System

Abstract

An object of a system according to an embodiment is to analyze sleep data, a stress level, and lifestyle information of an employee and provide appropriate advice.SOLUTION: A system includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects sleep data, a stress level, and lifestyle information of an employee. The analysis unit analyzes the information collected by the data collection unit. The advice providing unit provides appropriate advice on the basis of a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous technology had the problem of not being able to effectively utilize information on employees' sleep data, stress levels, and lifestyle habits to provide appropriate advice.

[0005] The system according to the embodiment aims to analyze employees' sleep data, stress levels, and lifestyle habits, and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects information on employees' sleep data, stress levels, and lifestyle habits. The analysis unit analyzes the information collected by the data collection unit. The advice provision unit provides appropriate advice based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze employees' sleep data, stress levels, and lifestyle habits, and provide appropriate advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The sleep coaching app according to an embodiment of the present invention is a system that provides sleep improvement advice tailored to each employee. This system utilizes AI to collect and analyze information such as employees' sleep data, stress levels, and lifestyle habits, and provides appropriate advice. This allows the sleep coaching app to improve employee productivity and well-being.

[0029] A sleep coaching app according to an embodiment includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects information such as an employee's sleep data, stress level, and lifestyle habits. For example, the data collection unit collects data using a device that records the employee's sleep time and sleep quality. The data collection unit may also use a device that records heart rate variability to measure the employee's stress level. The data collection unit may also conduct a survey to collect information about the employee's lifestyle habits. For example, the data collection unit collects information about the employee's dietary and exercise habits. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit may analyze the employee's sleep patterns using statistical analysis. The analysis unit may also predict the employee's stress level using a machine learning algorithm. The analysis unit may also assess health risks based on the employee's lifestyle habits. For example, the analysis unit may assess health risks based on the employee's dietary and exercise habits. The advice provision unit provides appropriate advice based on the results of the analysis by the analysis unit. For example, the advice provision unit may provide the employee with specific advice for improving their sleep. The advice providing unit can also provide specific advice for stress management to employees. Furthermore, the advice providing unit can also provide specific advice for improving lifestyle habits to employees. For example, the advice providing unit recommends that employees eat a balanced diet and exercise moderately. As a result, the sleep coaching app according to the embodiment is expected to improve productivity and well-being by providing sleep improvement advice tailored to each employee.

[0030] The data collection unit compares an employee's past sleep data with their current lifestyle habits, analyzes long-term changes in sleep patterns, and can provide advice. For example, the data collection unit collects an employee's sleep data from the past year and compares it with their current lifestyle habits. For example, it analyzes data such as sleep duration, time to fall asleep, and time to wake up to identify long-term changes. The data collection unit also analyzes changes in sleep patterns based on the past sleep data and current lifestyle habits and suggests specific improvement measures. For example, if an employee has started staying up late more often, it will suggest advice to go to bed earlier and get up earlier. The data collection unit also analyzes long-term changes in sleep patterns and provides individual advice to the employee. For example, if stress is causing a decline in sleep quality, it will suggest stress management methods. This allows for more accurate advice to be provided by analyzing long-term changes in sleep patterns.

[0031] The data collection unit can suggest optimal sleep and rest periods based on an employee's job content and work schedule. The data collection unit, for example, analyzes an employee's job content and work schedule to suggest optimal sleep periods. For example, it may recommend daytime sleep for employees who work many night shifts. The data collection unit also suggests appropriate rest periods based on the work schedule. For example, it may advise employees to take short breaks in the event of a series of long meetings. The data collection unit also suggests sleep periods according to job content. For example, it may advise employees doing creative work to concentrate during quieter hours at night. In this way, by providing advice based on job content and work schedules, employee productivity is improved.

[0032] The data collection unit can also analyze employees' dietary and exercise habits and provide comprehensive health management advice. For example, the data collection unit collects employees' dietary data and provides dietary advice that helps improve sleep. For example, it may suggest limiting caffeine intake. The data collection unit may also analyze exercise habits and provide exercise advice that helps improve sleep. For example, it may explain that moderate exercise improves sleep quality. The data collection unit may also comprehensively analyze dietary and exercise habits and provide health management advice. For example, it may emphasize the importance of a balanced diet and moderate exercise. In this way, advice that takes dietary and exercise habits into consideration improves the overall health of employees.

[0033] The data collection unit can propose sleep improvement measures at home, taking into account the employee's family structure and living environment. The data collection unit, for example, proposes sleep improvement measures at home, taking into account the employee's family structure. For example, for families with small children, it provides advice on how to get children to sleep. The data collection unit also analyzes the living environment and proposes sleep improvement measures at home. For example, in noisy environments, it recommends earplugs or a white noise machine. The data collection unit also provides individual sleep improvement measures according to the family structure and living environment. For example, for dual-income households, it suggests ways for the couple to cooperate and share housework. In this way, by providing advice that takes into account the family structure and living environment, sleep improvement at home is achieved.

