system

Abstract

The system according to this embodiment aims to propose health promotion menus based on employees' biometric information and to provide incentives to employees who implement them. [Solution] The system according to the embodiment comprises a recording unit, an analysis unit, a proposal unit, and an incentive unit. The recording unit records biological information. The analysis unit analyzes the biological information recorded by the recording unit. The proposal unit proposes health promotion menus based on the analysis results obtained by the analysis unit. The incentive unit provides incentives to employees who implement the menus proposed by the proposal unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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[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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, effective measures against insufficient exercise, increased body fat, and decreased physical strength of desk workers and home workers have not been fully taken, and there is room for improvement. <00,00027> The system according to the embodiment aims to propose a health promotion menu based on the biometric information of employees and give incentives to the employees who implement it.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recording unit, an analysis unit, a proposal unit, and an incentive unit. The recording unit records biological information. The analysis unit analyzes the biological information recorded by the recording unit. The proposal unit proposes health promotion menus based on the analysis results obtained by the analysis unit. The incentive unit provides incentives to employees who implement the menus proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose health promotion menus based on employees' biometric information and provide incentives to employees who implement them. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is a system for maintaining employee health and improving productivity. This health management system distributes smart devices to all employees free of charge and records their biometric information. Next, the health management system uses AI and IoT to analyze the biometric information, and the AI ​​generates and proposes an optimal health promotion menu for each employee. Furthermore, the health management system awards points as an incentive to employees who implement the proposed menu. Through this mechanism, employee health is maintained and productivity is improved. For example, the health management system can be linked with health checkup results and attendance management systems to enable more accurate health management. In addition, the health management system's AI, equipped with an emotion generation engine, recognizes employees' stress levels from emails, chats, facial expressions, etc., and proposes appropriate stress management methods. As a result, the health management system can maintain employee health and improve productivity.

[0029] The health management system according to this embodiment comprises a recording unit, an analysis unit, a proposal unit, and an incentive unit. The recording unit records biometric information. The recording unit can record biometric information such as heart rate, blood pressure, and body temperature. The recording unit acquires biometric information using a smart device and stores it in a database. The analysis unit analyzes the biometric information recorded by the recording unit. The analysis unit analyzes the biometric information using a data analysis algorithm, for example, and evaluates the health status. The analysis unit can also analyze the biometric information using AI and predict health risks. The proposal unit proposes health promotion menus based on the analysis results obtained by the analysis unit. The proposal unit proposes health promotion menus such as exercise programs and meal plans, for example. The proposal unit can also propose the most suitable health promotion menu for each employee using generative AI. The incentive unit provides incentives to employees who implement the menus proposed by the proposal unit. The incentive unit provides incentives such as rewards, benefits, and points, for example. The incentive unit can also adjust the type of incentive and the criteria for granting it using AI. As a result, the health management system according to this embodiment can maintain employee health and improve productivity through recording, analyzing, suggesting, and providing incentives for biometric information.

[0030] The recording unit records biometric information. For example, it can record biometric information such as heart rate, blood pressure, and body temperature. The recording unit acquires biometric information using smart devices and stores it in a database. Specifically, it uses wearable devices such as smartwatches and fitness trackers to acquire detailed biometric information in real time, including heart rate, blood pressure, body temperature, sleep patterns, and activity levels. These devices connect to smartphones and tablets via Bluetooth® or Wi-Fi and transmit the acquired data to a dedicated application. The application automatically saves the data to a cloud-based database, allowing users to access it at any time. Furthermore, the recording unit can also incorporate regular health checkups and test results from medical institutions. For example, it can acquire results from regular health checkups, blood test results, and electrocardiogram data from hospitals from electronic medical record systems and manage them comprehensively. This allows for a comprehensive understanding of the user's health status. The recording unit also prioritizes data security; acquired biometric information is encrypted, and mechanisms are in place to prevent unauthorized access by third parties. This allows users to record and manage their health data with peace of mind.

[0031] The analysis unit analyzes biometric information recorded by the recording unit. For example, the analysis unit uses data analysis algorithms to analyze biometric information and assess the user's health status. Specifically, it analyzes data such as heart rate, blood pressure, and body temperature in time series to detect anomalies and trends. For instance, it can detect sudden increases in heart rate or abnormal fluctuations in blood pressure, aiding in the early detection of health risks. The analysis unit can also use AI to analyze biometric information and predict health risks. The AI ​​learns from past data using machine learning algorithms to predict future health risks. For example, it can extract specific patterns from past data to predict risks of heart disease, hypertension, diabetes, and other conditions. Furthermore, the analysis unit also considers the user's lifestyle and environmental factors during its analysis. For example, it integrates information such as the user's exercise habits, diet, and stress levels to perform a more accurate health assessment. This allows the analysis unit to comprehensively evaluate the user's health status and identify individual health risks. The analysis results are provided to the user in a visually easy-to-understand format, enabling them to intuitively understand changes in their health status and risk factors.

[0032] The Proposal Department proposes health promotion menus based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes health promotion menus such as exercise programs and meal plans. Specifically, it creates individualized exercise programs tailored to the user's health condition and lifestyle, and proposes daily exercise volume and types. For example, based on heart rate and activity data, it proposes specific exercise menus such as walking, jogging, and strength training. Regarding meal plans, it proposes balanced meal menus tailored to the user's nutritional status and health goals. For example, it proposes specific breakfast, lunch, and dinner menus considering calorie intake and nutritional balance, and also advises on recipes and ingredient selection. The Proposal Department can also use generative AI to propose optimal health promotion menus for each employee. The generative AI generates optimal exercise programs and meal plans based on the user's past data and current health condition, providing individually customized proposals. For example, the generative AI proposes meal menus considering the user's preferences and allergy information, and adjusts exercise programs according to the user's physical fitness and goals. This allows the Proposal Department to provide users with easy-to-follow and effective health promotion menus.

[0033] The Incentives Department provides incentives to employees who implement the programs proposed by the Proposal Department. These incentives include rewards, benefits, and points. Specifically, when users complete a proposed exercise program or meal plan, they are awarded points based on their performance. Once a certain number of points are accumulated, they can be exchanged for rewards or benefits. For example, points are awarded for daily exercise or consistent implementation of a proposed meal plan, and these accumulated points can be exchanged for gift cards, vouchers, health-related goods, and more. The Incentives Department can also use AI to adjust the types and criteria for awarding incentives. The AI ​​analyzes user motivation and behavioral patterns to provide optimal incentives. For example, it predicts which incentives are most effective based on the user's past behavioral data and provides individually customized incentives. This allows the Incentives Department to increase user motivation and encourage the implementation of health-promoting programs. Furthermore, the Incentives Department collects user feedback to improve the incentive system. For example, it reviews the content and criteria for awarding incentives based on user opinions and requests to build a more effective system. This allows the incentive department to support the improvement of users' health and maximize the overall effectiveness of the system.

