system

Abstract

The system according to this embodiment aims to suggest appropriate rest times and health maintenance measures based on the user's work schedule and health data. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a suggestion unit, a monitoring unit, and a meal suggestion unit. The data collection unit collects the user's work schedule and health data. The analysis unit analyzes the data collected by the data collection unit and suggests appropriate rest times. The suggestion unit suggests relaxation, stretching, and simple exercises based on the results obtained by the analysis unit. The monitoring unit monitors the user's facial expressions. The meal suggestion unit suggests healthy meal timings and contents.

Description

Technical Field

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

Background Art

[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 an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, appropriate suggestions for rest timing and health maintenance based on the user's work schedule and health data have not been sufficiently made, and there is room for improvement.

[0005] The system according to the embodiment aims to make appropriate suggestions for rest timing and health maintenance based on the user's work schedule and health data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, a monitoring unit, and a meal suggestion unit. The data collection unit collects the user's work schedule and health data. The analysis unit analyzes the data collected by the data collection unit and suggests appropriate rest times. The suggestion unit suggests relaxation, stretching, and simple exercises based on the results obtained by the analysis unit. The monitoring unit monitors the user's facial expressions. The meal suggestion unit suggests healthy meal timings and contents. [Effects of the Invention]

[0007] The system according to this embodiment can suggest appropriate rest times and health maintenance measures based on the user's work schedule and health data. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health maintenance support system according to an embodiment of the present invention is a system that uses an AI agent to propose appropriate break times in real time based on work schedules and individual health data, and supports health maintenance by recommending relaxation, stretching, and simple exercises. The health maintenance support system periodically monitors the user's facial expressions, analyzes their health status and fatigue level from changes in their complexion and expressions, and achieves optimal health maintenance by proposing breaks and relaxations individually in real time. The health maintenance support system also proposes healthy meal timings and contents while taking into account the user's busy schedule. For example, it recommends proper home cooking when there is time, and suggests healthy meals that can be prepared in a short time on busy days. For example, the health maintenance support system collects the user's work schedule and health data. This includes information such as the user's working hours, break times, health status, and fatigue level. Next, based on the collected data, the health maintenance support system proposes appropriate break times in real time. For example, it notifies a user who is doing desk work for long hours to take a break after a certain amount of time has passed. It also recommends relaxation, stretching, and simple exercises to support the user in maintaining their health. Furthermore, the health maintenance support system periodically monitors the user's facial expressions. This allows the system to analyze health status and fatigue levels from changes in complexion and facial expressions, and to suggest breaks and relaxation in real time. For example, if a user's complexion worsens or their expression becomes tired, they will be notified to take a break. In this way, the system constantly monitors the user's health status and ensures optimal health maintenance. The health maintenance support system also suggests healthy meal timings and contents, taking into account the user's busy schedule. For example, it recommends proper home cooking when there is time, and suggests healthy meals that can be prepared quickly on busy days. This allows users to eat healthy meals even on busy days and maintain their health. This system supports health maintenance by providing appropriate break timings and healthy meal suggestions for business people who spend long hours at their desks, remote workers, and companies interested in health management.This improves concentration and productivity and reduces health risks. As a result, the health maintenance support system can comprehensively support the user's health.

[0029] The health maintenance support system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, a monitoring unit, and a meal proposal unit. The data collection unit collects the user's work schedule and health data. For example, the data collection unit collects information such as the user's working hours, break times, health status, and fatigue level. For example, the data collection unit records the user's working hours and understands the start and end times of work and break times. The data collection unit can also monitor the user's health status and collect data such as heart rate, blood pressure, and sleep data. For example, the data collection unit uses a wearable device to monitor the user's heart rate in real time and collect data. The analysis unit analyzes the data collected by the data collection unit and proposes appropriate break times. For example, the analysis unit evaluates the user's fatigue level and health status based on the collected data and determines when a break is needed. For example, the analysis unit proposes a break when the user's heart rate exceeds a certain standard. The analysis unit can also analyze the user's working hours and break times patterns and propose the optimal break timing. The suggestion unit proposes relaxation, stretching, and simple exercises based on the results obtained by the analysis unit. For example, the suggestion unit proposes relaxation methods such as deep breathing, meditation, and listening to music to the user. It can also propose stretches to relieve stiff shoulders and stretches to prevent lower back pain. Furthermore, it can propose simple exercises such as light jogging, walking, and squats. The monitoring unit periodically monitors the user's facial expressions and analyzes their health status and fatigue level from changes in complexion and facial expressions. For example, the monitoring unit uses a camera to capture the user's facial expressions and evaluates their health status using facial recognition technology. For example, the monitoring unit proposes a break if the user's complexion becomes poor or their facial expression looks tired. The monitoring unit can also accumulate the user's facial expression data and analyze changes in their health status over the long term. The meal suggestion unit proposes healthy meal timings and contents while taking into account the user's busy schedule. For example, the meal suggestion unit recommends proper home cooking when the user has time and proposes healthy meals that can be prepared quickly on busy days.The meal suggestion unit can, for example, suggest meals containing balanced nutrients or meals that take calorie restrictions into consideration. This allows the health maintenance support system according to the embodiment to comprehensively support the user's health.

[0030] The data collection unit collects users' work schedules and health data. Specifically, it collects information such as users' working hours, break times, health status, and fatigue levels. For example, the data collection unit records users' working hours, tracking start and end times and break times. This allows for a detailed understanding of the user's daily activity patterns. The data collection unit can also monitor users' health status and collect data such as heart rate, blood pressure, and sleep data. For example, the data collection unit uses a wearable device to monitor the user's heart rate in real time and collect data. The wearable device is attached to the user's wrist or chest and continuously collects data such as heart rate, blood pressure, body temperature, and activity level. This data is transmitted to a smartphone or cloud server via Bluetooth® or Wi-Fi and stored in a central database. Furthermore, the data collection unit can use smartphone apps or dedicated sleep trackers to collect users' sleep data. This allows for a detailed understanding of the user's sleep quality and sleep duration, which can be used to assess their health status. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit and proposes appropriate break times. Specifically, based on the collected data, it evaluates the user's fatigue level and health condition and determines when a break is needed. For example, the analysis unit may suggest a break if the user's heart rate exceeds a certain threshold. The analysis unit can also analyze the user's work and break patterns and propose the optimal break timing. The analysis unit uses AI to process data in real time and evaluate the user's health condition. For example, the AI ​​analyzes biometric data such as heart rate, blood pressure, and sleep data to evaluate the user's fatigue level and stress level. Furthermore, the AI ​​can utilize historical data and statistical information to predict long-term changes in health condition and propose appropriate break timings. For example, based on historical data, it can predict fluctuations in fatigue levels during specific time periods or situations and optimize break timing. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The suggestion department proposes relaxation, stretching, and simple exercises based on the results obtained by the analysis department. Specifically, it suggests relaxation methods such as deep breathing, meditation, and listening to music to the user. For example, the suggestion department can also suggest stretches to relieve stiff shoulders or prevent lower back pain. Furthermore, it can also suggest simple exercises such as light jogging, walking, and squats. The suggestion department proposes the most suitable relaxation method and exercise according to the user's health condition and fatigue level. For example, if the user's heart rate is high, it will suggest relaxation methods such as deep breathing and meditation to lower the heart rate. Also, if the user has stiff shoulders or lower back pain, it will suggest stretches to relieve stiff shoulders or prevent lower back pain to loosen muscle tension. Furthermore, if the user's activity level is low, it will suggest simple exercises such as light jogging, walking, and squats to encourage physical activity. The suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, based on feedback from users who have implemented the suggested relaxation methods and exercises, the system revises the suggestions and proposes more effective methods. Furthermore, the suggestion department can provide customized suggestions tailored to the user's preferences and lifestyle. This allows the suggestion department to propose optimal relaxation methods and exercises to users, supporting their health maintenance.

