monitoring unit can improve the accuracy of detecting abnormal values ​​by referring to the user's past health data. Examples of past health data include, but are not limited to, medical records, self-reports, and app usage history. For example, the monitoring unit can improve the accuracy of detecting abnormal values ​​based on the user's past health data. The monitoring unit can also analyze patterns of abnormal values ​​by referring to the user's past health data. Furthermore, the monitoring unit can improve the accuracy of predicting abnormal values ​​based on the user's past health data. This improves the accuracy of detecting abnormal values ​​based on the user's past health data. Some or all of the above-described processing in the monitoring unit can be performed using, or without, AI. For example, the monitoring unit can input the user's past health data into the generation AI and cause the generation AI to detect abnormal values.

[0107] During monitoring, the monitoring unit can correct data based on the user's living environment (temperature and humidity). Living environment includes, but is not limited to, for example, temperature, humidity, and residential environment. The monitoring unit corrects the monitoring data based on, for example, the user's living environment. The monitoring unit can also improve the accuracy of the data by taking into account the user's living environment (temperature, humidity, etc.). Furthermore, the monitoring unit can adjust the method of correcting the monitoring data based on the user's living environment. This allows the monitoring data to be corrected and the accuracy to be improved based on the user's living environment. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's living environment data into a generation AI and have the generation AI correct the data.

[0108] During monitoring, the monitoring unit may interpret the data taking into account the user's activity level. Examples of activity levels include, but are not limited to, those obtained from a pedometer, an exercise app, or self-reporting. The monitoring unit may interpret the monitoring data based on, for example, the user's activity level. The monitoring unit may also improve the accuracy of detecting abnormal values ​​by taking the user's activity level into account. Furthermore, the monitoring unit may adjust the method of interpreting the monitoring data based on the user's activity level. This allows the monitoring data to be interpreted based on the user's activity level, thereby improving the accuracy of detecting abnormal values. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the user's activity level data into a generation AI and have the generation AI interpret the data.

[0109] The monitoring unit can estimate the user's emotions and prioritize the monitoring data based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, when the user is feeling stressed, the monitoring unit can prioritize and display important data. When the user is relaxed, the monitoring unit can also display detailed data. When the user is in a hurry, the monitoring unit can also display data that focuses on the main points. This allows the prioritization of monitoring data according to the user's emotions and prioritizes the display of important data. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the monitoring data.

[0110] During monitoring, the monitoring unit can monitor region-specific health risks by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS, IP address, and manual user input. The monitoring unit monitors region-specific health risks, for example, based on the user's geographical location information. The monitoring unit can also provide region-specific health risk information based on the user's location information. Furthermore, the monitoring unit can correct the monitoring data by taking into account the user's geographical location information. This allows region-specific health risks to be monitored based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's geographical location data into the generation AI and cause the generation AI to monitor region-specific health risks.

[0111] During monitoring, the monitoring unit can analyze the user's social media activity and monitor related health data. Social media activity includes, but is not limited to, analysis of posted content and the number of followers. For example, the monitoring unit analyzes the user's social media activity and monitors related health data. The monitoring unit can also predict health risks based on the user's social media posts. Furthermore, the monitoring unit can monitor related health data with reference to the activities of the user's friends on social media. This makes it possible to monitor related health data based on the user's social media activity. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media data into a generation AI and cause the generation AI to monitor the health data.

[0112] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, questionnaires, user comments, and usage history. The monitoring unit can improve the monitoring method, for example, based on the user's past feedback. The monitoring unit can also adjust the display method of monitoring data by reflecting the user's feedback. Furthermore, the monitoring unit can improve the accuracy of monitoring based on the user's feedback. This allows the monitoring method to be customized and the accuracy to be improved based on the user's past feedback. Some or all of the above-described processing in the monitoring unit can be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's feedback data into the generation AI and have the generation AI customize the monitoring method.

[0113] The alert unit can estimate the user's emotions and adjust the alert notification method based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is nervous, the alert unit issues an alert with a gentle notification sound. Furthermore, if the user is relaxed, the alert unit can also notify the user of detailed alert content. Furthermore, if the user is in a hurry, the alert unit can also issue a concise alert. This allows the alert notification method to be adjusted according to the user's emotions and provide an appropriate notification. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the alert unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the alert unit can input the user's emotion data into the generation AI and have the generation AI adjust the alert notification method.