[0034] The data collection unit can monitor employees' stress levels in real time and provide immediate advice when stress increases. The data collection unit, for example, builds a system that monitors employees' stress levels in real time. For example, it uses devices that measure heart rate and electrodermal activity. The data collection unit also provides immediate advice when stress increases. For example, it suggests deep breathing or meditation techniques. The data collection unit also provides individual advice based on the stress data monitored in real time. For example, it suggests relaxation techniques during times when stress increases. In this way, employees' stress can be effectively managed by monitoring stress levels in real time and providing immediate advice.

[0035] The data collection unit can analyze data on the work environment and interpersonal relationships to identify causes of stress in employees and propose specific countermeasures. The data collection unit, for example, collects data on the work environment and identifies causes of stress. For example, it analyzes the effects of noise and lighting. The data collection unit also analyzes data on interpersonal relationships to identify causes of stress. For example, it evaluates relationships with superiors and colleagues. The data collection unit also proposes specific countermeasures to stress based on the data on the work environment and interpersonal relationships. For example, it proposes changes to the workplace layout or measures to improve communication. In this way, by analyzing data on the work environment and interpersonal relationships, it is possible to identify causes of stress and propose specific countermeasures.

[0036] The data collection unit can take into account the hobbies and interests of employees and suggest relaxing activities. The data collection unit, for example, collects the hobbies and interests of employees and suggests relaxing activities based on the collected information. For example, listening to music or reading is recommended. The data collection unit also suggests relaxation activities that match the hobbies and interests. For example, gardening or cooking is recommended. The data collection unit also takes into account the hobbies and interests of employees and suggests individual relaxation activities. For example, arts and crafts are recommended. In this way, suggesting activities that take into account hobbies and interests promotes employee relaxation.

[0037] The data collection department can make suggestions for adjusting the workload at work according to the stress level of the employee. For example, the data collection department monitors the stress level of the employee and makes suggestions for adjusting the workload. For example, if stress is increasing, the data collection department may assign some of the work to other employees. The data collection department also makes suggestions for adjusting the workload according to the stress level. For example, the data collection department may assign an important project during periods of low stress. The data collection department also makes specific suggestions for adjusting the workload at work. For example, the data collection department may advise an employee experiencing increasing stress to take a vacation. In this way, by suggesting an adjustment of the workload according to the stress level, the stress of the employee is reduced.

[0038] The data collection department can quantitatively evaluate the effectiveness of employees' relaxation techniques and suggest the most effective techniques. For example, the data collection department builds a system that quantitatively evaluates the effectiveness of relaxation techniques practiced by employees. For example, it measures changes in heart rate and stress levels. The data collection department also evaluates the effectiveness of relaxation techniques and suggests the most effective techniques. For example, it compares the effects of meditation and yoga and selects the optimal technique. The data collection department also suggests individual relaxation techniques to employees based on the quantitative evaluation data. For example, it recommends deep breathing if stress is rising. In this way, by quantitatively evaluating the effectiveness of relaxation techniques, it is possible to suggest the most effective techniques.

[0039] The data collection department can analyze employees' level of mastery of relaxation techniques and provide step-by-step instruction according to their level of mastery. The data collection department, for example, builds a system that analyzes employees' level of mastery of relaxation techniques. For example, it evaluates the number of times and effects of meditation or yoga practice. The data collection department also provides step-by-step instruction according to their level of mastery. For example, it may teach basic breathing techniques to beginners and advanced meditation techniques to intermediate employees. The data collection department also suggests individual relaxation techniques to employees based on their level of mastery. For example, it may recommend advanced relaxation techniques to employees with a high level of mastery. In this way, employees can effectively master relaxation techniques by providing instruction according to their level of mastery.

[0040] The data collection department can use virtual reality to provide relaxation techniques and provide an immersive relaxation experience. For example, the data collection department can use virtual reality (VR) to provide an immersive relaxation experience to employees. For example, natural scenery or a relaxing environment can be recreated in VR. The data collection department can also use VR technology to provide guides for employees to practice relaxation techniques. For example, meditation or yoga instruction can be provided in VR. The data collection department can also use VR to provide an environment where employees can relax. For example, relaxation music or the sounds of nature can be recreated in VR. In this way, the use of virtual reality (VR) can provide an immersive relaxation experience.

[0041] The data collection department can share the effects of employees' relaxation techniques with the entire workplace and use them as part of team building. For example, the data collection department can build a system to share the effects of employees' relaxation techniques with the entire workplace. For example, it can provide a platform for sharing the results of practicing relaxation techniques. The data collection department can also use the effects of relaxation techniques as part of team building. For example, it can hold an event where the entire team practices relaxation techniques. The data collection department can also share the effects of relaxation techniques with the entire workplace and increase team cohesion. For example, by sharing the results of practicing relaxation techniques, it can promote communication between team members. In this way, the effects of relaxation techniques can be shared with the entire workplace and used as part of team building.