[0033] The monitoring unit periodically monitors the user's facial expressions and analyzes their health status and fatigue level from changes in complexion and facial expressions. Specifically, it uses a camera to capture the user's facial expressions and uses facial recognition technology to evaluate their health status. For example, if the user's complexion worsens or their facial expression becomes fatigued, the monitoring unit will suggest a break. The monitoring unit can also accumulate user facial expression data and analyze long-term changes in health status. The monitoring unit uses AI to analyze facial expression data and evaluate the user's health status. For example, the AI ​​analyzes changes in the user's complexion and facial expressions to evaluate fatigue and stress levels. Furthermore, based on past facial expression data, the AI ​​can predict long-term changes in health status and suggest appropriate break times. For example, based on past data, it can predict fluctuations in fatigue levels at specific times or situations and optimize the timing of breaks. In addition, the monitoring unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue warnings early. As a result, the monitoring unit can not only grasp the situation in real time but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0034] The Meal Suggestion Department proposes healthy meal timings and content while taking into account the user's busy schedule. Specifically, it recommends proper home cooking when the user has time, and proposes healthy meals that can be prepared quickly on busy days. For example, the Meal Suggestion Department can propose meals containing balanced nutrients or meals that take calorie restrictions into consideration. The Meal Suggestion Department proposes the optimal meal content according to the user's health condition and lifestyle. For example, if the user is in good health, it will propose meals containing balanced nutrients to support maintaining good health. If the user wishes to lose weight, it will propose meals that take calorie restrictions into consideration to support healthy weight management. Furthermore, if the user is busy, it will propose healthy meals that can be prepared quickly, so that nutritional balance can be maintained even on busy days. The Meal Suggestion Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, based on feedback from users who have tried the suggested meals, it will revise the suggestions and propose more effective meal content. In addition, the Meal Suggestion Department can provide customized suggestions that take into account the user's preferences and allergy information. In this way, the Meal Suggestion Department can propose the optimal meal content for the user and support the maintenance of their health.

[0035] The data collection unit can collect information such as the user's working hours, break times, health status, and fatigue level. For example, the data collection unit can record the user's working hours and determine the start and end times of work and break times. For example, the data collection unit can monitor the user's health status and collect data such as heart rate, blood pressure, and sleep data. For example, the data collection unit can use a wearable device to monitor the user's heart rate in real time and collect data. By collecting information such as the user's working hours, break times, health status, and fatigue level, analysis and suggestions can be made based on more accurate data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a wearable device into an AI and have the AI ​​perform data analysis.

[0036] The analysis unit can suggest appropriate rest times in real time based on the collected data. For example, the analysis unit can evaluate the user's fatigue level and health condition based on the collected data and determine when a rest is needed. For example, the analysis unit can suggest a rest when the user's heart rate exceeds a certain threshold. The analysis unit can also analyze the user's work hours and rest time patterns and suggest the optimal rest time. In this way, by suggesting appropriate rest times in real time based on the collected data, it supports the maintenance of the user's health. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​suggest appropriate rest times.

[0037] The suggestion unit can suggest relaxation, stretching, and simple exercises. For example, the suggestion unit can suggest relaxation methods such as deep breathing, meditation, and listening to music to the user. The suggestion unit can also suggest stretches to relieve stiff shoulders or stretches to prevent lower back pain. Furthermore, the suggestion unit can suggest simple exercises such as light jogging, walking, and squats. In this way, by suggesting relaxation, stretching, and simple exercises, it supports the user in maintaining their health. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health status into the AI ​​and have the AI ​​perform suggestions for relaxation, stretching, and simple exercises.

[0038] The monitoring unit can periodically monitor the user's facial expressions and analyze their health status and fatigue level from changes in their complexion and facial expressions. For example, the monitoring unit can capture the user's facial expressions using a camera and evaluate their health status using facial recognition technology. For example, the monitoring unit can suggest a break if the user's complexion becomes poor or their facial expression appears tired. The monitoring unit can also accumulate user facial expression data and analyze long-term changes in their health status. This allows the system to periodically monitor the user's facial expressions and analyze their health status and fatigue level from changes in their complexion and facial expressions, thereby suggesting appropriate breaks and relaxation. Some or all of the above-described processes in the monitoring unit may be performed using AI, or they may not. For example, the monitoring unit can input user facial expression data captured by the camera into an AI and have the AI ​​perform the analysis of their health status and fatigue level.

[0039] The meal suggestion unit can propose healthy meal timings and contents while taking into account the user's busy schedule. For example, it can recommend proper home cooking when the user has time, and suggest healthy meals that can be prepared quickly on busy days. The meal suggestion unit can also suggest meals containing balanced nutrients or meals that take calorie restrictions into consideration. In this way, it supports the maintenance of health by suggesting healthy meal timings and contents while taking into account the user's busy schedule. Some or all of the above processing in the meal suggestion unit may be performed using AI, for example, or not using AI. For example, the meal suggestion unit can input data on the user's health status and busyness into the AI ​​and have the AI ​​suggest the optimal meal timing and contents.

[0040] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can concentrate data collection during specific time periods based on the user's past health data. For example, the data collection unit can focus data collection on specific health indicators based on the user's past health data. For example, the data collection unit can analyze the user's past health data and optimize the timing of data collection. This allows the optimal data collection method to be selected by analyzing the user's past health data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into AI and have the AI ​​select the optimal data collection method.

[0041] The data collection unit can filter data based on the user's current activity status and environment during data collection. For example, if the user is exercising, the data collection unit can prioritize collecting data related to exercise. For example, if the user is resting, the data collection unit can collect data related to relaxation. For example, if the user is working, the data collection unit can collect data related to work. This allows for the collection of highly relevant data by filtering the data based on the user's current activity status and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity status and environment data into AI and have the AI ​​perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in the office, the data collection unit can prioritize the collection of work-related data. For example, if the user is at home, the data collection unit can prioritize the collection of relaxation-related data. For example, if the user is out, the data collection unit can prioritize the collection of exercise-related data. By prioritizing the collection of highly relevant data while considering the user's geographical location information, more useful data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, if the user posts about feeling stressed on social media, the data collection unit can collect stress-related data. For example, if the user posts about relaxation, the data collection unit can collect relaxation-related data. For example, if the user posts about exercise, the data collection unit can collect exercise-related data. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit can perform a detailed analysis on data of high importance. For example, the analysis unit can perform a simplified analysis on data of low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data of moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis based on the importance of the data.

[0045] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit can apply a health analysis method to health data. For example, the analysis unit can apply a business analysis method to business data. For example, the analysis unit can apply an emotion analysis method to emotion data. By applying different analysis methods depending on the data category, more accurate analysis results can be obtained. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​perform the application of the appropriate analysis method.