[0114] When issuing an alert, the alert unit can improve the accuracy of the alert by referring to the user's past health data. Past health data includes, but is not limited to, medical records, self-reports, and app usage history. The alert unit can improve the accuracy of issuing an alert, for example, based on the user's past health data. The alert unit can also analyze abnormal value patterns by referring to the user's past health data. Furthermore, the alert unit can improve the accuracy of alert predictions based on the user's past health data. This improves the accuracy of issuing an alert based on the user's past health data. Some or all of the above-described processing in the alert unit can be performed, for example, using AI or without AI. For example, the alert unit can input the user's past health data into the generation AI and cause the generation AI to improve the accuracy of issuing an alert.

[0115] When issuing an alert, the alert unit can select the optimal notification timing taking into account the user's lifestyle rhythm. Lifestyle rhythms include, but are not limited to, sleep patterns, activity times, meal times, etc. The alert unit selects the optimal alert notification timing, for example, based on the user's lifestyle rhythm. The alert unit can also adjust the alert notification method taking into account the user's lifestyle rhythm. Furthermore, the alert unit can also optimize the timing of issuing an alert based on the user's lifestyle rhythm. This makes it possible to select the optimal alert notification timing based on the user's lifestyle rhythm. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input the user's lifestyle rhythm data into a generation AI and cause the generation AI to select the notification timing.

[0116] When issuing an alert, the alert unit can suggest an appropriate medical institution taking into account the user's medical history. Medical history includes, but is not limited to, medical records, self-reports, and app usage history. The alert unit can, for example, suggest an appropriate medical institution based on the user's medical history. The alert unit can also select an optimal medical institution taking into account the user's medical history. Furthermore, the alert unit can adjust the method of suggesting medical institutions based on the user's medical history. This makes it possible to suggest an appropriate medical institution based on the user's medical history. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input the user's medical history data into the generation AI and cause the generation AI to suggest medical institutions.

[0117] The alert unit can estimate the user's emotions and determine the priority of alerts based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is feeling stressed, the alert unit can prioritize important alerts. If the user is relaxed, the alert unit can also notify detailed alerts. Furthermore, if the user is in a hurry, the alert unit can also notify alerts that focus on the main points. This allows the priority of alerts to be determined according to the user's emotions and important alerts to be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, an AI. For example, the alert unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of alerts.

[0118] When issuing an alert, the alert unit can suggest regional medical institutions taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS, IP address, and manual user input. The alert unit can suggest regional medical institutions based on, for example, the user's geographical location information. The alert unit can also provide regional medical institution information based on the user's location information. Furthermore, the alert unit can suggest the most appropriate medical institution taking into account the user's geographical location information. This makes it possible to suggest regional medical institutions based on the user's geographical location information. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input the user's geographical location data into the generation AI and cause the generation AI to suggest regional medical institutions.

[0119] When issuing an alert, the alert unit can analyze the user's social media activity and issue a related alert. Social media activity includes, but is not limited to, analysis of post content and follower count. The alert unit, for example, analyzes the user's social media activity and issues a related alert. The alert unit can also predict health risks based on the user's social media posts. Furthermore, the alert unit can also issue a related alert based on the activity of the user's friends on social media. This makes it possible to issue a related alert based on the user's social media activity. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input the user's social media data into a generation AI and cause the generation AI to issue an alert.

[0120] When issuing an alert, the alert unit can customize the alert method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, surveys, user comments, and usage history. The alert unit can improve the alert method, for example, based on the user's past feedback. The alert unit can also adjust the alert notification method by reflecting the user's feedback. Furthermore, the alert unit can also improve the accuracy of issuing alerts based on the user's feedback. This allows the alert method to be customized and the accuracy to be improved based on the user's past feedback. Some or all of the above-described processing in the alert unit may be performed, for example, using AI, or may be performed without using AI. For example, the alert unit can input user feedback data into the generation AI and have the generation AI customize the alert method.

