System

Abstract

An object of a system according to an embodiment is to analyze sleep data and lifestyle data of an employee and provide individual advice.SOLUTION: A system includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects sleep data and lifestyle data of an employee. The analysis unit analyzes the data collected by the data collection unit. The advice providing unit provides individual 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] Conventional technologies do not adequately provide effective solutions to the reduced productivity and stress caused by lack of sleep among employees, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze sleep data and lifestyle habit data of employees and provide them with individualized 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 sleep data and lifestyle habit data of employees. The analysis unit analyzes the data collected by the data collection unit. The advice provision unit provides individual 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 sleep data and lifestyle habit data of employees and provide personalized 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) A sleep improvement system according to an embodiment of the present invention is a sleep improvement program provided to companies and their employees, and uses generative AI to provide personalized advice. This system solves the problems of reduced productivity and stress caused by lack of sleep, and provides business benefits to companies, such as increased productivity, improved employee happiness, and improved retention rates.

[0029] A sleep improvement system according to an embodiment includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects sleep data and lifestyle habit data of employees. For example, the data collection unit collects sleep data recorded by employees using smartwatches or smartphones. The data collection unit can also collect lifestyle habit data, such as employees' diet, exercise, smoking, and drinking habits. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the sleep data and lifestyle habit data of employees using statistical analysis or machine learning algorithms. The analysis unit can also analyze the employee's emotional data and provide sleep improvement advice based on the employee's emotional state. The advice provision unit provides personalized advice based on the results of the analysis by the analysis unit. For example, the advice provision unit can suggest optimal sleep habits and improvement measures to the employee. The advice provision unit can also suggest relaxation methods based on the employee's emotional state. As a result, the sleep improvement system according to an embodiment can resolve productivity declines and stress caused by lack of sleep by analyzing the employee's sleep data and lifestyle habit data and providing personalized advice.

[0030] The data collection unit collects the employee's past sleep data and current lifestyle habits, and the analysis unit compares the past sleep data with the current lifestyle habits to propose long-term improvement measures. The data collection unit, for example, collects the employee's past sleep data and current lifestyle habits. For example, the data collection unit collects data such as sleep duration and sleep quality over the past year. The data collection unit can also collect lifestyle habits such as the employee's current diet, exercise, smoking, and drinking habits. The analysis unit, for example, compares the collected past sleep data with the employee's current lifestyle habits to propose long-term improvement measures. For example, the analysis unit can identify the cause of declining sleep quality from the past data and propose a lifestyle review. The analysis unit can also adjust the employee's current lifestyle habits by referring to lifestyle habits from times when the employee had good sleep in the past. In this way, long-term improvement measures can be proposed by comparing the past data with the employee's current lifestyle habits.

[0031] The data collection unit collects the job details of employees, and the analysis unit can customize an optimal sleep schedule based on the job details. The data collection unit, for example, collects the job details of employees. For example, the data collection unit collects data such as the type of work, working hours, and workload of employees. The analysis unit customizes an optimal sleep schedule based on the collected job details. For example, it recommends daytime sleep for employees who work many night shifts, and nighttime sleep for employees who work many day shifts. The analysis unit can also suggest taking a short nap if a project deadline is approaching. In this way, an optimal sleep schedule can be customized based on the job details of employees.

[0032] The data collection unit collects employee dietary data, and the analysis unit can analyze the association between the dietary data and sleep data and provide advice. The data collection unit, for example, collects employee dietary data. For example, the data collection unit collects data such as the employee's dietary content, meal timing, and nutrient intake. The analysis unit, for example, analyzes the association between the collected dietary data and sleep data and provides advice. For example, the analysis unit analyzes the impact of a specific meal on sleep quality and suggests dietary improvement measures. The analysis unit can also analyze the impact of dinner time and content on sleep and suggest optimal meal times. In this way, by analyzing the association between dietary data and sleep data, it is possible to suggest dietary improvement measures.

[0033] The data collection unit collects the employee's exercise data, and the analysis unit can analyze the correlation between the exercise data and sleep data and provide advice. The data collection unit, for example, collects the employee's exercise data. For example, the data collection unit collects data such as the employee's type of exercise, exercise time, and exercise intensity. The analysis unit, for example, analyzes the correlation between the collected exercise data and sleep data and provides advice. For example, it analyzes the effect of exercise frequency and intensity on sleep and suggests exercise improvement measures. The analysis unit can also analyze the effect of exercise timing and type on sleep and suggest optimal exercise habits. In this way, by analyzing the correlation between exercise data and sleep data, it is possible to suggest exercise improvement measures.