[0034] The health management system includes a linking unit that connects with health checkup results and attendance management systems. This linking unit synchronizes data with the health checkup results and attendance management systems. For example, the linking unit stores health checkup results in a database, and the analysis unit uses this data for analysis. The linking unit can also connect with the attendance management system to understand employees' work status. As a result, the health management system, by linking with health checkup results and attendance management systems, enables more accurate health management.

[0035] The health management system includes an emotion recognition unit equipped with an emotion generation engine. The emotion recognition unit uses the emotion generation engine to recognize employees' emotions. For example, the emotion recognition unit can recognize employees' stress levels from emails, chats, facial expressions, etc. The emotion recognition unit can also use AI to recognize emotions and evaluate stress levels. Therefore, by incorporating the emotion generation engine, the health management system can recognize employees' stress levels and propose appropriate stress management methods.

[0036] The health management system includes a stress management department that proposes stress management methods. This department assesses employees' stress levels and proposes appropriate stress management methods. For example, it might suggest relaxation techniques or counseling methods. The stress management department can also use AI to propose stress management methods. As a result, the health management system can reduce employee stress and maintain their health by proposing stress management methods.

[0037] The recording unit can record biometric information such as heart rate, ECG, blood oxygen saturation, sleep tracking, activity level, body temperature, stress level, and blood pressure from smart devices. For example, the recording unit can record heart rate and ECG using a smartwatch. It can also record activity level and sleep tracking using a fitness tracker. Furthermore, the recording unit can record blood oxygen saturation and body temperature using a smart device. As a result, the recording unit enables comprehensive health management by recording diverse biometric information from smart devices.

[0038] The proposal department can use generative AI to suggest optimal health promotion menus for each employee. For example, the proposal department can use generative AI to suggest meal menus. The proposal department can also use generative AI to suggest exercise menus. Furthermore, the proposal department can use generative AI to suggest lifestyle improvement menus. In this way, the proposal department can use generative AI to suggest optimal health promotion menus for each employee.

[0039] The recording unit can select the optimal recording method by referring to the user's past health data during recording. For example, the recording unit can refer to the user's past heart rate data and prioritize recording an electrocardiogram if an abnormality is detected. The recording unit can also refer to the user's past sleep data and enhance sleep tracking if sleep quality is poor. Furthermore, the recording unit can refer to the user's past activity level data and increase activity level recording if there is a prolonged lack of exercise. In this way, the recording unit can select the optimal recording method by referring to past health data and improve the accuracy of health management.

[0040] The recording unit can select the types of biometric data to record based on the user's current activity level. For example, if the user is exercising, the recording unit will prioritize recording heart rate and activity level. If the user is resting, the recording unit can also prioritize recording blood oxygen saturation and body temperature. Furthermore, if the user is sleeping, the recording unit can prioritize sleep tracking and heart rate recording. This allows the recording unit to select the types of biometric data based on the user's current activity level, enabling more appropriate health management.

[0041] The recording unit can prioritize recording highly relevant biometric information by considering the user's geographical location during recording. For example, if the user is at high altitude, the recording unit will prioritize recording blood oxygen saturation. If the user is in a cold region, the recording unit can also prioritize recording body temperature. Furthermore, if the user is at an exercise facility, the recording unit can prioritize recording activity level and heart rate. In this way, by considering geographical location information, the recording unit can prioritize recording highly relevant biometric information and improve the accuracy of health management.

[0042] The recording unit can analyze the user's social media activity and record relevant biometric information during recording. For example, if the user posts about feeling stressed, the recording unit will prioritize recording heart rate and stress level. If the user posts about exercise, the recording unit can also prioritize recording activity level and heart rate. Furthermore, if the user posts about sleep, the recording unit can prioritize sleep tracking. In this way, the recording unit can improve the accuracy of health management by recording relevant biometric information through analysis of social media activity.

[0043] The analysis unit can improve the accuracy of its analysis by referring to past analysis data during the analysis process. For example, the analysis unit can refer to the user's past heart rate data and enhance the electrocardiogram analysis if an abnormality is detected. The analysis unit can also refer to the user's past sleep data and enhance the sleep data analysis if the sleep quality is poor. Furthermore, the analysis unit can refer to the user's past activity level data and enhance the activity level data analysis if there is a prolonged lack of exercise. In this way, the analysis unit can improve the accuracy of its analysis by referring to past analysis data.

[0044] The analysis unit can perform analysis while taking into account the user's health check results. For example, the analysis unit can enhance the analysis of heart rate data based on the user's health check results. The analysis unit can also enhance the analysis of blood oxygen saturation data based on the user's health check results. Furthermore, the analysis unit can enhance the analysis of body temperature data based on the user's health check results. As a result, the analysis unit can perform more accurate analysis by taking health check results into consideration.

[0045] The analysis unit can perform analysis while considering the user's geographical location. For example, if the user is at high altitude, the analysis unit can enhance the analysis of blood oxygen concentration data. If the user is in a cold region, the analysis unit can also enhance the analysis of body temperature data. Furthermore, if the user is at an exercise facility, the analysis unit can enhance the analysis of activity level data. As a result, the analysis unit can perform more appropriate analysis by considering geographical location information.

[0046] The proposal department can adjust the level of detail in its proposals based on the importance of each health promotion menu item. For example, it can provide detailed explanations for highly important menu items, while providing concise explanations for less important items. Furthermore, the proposal department can adjust the level of detail in its proposals in stages according to importance. This allows the proposal department to provide more effective proposals by adjusting the level of detail based on the importance of each health promotion menu item.

[0047] The suggestion unit can propose the most suitable menu by considering the user's health check results. For example, the suggestion unit can propose a meal menu based on the user's health check results. The suggestion unit can also propose an exercise menu based on the user's health check results. Furthermore, the suggestion unit can propose a lifestyle improvement menu based on the user's health check results. In this way, the suggestion unit can propose a more appropriate health promotion menu by considering the health check results.

[0048] The suggestion unit can determine the priority of suggestions based on the user's lifestyle when making suggestions. For example, the suggestion unit can determine the priority of meal menus based on the user's lifestyle. The suggestion unit can also determine the priority of exercise menus based on the user's lifestyle. Furthermore, the suggestion unit can also determine the priority of lifestyle improvement menus based on the user's lifestyle. As a result, the suggestion unit can make more effective suggestions by determining the priority of suggestions based on the user's lifestyle.

[0049] The suggestion function can analyze a user's social media activity and propose relevant menus when making suggestions. For example, if a user posts about food, the suggestion function can suggest meal menus. If a user posts about exercise, the suggestion function can also suggest exercise menus. Furthermore, if a user posts about lifestyle habits, the suggestion function can also suggest lifestyle improvement menus. In this way, by analyzing social media activity, the suggestion function can propose relevant menus and improve the accuracy of health management.