[0121] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring data based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is nervous, the monitoring unit can provide a simple, highly visible display method. If the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can also provide a display method that focuses on the main points. This allows the display method of the monitoring data to be adjusted according to the user's emotions and improve visibility. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the monitoring data.

[0122] During monitoring, the monitoring unit can improve the accuracy of the monitoring by referring to the user's past mood and stress level. Examples of past mood and stress levels include, but are not limited to, questionnaires, self-reports, and sensor measurements. The monitoring unit can improve the accuracy of the monitoring based on, for example, the user's past mood and stress level. The monitoring unit can also analyze abnormal value patterns by referring to the user's past mood and stress level. Furthermore, the monitoring unit can improve the accuracy of predicting abnormal values ​​based on the user's past mood and stress level. This allows the accuracy of monitoring to be improved based on the user's past mood and stress level. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past mood and stress level data into the generation AI and cause the generation AI to improve the accuracy of the monitoring.

[0123] During monitoring, the monitoring unit can correct data based on the user's living environment (work and home). Living environments include, but are not limited to, work environment, home environment, and residential environment. The monitoring unit corrects the monitoring data based on, for example, the user's living environment. The monitoring unit can also improve the accuracy of the data by taking into account the user's living environment (work, home, etc.). Furthermore, the monitoring unit can adjust the method of correcting the monitoring data based on the user's living environment. This allows the monitoring data to be corrected and its accuracy improved based on the user's living environment. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's living environment data into a generation AI and have the generation AI correct the data.

[0124] During monitoring, the monitoring unit may interpret the data taking into account the user's activity level. Examples of activity levels include, but are not limited to, pedometers, exercise apps, and self-reports. The monitoring unit may interpret the monitoring data based on, for example, the user's activity level. The monitoring unit may also improve the accuracy of detecting abnormal values ​​by taking the user's activity level into account. Furthermore, the monitoring unit may adjust the method of interpreting the monitoring data based on the user's activity level. This allows the monitoring data to be interpreted based on the user's activity level, thereby improving the accuracy of detecting abnormal values. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the user's activity level data to a generation AI and have the generation AI interpret the data.

[0125] The monitoring unit can estimate the user's emotions and prioritize the monitoring data based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is feeling stressed, the monitoring unit can prioritize and display important data. The monitoring unit can also display detailed data if the user is relaxed. Furthermore, if the user is in a hurry, the monitoring unit can display data that focuses on the main points. This allows the monitoring data to be prioritized according to the user's emotions and important data to be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the monitoring data.

[0126] During monitoring, the monitoring unit can monitor region-specific stress factors by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS, IP address, and manual user input. The monitoring unit can monitor region-specific stress factors, for example, based on the user's geographical location information. The monitoring unit can also provide region-specific stress factor information based on the user's location information. Furthermore, the monitoring unit can correct the monitoring data by taking into account the user's geographical location information. This allows region-specific stress factors to be monitored based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's geographical location data to the generation AI and cause the generation AI to monitor region-specific stress factors.

[0127] During monitoring, the monitoring unit can analyze the user's social media activity and monitor related stress factors. Social media activity includes, but is not limited to, analysis of posted content and the number of followers. The monitoring unit, for example, analyzes the user's social media activity and monitors related stress factors. The monitoring unit can also predict stress factors based on the user's social media posts. Furthermore, the monitoring unit can monitor related stress factors with reference to the activities of the user's friends on social media. This makes it possible to monitor related stress factors based on the user's social media activity. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media data into a generation AI and cause the generation AI to monitor stress factors.

[0128] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, questionnaires, user comments, and usage history. The monitoring unit can improve the monitoring method, for example, based on the user's past feedback. The monitoring unit can also adjust the display method of the monitoring data by reflecting the user's feedback. Furthermore, the monitoring unit can improve the accuracy of monitoring based on the user's feedback. This allows the monitoring method to be customized and the accuracy to be improved based on the user's past feedback. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's feedback data into the generation AI and have the generation AI customize the monitoring method.

[0129] The support unit can estimate the user's emotions and adjust the support method based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is feeling stressed, the support unit can suggest a relaxation method. If the user is relaxed, the support unit can also suggest counseling. Furthermore, if the user is in a hurry, the support unit can suggest an easy-to-implement support method. This allows the support method to be adjusted according to the user's emotions and provide appropriate support. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's emotion data into the generation AI and have the generation AI adjust the support method.