[0042] The data collection unit can periodically monitor employees' sleeping environments and provide optimization advice in response to changes in the environment. The data collection unit, for example, builds a system that periodically monitors employees' sleeping environments. For example, it collects data on temperature, humidity, and lighting. The data collection unit also provides optimization advice in response to changes in the environment. For example, it suggests how to select bedding depending on the change in season. The data collection unit also provides individual sleep environment optimization advice to employees based on the regularly monitored data. For example, it recommends using a dehumidifier when humidity is high. In this way, by periodically monitoring the sleeping environment and providing advice in response to changes in the environment, the quality of employees' sleep is improved.

[0043] The data collection unit can analyze the effects of sound and light in an employee's sleep environment and suggest the optimal environment. For example, the data collection unit builds a system that analyzes the effects of sound in an employee's sleep environment. For example, it measures noise levels and recommends a quiet environment. The data collection unit also analyzes the effects of light and suggests the optimal lighting environment. For example, it provides advice on adjusting bedroom lighting. The data collection unit also comprehensively analyzes the effects of sound and light and provides individual advice to employees to optimize their sleep environment. For example, it recommends the use of blackout curtains and earplugs. In this way, the optimal sleep environment can be suggested by analyzing the effects of sound and light.

[0044] The data collection unit can link smart home devices to automatically adjust the environment to optimize employees' sleeping environments. The data collection unit, for example, links smart home devices to build a system that automatically adjusts employees' sleeping environments. For example, it automatically adjusts the temperature and lighting. The data collection unit also uses smart home devices to optimize employees' sleeping environments. For example, it uses smart thermostats and smart lights. The data collection unit also links smart home devices to develop a system that adjusts employees' sleeping environments in real time. For example, it makes automatic adjustments based on environmental data during sleep. In this way, by linking smart home devices, it is possible to automatically optimize employees' sleeping environments.

[0045] The data collection department can share the optimization of employees' sleep environments with the entire workplace and use it to improve the office environment. For example, the data collection department can build a system to share optimization data of employees' sleep environments with the entire workplace. For example, it can share optimal temperature and lighting settings. The data collection department can also use the optimization data of sleep environments with the entire workplace to improve the office environment. For example, it can adjust the lighting and temperature in the office. The data collection department can also improve the environment of the entire workplace based on the optimization data of sleep environments. For example, it can propose changes to the office layout or the introduction of new equipment. In this way, the optimization data of sleep environments can be shared with the entire workplace and used to improve the office environment.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data collection department can take into account employees' hobbies and interests and suggest relaxing activities. For example, it collects employees' hobbies and interests and suggests relaxing activities based on them. For example, it can recommend listening to music or reading. The data collection department can also suggest relaxation activities that suit employees' hobbies and interests. For example, it can recommend gardening or cooking. The data collection department can also take into account employees' hobbies and interests and suggest individual relaxation activities. For example, it can recommend arts and crafts. In this way, it is possible to promote relaxation for employees by suggesting activities that take into account hobbies and interests.

[0048] The data collection unit can propose sleep improvement measures at home, taking into account the employee's family structure and living environment. For example, it proposes sleep improvement measures at home, taking into account the employee's family structure. For example, for families with small children, it can provide advice on how to get children to sleep. The data collection unit also analyzes the living environment and proposes sleep improvement measures at home. For example, in noisy environments, it can recommend earplugs or a white noise machine. The data collection unit also provides individual sleep improvement measures according to the family structure and living environment. For example, for dual-income households, it can suggest ways for the couple to cooperate and share housework. In this way, it is possible to improve sleep at home by providing advice that takes into account the family structure and living environment.

[0049] The data collection unit can suggest optimal sleep and rest times based on an employee's job content and work schedule. For example, it can analyze an employee's job content and work schedule to suggest optimal sleep times. For example, it can recommend daytime sleep for employees who work many night shifts. The data collection unit can also suggest appropriate rest times based on the work schedule. For example, it can advise employees who have a series of long meetings to take short breaks. The data collection unit can also suggest sleep times according to job content. For example, it can advise employees who do creative work to concentrate during quieter hours at night. In this way, it is possible to improve employee productivity by providing advice based on job content and work schedules.

[0050] The data collection unit can monitor employees' stress levels in real time and provide immediate advice when stress increases. For example, a system can be built that monitors employees' stress levels in real time. For example, devices that measure heart rate and electrodermal activity can be used. The data collection unit can also provide immediate advice when stress increases. For example, it can suggest deep breathing or meditation techniques. The data collection unit can also provide individual advice based on the stress data monitored in real time. For example, it can suggest relaxation techniques during times when stress increases. In this way, employees' stress can be effectively managed by monitoring stress levels in real time and providing immediate advice.