[0050] The incentive unit can select the most appropriate incentive by referring to the user's past performance when awarding incentives. For example, the incentive unit can provide exercise-related incentives based on the user's past exercise performance. It can also provide diet-related incentives based on the user's past eating habits. Furthermore, it can provide health management-related incentives based on the user's past health management performance. In this way, the incentive unit can select the most appropriate incentive by referring to past performance, thereby enhancing the effectiveness of the incentives.

[0051] The incentive unit can adjust the amount of incentive based on the user's current health status when awarding incentives. For example, if the user's health is good, the incentive unit can increase the amount of incentive. If the user's health is deteriorating, the incentive unit can also decrease the amount of incentive. Furthermore, the incentive unit can adjust the amount of incentive in stages according to the user's health status. This allows the incentive unit to provide more appropriate incentives by adjusting the amount of incentive based on the user's current health status.

[0052] The incentive unit can select the most appropriate incentive by considering the user's geographical location when awarding incentives. For example, if the user is at a high altitude, the incentive unit can offer a voucher for an oxygen bar as an incentive. If the user is in a cold region, the incentive unit can offer a voucher for a hot spring facility as an incentive. Furthermore, if the user is at an exercise facility, the incentive unit can offer exercise-related incentives. In this way, the incentive unit can select the most appropriate incentive by considering geographical location information, thereby enhancing the effectiveness of the incentives.

[0053] The incentive department can analyze users' social media activity when awarding incentives and provide relevant incentives. For example, if a user posts about stress, the incentive department can provide relaxation-related incentives. If a user posts about exercise, the incentive department can also provide exercise-related incentives. Furthermore, if a user posts about food, the incentive department can provide food-related incentives. In this way, the incentive department can analyze social media activity to provide relevant incentives and enhance the effectiveness of the incentives.

[0054] The integration unit can improve the accuracy of integration by referring to past integration data during integration. For example, the integration unit can refer to the user's past integration data and improve the accuracy of integration if an anomaly is detected. The integration unit can also refer to the user's past integration data and adjust the frequency of integration. Furthermore, the integration unit can refer to the user's past integration data and adjust the type of data to be integrated. In this way, the integration unit can improve the accuracy of integration by referring to past integration data.

[0055] The integration unit can select the optimal integration method by considering the user's geographical location information during integration. For example, if the user is at high altitude, the integration unit may prioritize integrating oxygen concentration data. If the user is in a cold region, the integration unit may prioritize integrating body temperature data. Furthermore, if the user is at an exercise facility, the integration unit may prioritize integrating activity level data. In this way, the integration unit can select the optimal integration method by considering geographical location information and improve the accuracy of the integration.

[0056] The emotion recognition unit can improve the accuracy of its recognition by referring to past emotion data during emotion recognition. For example, the emotion recognition unit can refer to the user's past emotion data and improve the accuracy of recognition if an anomaly is detected. The emotion recognition unit can also refer to the user's past emotion data and predict changes in emotion. Furthermore, the emotion recognition unit can refer to the user's past emotion data and adjust the emotion recognition algorithm. In this way, the emotion recognition unit can improve the accuracy of its recognition by referring to past emotion data.

[0057] The emotion recognition unit can perform emotion recognition while considering the user's geographical location information. For example, if the user is at high altitude, the emotion recognition unit can prioritize oxygen concentration data for emotion recognition. If the user is in a cold region, the emotion recognition unit can prioritize body temperature data for emotion recognition. Furthermore, if the user is in a sports facility, the emotion recognition unit can prioritize activity level data for emotion recognition. As a result, the emotion recognition unit can perform emotion recognition with higher accuracy by considering geographical location information.

[0058] The stress management unit can optimize its stress management methods by referring to past stress data during stress management. For example, the stress management unit can adjust stress management methods by referring to the user's past stress data. The stress management unit can also predict changes in stress by referring to the user's past stress data. Furthermore, the stress management unit can adjust the stress management algorithm by referring to the user's past stress data. As a result, the stress management unit can optimize its management methods by referring to past stress data, enabling more effective stress management.

[0059] The stress management unit can select the optimal management method when managing stress, taking into account the user's geographical location. For example, if the user is at high altitude, the stress management unit can prioritize oxygen concentration data for stress management. If the user is in a cold region, the stress management unit can also prioritize body temperature data for stress management. Furthermore, if the user is at an exercise facility, the stress management unit can prioritize activity level data for stress management. In this way, the stress management unit can select the optimal management method by considering geographical location information, enabling more effective stress management.

[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] The health management system can also include a communications department. This department facilitates communication among employees and supports the implementation of health promotion programs. For example, it can provide a chat function for employees to share health promotion programs and encourage each other. It can also provide a forum for employees to report on their progress on health promotion programs. Furthermore, it can provide a Q&A function for employees to ask questions and seek advice regarding health promotion programs. In this way, the communications department can maintain employee health and improve productivity by facilitating communication among employees and supporting the implementation of health promotion programs.

[0062] A health management system can also include a feedback section. This section collects feedback from employees and uses it to improve the health management system. For example, the feedback section can provide a survey function for employees to evaluate the effectiveness and ease of use of health promotion programs. It can also provide a feedback form for employees to submit opinions and requests regarding the health management system. Furthermore, the feedback section can provide data analysis functions to analyze employee feedback and identify areas for improvement in the health management system. In this way, the feedback section can collect employee feedback and use it to improve the health management system, thereby maintaining employee health and increasing productivity.

[0063] A health management system can also include an education department. This department provides employees with health-related knowledge and skills, enhancing the effectiveness of health promotion programs. For example, the education department can offer online courses for employees to learn about health. It can also provide training programs for employees to implement health promotion programs. Furthermore, the education department can offer expert counseling services to help employees resolve health-related questions and concerns. In this way, the education department can maintain employee health and improve productivity by providing employees with health-related knowledge and skills, thereby enhancing the effectiveness of health promotion programs.

[0064] A health management system can also include a motivation section. This section aims to boost employee motivation and encourage the implementation of health promotion programs. For example, the motivation section can provide goal-setting functions to help employees achieve their health goals. It can also offer incentive programs that reward employees with benefits or perks upon achieving their goals. Furthermore, the motivation section can provide a dashboard function to visualize the progress of employees in their health promotion programs. In this way, the motivation section can maintain employee health and improve productivity by boosting employee motivation and encouraging the implementation of health promotion programs.

[0065] The health management system can also include a reminder function. This reminder function provides reminders to ensure employees remember to implement health promotion activities. For example, it can send notifications to employees to remind them to exercise or eat meals. It can also provide reminders for employees to undergo health checkups or regular check-ups. Furthermore, it can provide reminders for employees to implement stress management techniques. In this way, the reminder function helps maintain employee health and improve productivity by providing reminders to ensure employees remember to implement health promotion activities.