[0034] The data collection unit collects the employee's sleep data in real time, and the analysis unit can instantly analyze the sleep data and provide feedback. The data collection unit, for example, collects the employee's sleep data in real time. For example, the data collection unit collects data such as the employee's heart rate and breathing rate in real time. The analysis unit, for example, instantly analyzes the collected sleep data and provides feedback. For example, the analysis unit analyzes the heart rate and breathing rate during sleep to evaluate the quality of sleep. The analysis unit can also issue an alert if an abnormality is detected during sleep. In this way, by collecting sleep data in real time and providing instant feedback, the quality of sleep can be improved.

[0035] The data collection unit collects employee lifestyle data over a long period of time, and the analysis unit can analyze the lifestyle data to identify trends. The data collection unit, for example, collects employee lifestyle data over a long period of time. For example, the data collection unit collects data on employees' diet, exercise, sleep, etc. over a period of several months to several years. The analysis unit, for example, analyzes the collected lifestyle data to identify trends. For example, the data on diet, exercise, and sleep can be integrated to suggest lifestyle improvement measures. The analysis unit can also analyze seasonal variations in lifestyle habits and suggest optimal lifestyle habits. In this way, by analyzing long-term data, lifestyle trends can be identified and improvement measures can be suggested.

[0036] The data collection unit anonymizes employee data, and the analysis unit analyzes the anonymized data to provide advice while protecting privacy. The data collection unit, for example, anonymizes employee data. For example, the data collection unit deletes personal identifying information and masks the data. The analysis unit analyzes the anonymized data to provide advice while protecting privacy. For example, the analysis unit collects data in a form that does not identify individuals and provides the analysis results. The analysis unit can also encrypt the data to protect personal information. By anonymizing the data, analysis can be performed while protecting privacy, creating an environment in which data can be provided with peace of mind.

[0037] The data collection unit collects employee health data, and the analysis unit can integrate and analyze the health data and sleep data. The data collection unit, for example, collects employee health data. For example, the data collection unit collects data such as the employee's heart rate, blood pressure, and body temperature. The analysis unit, for example, integrates and analyzes the collected health data and sleep data to evaluate the employee's overall health condition. For example, the analysis unit evaluates the quality of sleep based on the heart rate and blood pressure data. The analysis unit can also analyze the impact of fluctuations in heart rate and blood pressure on sleep and suggest measures to improve the health condition. In this way, by integrating and analyzing the health data and sleep data, the employee can evaluate the employee's overall health condition and suggest measures to improve it.

[0038] The data collection unit collects employee data by region, and the analysis unit analyzes the data to provide advice based on lifestyle habits specific to the region. The data collection unit, for example, collects employee data by region. For example, the data collection unit collects data by region, such as by city, prefecture, or country. The analysis unit, for example, analyzes the collected data to provide advice based on lifestyle habits specific to the region. For example, it proposes lifestyle improvement measures tailored to the climate and culture of each region. The analysis unit can also provide advice that takes into account the dietary and exercise habits of each region. As a result, by providing advice based on lifestyle habits specific to each region, more effective improvement measures can be proposed.

[0039] The analysis unit can analyze employees' work performance data and quantify the impact of sleep improvement on productivity. For example, the analysis unit can analyze employees' work performance data and quantify the impact of sleep improvement on productivity. For example, it can numerically show the change in work efficiency when sleep quality improves. The analysis unit can also quantify the improvement in concentration and judgment when sleep time increases. This makes it possible to quantify the impact of sleep improvement on productivity and propose specific improvement measures.

[0040] The analysis unit can analyze an employee's work schedule and suggest the optimal timing for taking a break. The analysis unit, for example, analyzes an employee's work schedule and suggests the optimal timing for taking a break. For example, it can recommend taking a break at a time when concentration levels are low. The analysis unit can also show that taking a short break after a long meeting improves work efficiency. This makes it possible to improve productivity by suggesting the optimal timing for taking a break.