[0051] The data collection department can quantitatively evaluate the effectiveness of employees' relaxation techniques and suggest the most effective techniques. For example, a system can be built to quantitatively evaluate the effectiveness of relaxation techniques practiced by employees. For example, changes in heart rate and stress levels can be measured. The data collection department can also evaluate the effectiveness of relaxation techniques and suggest the most effective techniques. For example, it can compare the effects of meditation and yoga and select the most suitable technique. The data collection department can also suggest individual relaxation techniques to employees based on the quantitative evaluation data. For example, it can recommend deep breathing if stress is rising. In this way, the effectiveness of relaxation techniques can be quantitatively evaluated and the most effective techniques can be suggested.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The data collection unit collects information on employees' sleep data, stress levels, and lifestyle habits. For example, the data collection unit collects data using a device that records employees' sleep duration and sleep quality. The data collection unit may also use a device that records heart rate variability to measure employees' stress levels. Furthermore, the data collection unit may conduct a questionnaire to collect information on employees' lifestyle habits. For example, the data collection unit may collect information on employees' dietary and exercise habits. Step 2: The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit may analyze employees' sleep patterns using statistical analysis. The analysis unit may also predict employees' stress levels using machine learning algorithms. Furthermore, the analysis unit may assess health risks based on employees' lifestyle habits. For example, the analysis unit may assess health risks based on employees' dietary and exercise habits. Step 3: The advice providing unit provides appropriate advice based on the results of the analysis by the analysis unit. For example, the advice providing unit provides the employee with specific advice on how to improve their sleep. The advice providing unit can also provide the employee with specific advice on how to manage stress. Furthermore, the advice providing unit can also provide the employee with specific advice on how to improve their lifestyle habits. For example, the advice providing unit recommends that the employee eat a balanced diet and exercise moderately.

[0054] (Example 2) The sleep coaching app according to an embodiment of the present invention is a system that provides sleep improvement advice tailored to each employee. This system utilizes AI to collect and analyze information such as employees' sleep data, stress levels, and lifestyle habits, and provides appropriate advice. This allows the sleep coaching app to improve employee productivity and well-being.

[0055] A sleep coaching app according to an embodiment includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects information such as an employee's sleep data, stress level, and lifestyle habits. For example, the data collection unit collects data using a device that records the employee's sleep time and sleep quality. The data collection unit may also use a device that records heart rate variability to measure the employee's stress level. The data collection unit may also conduct a survey to collect information about the employee's lifestyle habits. For example, the data collection unit collects information about the employee's dietary and exercise habits. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit may analyze the employee's sleep patterns using statistical analysis. The analysis unit may also predict the employee's stress level using a machine learning algorithm. The analysis unit may also assess health risks based on the employee's lifestyle habits. For example, the analysis unit may assess health risks based on the employee's dietary and exercise habits. The advice provision unit provides appropriate advice based on the results of the analysis by the analysis unit. For example, the advice provision unit may provide the employee with specific advice for improving their sleep. The advice providing unit can also provide specific advice for stress management to employees. Furthermore, the advice providing unit can also provide specific advice for improving lifestyle habits to employees. For example, the advice providing unit recommends that employees eat a balanced diet and exercise moderately. As a result, the sleep coaching app according to the embodiment is expected to improve productivity and well-being by providing sleep improvement advice tailored to each employee.

[0056] The data collection unit compares an employee's past sleep data with their current lifestyle habits, analyzes long-term changes in sleep patterns, and can provide advice. For example, the data collection unit collects an employee's sleep data from the past year and compares it with their current lifestyle habits. For example, it analyzes data such as sleep duration, time to fall asleep, and time to wake up to identify long-term changes. The data collection unit also analyzes changes in sleep patterns based on the past sleep data and current lifestyle habits and suggests specific improvement measures. For example, if an employee has started staying up late more often, it will suggest advice to go to bed earlier and get up earlier. The data collection unit also analyzes long-term changes in sleep patterns and provides individual advice to the employee. For example, if stress is causing a decline in sleep quality, it will suggest stress management methods. This allows for more accurate advice to be provided by analyzing long-term changes in sleep patterns.

[0057] The data collection unit can suggest optimal sleep and rest periods based on an employee's job content and work schedule. The data collection unit, for example, analyzes an employee's job content and work schedule to suggest optimal sleep periods. For example, it may recommend daytime sleep for employees who work many night shifts. The data collection unit also suggests appropriate rest periods based on the work schedule. For example, it may advise employees to take short breaks in the event of a series of long meetings. The data collection unit also suggests sleep periods according to job content. For example, it may advise employees doing creative work to concentrate during quieter hours at night. In this way, by providing advice based on job content and work schedules, employee productivity is improved.

[0058] The data collection unit can use the emotion estimation function to analyze the emotional state of employees and provide sleep improvement advice according to their emotions. The data collection unit, for example, uses the emotion estimation function to analyze the emotional state of employees in real time. For example, if stress or anxiety is high, it suggests relaxation techniques. The data collection unit also provides sleep improvement advice according to the emotional state. For example, if positive emotions are strong, it recommends sleeping in a relaxed state. The data collection unit also provides individual sleep improvement advice to employees based on the emotion estimation data. For example, if negative emotions are strong, it suggests stress management methods. In this way, by providing advice according to the emotional state, the quality of employees' sleep is improved.