[0130] When providing support, the support unit can select an optimal support method by referring to the user's past stress level. Examples of past stress levels include, but are not limited to, questionnaires, self-reports, and sensor measurements. The support unit can, for example, suggest an optimal relaxation method based on the user's past stress level. The support unit can also determine the need for counseling by referring to the user's past stress level. Furthermore, the support unit can predict the effectiveness of a support method based on the user's past stress level. This allows the optimal support method to be selected based on the user's past stress level. Some or all of the above-described processing in the support unit may be performed using, or without, AI. For example, the support unit can input the user's past stress level data into the generation AI and have the generation AI select a support method.

[0131] The support unit can customize the support content taking into account the user's living environment (work, home, etc.) when providing support. Examples of living environment include, but are not limited to, a work environment, a home environment, and a residential environment. The support unit can, for example, propose an optimal support method based on the user's living environment. The support unit can also adjust the support content taking into account the user's living environment (work, home, etc.). Furthermore, the support unit can predict the effectiveness of the support method based on the user's living environment. This makes it possible to customize the support content and provide appropriate support based on the user's living environment. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without using AI. For example, the support unit can input the user's living environment data into a generation AI and cause the generation AI to customize the support content.

[0132] The support unit can improve the support method by reflecting user feedback when providing support. Feedback includes, but is not limited to, for example, questionnaires, user comments, and usage history. The support unit can improve the support method, for example, based on user feedback. The support unit can also adjust the support content by reflecting user feedback. Furthermore, the support unit can predict the effectiveness of the support method based on user feedback. This allows the support method to be improved and its effectiveness to be enhanced based on user feedback. Some or all of the above-described processing in the support unit can be performed, for example, using AI or without AI. For example, the support unit can input user feedback data into a generation AI and have the generation AI improve the support method.

[0133] The support unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is feeling stressed, the support unit can prioritize suggesting relaxation methods. Furthermore, if the user is relaxed, the support unit can prioritize suggesting counseling. Furthermore, if the user is in a hurry, the support unit can prioritize suggesting easy-to-implement support methods. This allows support priorities to be determined according to the user's emotions and important support to be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or without AI. For example, the support unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of support.

[0134] When providing assistance, the assistance unit can propose a region-specific assistance method by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS, IP address, and manual user input. For example, the assistance unit can propose a region-specific relaxation method based on the user's geographical location information. The assistance unit can also propose local counseling services based on the user's location information. Furthermore, the assistance unit can propose an optimal assistance method by taking into account the user's geographical location information. This allows the region-specific assistance method to be proposed based on the user's geographical location information. Some or all of the above-described processing in the assistance unit may be performed using, for example, AI, or may be performed without using AI. For example, the assistance unit can input the user's geographical location data into a generation AI and cause the generation AI to propose a region-specific assistance method.

[0135] During support, the support unit can analyze the user's social media activity and suggest relevant support methods. Examples of social media activity include, but are not limited to, analyzing the content of posts and the number of followers. For example, the support unit can analyze the user's social media activity and suggest relevant relaxation methods. The support unit can also determine the need for counseling based on the content of the user's social media posts. Furthermore, the support unit can suggest relevant support methods by referring to the activities of the user's friends on social media. This allows relevant support methods to be suggested based on the user's social media activity. Some or all of the above-described processing in the support unit can be performed, for example, using AI or without AI. For example, the support unit can input the user's social media data into a generation AI and have the generation AI suggest support methods.