[0046] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may prioritize the most recent data while referring to past data. For example, the analysis unit may focus on analyzing data from a specific period. This allows for the prioritization of analysis based on the data collection period, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into the AI ​​and have the AI ​​determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0048] The suggestion function can adjust the level of detail in its suggestions based on the importance of relaxation, stretching, and exercise. For example, if relaxation is important, the suggestion function can suggest detailed relaxation methods. For example, if stretching is important, the suggestion function can suggest detailed stretching methods. For example, if exercise is important, the suggestion function can suggest detailed exercise methods. By adjusting the level of detail in suggestions based on the importance of relaxation, stretching, and exercise, more effective suggestions become possible. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the importance of relaxation, stretching, and exercise into the AI ​​and have the AI ​​adjust the level of detail in the suggestions.

[0049] The suggestion unit can apply different suggestion algorithms depending on the user's health condition when making suggestions. For example, if the user is tired, the suggestion unit can make suggestions that focus on fatigue recovery. For example, if the user is relaxed, the suggestion unit can make suggestions that focus on relaxation. For example, if the user is stressed, the suggestion unit can make suggestions that focus on stress reduction. By applying different suggestion algorithms according to the user's health condition, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health condition data into AI and have AI execute the application of the suggestion algorithm.

[0050] The suggestion unit can determine the priority of suggestions based on the user's activity status when making suggestions. For example, if the user is exercising, the suggestion unit will prioritize suggestions related to exercise. For example, if the user is resting, the suggestion unit can prioritize suggestions related to relaxation. For example, if the user is working, the suggestion unit can prioritize suggestions related to work. By determining the priority of suggestions based on the user's activity status, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user activity data into AI and have the AI ​​perform the task of determining the priority of suggestions.

[0051] The suggestion unit can adjust the order of suggestions based on the user's past responses when making suggestions. For example, the suggestion unit may prioritize suggestions that the user has previously accepted favorably. For example, the suggestion unit may postpone suggestions that the user has previously rejected. For example, the suggestion unit can dynamically adjust the order of suggestions based on the user's past responses. This allows for more appropriate suggestions by adjusting the order of suggestions based on the user's past responses. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past response data into AI and have the AI ​​perform the adjustment of the suggestion order.

[0052] The monitoring unit can improve the accuracy of monitoring by referring to the user's past facial expression data during monitoring. For example, the monitoring unit accurately analyzes the user's current facial expression based on the user's past facial expression data. For example, the monitoring unit can detect abnormal changes by referring to the user's past facial expression data. For example, the monitoring unit can improve the accuracy of monitoring by analyzing the user's past facial expression data. In this way, the accuracy of monitoring can be improved by referring to the user's past facial expression data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the user's past facial expression data into AI and have AI perform the improvement of monitoring accuracy.

[0053] The monitoring unit can customize its monitoring methods based on the user's health status during monitoring. For example, if the user is tired, the monitoring unit can focus on monitoring fatigue levels. For example, if the user is stressed, the monitoring unit can focus on monitoring stress levels. For example, if the user is relaxed, the monitoring unit can focus on monitoring relaxation levels. By customizing the monitoring methods based on the user's health status, more appropriate monitoring becomes possible. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user health status data into AI and have the AI ​​perform the customization of the monitoring methods.

[0054] The monitoring unit can determine monitoring priorities by considering the user's activity status during monitoring. For example, if the user is exercising, the monitoring unit can prioritize exercise-related monitoring. For example, if the user is resting, the monitoring unit can prioritize relaxation-related monitoring. For example, if the user is working, the monitoring unit can prioritize work-related monitoring. This allows for more appropriate monitoring by determining monitoring priorities by considering the user's activity status. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user activity data into AI and have the AI ​​determine the monitoring priorities.

[0055] The monitoring unit can adjust its monitoring method by referring to the user's past health data during monitoring. For example, the monitoring unit can accurately analyze the user's current health status based on the user's past health data. For example, the monitoring unit can detect abnormal changes by referring to the user's past health data. For example, the monitoring unit can improve its monitoring method by analyzing the user's past health data. This allows for more accurate monitoring by adjusting the monitoring method by referring to the user's past health data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past health data into AI and have AI perform the adjustment of the monitoring method.

[0056] The meal suggestion unit can provide optimal suggestions by referring to the user's past eating history when suggesting meals. For example, the meal suggestion unit can suggest meals using the user's preferred ingredients based on their past eating history. For example, the meal suggestion unit can suggest nutritionally balanced meals based on the user's past eating history. For example, the meal suggestion unit can analyze the user's past eating history and suggest meals tailored to their health condition. This makes it possible to provide more appropriate meal suggestions by referring to the user's past eating history. Some or all of the above processes in the meal suggestion unit may be performed using AI, for example, or without AI. For example, the meal suggestion unit can input the user's past eating history data into AI and have the AI ​​perform optimal meal suggestions.

[0057] The meal suggestion unit can customize its meal suggestion method based on the user's health condition. For example, if the user is tired, the meal suggestion unit can suggest meals that are effective for fatigue recovery. For example, if the user is stressed, the meal suggestion unit can suggest meals that are effective for stress reduction. For example, if the user is relaxed, the meal suggestion unit can suggest meals that are effective for relaxation. By customizing the meal suggestion method based on the user's health condition, more appropriate meal suggestions become possible. Some or all of the above processing in the meal suggestion unit may be performed using AI, for example, or without AI. For example, the meal suggestion unit can input the user's health condition data into AI and have the AI ​​perform the customization of the meal suggestion method.

[0058] The meal suggestion unit can provide optimal suggestions by considering the user's geographical location when suggesting meals. For example, if the user is at home, the meal suggestion unit can suggest meals suitable for home cooking. If the user is out, the meal suggestion unit can suggest healthy meals suitable for eating out. If the user is at the office, the meal suggestion unit can suggest meals that can be easily prepared at the office. This makes it possible to provide more appropriate meal suggestions by considering the user's geographical location. Some or all of the above processing in the meal suggestion unit may be performed using AI, for example, or without AI. For example, the meal suggestion unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the optimal meal suggestion.

[0059] The meal suggestion unit can analyze the user's social media activity and adjust the content of the meal suggestion when making a suggestion. For example, if the user posts about healthy eating on social media, the meal suggestion unit can suggest healthy meals. For example, if the user posts about a specific ingredient on social media, the meal suggestion unit can suggest meals using that ingredient. For example, if the user posts about dieting on social media, the meal suggestion unit can suggest meals suitable for dieting. In this way, analyzing the user's social media activity makes it possible to make more appropriate meal suggestions. Some or all of the above processing in the meal suggestion unit may be performed using AI, for example, or not using AI. For example, the meal suggestion unit can input the user's social media activity data into AI and have the AI ​​adjust the content of the meal suggestion.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] A health maintenance support system can analyze a user's past health data and select the optimal data collection method. For example, it can concentrate data collection during specific time periods based on the user's past health data. Based on the user's past health data, it can focus data collection on specific health indicators. Furthermore, it can analyze the user's past health data and optimize the timing of data collection. In this way, by analyzing the user's past health data, the optimal data collection method can be selected.