[0059] The data collection unit can also analyze employees' dietary and exercise habits and provide comprehensive health management advice. For example, the data collection unit collects employees' dietary data and provides dietary advice that helps improve sleep. For example, it may suggest limiting caffeine intake. The data collection unit may also analyze exercise habits and provide exercise advice that helps improve sleep. For example, it may explain that moderate exercise improves sleep quality. The data collection unit may also comprehensively analyze dietary and exercise habits and provide health management advice. For example, it may emphasize the importance of a balanced diet and moderate exercise. In this way, advice that takes dietary and exercise habits into consideration improves the overall health of employees.

[0060] The data collection unit can propose sleep improvement measures at home, taking into account the employee's family structure and living environment. The data collection unit, for example, proposes sleep improvement measures at home, taking into account the employee's family structure. For example, for families with small children, it provides advice on how to get children to sleep. The data collection unit also analyzes the living environment and proposes sleep improvement measures at home. For example, in noisy environments, it recommends earplugs or a white noise machine. The data collection unit also provides individual sleep improvement measures according to the family structure and living environment. For example, for dual-income households, it suggests ways for the couple to cooperate and share housework. In this way, by providing advice that takes into account the family structure and living environment, sleep improvement at home is achieved.

[0061] The data collection unit can use the emotion estimation function to analyze the emotions of employees when they use the app in real time and provide advice to elicit positive emotions. The data collection unit, for example, uses the emotion estimation function to analyze the emotions of employees when they use the app in real time. For example, if stress is rising, advice to relax is provided. The data collection unit also provides advice to elicit positive emotions. For example, a message encouraging gratitude or positive thinking is displayed. The data collection unit also provides individual advice to employees based on the emotion estimation data. For example, if negative emotions are strong, relaxation techniques are suggested. In this way, advice to elicit positive emotions is provided, thereby improving employee well-being.

[0062] The data collection unit can monitor employees' stress levels in real time and provide immediate advice when stress increases. The data collection unit, for example, builds a system that monitors employees' stress levels in real time. For example, it uses devices that measure heart rate and electrodermal activity. The data collection unit also provides immediate advice when stress increases. For example, it suggests deep breathing or meditation techniques. The data collection unit also provides individual advice based on the stress data monitored in real time. For example, it suggests relaxation techniques during times when stress increases. In this way, employees' stress can be effectively managed by monitoring stress levels in real time and providing immediate advice.

[0063] The data collection unit can analyze data on the work environment and interpersonal relationships to identify causes of stress in employees and propose specific countermeasures. The data collection unit, for example, collects data on the work environment and identifies causes of stress. For example, it analyzes the effects of noise and lighting. The data collection unit also analyzes data on interpersonal relationships to identify causes of stress. For example, it evaluates relationships with superiors and colleagues. The data collection unit also proposes specific countermeasures to stress based on the data on the work environment and interpersonal relationships. For example, it proposes changes to the workplace layout or measures to improve communication. In this way, by analyzing data on the work environment and interpersonal relationships, it is possible to identify causes of stress and propose specific countermeasures.

[0064] The data collection unit can use the emotion estimation function to analyze the emotional state of employees and provide stress management advice according to the emotions. The data collection unit, for example, uses the emotion estimation function to analyze the emotional state of employees in real time. For example, if stress or anxiety is high, relaxation techniques are suggested. The data collection unit also provides stress management advice according to the emotional state. For example, if positive emotions are strong, a stress management method in a relaxed state is recommended. The data collection unit also provides individual stress management advice to employees based on the emotion estimation data. For example, if negative emotions are strong, a stress management method is suggested. In this way, by providing stress management advice according to the emotional state, employees' stress can be effectively managed.

[0065] The data collection unit can take into account the hobbies and interests of employees and suggest relaxing activities. The data collection unit, for example, collects the hobbies and interests of employees and suggests relaxing activities based on the collected information. For example, listening to music or reading is recommended. The data collection unit also suggests relaxation activities that match the hobbies and interests. For example, gardening or cooking is recommended. The data collection unit also takes into account the hobbies and interests of employees and suggests individual relaxation activities. For example, arts and crafts are recommended. In this way, suggesting activities that take into account hobbies and interests promotes employee relaxation.

[0066] The data collection department can make suggestions for adjusting the workload at work according to the stress level of the employee. For example, the data collection department monitors the stress level of the employee and makes suggestions for adjusting the workload. For example, if stress is increasing, the data collection department may assign some of the work to other employees. The data collection department also makes suggestions for adjusting the workload according to the stress level. For example, the data collection department may assign an important project during periods of low stress. The data collection department also makes specific suggestions for adjusting the workload at work. For example, the data collection department may advise an employee experiencing increasing stress to take a vacation. In this way, by suggesting an adjustment of the workload according to the stress level, the stress of the employee is reduced.

[0067] The data collection unit can use the emotion estimation function to analyze the emotions of employees when they feel stressed in real time and provide advice to elicit positive emotions. The data collection unit, for example, uses the emotion estimation function to analyze the emotions of employees when they feel stressed in real time. For example, if stress is rising, advice to relax is provided. The data collection unit also provides advice to elicit positive emotions. For example, a message encouraging gratitude or positive thinking is displayed. The data collection unit also provides individual advice to employees based on the emotion estimation data. For example, if negative emotions are strong, relaxation techniques are suggested. In this way, advice to elicit positive emotions is provided, thereby reducing stress in employees.