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

[0067] Step 1: The recording unit records biometric information. The recording unit can record biometric information such as heart rate, blood pressure, and body temperature. The recording unit acquires biometric information using a smart device and stores it in a database. Step 2: The analysis unit analyzes the biometric information recorded by the recording unit. The analysis unit analyzes the biometric information using, for example, a data analysis algorithm to evaluate the health status. The analysis unit can also use AI to analyze the biometric information and predict health risks. Step 3: The proposal department proposes health promotion menus based on the analysis results obtained by the analysis department. The proposal department proposes health promotion menus such as exercise programs and meal plans. The proposal department can also use generative AI to propose the most suitable health promotion menu for each employee. Step 4: The Incentives Department provides incentives to employees who implement the menus proposed by the Proposal Department. The Incentives Department provides incentives such as rewards, perks, or points. The Incentives Department can also use AI to adjust the types of incentives and the criteria for granting them.

[0068] (Example of form 2) The health management system according to an embodiment of the present invention is a system for maintaining employee health and improving productivity. This health management system distributes smart devices to all employees free of charge and records their biometric information. Next, the health management system uses AI and IoT to analyze the biometric information, and the AI ​​generates and proposes an optimal health promotion menu for each employee. Furthermore, the health management system awards points as an incentive to employees who implement the proposed menu. Through this mechanism, employee health is maintained and productivity is improved. For example, the health management system can be linked with health checkup results and attendance management systems to enable more accurate health management. In addition, the health management system's AI, equipped with an emotion generation engine, recognizes employees' stress levels from emails, chats, facial expressions, etc., and proposes appropriate stress management methods. As a result, the health management system can maintain employee health and improve productivity.

[0069] The health management system according to this embodiment comprises a recording unit, an analysis unit, a proposal unit, and an incentive unit. The recording unit records biometric information. The recording unit can record biometric information such as heart rate, blood pressure, and body temperature. The recording unit acquires biometric information using a smart device and stores it in a database. The analysis unit analyzes the biometric information recorded by the recording unit. The analysis unit analyzes the biometric information using a data analysis algorithm, for example, and evaluates the health status. The analysis unit can also analyze the biometric information using AI and predict health risks. The proposal unit proposes health promotion menus based on the analysis results obtained by the analysis unit. The proposal unit proposes health promotion menus such as exercise programs and meal plans, for example. The proposal unit can also propose the most suitable health promotion menu for each employee using generative AI. The incentive unit provides incentives to employees who implement the menus proposed by the proposal unit. The incentive unit provides incentives such as rewards, benefits, and points, for example. The incentive unit can also adjust the type of incentive and the criteria for granting it using AI. As a result, the health management system according to this embodiment can maintain employee health and improve productivity through recording, analyzing, suggesting, and providing incentives for biometric information.

[0070] The recording unit records biometric information. For example, it can record biometric information such as heart rate, blood pressure, and body temperature. The recording unit acquires biometric information using smart devices and stores it in a database. Specifically, it uses wearable devices such as smartwatches and fitness trackers to acquire detailed biometric information in real time, including heart rate, blood pressure, body temperature, sleep patterns, and activity levels. These devices connect to smartphones and tablets via Bluetooth or Wi-Fi and transmit the acquired data to a dedicated application. The application automatically saves the data to a cloud-based database, allowing users to access it at any time. Furthermore, the recording unit can also incorporate regular health checkups and test results from medical institutions. For example, it can acquire and integrate data from regular health checkups, hospital blood tests, and electrocardiogram tests from electronic medical record systems. This allows for a comprehensive understanding of the user's health status. The recording unit also prioritizes data security; acquired biometric information is encrypted, and mechanisms are in place to prevent unauthorized access by third parties. This allows users to record and manage their health data with peace of mind.

[0071] The analysis unit analyzes biometric information recorded by the recording unit. For example, the analysis unit uses data analysis algorithms to analyze biometric information and assess the user's health status. Specifically, it analyzes data such as heart rate, blood pressure, and body temperature in time series to detect anomalies and trends. For instance, it can detect sudden increases in heart rate or abnormal fluctuations in blood pressure, aiding in the early detection of health risks. The analysis unit can also use AI to analyze biometric information and predict health risks. The AI ​​learns from past data using machine learning algorithms to predict future health risks. For example, it can extract specific patterns from past data to predict risks of heart disease, hypertension, diabetes, and other conditions. Furthermore, the analysis unit also considers the user's lifestyle and environmental factors during its analysis. For example, it integrates information such as the user's exercise habits, diet, and stress levels to perform a more accurate health assessment. This allows the analysis unit to comprehensively evaluate the user's health status and identify individual health risks. The analysis results are provided to the user in a visually easy-to-understand format, enabling them to intuitively understand changes in their health status and risk factors.

[0072] The Proposal Department proposes health promotion menus based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes health promotion menus such as exercise programs and meal plans. Specifically, it creates individualized exercise programs tailored to the user's health condition and lifestyle, and proposes daily exercise volume and types. For example, based on heart rate and activity data, it proposes specific exercise menus such as walking, jogging, and strength training. Regarding meal plans, it proposes balanced meal menus tailored to the user's nutritional status and health goals. For example, it proposes specific breakfast, lunch, and dinner menus considering calorie intake and nutritional balance, and also advises on recipes and ingredient selection. The Proposal Department can also use generative AI to propose optimal health promotion menus for each employee. The generative AI generates optimal exercise programs and meal plans based on the user's past data and current health condition, providing individually customized proposals. For example, the generative AI proposes meal menus considering the user's preferences and allergy information, and adjusts exercise programs according to the user's physical fitness and goals. This allows the Proposal Department to provide users with easy-to-follow and effective health promotion menus.

[0073] The Incentives Department provides incentives to employees who implement the programs proposed by the Proposal Department. These incentives include rewards, benefits, and points. Specifically, when users complete a proposed exercise program or meal plan, they are awarded points based on their performance. Once a certain number of points are accumulated, they can be exchanged for rewards or benefits. For example, points are awarded for daily exercise or consistent implementation of a proposed meal plan, and these accumulated points can be exchanged for gift cards, vouchers, health-related goods, and more. The Incentives Department can also use AI to adjust the types and criteria for awarding incentives. The AI ​​analyzes user motivation and behavioral patterns to provide optimal incentives. For example, it predicts which incentives are most effective based on the user's past behavioral data and provides individually customized incentives. This allows the Incentives Department to increase user motivation and encourage the implementation of health-promoting programs. Furthermore, the Incentives Department collects user feedback to improve the incentive system. For example, it reviews the content and criteria for awarding incentives based on user opinions and requests to build a more effective system. This allows the incentive department to support the improvement of users' health and maximize the overall effectiveness of the system.

[0074] The health management system includes a linking unit that connects with health checkup results and attendance management systems. This linking unit synchronizes data with the health checkup results and attendance management systems. For example, the linking unit stores health checkup results in a database, and the analysis unit uses this data for analysis. The linking unit can also connect with the attendance management system to understand employees' work status. As a result, the health management system, by linking with health checkup results and attendance management systems, enables more accurate health management.