[0062] The health maintenance support system can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is in the office, it can prioritize the collection of work-related data. If the user is at home, it can prioritize the collection of relaxation-related data. Furthermore, if the user is out and about, it can prioritize the collection of exercise-related data. By prioritizing the collection of highly relevant data while considering the user's geographical location, the system can collect more useful data.

[0063] The health maintenance support system can analyze users' social media activity and collect relevant data. For example, if a user posts about feeling stressed on social media, it can collect stress-related data. If a user posts about relaxation, it can collect relaxation-related data. Furthermore, if a user posts about exercise, it can collect exercise-related data. In this way, relevant data can be collected by analyzing users' social media activity.

[0064] The health maintenance support system can improve monitoring accuracy by referring to the user's past facial expression data. For example, it can accurately analyze the user's current facial expression based on their past facial expression data. By referring to the user's past facial expression data, it can detect abnormal changes. Furthermore, by analyzing the user's past facial expression data, the accuracy of monitoring can be improved. In this way, by referring to the user's past facial expression data, the accuracy of monitoring can be improved.

[0065] A health maintenance support system can provide optimal suggestions by referring to the user's past eating history. For example, it can suggest meals using the user's preferred ingredients based on their past eating history. It can also suggest nutritionally balanced meals based on the user's past eating history. Furthermore, it can analyze the user's past eating history and suggest meals tailored to their health condition. In this way, referring to the user's past eating history enables more appropriate meal suggestions.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects the user's work schedule and health data. For example, it collects information such as the user's working hours, break times, health status, and fatigue level. The data collection unit records the user's working hours and tracks start and end times, as well as break times. The data collection unit also monitors the user's health status and collects data such as heart rate, blood pressure, and sleep data. For example, it uses a wearable device to monitor the user's heart rate in real time and collect data. Step 2: The analysis unit analyzes the data collected by the data collection unit and proposes appropriate break times. For example, based on the collected data, it evaluates the user's fatigue level and health condition and determines when a break is needed. It proposes a break if the user's heart rate exceeds a certain threshold. It also analyzes the user's work hours and break times patterns and proposes the optimal break times. Step 3: The suggestion unit proposes relaxation, stretching, and simple exercises based on the results obtained by the analysis unit. For example, it may suggest relaxation methods such as deep breathing, meditation, and listening to music to the user. It may also suggest stretches to relieve stiff shoulders, stretches to prevent lower back pain, and simple exercises such as light jogging, walking, and squats. Step 4: The monitoring unit periodically monitors the user's facial expressions and analyzes their health status and fatigue level from changes in complexion and facial expressions. For example, it uses a camera to capture the user's facial expressions and uses facial recognition technology to evaluate their health status. If the user's complexion worsens or their facial expression becomes tired, it suggests they take a break. It also accumulates the user's facial expression data and analyzes long-term changes in their health status. Step 5: The meal suggestion team proposes healthy meal timings and contents while taking into account the user's busy schedule. For example, they recommend proper home cooking when the user has time, and suggest healthy meals that can be prepared quickly on busy days. They also suggest meals that include balanced nutrients and meals that take calorie restrictions into consideration.

[0068] (Example of form 2) The health maintenance support system according to an embodiment of the present invention is a system that uses an AI agent to propose appropriate break times in real time based on work schedules and individual health data, and supports health maintenance by recommending relaxation, stretching, and simple exercises. The health maintenance support system periodically monitors the user's facial expressions, analyzes their health status and fatigue level from changes in their complexion and expressions, and achieves optimal health maintenance by proposing breaks and relaxations individually in real time. The health maintenance support system also proposes healthy meal timings and contents while taking into account the user's busy schedule. For example, it recommends proper home cooking when there is time, and suggests healthy meals that can be prepared in a short time on busy days. For example, the health maintenance support system collects the user's work schedule and health data. This includes information such as the user's working hours, break times, health status, and fatigue level. Next, based on the collected data, the health maintenance support system proposes appropriate break times in real time. For example, it notifies a user who is doing desk work for long hours to take a break after a certain amount of time has passed. It also recommends relaxation, stretching, and simple exercises to support the user in maintaining their health. Furthermore, the health maintenance support system periodically monitors the user's facial expressions. This allows the system to analyze health status and fatigue levels from changes in complexion and facial expressions, and to suggest breaks and relaxation in real time. For example, if a user's complexion worsens or their expression becomes tired, they will be notified to take a break. In this way, the system constantly monitors the user's health status and ensures optimal health maintenance. The health maintenance support system also suggests healthy meal timings and contents, taking into account the user's busy schedule. For example, it recommends proper home cooking when there is time, and suggests healthy meals that can be prepared quickly on busy days. This allows users to eat healthy meals even on busy days and maintain their health. This system supports health maintenance by providing appropriate break timings and healthy meal suggestions for business people who spend long hours at their desks, remote workers, and companies interested in health management.This improves concentration and productivity and reduces health risks. As a result, the health maintenance support system can comprehensively support the user's health.

[0069] The health maintenance support system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, a monitoring unit, and a meal proposal unit. The data collection unit collects the user's work schedule and health data. For example, the data collection unit collects information such as the user's working hours, break times, health status, and fatigue level. For example, the data collection unit records the user's working hours and understands the start and end times of work and break times. The data collection unit can also monitor the user's health status and collect data such as heart rate, blood pressure, and sleep data. For example, the data collection unit uses a wearable device to monitor the user's heart rate in real time and collect data. The analysis unit analyzes the data collected by the data collection unit and proposes appropriate break times. For example, the analysis unit evaluates the user's fatigue level and health status based on the collected data and determines when a break is needed. For example, the analysis unit proposes a break when the user's heart rate exceeds a certain standard. The analysis unit can also analyze the user's working hours and break times patterns and propose the optimal break timing. The suggestion unit proposes relaxation, stretching, and simple exercises based on the results obtained by the analysis unit. For example, the suggestion unit proposes relaxation methods such as deep breathing, meditation, and listening to music to the user. It can also propose stretches to relieve stiff shoulders and stretches to prevent lower back pain. Furthermore, it can propose simple exercises such as light jogging, walking, and squats. The monitoring unit periodically monitors the user's facial expressions and analyzes their health status and fatigue level from changes in complexion and facial expressions. For example, the monitoring unit uses a camera to capture the user's facial expressions and evaluates their health status using facial recognition technology. For example, the monitoring unit proposes a break if the user's complexion becomes poor or their facial expression looks tired. The monitoring unit can also accumulate the user's facial expression data and analyze changes in their health status over the long term. The meal suggestion unit proposes healthy meal timings and contents while taking into account the user's busy schedule. For example, the meal suggestion unit recommends proper home cooking when the user has time and proposes healthy meals that can be prepared quickly on busy days.The meal suggestion unit can, for example, suggest meals containing balanced nutrients or meals that take calorie restrictions into consideration. This allows the health maintenance support system according to the embodiment to comprehensively support the user's health.