[0136] When providing assistance, the assistance unit can customize the assistance method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, questionnaires, user comments, and usage history. The assistance unit can improve the assistance method, for example, based on the user's past feedback. The assistance unit can also adjust the assistance content by reflecting the user's feedback. Furthermore, the assistance unit can predict the effectiveness of the assistance method based on the user's feedback. This allows the assistance method to be customized and its effectiveness improved based on the user's past feedback. Some or all of the above-described processing in the assistance unit may be performed using, for example, AI, or may be performed without using AI. For example, the assistance unit can input the user's feedback data into a generation AI and have the generation AI customize the assistance method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, monitoring unit, alert unit, monitoring unit, and support unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit inputs basic information of the user using the reception device 38 of the smart device 14. The suggestion unit personalizes the meal plan using the specific processing unit 290 of the data processing device 12. The monitoring unit periodically monitors health data using the camera 42 and sensors of the smart device 14. The alert unit issues an alert when an abnormality is detected by the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's stress level and mental state using the reception device 38 of the smart device 14. The support unit provides psychological support using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, monitoring unit, alert unit, monitoring unit, and support unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit inputs basic information of the user using the microphone 238 of the smart glasses 214. The suggestion unit personalizes the meal plan using the specific processing unit 290 of the data processing device 12. The monitoring unit periodically monitors health data using the camera 42 and sensors of the smart glasses 214. The alert unit issues an alert when an abnormality is detected by the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's stress level and mental state using the microphone 238 of the smart glasses 214. The support unit provides psychological support using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, monitoring unit, alert unit, monitoring unit, and support unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit inputs basic information about the user using the microphone 238 of the headset-type terminal 314. The suggestion unit personalizes the meal plan using the specific processing unit 290 of the data processing device 12. The monitoring unit periodically monitors health data using the camera 42 and sensors of the headset-type terminal 314. The alert unit issues an alert when an abnormality is detected by the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's stress level and mental state using the microphone 238 of the headset-type terminal 314. The support unit provides psychological support using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, monitoring unit, alert unit, monitoring unit, and support unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit inputs basic information about the user using the microphone 238 of the robot 414. The suggestion unit personalizes the meal plan using the specific processing unit 290 of the data processing device 12. The monitoring unit periodically monitors health data using the camera 42 and sensors of the robot 414. The alert unit issues an alert when an abnormality is detected by the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the stress level and mental state of the user using the microphone 238 of the robot 414. The support unit provides psychological support using the specific processing unit 290 of the data processing device 12.

[0137] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0138] When inputting the user's basic information, the reception unit can improve input efficiency by referring to the user's past input history. For example, input time can be reduced by automatically displaying information previously input by the user as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information that will be used in a specific time period based on the user's past input history. This provides an auto-complete function based on the user's past input history, improving input efficiency.

[0139] The suggestion unit can estimate the user's emotions and adjust the method of suggesting meal menus based on the estimated user emotions. For example, if the user is feeling stressed, it can suggest a menu using ingredients that have a relaxing effect. Also, if the user is relaxed, it can suggest a nutritionally balanced menu. Furthermore, if the user is in a hurry, it can suggest an easy-to-prepare menu. In this way, it is possible to adjust the method of suggesting meal menus according to the user's emotions and provide more appropriate menus.

[0140] The monitoring unit can correct data based on the user's living environment (temperature and humidity). For example, the monitoring data is corrected based on the user's living environment. The accuracy of the data can also be improved by taking the user's living environment (temperature, humidity, etc.) into consideration. Furthermore, the method of correcting the monitoring data can also be adjusted based on the user's living environment. This allows the monitoring data to be corrected based on the user's living environment, improving accuracy.

[0141] The alert unit can estimate the user's emotions and adjust the alert notification method based on the estimated user's emotions. For example, if the user is nervous, the alert can be issued with a gentle notification sound. If the user is relaxed, the alert can be issued with detailed alert content. Furthermore, if the user is in a hurry, the alert can be issued in a concise manner. This allows the alert notification method to be adjusted according to the user's emotions, making it possible to provide appropriate notifications.

[0142] The monitoring unit can analyze the user's social media activities and monitor related stress factors. For example, the monitoring unit can analyze the user's social media activities and monitor related stress factors. It can also predict stress factors based on the content of the user's social media posts. It can also monitor related stress factors by referring to the activities of the user's friends on social media. In this way, it is possible to monitor related stress factors based on the user's social media activities.

[0143] The support unit can estimate the user's emotions and adjust the support method based on the estimated user emotions. For example, if the user is feeling stressed, it can suggest relaxation methods. If the user is relaxed, it can also suggest counseling. Furthermore, if the user is in a hurry, it can also suggest an easy-to-implement support method. In this way, it is possible to adjust the support method according to the user's emotions and provide appropriate support.