[0075] The health management system includes an emotion recognition unit equipped with an emotion generation engine. The emotion recognition unit uses the emotion generation engine to recognize employees' emotions. For example, the emotion recognition unit can recognize employees' stress levels from emails, chats, facial expressions, etc. The emotion recognition unit can also use AI to recognize emotions and evaluate stress levels. Therefore, by incorporating the emotion generation engine, the health management system can recognize employees' stress levels and propose appropriate stress management methods.

[0076] The health management system includes a stress management department that proposes stress management methods. This department assesses employees' stress levels and proposes appropriate stress management methods. For example, it might suggest relaxation techniques or counseling methods. The stress management department can also use AI to propose stress management methods. As a result, the health management system can reduce employee stress and maintain their health by proposing stress management methods.

[0077] The recording unit can record biometric information such as heart rate, ECG, blood oxygen saturation, sleep tracking, activity level, body temperature, stress level, and blood pressure from smart devices. For example, the recording unit can record heart rate and ECG using a smartwatch. It can also record activity level and sleep tracking using a fitness tracker. Furthermore, the recording unit can record blood oxygen saturation and body temperature using a smart device. As a result, the recording unit enables comprehensive health management by recording diverse biometric information from smart devices.

[0078] The proposal department can use generative AI to suggest optimal health promotion menus for each employee. For example, the proposal department can use generative AI to suggest meal menus. The proposal department can also use generative AI to suggest exercise menus. Furthermore, the proposal department can use generative AI to suggest lifestyle improvement menus. In this way, the proposal department can use generative AI to suggest optimal health promotion menus for each employee.

[0079] The recording unit can estimate the user's emotions and adjust the frequency of biometric data recording based on the estimated emotions. For example, if the user is stressed, the recording unit can increase the frequency of recording heart rate and stress level. If the user is relaxed, the recording unit can also increase the frequency of sleep tracking recording. Furthermore, if the user is exercising, the recording unit can increase the frequency of recording activity level and heart rate. This allows for more appropriate health management by adjusting the frequency of biometric data recording according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The recording unit can select the optimal recording method by referring to the user's past health data during recording. For example, the recording unit can refer to the user's past heart rate data and prioritize recording an electrocardiogram if an abnormality is detected. The recording unit can also refer to the user's past sleep data and enhance sleep tracking if sleep quality is poor. Furthermore, the recording unit can refer to the user's past activity level data and increase activity level recording if there is a prolonged lack of exercise. In this way, the recording unit can select the optimal recording method by referring to past health data and improve the accuracy of health management.

[0081] The recording unit can select the types of biometric data to record based on the user's current activity level. For example, if the user is exercising, the recording unit will prioritize recording heart rate and activity level. If the user is resting, the recording unit can also prioritize recording blood oxygen saturation and body temperature. Furthermore, if the user is sleeping, the recording unit can prioritize sleep tracking and heart rate recording. This allows the recording unit to select the types of biometric data based on the user's current activity level, enabling more appropriate health management.

[0082] The recording unit can estimate the user's emotions and prioritize the biometric data to record based on the estimated emotions. For example, if the user is stressed, the recording unit will prioritize recording heart rate and stress level. If the user is relaxed, the recording unit may also prioritize sleep tracking and blood oxygen saturation. Furthermore, if the user is exercising, the recording unit may prioritize recording activity level and heart rate. This allows for more appropriate health management by prioritizing biometric data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The recording unit can prioritize recording highly relevant biometric information by considering the user's geographical location during recording. For example, if the user is at high altitude, the recording unit will prioritize recording blood oxygen saturation. If the user is in a cold region, the recording unit can also prioritize recording body temperature. Furthermore, if the user is at an exercise facility, the recording unit can prioritize recording activity level and heart rate. In this way, by considering geographical location information, the recording unit can prioritize recording highly relevant biometric information and improve the accuracy of health management.

[0084] The recording unit can analyze the user's social media activity and record relevant biometric information during recording. For example, if the user posts about feeling stressed, the recording unit will prioritize recording heart rate and stress level. If the user posts about exercise, the recording unit can also prioritize recording activity level and heart rate. Furthermore, if the user posts about sleep, the recording unit can prioritize sleep tracking. In this way, the recording unit can improve the accuracy of health management by recording relevant biometric information through analysis of social media activity.

[0085] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can enhance the analysis of stress levels. If the user is relaxed, the analysis unit can also enhance the analysis of sleep data. Furthermore, if the user is exercising, the analysis unit can enhance the analysis of activity level data. This allows the analysis unit to obtain more appropriate analysis results by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The analysis unit can improve the accuracy of its analysis by referring to past analysis data during the analysis process. For example, the analysis unit can refer to the user's past heart rate data and enhance the electrocardiogram analysis if an abnormality is detected. The analysis unit can also refer to the user's past sleep data and enhance the sleep data analysis if the sleep quality is poor. Furthermore, the analysis unit can refer to the user's past activity level data and enhance the activity level data analysis if there is a prolonged lack of exercise. In this way, the analysis unit can improve the accuracy of its analysis by referring to past analysis data.

[0087] The analysis unit can perform analysis while taking into account the user's health check results. For example, the analysis unit can enhance the analysis of heart rate data based on the user's health check results. The analysis unit can also enhance the analysis of blood oxygen saturation data based on the user's health check results. Furthermore, the analysis unit can enhance the analysis of body temperature data based on the user's health check results. As a result, the analysis unit can perform more accurate analysis by taking health check results into consideration.

[0088] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, the analysis unit can provide more appropriate information by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The analysis unit can perform analysis while considering the user's geographical location. For example, if the user is at high altitude, the analysis unit can enhance the analysis of blood oxygen concentration data. If the user is in a cold region, the analysis unit can also enhance the analysis of body temperature data. Furthermore, if the user is at an exercise facility, the analysis unit can enhance the analysis of activity level data. As a result, the analysis unit can perform more appropriate analysis by considering geographical location information.

[0090] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function can provide simple and easily understandable suggestions. If the user is relaxed, it can also provide suggestions that include more detailed information. Furthermore, if the user is in a hurry, it can provide suggestions that get straight to the point. This allows the suggestion function to provide more appropriate suggestions by adjusting the way it presents suggestions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The proposal department can adjust the level of detail in its proposals based on the importance of each health promotion menu item. For example, it can provide detailed explanations for highly important menu items, while providing concise explanations for less important items. Furthermore, the proposal department can adjust the level of detail in its proposals in stages according to importance. This allows the proposal department to provide more effective proposals by adjusting the level of detail based on the importance of each health promotion menu item.

[0092] The suggestion unit can propose the most suitable menu by considering the user's health check results. For example, the suggestion unit can propose a meal menu based on the user's health check results. The suggestion unit can also propose an exercise menu based on the user's health check results. Furthermore, the suggestion unit can propose a lifestyle improvement menu based on the user's health check results. In this way, the suggestion unit can propose a more appropriate health promotion menu by considering the health check results.