[0070] The data collection unit collects users' work schedules and health data. Specifically, it collects information such as users' working hours, break times, health status, and fatigue levels. For example, the data collection unit records users' working hours, tracking start and end times and break times. This allows for a detailed understanding of the user's daily activity patterns. The data collection unit can also monitor users' health status and collect data such as heart rate, blood pressure, and sleep data. For example, the data collection unit uses wearable devices to monitor users' heart rates in real time and collect data. Wearable devices are attached to the user's wrist or chest and continuously collect data such as heart rate, blood pressure, body temperature, and activity level. This data is transmitted via Bluetooth or Wi-Fi to smartphones or cloud servers and stored in a central database. Furthermore, the data collection unit can use smartphone apps or dedicated sleep trackers to collect users' sleep data. This allows for a detailed understanding of the user's sleep quality and sleep duration, which can be used to assess their health status. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0071] The analysis unit analyzes the data collected by the data collection unit and proposes appropriate break times. Specifically, based on the collected data, it evaluates the user's fatigue level and health condition and determines when a break is needed. For example, the analysis unit may suggest a break if the user's heart rate exceeds a certain threshold. The analysis unit can also analyze the user's work and break patterns and propose the optimal break timing. The analysis unit uses AI to process data in real time and evaluate the user's health condition. For example, the AI ​​analyzes biometric data such as heart rate, blood pressure, and sleep data to evaluate the user's fatigue level and stress level. Furthermore, the AI ​​can utilize historical data and statistical information to predict long-term changes in health condition and propose appropriate break timings. For example, based on historical data, it can predict fluctuations in fatigue levels during specific time periods or situations and optimize break timing. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0072] The suggestion department proposes relaxation, stretching, and simple exercises based on the results obtained by the analysis department. Specifically, it suggests relaxation methods such as deep breathing, meditation, and listening to music to the user. For example, the suggestion department can also suggest stretches to relieve stiff shoulders or prevent lower back pain. Furthermore, it can also suggest simple exercises such as light jogging, walking, and squats. The suggestion department proposes the most suitable relaxation method and exercise according to the user's health condition and fatigue level. For example, if the user's heart rate is high, it will suggest relaxation methods such as deep breathing and meditation to lower the heart rate. Also, if the user has stiff shoulders or lower back pain, it will suggest stretches to relieve stiff shoulders or prevent lower back pain to loosen muscle tension. Furthermore, if the user's activity level is low, it will suggest simple exercises such as light jogging, walking, and squats to encourage physical activity. The suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, based on feedback from users who have implemented the suggested relaxation methods and exercises, the system revises the suggestions and proposes more effective methods. Furthermore, the suggestion department can provide customized suggestions tailored to the user's preferences and lifestyle. This allows the suggestion department to propose optimal relaxation methods and exercises to users, supporting their health maintenance.

[0073] The monitoring unit periodically monitors the user's facial expressions and analyzes their health status and fatigue level from changes in complexion and facial expressions. Specifically, it uses a camera to capture the user's facial expressions and uses facial recognition technology to evaluate their health status. For example, if the user's complexion worsens or their facial expression becomes fatigued, the monitoring unit will suggest a break. The monitoring unit can also accumulate user facial expression data and analyze long-term changes in health status. The monitoring unit uses AI to analyze facial expression data and evaluate the user's health status. For example, the AI ​​analyzes changes in the user's complexion and facial expressions to evaluate fatigue and stress levels. Furthermore, based on past facial expression data, the AI ​​can predict long-term changes in health status and suggest appropriate break times. For example, based on past data, it can predict fluctuations in fatigue levels at specific times or situations and optimize the timing of breaks. In addition, the monitoring unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue warnings early. As a result, the monitoring unit can not only grasp the situation in real time but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0074] The Meal Suggestion Department proposes healthy meal timings and content while taking into account the user's busy schedule. Specifically, it recommends proper home cooking when the user has time, and proposes healthy meals that can be prepared quickly on busy days. For example, the Meal Suggestion Department can propose meals containing balanced nutrients or meals that take calorie restrictions into consideration. The Meal Suggestion Department proposes the optimal meal content according to the user's health condition and lifestyle. For example, if the user is in good health, it will propose meals containing balanced nutrients to support maintaining good health. If the user wishes to lose weight, it will propose meals that take calorie restrictions into consideration to support healthy weight management. Furthermore, if the user is busy, it will propose healthy meals that can be prepared quickly, so that nutritional balance can be maintained even on busy days. The Meal Suggestion Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, based on feedback from users who have tried the suggested meals, it will revise the suggestions and propose more effective meal content. In addition, the Meal Suggestion Department can provide customized suggestions that take into account the user's preferences and allergy information. In this way, the Meal Suggestion Department can propose the optimal meal content for the user and support the maintenance of their health.

[0075] The data collection unit can collect information such as the user's working hours, break times, health status, and fatigue level. For example, the data collection unit can record the user's working hours and determine the start and end times of work and break times. For example, the data collection unit can monitor the user's health status and collect data such as heart rate, blood pressure, and sleep data. For example, the data collection unit can use a wearable device to monitor the user's heart rate in real time and collect data. By collecting information such as the user's working hours, break times, health status, and fatigue level, analysis and suggestions can be made based on more accurate data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a wearable device into an AI and have the AI ​​perform data analysis.

[0076] The analysis unit can suggest appropriate rest times in real time based on the collected data. For example, the analysis unit can evaluate the user's fatigue level and health condition based on the collected data and determine when a rest is needed. For example, the analysis unit can suggest a rest when the user's heart rate exceeds a certain threshold. The analysis unit can also analyze the user's work hours and rest time patterns and suggest the optimal rest time. In this way, by suggesting appropriate rest times in real time based on the collected data, it supports the maintenance of the user's health. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​suggest appropriate rest times.

[0077] The suggestion unit can suggest relaxation, stretching, and simple exercises. For example, the suggestion unit can suggest relaxation methods such as deep breathing, meditation, and listening to music to the user. The suggestion unit can also suggest stretches to relieve stiff shoulders or stretches to prevent lower back pain. Furthermore, the suggestion unit can suggest simple exercises such as light jogging, walking, and squats. In this way, by suggesting relaxation, stretching, and simple exercises, it supports the user in maintaining their health. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health status into the AI ​​and have the AI ​​perform suggestions for relaxation, stretching, and simple exercises.

[0078] The monitoring unit can periodically monitor the user's facial expressions and analyze their health status and fatigue level from changes in their complexion and facial expressions. For example, the monitoring unit can capture the user's facial expressions using a camera and evaluate their health status using facial recognition technology. For example, the monitoring unit can suggest a break if the user's complexion becomes poor or their facial expression appears tired. The monitoring unit can also accumulate user facial expression data and analyze long-term changes in their health status. This allows the system to periodically monitor the user's facial expressions and analyze their health status and fatigue level from changes in their complexion and facial expressions, thereby suggesting appropriate breaks and relaxation. Some or all of the above-described processes in the monitoring unit may be performed using AI, or they may not. For example, the monitoring unit can input user facial expression data captured by the camera into an AI and have the AI ​​perform the analysis of their health status and fatigue level.

[0079] The meal suggestion unit can propose healthy meal timings and contents while taking into account the user's busy schedule. For example, it can recommend proper home cooking when the user has time, and suggest healthy meals that can be prepared quickly on busy days. The meal suggestion unit can also suggest meals containing balanced nutrients or meals that take calorie restrictions into consideration. In this way, it supports the maintenance of health by suggesting healthy meal timings and contents while taking into account the user's busy schedule. Some or all of the above processing in the meal suggestion unit may be performed using AI, for example, or not using AI. For example, the meal suggestion unit can input data on the user's health status and busyness into the AI ​​and have the AI ​​suggest the optimal meal timing and contents.