[0068] The data collection department can quantitatively evaluate the effectiveness of employees' relaxation techniques and suggest the most effective techniques. For example, the data collection department builds a system that quantitatively evaluates the effectiveness of relaxation techniques practiced by employees. For example, it measures changes in heart rate and stress levels. The data collection department also evaluates the effectiveness of relaxation techniques and suggests the most effective techniques. For example, it compares the effects of meditation and yoga and selects the optimal technique. The data collection department also suggests individual relaxation techniques to employees based on the quantitative evaluation data. For example, it recommends deep breathing if stress is rising. In this way, by quantitatively evaluating the effectiveness of relaxation techniques, it is possible to suggest the most effective techniques.

[0069] The data collection department can analyze employees' level of mastery of relaxation techniques and provide step-by-step instruction according to their level of mastery. The data collection department, for example, builds a system that analyzes employees' level of mastery of relaxation techniques. For example, it evaluates the number of times and effects of meditation or yoga practice. The data collection department also provides step-by-step instruction according to their level of mastery. For example, it may teach basic breathing techniques to beginners and advanced meditation techniques to intermediate employees. The data collection department also suggests individual relaxation techniques to employees based on their level of mastery. For example, it may recommend advanced relaxation techniques to employees with a high level of mastery. In this way, employees can effectively master relaxation techniques by providing instruction according to their level of mastery.

[0070] The data collection unit can use the emotion estimation function to analyze the emotional state of employees and provide relaxation techniques according to the emotions. The data collection unit, for example, uses the emotion estimation function to analyze the emotional state of employees in real time. For example, if stress or anxiety is high, it suggests relaxation techniques. The data collection unit also provides relaxation techniques according to the emotional state. For example, if positive emotions are strong, it recommends relaxation methods in a relaxed state. The data collection unit also provides individual relaxation techniques to employees based on the emotion estimation data. For example, if negative emotions are strong, it suggests meditation or deep breathing. In this way, by providing relaxation techniques according to the emotional state, the relaxation effect of employees is enhanced.

[0071] The data collection department can use virtual reality to provide relaxation techniques and provide an immersive relaxation experience. For example, the data collection department can use virtual reality (VR) to provide an immersive relaxation experience to employees. For example, natural scenery or a relaxing environment can be recreated in VR. The data collection department can also use VR technology to provide guides for employees to practice relaxation techniques. For example, meditation or yoga instruction can be provided in VR. The data collection department can also use VR to provide an environment where employees can relax. For example, relaxation music or the sounds of nature can be recreated in VR. In this way, the use of virtual reality (VR) can provide an immersive relaxation experience.

[0072] The data collection department can share the effects of employees' relaxation techniques with the entire workplace and use them as part of team building. For example, the data collection department can build a system to share the effects of employees' relaxation techniques with the entire workplace. For example, it can provide a platform for sharing the results of practicing relaxation techniques. The data collection department can also use the effects of relaxation techniques as part of team building. For example, it can hold an event where the entire team practices relaxation techniques. The data collection department can also share the effects of relaxation techniques with the entire workplace and increase team cohesion. For example, by sharing the results of practicing relaxation techniques, it can promote communication between team members. In this way, the effects of relaxation techniques can be shared with the entire workplace and used as part of team building.

[0073] The data collection unit can use the emotion estimation function to analyze emotions in real time when employees practice relaxation techniques, and provide technology to elicit positive emotions. For example, the data collection unit uses the emotion estimation function to analyze emotions in real time when employees practice relaxation techniques. For example, if stress is rising, technology to help employees relax is provided. The data collection unit also provides relaxation technology to elicit positive emotions. For example, a message encouraging gratitude or positive thinking is displayed. The data collection unit also provides individual relaxation techniques to employees based on the emotion estimation data. For example, if negative emotions are strong, meditation or deep breathing is suggested. In this way, technology to elicit positive emotions is provided, thereby enhancing the relaxation effect of employees.

[0074] The data collection unit can periodically monitor employees' sleeping environments and provide optimization advice in response to changes in the environment. The data collection unit, for example, builds a system that periodically monitors employees' sleeping environments. For example, it collects data on temperature, humidity, and lighting. The data collection unit also provides optimization advice in response to changes in the environment. For example, it suggests how to select bedding depending on the change in season. The data collection unit also provides individual sleep environment optimization advice to employees based on the regularly monitored data. For example, it recommends using a dehumidifier when humidity is high. In this way, by periodically monitoring the sleeping environment and providing advice in response to changes in the environment, the quality of employees' sleep is improved.