[0093] The suggestion function can estimate the user's emotions and adjust the length of its suggestions based on those emotions. For example, if the user is stressed, the suggestion function will provide short, concise suggestions. If the user is relaxed, it can provide longer suggestions with more detailed explanations. Furthermore, if the user is in a hurry, it can provide quick and concise suggestions. This allows the suggestion function to provide more appropriate suggestions by adjusting the length of suggestions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The suggestion unit can determine the priority of suggestions based on the user's lifestyle when making suggestions. For example, the suggestion unit can determine the priority of meal menus based on the user's lifestyle. The suggestion unit can also determine the priority of exercise menus based on the user's lifestyle. Furthermore, the suggestion unit can also determine the priority of lifestyle improvement menus based on the user's lifestyle. As a result, the suggestion unit can make more effective suggestions by determining the priority of suggestions based on the user's lifestyle.

[0095] The suggestion function can analyze a user's social media activity and propose relevant menus when making suggestions. For example, if a user posts about food, the suggestion function can suggest meal menus. If a user posts about exercise, the suggestion function can also suggest exercise menus. Furthermore, if a user posts about lifestyle habits, the suggestion function can also suggest lifestyle improvement menus. In this way, by analyzing social media activity, the suggestion function can propose relevant menus and improve the accuracy of health management.

[0096] The incentive unit can estimate the user's emotions and adjust the type of incentive based on the estimated emotions. For example, if the user is feeling stressed, the incentive unit can provide relaxation-related incentives. If the user is relaxed, the incentive unit can also provide exercise-related incentives. Furthermore, if the user is exercising, the incentive unit can also provide food-related incentives. In this way, the incentive unit can provide more appropriate incentives by adjusting the type of incentive according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The incentive unit can select the most appropriate incentive by referring to the user's past performance when awarding incentives. For example, the incentive unit can provide exercise-related incentives based on the user's past exercise performance. It can also provide diet-related incentives based on the user's past eating habits. Furthermore, it can provide health management-related incentives based on the user's past health management performance. In this way, the incentive unit can select the most appropriate incentive by referring to past performance, thereby enhancing the effectiveness of the incentives.

[0098] The incentive unit can adjust the amount of incentive based on the user's current health status when awarding incentives. For example, if the user's health is good, the incentive unit can increase the amount of incentive. If the user's health is deteriorating, the incentive unit can also decrease the amount of incentive. Furthermore, the incentive unit can adjust the amount of incentive in stages according to the user's health status. This allows the incentive unit to provide more appropriate incentives by adjusting the amount of incentive based on the user's current health status.

[0099] The incentive unit can estimate the user's emotions and determine the priority of incentives based on the estimated emotions. For example, if the user is stressed, the incentive unit will prioritize incentives related to relaxation. If the user is relaxed, the incentive unit may also prioritize incentives related to exercise. Furthermore, if the user is exercising, the incentive unit may also prioritize incentives related to food. In this way, the incentive unit can provide more effective incentives by determining the priority of incentives according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The incentive unit can select the most appropriate incentive by considering the user's geographical location when awarding incentives. For example, if the user is at a high altitude, the incentive unit can offer a voucher for an oxygen bar as an incentive. If the user is in a cold region, the incentive unit can offer a voucher for a hot spring facility as an incentive. Furthermore, if the user is at an exercise facility, the incentive unit can offer exercise-related incentives. In this way, the incentive unit can select the most appropriate incentive by considering geographical location information, thereby enhancing the effectiveness of the incentives.

[0101] The incentive department can analyze users' social media activity when awarding incentives and provide relevant incentives. For example, if a user posts about stress, the incentive department can provide relaxation-related incentives. If a user posts about exercise, the incentive department can also provide exercise-related incentives. Furthermore, if a user posts about food, the incentive department can provide food-related incentives. In this way, the incentive department can analyze social media activity to provide relevant incentives and enhance the effectiveness of the incentives.

[0102] The integration unit can estimate the user's emotions and adjust the types of data it integrates based on the estimated emotions. For example, if the user is stressed, the integration unit will prioritize integrating stress level and heart rate data. If the user is relaxed, the integration unit can also prioritize integrating sleep data and blood oxygen saturation data. Furthermore, if the user is exercising, the integration unit can prioritize integrating activity level data and heart rate data. This allows the integration unit to integrate more appropriate data by adjusting the types of data it integrates according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The integration unit can improve the accuracy of integration by referring to past integration data during integration. For example, the integration unit can refer to the user's past integration data and improve the accuracy of integration if an anomaly is detected. The integration unit can also refer to the user's past integration data and adjust the frequency of integration. Furthermore, the integration unit can refer to the user's past integration data and adjust the type of data to be integrated. In this way, the integration unit can improve the accuracy of integration by referring to past integration data.

[0104] The interaction unit can estimate the user's emotions and adjust the frequency of interaction based on the estimated emotions. For example, if the user is stressed, the interaction unit can increase the frequency of interaction. If the user is relaxed, the interaction unit can also decrease the frequency of interaction. Furthermore, if the user is exercising, the interaction unit can increase the frequency of interaction. In this way, the interaction unit can enable more appropriate data interaction by adjusting the frequency of interaction according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The integration unit can select the optimal integration method by considering the user's geographical location information during integration. For example, if the user is at high altitude, the integration unit may prioritize integrating oxygen concentration data. If the user is in a cold region, the integration unit may prioritize integrating body temperature data. Furthermore, if the user is at an exercise facility, the integration unit may prioritize integrating activity level data. In this way, the integration unit can select the optimal integration method by considering geographical location information and improve the accuracy of the integration.

[0106] The emotion recognition unit can estimate the user's emotions and adjust the emotion recognition algorithm based on the estimated emotions. For example, if the user is stressed, the emotion recognition unit can enhance the recognition of stress levels. If the user is relaxed, the emotion recognition unit can also enhance the recognition of relaxation levels. Furthermore, if the user is exercising, the emotion recognition unit can enhance the recognition of emotional changes due to exercise. In this way, the emotion recognition unit can achieve more accurate emotion recognition by adjusting the emotion recognition algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The emotion recognition unit can improve the accuracy of its recognition by referring to past emotion data during emotion recognition. For example, the emotion recognition unit can refer to the user's past emotion data and improve the accuracy of recognition if an anomaly is detected. The emotion recognition unit can also refer to the user's past emotion data and predict changes in emotion. Furthermore, the emotion recognition unit can refer to the user's past emotion data and adjust the emotion recognition algorithm. In this way, the emotion recognition unit can improve the accuracy of its recognition by referring to past emotion data.