[0080] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can minimize the frequency of data collection to quickly collect the necessary data. This allows for the collection of detailed data while reducing the user's burden by adjusting the frequency of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​adjust the frequency of data collection.

[0081] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can concentrate data collection during specific time periods based on the user's past health data. For example, the data collection unit can focus data collection on specific health indicators based on the user's past health data. For example, the data collection unit can analyze the user's past health data and optimize the timing of data collection. This allows the optimal data collection method to be selected by analyzing the user's past health data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into AI and have the AI ​​select the optimal data collection method.

[0082] The data collection unit can filter data based on the user's current activity status and environment during data collection. For example, if the user is exercising, the data collection unit can prioritize collecting data related to exercise. For example, if the user is resting, the data collection unit can collect data related to relaxation. For example, if the user is working, the data collection unit can collect data related to work. This allows for the collection of highly relevant data by filtering the data based on the user's current activity status and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity status and environment data into AI and have the AI ​​perform data filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting stress-related data. For example, if the user is relaxed, the data collection unit can prioritize collecting relaxation-related data. For example, if the user is tired, the data collection unit can prioritize collecting fatigue-related data. This allows for the priority collection of important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform the data priority determination.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in the office, the data collection unit can prioritize the collection of work-related data. For example, if the user is at home, the data collection unit can prioritize the collection of relaxation-related data. For example, if the user is out, the data collection unit can prioritize the collection of exercise-related data. By prioritizing the collection of highly relevant data while considering the user's geographical location information, more useful data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have AI perform the collection of highly relevant data.

[0085] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, if the user posts about feeling stressed on social media, the data collection unit can collect stress-related data. For example, if the user posts about relaxation, the data collection unit can collect relaxation-related data. For example, if the user posts about exercise, the data collection unit can collect exercise-related data. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is stressed, the analysis unit can apply an analysis algorithm that focuses on stress reduction. For example, if the user is relaxed, the analysis unit can apply an analysis algorithm that focuses on relaxation. For example, if the user is tired, the analysis unit can apply an analysis algorithm that focuses on fatigue recovery. By adjusting the analysis algorithm based on the user's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​perform the adjustment of the analysis algorithm.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit can perform a detailed analysis on data of high importance. For example, the analysis unit can perform a simplified analysis on data of low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data of moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis based on the importance of the data.

[0088] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit can apply a health analysis method to health data. For example, the analysis unit can apply a business analysis method to business data. For example, the analysis unit can apply an emotion analysis method to emotion data. By applying different analysis methods depending on the data category, more accurate analysis results can be obtained. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​perform the application of the appropriate analysis method.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​adjust the display method of the analysis results.

[0090] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may prioritize the most recent data while referring to past data. For example, the analysis unit may focus on analyzing data from a specific period. This allows for the prioritization of analysis based on the data collection period, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into the AI ​​and have the AI ​​determine the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0092] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide suggestions that include detailed information. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the way suggestions are presented.

[0093] The suggestion function can adjust the level of detail in its suggestions based on the importance of relaxation, stretching, and exercise. For example, if relaxation is important, the suggestion function can suggest detailed relaxation methods. For example, if stretching is important, the suggestion function can suggest detailed stretching methods. For example, if exercise is important, the suggestion function can suggest detailed exercise methods. By adjusting the level of detail in suggestions based on the importance of relaxation, stretching, and exercise, more effective suggestions become possible. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the importance of relaxation, stretching, and exercise into the AI ​​and have the AI ​​adjust the level of detail in the suggestions.

[0094] The suggestion unit can apply different suggestion algorithms depending on the user's health condition when making suggestions. For example, if the user is tired, the suggestion unit can make suggestions that focus on fatigue recovery. For example, if the user is relaxed, the suggestion unit can make suggestions that focus on relaxation. For example, if the user is stressed, the suggestion unit can make suggestions that focus on stress reduction. By applying different suggestion algorithms according to the user's health condition, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health condition data into AI and have AI execute the application of the suggestion algorithm.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the length of the suggestions.

[0096] The suggestion unit can determine the priority of suggestions based on the user's activity status when making suggestions. For example, if the user is exercising, the suggestion unit will prioritize suggestions related to exercise. For example, if the user is resting, the suggestion unit can prioritize suggestions related to relaxation. For example, if the user is working, the suggestion unit can prioritize suggestions related to work. By determining the priority of suggestions based on the user's activity status, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user activity data into AI and have the AI ​​perform the task of determining the priority of suggestions.

[0097] The suggestion unit can adjust the order of suggestions based on the user's past responses when making suggestions. For example, the suggestion unit may prioritize suggestions that the user has previously accepted favorably. For example, the suggestion unit may postpone suggestions that the user has previously rejected. For example, the suggestion unit can dynamically adjust the order of suggestions based on the user's past responses. This allows for more appropriate suggestions by adjusting the order of suggestions based on the user's past responses. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past response data into AI and have the AI ​​perform the adjustment of the suggestion order.

[0098] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit can increase the monitoring frequency and collect more detailed data. For example, if the user is relaxed, the monitoring unit can decrease the monitoring frequency to reduce the user's burden. For example, if the user is in a hurry, the monitoring unit can minimize the monitoring frequency and quickly collect the necessary data. This allows for more appropriate monitoring by adjusting the monitoring frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into AI and have the AI ​​adjust the monitoring frequency.

[0099] The monitoring unit can improve the accuracy of monitoring by referring to the user's past facial expression data during monitoring. For example, the monitoring unit accurately analyzes the user's current facial expression based on the user's past facial expression data. For example, the monitoring unit can detect abnormal changes by referring to the user's past facial expression data. For example, the monitoring unit can improve the accuracy of monitoring by analyzing the user's past facial expression data. In this way, the accuracy of monitoring can be improved by referring to the user's past facial expression data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the user's past facial expression data into AI and have AI perform the improvement of monitoring accuracy.

[0100] The monitoring unit can customize its monitoring methods based on the user's health status during monitoring. For example, if the user is tired, the monitoring unit can focus on monitoring fatigue levels. For example, if the user is stressed, the monitoring unit can focus on monitoring stress levels. For example, if the user is relaxed, the monitoring unit can focus on monitoring relaxation levels. By customizing the monitoring methods based on the user's health status, more appropriate monitoring becomes possible. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user health status data into AI and have the AI ​​perform the customization of the monitoring methods.

[0101] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is tense, the monitoring unit can provide a simple and highly visible display method. For example, if the user is relaxed, the monitoring unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the monitoring unit can provide a display method that gets straight to the point. By adjusting the display method of the monitoring results based on the user's emotions, a more user-friendly display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into AI and have the AI ​​adjust the display method of the monitoring results.

[0102] The monitoring unit can determine monitoring priorities by considering the user's activity status during monitoring. For example, if the user is exercising, the monitoring unit can prioritize exercise-related monitoring. For example, if the user is resting, the monitoring unit can prioritize relaxation-related monitoring. For example, if the user is working, the monitoring unit can prioritize work-related monitoring. This allows for more appropriate monitoring by determining monitoring priorities by considering the user's activity status. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user activity data into AI and have the AI ​​determine the monitoring priorities.