[0075] The data collection unit can analyze the effects of sound and light in an employee's sleep environment and suggest the optimal environment. For example, the data collection unit builds a system that analyzes the effects of sound in an employee's sleep environment. For example, it measures noise levels and recommends a quiet environment. The data collection unit also analyzes the effects of light and suggests the optimal lighting environment. For example, it provides advice on adjusting bedroom lighting. The data collection unit also comprehensively analyzes the effects of sound and light and provides individual advice to employees to optimize their sleep environment. For example, it recommends the use of blackout curtains and earplugs. In this way, the optimal sleep environment can be suggested by analyzing the effects of sound and light.

[0076] The data collection unit can link smart home devices to automatically adjust the environment to optimize employees' sleeping environments. The data collection unit, for example, links smart home devices to build a system that automatically adjusts employees' sleeping environments. For example, it automatically adjusts the temperature and lighting. The data collection unit also uses smart home devices to optimize employees' sleeping environments. For example, it uses smart thermostats and smart lights. The data collection unit also links smart home devices to develop a system that adjusts employees' sleeping environments in real time. For example, it makes automatic adjustments based on environmental data during sleep. In this way, by linking smart home devices, it is possible to automatically optimize employees' sleeping environments.

[0077] The data collection department can share the optimization of employees' sleep environments with the entire workplace and use it to improve the office environment. For example, the data collection department can build a system to share optimization data of employees' sleep environments with the entire workplace. For example, it can share optimal temperature and lighting settings. The data collection department can also use the optimization data of sleep environments with the entire workplace to improve the office environment. For example, it can adjust the lighting and temperature in the office. The data collection department can also improve the environment of the entire workplace based on the optimization data of sleep environments. For example, it can propose changes to the office layout or the introduction of new equipment. In this way, the optimization data of sleep environments can be shared with the entire workplace and used to improve the office environment.

[0078] The data collection unit can use the emotion estimation function to analyze in real time the emotions that employees feel when adjusting their sleep environment, and provide environmental adjustments that elicit positive emotions. For example, the data collection unit uses the emotion estimation function to analyze in real time the emotions that employees feel when adjusting their sleep environment. For example, if stress is high, environmental adjustments are provided to help employees relax. The data collection unit also provides environmental adjustments to elicit positive emotions. For example, messages encouraging gratitude and positive thinking are displayed. The data collection unit also provides individual environmental adjustments to employees based on the emotion estimation data. For example, if negative emotions are strong, relaxing music or aromas are suggested. In this way, environmental adjustments that elicit positive emotions are provided, thereby improving the quality of employees' sleep.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The data collection department can take into account employees' hobbies and interests and suggest relaxing activities. For example, it collects employees' hobbies and interests and suggests relaxing activities based on them. For example, it can recommend listening to music or reading. The data collection department can also suggest relaxation activities that suit employees' hobbies and interests. For example, it can recommend gardening or cooking. The data collection department can also take into account employees' hobbies and interests and suggest individual relaxation activities. For example, it can recommend arts and crafts. In this way, it is possible to promote relaxation for employees by suggesting activities that take into account hobbies and interests.

[0081] The data collection unit can propose sleep improvement measures at home, taking into account the employee's family structure and living environment. For example, it proposes sleep improvement measures at home, taking into account the employee's family structure. For example, for families with small children, it can provide advice on how to get children to sleep. The data collection unit also analyzes the living environment and proposes sleep improvement measures at home. For example, in noisy environments, it can recommend earplugs or a white noise machine. The data collection unit also provides individual sleep improvement measures according to the family structure and living environment. For example, for dual-income households, it can suggest ways for the couple to cooperate and share housework. In this way, it is possible to improve sleep at home by providing advice that takes into account the family structure and living environment.

[0082] The data collection unit can suggest optimal sleep and rest times based on an employee's job content and work schedule. For example, it can analyze an employee's job content and work schedule to suggest optimal sleep times. For example, it can recommend daytime sleep for employees who work many night shifts. The data collection unit can also suggest appropriate rest times based on the work schedule. For example, it can advise employees who have a series of long meetings to take short breaks. The data collection unit can also suggest sleep times according to job content. For example, it can advise employees who do creative work to concentrate during quieter hours at night. In this way, it is possible to improve employee productivity by providing advice based on job content and work schedules.

[0083] The data collection unit can monitor employees' stress levels in real time and provide immediate advice when stress increases. For example, a system can be built that monitors employees' stress levels in real time. For example, devices that measure heart rate and electrodermal activity can be used. The data collection unit can also provide immediate advice when stress increases. For example, it can suggest deep breathing or meditation techniques. The data collection unit can also provide individual advice based on the stress data monitored in real time. For example, it can suggest relaxation techniques during times when stress increases. In this way, employees' stress can be effectively managed by monitoring stress levels in real time and providing immediate advice.

[0084] The data collection department can quantitatively evaluate the effectiveness of employees' relaxation techniques and suggest the most effective techniques. For example, a system can be built to quantitatively evaluate the effectiveness of relaxation techniques practiced by employees. For example, changes in heart rate and stress levels can be measured. The data collection department can also evaluate the effectiveness of relaxation techniques and suggest the most effective techniques. For example, it can compare the effects of meditation and yoga and select the most suitable technique. The data collection department can also suggest individual relaxation techniques to employees based on the quantitative evaluation data. For example, it can recommend deep breathing if stress is rising. In this way, the effectiveness of relaxation techniques can be quantitatively evaluated and the most effective techniques can be suggested.