[0108] The emotion recognition unit can estimate the user's emotions and adjust the way it displays the emotion recognition results based on the estimated emotions. For example, if the user is stressed, the emotion recognition unit can provide a simple and highly visible display method. If the user is relaxed, the emotion recognition unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the emotion recognition unit can provide a concise display method. In this way, the emotion recognition unit can provide more appropriate information by adjusting the way it displays the emotion recognition results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The emotion recognition unit can perform emotion recognition while considering the user's geographical location information. For example, if the user is at high altitude, the emotion recognition unit can prioritize oxygen concentration data for emotion recognition. If the user is in a cold region, the emotion recognition unit can prioritize body temperature data for emotion recognition. Furthermore, if the user is in a sports facility, the emotion recognition unit can prioritize activity level data for emotion recognition. As a result, the emotion recognition unit can perform emotion recognition with higher accuracy by considering geographical location information.

[0110] The stress management unit can estimate the user's emotions and adjust stress management methods based on the estimated emotions. For example, if the user is feeling stressed, the stress management unit can suggest relaxation methods. If the user is relaxed, the stress management unit can also suggest exercise methods. Furthermore, if the user is exercising, the stress management unit can also suggest stretching methods. In this way, the stress management unit can provide more appropriate stress management by adjusting stress management methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The stress management unit can optimize its stress management methods by referring to past stress data during stress management. For example, the stress management unit can adjust stress management methods by referring to the user's past stress data. The stress management unit can also predict changes in stress by referring to the user's past stress data. Furthermore, the stress management unit can adjust the stress management algorithm by referring to the user's past stress data. As a result, the stress management unit can optimize its management methods by referring to past stress data, enabling more effective stress management.

[0112] The stress management unit can estimate the user's emotions and determine stress management priorities based on those estimated emotions. For example, if the user is feeling stressed, the stress management unit will prioritize relaxation methods. If the user is relaxed, the stress management unit may also prioritize exercise methods. Furthermore, if the user is exercising, the stress management unit may also prioritize stretching methods. This allows the stress management unit to determine stress management priorities according to the user's emotions, enabling more effective stress management. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The stress management unit can select the optimal management method when managing stress, taking into account the user's geographical location. For example, if the user is at high altitude, the stress management unit can prioritize oxygen concentration data for stress management. If the user is in a cold region, the stress management unit can also prioritize body temperature data for stress management. Furthermore, if the user is at an exercise facility, the stress management unit can prioritize activity level data for stress management. In this way, the stress management unit can select the optimal management method by considering geographical location information, enabling more effective stress management.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The health management system can also include a communications department. This department facilitates communication among employees and supports the implementation of health promotion programs. For example, it can provide a chat function for employees to share health promotion programs and encourage each other. It can also provide a forum for employees to report on their progress on health promotion programs. Furthermore, it can provide a Q&A function for employees to ask questions and seek advice regarding health promotion programs. In this way, the communications department can maintain employee health and improve productivity by facilitating communication among employees and supporting the implementation of health promotion programs.

[0116] A health management system can also include a feedback section. This section collects feedback from employees and uses it to improve the health management system. For example, the feedback section can provide a survey function for employees to evaluate the effectiveness and ease of use of health promotion programs. It can also provide a feedback form for employees to submit opinions and requests regarding the health management system. Furthermore, the feedback section can provide data analysis functions to analyze employee feedback and identify areas for improvement in the health management system. In this way, the feedback section can collect employee feedback and use it to improve the health management system, thereby maintaining employee health and increasing productivity.

[0117] A health management system can also include an education department. This department provides employees with health-related knowledge and skills, enhancing the effectiveness of health promotion programs. For example, the education department can offer online courses for employees to learn about health. It can also provide training programs for employees to implement health promotion programs. Furthermore, the education department can offer expert counseling services to help employees resolve health-related questions and concerns. In this way, the education department can maintain employee health and improve productivity by providing employees with health-related knowledge and skills, thereby enhancing the effectiveness of health promotion programs.

[0118] A health management system can also include a motivation section. This section aims to boost employee motivation and encourage the implementation of health promotion programs. For example, the motivation section can provide goal-setting functions to help employees achieve their health goals. It can also offer incentive programs that reward employees with benefits or perks upon achieving their goals. Furthermore, the motivation section can provide a dashboard function to visualize the progress of employees in their health promotion programs. In this way, the motivation section can maintain employee health and improve productivity by boosting employee motivation and encouraging the implementation of health promotion programs.

[0119] The health management system can also include a reminder function. This reminder function provides reminders to ensure employees remember to implement health promotion activities. For example, it can send notifications to employees to remind them to exercise or eat meals. It can also provide reminders for employees to undergo health checkups or regular check-ups. Furthermore, it can provide reminders for employees to implement stress management techniques. In this way, the reminder function helps maintain employee health and improve productivity by providing reminders to ensure employees remember to implement health promotion activities.

[0120] The health management system can also be equipped with an emotion estimation unit. This unit estimates an employee's emotions and adjusts the health promotion menu based on those emotions. For example, if an employee is feeling stressed, the emotion estimation unit can prioritize suggesting relaxation activities. If an employee is relaxed, it can prioritize suggesting exercise activities. Furthermore, if an employee is exercising, it can prioritize suggesting stretching activities. This allows the emotion estimation unit to adjust the health promotion menu according to the employee's emotions, enabling more appropriate health management.

[0121] The health management system can also include an emotional feedback unit. This unit provides feedback based on employees' emotions, enhancing the effectiveness of health promotion programs. For example, if an employee is feeling stressed, the emotional feedback unit can provide feedback on relaxation techniques. If an employee is relaxed, it can also provide feedback on exercise techniques. Furthermore, if an employee is exercising, it can provide feedback on stretching techniques. In this way, the emotional feedback unit can enhance the effectiveness of health promotion programs by providing feedback based on employees' emotions.

[0122] The health management system can also include an emotion monitoring unit. This unit continuously monitors employees' emotions and evaluates the effectiveness of health promotion programs. For example, it can identify periods when employees are stressed and evaluate the effectiveness of health promotion programs during those periods. It can also identify periods when employees are relaxed and evaluate the effectiveness of health promotion programs during those periods. Furthermore, it can monitor changes in employees' emotions during exercise and adjust health promotion programs based on those changes. This allows the emotion monitoring unit to continuously monitor employees' emotions and evaluate the effectiveness of health promotion programs, enabling more appropriate health management.

[0123] The health management system can also be equipped with an emotional alert unit. This unit issues an alert when it detects an abnormality in an employee's emotions. For example, it can alert if an employee is experiencing extreme stress. It can also alert if an employee has been unable to relax for an extended period. Furthermore, it can alert if an employee exhibits abnormal emotional changes during exercise. This allows for early intervention by detecting abnormalities in an employee's emotions and issuing an alert.