[0103] The monitoring unit can adjust its monitoring method by referring to the user's past health data during monitoring. For example, the monitoring unit can accurately analyze the user's current health status based on the user's past health data. For example, the monitoring unit can detect abnormal changes by referring to the user's past health data. For example, the monitoring unit can improve its monitoring method by analyzing the user's past health data. This allows for more accurate monitoring by adjusting the monitoring method by referring to the user's past health data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past health data into AI and have AI perform the adjustment of the monitoring method.

[0104] The meal suggestion unit can estimate the user's emotions and adjust the content of meal suggestions based on the estimated emotions. For example, if the user is feeling stressed, the meal suggestion unit can suggest meals that are effective in reducing stress. For example, if the user is relaxed, the meal suggestion unit can suggest meals that are effective in promoting relaxation. For example, if the user is tired, the meal suggestion unit can suggest meals that are effective in promoting fatigue recovery. By adjusting the content of meal suggestions based on the user's emotions, more appropriate meal suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the meal suggestion unit may be performed using AI, for example, or without AI. For example, the meal suggestion unit can input user emotion data into AI and have the AI ​​adjust the content of meal suggestions.

[0105] The meal suggestion unit can provide optimal suggestions by referring to the user's past eating history when suggesting meals. For example, the meal suggestion unit can suggest meals using the user's preferred ingredients based on their past eating history. For example, the meal suggestion unit can suggest nutritionally balanced meals based on the user's past eating history. For example, the meal suggestion unit can analyze the user's past eating history and suggest meals tailored to their health condition. This makes it possible to provide more appropriate meal suggestions by referring to the user's past eating history. Some or all of the above processes in the meal suggestion unit may be performed using AI, for example, or without AI. For example, the meal suggestion unit can input the user's past eating history data into AI and have the AI ​​perform optimal meal suggestions.

[0106] The meal suggestion unit can customize its meal suggestion method based on the user's health condition. For example, if the user is tired, the meal suggestion unit can suggest meals that are effective for fatigue recovery. For example, if the user is stressed, the meal suggestion unit can suggest meals that are effective for stress reduction. For example, if the user is relaxed, the meal suggestion unit can suggest meals that are effective for relaxation. By customizing the meal suggestion method based on the user's health condition, more appropriate meal suggestions become possible. Some or all of the above processing in the meal suggestion unit may be performed using AI, for example, or without AI. For example, the meal suggestion unit can input the user's health condition data into AI and have the AI ​​perform the customization of the meal suggestion method.

[0107] The meal suggestion unit can estimate the user's emotions and determine the priority of meal suggestions based on the estimated emotions. For example, if the user is feeling stressed, the meal suggestion unit can prioritize suggesting meals that are effective in reducing stress. For example, if the user is relaxed, the meal suggestion unit can prioritize suggesting meals that are effective in promoting relaxation. For example, if the user is tired, the meal suggestion unit can prioritize suggesting meals that are effective in relieving fatigue. By determining the priority of meal suggestions based on the user's emotions, more appropriate meal suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the meal suggestion unit may be performed using AI, for example, or not using AI. For example, the meal suggestion unit can input user emotion data into AI and have the AI ​​determine the priority of meal suggestions.

[0108] The meal suggestion unit can provide optimal suggestions by considering the user's geographical location when suggesting meals. For example, if the user is at home, the meal suggestion unit can suggest meals suitable for home cooking. If the user is out, the meal suggestion unit can suggest healthy meals suitable for eating out. If the user is at the office, the meal suggestion unit can suggest meals that can be easily prepared at the office. This makes it possible to provide more appropriate meal suggestions by considering the user's geographical location. Some or all of the above processing in the meal suggestion unit may be performed using AI, for example, or without AI. For example, the meal suggestion unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the optimal meal suggestion.

[0109] The meal suggestion unit can analyze the user's social media activity and adjust the content of the meal suggestion when making a suggestion. For example, if the user posts about healthy eating on social media, the meal suggestion unit can suggest healthy meals. For example, if the user posts about a specific ingredient on social media, the meal suggestion unit can suggest meals using that ingredient. For example, if the user posts about dieting on social media, the meal suggestion unit can suggest meals suitable for dieting. In this way, analyzing the user's social media activity makes it possible to make more appropriate meal suggestions. Some or all of the above processing in the meal suggestion unit may be performed using AI, for example, or not using AI. For example, the meal suggestion unit can input the user's social media activity data into AI and have the AI ​​adjust the content of the meal suggestion.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The health maintenance support system can estimate the user's emotions and customize its suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest relaxation methods that are effective in reducing stress. If the user is relaxed, it can suggest ways to further deepen their relaxation. Furthermore, if the user is tired, it can suggest stretches or exercises that are effective in relieving fatigue. This allows for optimal suggestions tailored to the user's emotions, providing more effective support for maintaining health.

[0112] A health maintenance support system can analyze a user's past health data and select the optimal data collection method. For example, it can concentrate data collection during specific time periods based on the user's past health data. Based on the user's past health data, it can focus data collection on specific health indicators. Furthermore, it can analyze the user's past health data and optimize the timing of data collection. In this way, by analyzing the user's past health data, the optimal data collection method can be selected.

[0113] The health maintenance support system can estimate the user's emotions and adjust its analysis algorithm based on those emotions. For example, if the user is stressed, an analysis algorithm focused on stress reduction can be applied. If the user is relaxed, an analysis algorithm focused on relaxation can be applied. Furthermore, if the user is tired, an analysis algorithm focused on fatigue recovery can be applied. This allows for more appropriate analysis results by adjusting the analysis algorithm based on the user's emotions.

[0114] The health maintenance support system can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is in the office, it can prioritize the collection of work-related data. If the user is at home, it can prioritize the collection of relaxation-related data. Furthermore, if the user is out and about, it can prioritize the collection of exercise-related data. By prioritizing the collection of highly relevant data while considering the user's geographical location, the system can collect more useful data.

[0115] The health maintenance support system can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, it can provide simple and highly visible suggestions. If the user is relaxed, it can provide suggestions that include detailed information. If the user is in a hurry, it can provide concise suggestions. By adjusting the way suggestions are presented based on the user's emotions, the system can provide more appropriate suggestions.

[0116] The health maintenance support system can analyze users' social media activity and collect relevant data. For example, if a user posts about feeling stressed on social media, it can collect stress-related data. If a user posts about relaxation, it can collect relaxation-related data. Furthermore, if a user posts about exercise, it can collect exercise-related data. In this way, relevant data can be collected by analyzing users' social media activity.

[0117] The health maintenance support system can estimate the user's emotions and adjust the monitoring frequency based on those estimates. For example, if the user is stressed, the monitoring frequency can be increased to collect more detailed data. If the user is relaxed, the monitoring frequency can be decreased to reduce the user's burden. Also, if the user is in a hurry, the monitoring frequency can be minimized to quickly collect the necessary data. By adjusting the monitoring frequency based on the user's emotions, more appropriate monitoring becomes possible.