[0144] When proposing a meal menu, the suggestion unit can adjust the suggestion content by taking into account the season and local specialties. For example, it can suggest a menu using seasonal ingredients. It can also suggest a menu using local specialties. It can also suggest a menu that combines seasonal ingredients and local specialties. This makes it possible to suggest a more appropriate meal menu by taking into account the season and local specialties.

[0145] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring data based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the display method of the monitoring data can be adjusted according to the user's emotions, and visibility can be improved.

[0146] When issuing an alert, the alert unit can select the optimal notification timing taking into account the user's lifestyle. For example, the optimal alert notification timing is selected based on the user's lifestyle. The alert notification method can also be adjusted taking into account the user's lifestyle. Furthermore, the alert notification timing can be optimized based on the user's lifestyle. This makes it possible to select the optimal alert notification timing based on the user's lifestyle.

[0147] When providing support, the support unit can select the optimal support method by referring to the user's past stress level. For example, the support unit can suggest the optimal relaxation method based on the user's past stress level. The support unit can also refer to the user's past stress level to determine the need for counseling. Furthermore, the support unit can predict the effectiveness of the support method based on the user's past stress level. This makes it possible to select the optimal support method based on the user's past stress level.

[0148] The processing flow of the second embodiment will be briefly explained below.

[0149] Step 1: The reception unit inputs the user's basic information. This basic information includes name, age, gender, height, weight, etc. The reception unit provides an interface for the user to input the basic information into the application. The basic information can also be acquired automatically using voice input or image recognition. Step 2: The suggestion unit personalizes the meal plan based on the information input by the reception unit. The meal plan includes the number of calories, nutritional balance, meal frequency, etc. The suggestion unit proposes a meal menu that takes nutritional balance into consideration. Step 3: The monitoring unit periodically monitors the user's health data. The health data includes blood pressure, weight, heart rate, blood sugar level, etc. The monitoring unit measures the user's blood pressure every day and records the data. It can also measure the user's weight every week and record the data. Step 4: The alert unit issues an alert if an abnormality is detected by the monitoring unit. The alert includes a notification urging the user to consult a doctor if blood pressure rises sharply. The alert unit sends a notification urging the user to consult a doctor if the user's blood pressure exceeds a certain standard. Step 5: The monitoring unit monitors the user's stress level and mental state. The stress level and mental state include daily mood, stress level, psychological test results, etc. The monitoring unit collects data by having the user input their daily mood into the application. The monitoring unit can also collect data by measuring the user's stress level with a sensor. Step 6: The support unit provides psychological support based on the data monitored by the monitoring unit. The support includes suggestions for relaxation methods and counseling. The support unit suggests relaxation methods if the user's stress level is high. It can also suggest counseling depending on the user's mental state.

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0155] 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.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0164] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0171] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0172] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0187] 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.

[0188] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0189] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0190] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0191] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0192] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0193] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0194] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0195] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0196] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0197] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0198] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0199] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0200] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0201] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0202] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0203] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0204] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0205] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0206] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0207] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0208] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0209] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0210] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0211] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0212] 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.

[0213] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0214] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0215] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0216] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0217] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0218] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0219] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0220] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0221] [Explanation of symbols]

[0222] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting basic information of a user; a suggestion unit that personalizes a meal plan based on the information input by the reception unit; a monitoring unit that monitors health data of a user; an alert unit that issues an alert when an abnormality is detected by the monitoring unit; a monitoring unit for monitoring the stress level and mental state of the user; a support unit that provides psychological support based on the data monitored by the monitoring unit; Equipped with A system characterized by:

2. The proposal unit Proposing nutritionally balanced meal menus 2. The system of claim 1.

3. The monitoring unit Regularly monitor the user's blood pressure, weight, heart rate and other health data 2. The system of claim 1.

4. The alert unit Sends notifications to advise doctor consultation if blood pressure spikes 2. The system of claim 1.

5. The monitoring unit Monitor your daily mood and stress levels 2. The system of claim 1.

6. The support unit Suggest relaxation techniques and counseling if stress levels are high 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and provides an auto-completion function to improve input efficiency.

2. The system of claim 1.

Images