[0124] The health management system can also include an emotion reporting section. This section compiles employee emotion data into reports, which are used as a reference for health management. For example, the emotion reporting section can compile employee emotion data into weekly or monthly reports. It can also visualize employee emotion data as graphs and charts. Furthermore, the emotion reporting section can combine employee emotion data with other health data to create comprehensive reports. This allows for more appropriate health management by compiling employee emotion data into reports and using them as a reference.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The recording unit records biometric information. The recording unit can record biometric information such as heart rate, blood pressure, and body temperature. The recording unit acquires biometric information using a smart device and stores it in a database. Step 2: The analysis unit analyzes the biometric information recorded by the recording unit. The analysis unit analyzes the biometric information using, for example, a data analysis algorithm to evaluate the health status. The analysis unit can also use AI to analyze the biometric information and predict health risks. Step 3: The proposal department proposes health promotion menus based on the analysis results obtained by the analysis department. The proposal department proposes health promotion menus such as exercise programs and meal plans. The proposal department can also use generative AI to propose the most suitable health promotion menu for each employee. Step 4: The Incentives Department provides incentives to employees who implement the menus proposed by the Proposal Department. The Incentives Department provides incentives such as rewards, perks, or points. The Incentives Department can also use AI to adjust the types of incentives and the criteria for granting them.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the recording unit, analysis unit, proposal unit, incentive unit, collaboration unit, emotion recognition unit, and stress management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recording unit is implemented by the control unit 46A of the smart device 14, which acquires biometric information and stores it in the database 24. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes biometric information and evaluates health status. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes health promotion menus. The incentive unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides incentives. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12, which synchronizes data with health checkup results and attendance management systems. The emotion recognition unit is implemented by the control unit 46A of the smart device 14, which recognizes the emotions of employees. The stress management unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes stress management methods. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the recording unit, analysis unit, proposal unit, incentive unit, collaboration unit, emotion recognition unit, and stress management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the recording unit is implemented by the control unit 46A of the smart glasses 214, which acquires biometric information and stores it in the database 24. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes biometric information and evaluates health status. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes health promotion menus. The incentive unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides incentives. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12, which synchronizes data with health checkup results and attendance management systems. The emotion recognition unit is implemented by the control unit 46A of the smart glasses 214, which recognizes the emotions of employees. The stress management unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes stress management methods. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the recording unit, analysis unit, proposal unit, incentive unit, collaboration unit, emotion recognition unit, and stress management unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the recording unit is implemented by the control unit 46A of the headset terminal 314, which acquires biometric information and stores it in the database 24. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes biometric information and evaluates health status. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes health promotion menus. The incentive unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides incentives. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12, which synchronizes data with health checkup results and attendance management systems. The emotion recognition unit is implemented by the control unit 46A of the headset terminal 314, which recognizes the emotions of employees. The stress management unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes stress management methods. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each of the multiple elements described above, including the recording unit, analysis unit, proposal unit, incentive unit, collaboration unit, emotion recognition unit, and stress management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the recording unit is implemented by the control unit 46A of the robot 414, which acquires biometric information and stores it in the database 24. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes biometric information and evaluates health status. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes health promotion menus. The incentive unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides incentives. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12, which synchronizes data with health checkup results and attendance management systems. The emotion recognition unit is implemented by the control unit 46A of the robot 414, which recognizes the emotions of employees. The stress management unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes stress management methods. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) A recording unit for recording biological information, An analysis unit that analyzes the biological information recorded by the recording unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a health promotion menu, An incentive department provides incentives to employees who implement the menus proposed by the aforementioned proposal department, Equipped with A system characterized by the following features. (Note 2) It has a liaison department that connects with health checkup results and attendance management systems. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with an emotion recognition unit that has an emotion generation engine. The system described in Appendix 1, characterized by the features described herein. (Note 4) We have a stress management department that proposes stress management methods. The system described in Appendix 1, characterized by the features described herein. (Note 5) The recording unit is, Smart devices record biometric information such as heart rate, ECG, blood oxygen saturation, sleep tracking, activity level, body temperature, stress level, and blood pressure. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, The AI ​​generates personalized health promotion menus for each employee. The system described in Appendix 1, characterized by the features described herein. (Note 7) The recording unit is, The system estimates the user's emotions and adjusts the frequency of biometric data recording based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The recording unit is, During recording, the system selects the optimal recording method by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The recording unit is, During recording, the system selects the type of biometric information to record based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The recording unit is, It estimates the user's emotions and determines the priority of biometric data to record based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The recording unit is, During recording, the system prioritizes recording highly relevant biometric information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The recording unit is, During recording, the system analyzes the user's social media activity and records relevant biometric information. 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, past analysis data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the user's health check results will be taken into consideration. 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 user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the health promotion menu. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we will suggest the most suitable menu considering the user's health check results. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) The aforementioned proposal section is, When making suggestions, prioritize them based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest relevant options. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned incentive unit is The system estimates the user's emotions and adjusts the type of incentive based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned incentive unit is When awarding incentives, the system selects the most suitable incentive by referring to the user's past performance. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned incentive unit is When awarding incentives, adjust the amount of the incentive based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned incentive unit is It estimates user emotions and determines incentive priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned incentive unit is When awarding incentives, the system selects the most suitable incentive by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned incentive unit is When awarding incentives, analyze users' social media activity and provide relevant incentives. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the types of data it links based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned linkage unit is, During integration, past integration data is referenced to improve the accuracy of the integration. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the frequency of interaction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, When integrating, the system selects the optimal integration method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 34) The emotion recognition unit, It estimates the user's emotions and adjusts the emotion recognition algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The emotion recognition unit, When recognizing emotions, past emotion data is referenced to improve the accuracy of recognition. The system described in Appendix 1, characterized by the features described herein. (Note 36) The emotion recognition unit, Adjusting how we estimate user emotions and display emotion recognition results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The emotion recognition unit, When recognizing emotions, the system takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned stress management unit, It estimates the user's emotions and adjusts stress management methods based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned stress management unit, When managing stress, refer to past stress data to optimize the management method. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned stress management unit, It estimates the user's emotions and determines stress management priorities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned stress management unit, When managing stress, the optimal management method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recording unit for recording biological information, An analysis unit that analyzes the biological information recorded by the recording unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a health promotion menu, An incentive department provides incentives to employees who implement the menus proposed by the aforementioned proposal department, Equipped with A system characterized by the following features.

2. It has a liaison department that connects with health checkup results and attendance management systems. The system according to feature 1.

3. It is equipped with an emotion recognition unit that has an emotion generation engine. The system according to feature 1.

4. We have a stress management department that proposes stress management methods. The system according to feature 1.

5. The aforementioned recording unit is Smart devices record biometric information such as heart rate, ECG, blood oxygen saturation, sleep tracking, activity level, body temperature, stress level, and blood pressure. The system according to feature 1.

6. The aforementioned proposal section is, The AI ​​generates personalized health promotion menus for each employee. The system according to feature 1.

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

8. The aforementioned recording unit is During recording, the system selects the optimal recording method by referring to the user's past health data. The system according to feature 1.

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