[0085] The data collection unit can use the emotion estimation function to analyze the emotional state of employees and provide sleep improvement advice according to their emotions. For example, the emotion estimation function is used to analyze the emotional state of employees in real time. For example, if stress or anxiety is high, relaxation techniques are suggested. The data collection unit also provides sleep improvement advice according to the emotional state. For example, if positive emotions are strong, it recommends sleeping in a relaxed state. The data collection unit also provides individual sleep improvement advice to employees based on the emotion estimation data. For example, if negative emotions are strong, it suggests stress management methods. In this way, by providing advice according to the emotional state, it is possible to improve the quality of employees' sleep.

[0086] The data collection unit can use the emotion estimation function to analyze the emotions of employees when they use the app in real time and provide advice to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions of employees when they use the app in real time. For example, if stress is rising, advice to relax is provided. The data collection unit also provides advice to elicit positive emotions. For example, a message encouraging gratitude or positive thinking is displayed. The data collection unit also provides individual advice to employees based on the emotion estimation data. For example, if negative emotions are strong, relaxation techniques are suggested. In this way, by providing advice to elicit positive emotions, it is possible to improve employee well-being.

[0087] The data collection unit can use the emotion estimation function to analyze the emotional state of employees and provide stress management advice according to their emotions. For example, the emotion estimation function is used to analyze the emotional state of employees in real time. For example, if stress or anxiety is high, relaxation techniques are suggested. The data collection unit also provides stress management advice according to their emotional state. For example, if positive emotions are strong, stress management methods in a relaxed state are recommended. The data collection unit also provides individual stress management advice to employees based on the emotion estimation data. For example, if negative emotions are strong, stress management methods are suggested. In this way, by providing stress management advice according to their emotional state, employees' stress can be effectively managed.

[0088] The data collection unit can use the emotion estimation function to analyze the emotions of employees when they feel stressed in real time and provide advice to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions of employees when they feel stressed in real time. For example, if stress is rising, advice to relax is provided. The data collection unit also provides advice to elicit positive emotions. For example, a message encouraging gratitude or positive thinking is displayed. The data collection unit also provides individual advice to employees based on the emotion estimation data. For example, if negative emotions are strong, relaxation techniques are suggested. In this way, by providing advice to elicit positive emotions, stress in employees can be reduced.

[0089] The data collection unit can use the emotion estimation function to analyze the emotional state of employees and provide relaxation techniques according to the emotions. For example, the emotion estimation function is used to analyze the emotional state of employees in real time. For example, if stress or anxiety is high, relaxation techniques are suggested. The data collection unit also provides relaxation techniques according to the emotional state. For example, if positive emotions are strong, relaxation methods in a relaxed state are recommended. The data collection unit also provides individual relaxation techniques to employees based on the emotion estimation data. For example, if negative emotions are strong, meditation or deep breathing is suggested. In this way, by providing relaxation techniques according to the emotional state, the relaxation effect of employees can be increased.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The data collection unit collects information on employees' sleep data, stress levels, and lifestyle habits. For example, the data collection unit collects data using a device that records employees' sleep duration and sleep quality. The data collection unit may also use a device that records heart rate variability to measure employees' stress levels. Furthermore, the data collection unit may conduct a questionnaire to collect information on employees' lifestyle habits. For example, the data collection unit may collect information on employees' dietary and exercise habits. Step 2: The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit may analyze employees' sleep patterns using statistical analysis. The analysis unit may also predict employees' stress levels using machine learning algorithms. Furthermore, the analysis unit may assess health risks based on employees' lifestyle habits. For example, the analysis unit may assess health risks based on employees' dietary and exercise habits. Step 3: The advice providing unit provides appropriate advice based on the results of the analysis by the analysis unit. For example, the advice providing unit provides the employee with specific advice on how to improve their sleep. The advice providing unit can also provide the employee with specific advice on how to manage stress. Furthermore, the advice providing unit can also provide the employee with specific advice on how to improve their lifestyle habits. For example, the advice providing unit recommends that the employee eat a balanced diet and exercise moderately.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] 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.

[0151] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection department that collects employee sleep data, stress levels, and lifestyle information; an analysis unit that analyzes the information collected by the data collection unit; an advice providing unit that provides appropriate advice based on the results of the analysis by the analysis unit; A system characterized by:

2. The data collection unit Monitor employee stress levels in real time and provide immediate advice when stress levels rise The system of claim 1 .

3. The data collection unit To quantitatively evaluate the effectiveness of relaxation techniques for employees and propose the most effective techniques. The system of claim 1 .

4. The data collection unit Regularly monitor employees' sleep environments and provide optimization advice in response to changes in the environment. The system of claim 1 .

5. The data collection unit Analyzing employees' emotional state and providing sleep improvement advice according to their emotions The system of claim 1 .

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