[0118] The health maintenance support system can improve monitoring accuracy by referring to the user's past facial expression data. For example, it can accurately analyze the user's current facial expression based on their past facial expression data. By referring to the user's past facial expression data, it can detect abnormal changes. Furthermore, by analyzing the user's past facial expression data, the accuracy of monitoring can be improved. In this way, by referring to the user's past facial expression data, the accuracy of monitoring can be improved.

[0119] The health maintenance support system can estimate the user's emotions and adjust the display method of monitoring results based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-read display. If the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can provide a display method that focuses on the essentials. By adjusting the display method of monitoring results based on the user's emotions, a more user-friendly display becomes possible.

[0120] A health maintenance support system can provide optimal suggestions by referring to the user's past eating history. For example, it can suggest meals using the user's preferred ingredients based on their past eating history. It can also suggest nutritionally balanced meals based on the user's past eating history. Furthermore, it can analyze the user's past eating history and suggest meals tailored to their health condition. In this way, referring to the user's past eating history enables more appropriate meal suggestions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects the user's work schedule and health data. For example, it collects information such as the user's working hours, break times, health status, and fatigue level. The data collection unit records the user's working hours and tracks start and end times, as well as break times. The data collection unit also monitors the user's health status and collects data such as heart rate, blood pressure, and sleep data. For example, it uses a wearable device to monitor the user's heart rate in real time and collect data. Step 2: The analysis unit analyzes the data collected by the data collection unit and proposes appropriate break times. For example, based on the collected data, it evaluates the user's fatigue level and health condition and determines when a break is needed. It proposes a break if the user's heart rate exceeds a certain threshold. It also analyzes the user's work hours and break times patterns and proposes the optimal break times. Step 3: The suggestion unit proposes relaxation, stretching, and simple exercises based on the results obtained by the analysis unit. For example, it may suggest relaxation methods such as deep breathing, meditation, and listening to music to the user. It may also suggest stretches to relieve stiff shoulders, stretches to prevent lower back pain, and simple exercises such as light jogging, walking, and squats. Step 4: The monitoring unit periodically monitors the user's facial expressions and analyzes their health status and fatigue level from changes in complexion and facial expressions. For example, it uses a camera to capture the user's facial expressions and uses facial recognition technology to evaluate their health status. If the user's complexion worsens or their facial expression becomes tired, it suggests they take a break. It also accumulates the user's facial expression data and analyzes long-term changes in their health status. Step 5: The meal suggestion team proposes healthy meal timings and contents while taking into account the user's busy schedule. For example, they recommend proper home cooking when the user has time, and suggest healthy meals that can be prepared quickly on busy days. They also suggest meals that include balanced nutrients and meals that take calorie restrictions into consideration.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, monitoring unit, and meal proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects the user's work schedule and health data using sensors or wearable devices of the smart device 14. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 and proposes appropriate rest times. The proposal unit proposes relaxation, stretching, and simple exercises using, for example, the control unit 46A of the smart device 14. The monitoring unit periodically monitors the user's facial expressions using, for example, the camera 42 of the smart device 14 and analyzes their health status and fatigue level. The meal proposal unit proposes healthy meal timings and contents that take into account the user's busy schedule using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, monitoring unit, and meal proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's work schedule and health data using sensors and wearable devices of the smart glasses 214. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 and proposes appropriate rest times. The proposal unit proposes relaxation, stretching, and simple exercises using, for example, the control unit 46A of the smart glasses 214. The monitoring unit periodically monitors the user's facial expressions using, for example, the camera 42 of the smart glasses 214 and analyzes their health status and fatigue level. The meal proposal unit proposes healthy meal timings and contents that take into account the user's busy schedule using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, monitoring unit, and meal proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects the user's work schedule and health data using sensors and wearable devices of the headset terminal 314. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 and proposes appropriate rest times. The proposal unit suggests relaxation, stretching, and simple exercises using, for example, the control unit 46A of the headset terminal 314. The monitoring unit periodically monitors the user's facial expressions using, for example, the camera 42 of the headset terminal 314 and analyzes their health status and fatigue level. The meal proposal unit suggests healthy meal timings and contents that take into account the user's busy schedule using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, monitoring unit, and meal proposal unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects the user's work schedule and health data using sensors and wearable devices of the robot 414. The analysis unit analyzes the collected data, for example, by the specific processing unit 290 of the data processing unit 12, and proposes appropriate rest times. The proposal unit proposes relaxation, stretching, and simple exercises, for example, by the control unit 46A of the robot 414. The monitoring unit periodically monitors the user's facial expressions using the camera 42 of the robot 414 and analyzes their health status and fatigue level. The meal proposal unit proposes healthy meal timings and contents that take into account the user's busy schedule, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The data collection unit collects users' work schedules and health data, An analysis unit analyzes the data collected by the aforementioned collection unit and proposes an appropriate rest timing, Based on the results obtained by the analysis unit, the proposal unit suggests relaxation, stretching, and simple exercises. A monitoring unit that monitors the user's facial expressions, It includes a meal planning department that proposes healthy meal timings and contents. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information such as users' working hours, break times, health status, and fatigue levels. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, the system suggests appropriate break times in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We suggest relaxation, stretching, and simple exercises. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, The system periodically monitors the user's facial expressions and analyzes their health status and fatigue level based on changes in their complexion and facial expressions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned meal proposal department, We suggest healthy meal timings and contents while taking into consideration the user's busy schedule. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of relaxation, stretching, and exercise. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on the user's activity level. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the user's past responses. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, the system improves monitoring accuracy by referencing the user's past facial expression data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, During monitoring, the monitoring method is customized based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, During monitoring, the monitoring priority is determined by considering the user's activity status. The system described in Appendix 1, characterized by the features described herein. (Note 30) The monitoring unit, During monitoring, the monitoring method is adjusted by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned meal proposal department, The system estimates the user's emotions and adjusts the meal suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned meal proposal department, When suggesting meals, the system provides optimal suggestions by referencing the user's past meal history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned meal proposal department, When suggesting meals, the method of suggesting meals is customized based on the user's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned meal proposal department, The system estimates the user's emotions and prioritizes meal suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned meal proposal department, When suggesting meals, the system provides optimal suggestions by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned meal proposal department, When suggesting meals, we analyze the user's social media activity and adjust the content of the meal suggestions accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection unit collects users' work schedules and health data, An analysis unit analyzes the data collected by the aforementioned collection unit and proposes an appropriate rest timing, Based on the results obtained by the analysis unit, the proposal unit suggests relaxation, stretching, and simple exercises. A monitoring unit that monitors the user's facial expressions, It includes a meal planning department that proposes healthy meal timings and contents. A system characterized by the following features.

2. The aforementioned collection unit is Collect information such as users' working hours, break times, health status, and fatigue levels. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, the system suggests appropriate break times in real time. The system according to feature 1.

4. The aforementioned proposal section is, We suggest relaxation, stretching, and simple exercises. The system according to feature 1.

5. The monitoring unit, The system periodically monitors the user's facial expressions and analyzes their health status and fatigue level based on changes in their complexion and facial expressions. The system according to feature 1.

6. The aforementioned meal proposal department, We suggest healthy meal timings and contents while taking into consideration the user's busy schedule. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system according to feature 1.

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