[0041] The analysis unit can analyze the employee's concentration data and suggest sleeping habits to maintain concentration. The analysis unit, for example, analyzes the employee's concentration data and suggests sleeping habits to maintain concentration. For example, it suggests a sleep schedule tailored to the time periods when concentration is low. The analysis unit can also show that improving sleep quality will extend the duration of concentration. This makes it possible to improve productivity by suggesting sleeping habits to maintain concentration.

[0042] The analysis unit can analyze data for the entire employee team and propose measures to improve productivity for the entire team. For example, the analysis unit can analyze data for the entire employee team and propose measures to improve productivity for the entire team. For example, the analysis unit can propose optimal meeting times based on the sleep data of the entire team. The analysis unit can also propose schedule adjustments that take into account the sleep patterns of team members. In this way, by analyzing the data of the entire team, it is possible to propose measures to improve productivity for the entire team.

[0043] The analysis unit can analyze the work content of employees and provide customized advice to improve productivity. The analysis unit can, for example, analyze the work content of employees and provide customized advice to improve productivity. For example, the analysis unit can suggest relaxing breaks for creative work. The analysis unit can also recommend regular stretching for employees who do a lot of desk work. In this way, work efficiency can be improved by providing customized advice to improve productivity according to the work content.

[0044] The analysis unit can analyze employees' stress levels and suggest sleep improvement measures to reduce stress. The analysis unit, for example, analyzes employees' stress levels and suggests sleep improvement measures to reduce stress. For example, if stress is high, it can suggest a sleeping environment that has a relaxing effect. The analysis unit can also integrate stress levels and sleep data to show that sleep quality declines during periods of high stress and suggest stress reduction measures. In this way, by analyzing stress levels and suggesting sleep improvement measures to reduce stress, it is possible to improve employees' mental health.

[0045] The analysis unit can analyze employees' mental health data and suggest sleeping habits to improve their mental health. For example, the analysis unit can analyze employees' mental health data and suggest sleeping habits to improve their mental health. For example, if mental health is declining, the analysis unit can suggest a sleeping environment that has a relaxing effect. The analysis unit can also integrate mental health data and sleep data to indicate that sleep quality declines when mental health is declining and suggest measures to improve mental health. In this way, by analyzing mental health data and suggesting sleeping habits to improve mental health, it is possible to improve employees' mental health.

[0046] The analysis unit can analyze employee happiness data and provide a specific action plan to improve happiness. For example, the analysis unit can analyze employee happiness data and provide a specific action plan to improve happiness. For example, if happiness is declining, the analysis unit can suggest activities that have a relaxing effect. The analysis unit can also integrate happiness data with sleep data, indicate that sleep quality declines when happiness levels decline, and suggest measures to improve happiness. In this way, employee happiness can be improved by analyzing happiness data and providing a specific action plan.

[0047] The analysis unit can analyze the employee's family data and propose measures to improve the happiness of the entire family. For example, the analysis unit can analyze the employee's family data and propose measures to improve the happiness of the entire family. For example, it can propose an environment where the whole family can relax. The analysis unit can also integrate the family data and happiness data and propose activities that have a relaxing effect when the happiness of the whole family is declining. In this way, by analyzing the family data and proposing measures to improve the happiness of the whole family, it is possible to improve the mental health of the whole family.

[0048] The analysis unit can analyze employees' hobbies and interests and suggest relaxation methods. For example, the analysis unit can analyze employees' hobbies and interests and suggest relaxation methods. For example, it can suggest activities related to hobbies. For example, it can recommend listening to music that has a relaxing effect to an employee who likes music. The analysis unit can also integrate hobby and interest data with happiness data, analyze the impact of hobby-related activities on happiness, and suggest optimal relaxation methods. In this way, by analyzing hobbies and interests and suggesting relaxation methods, it is possible to improve employee happiness.

[0049] The analysis unit can analyze employee turnover risk and propose sleep improvement measures to reduce the risk. The analysis unit, for example, analyzes employee turnover risk and proposes sleep improvement measures to reduce the risk. For example, it can propose a relaxing sleep environment for employees at high turnover risk. The analysis unit can also integrate turnover risk data with sleep data, show that sleep quality declines during periods of high turnover risk, and propose risk reduction measures. In this way, by analyzing turnover risk and proposing sleep improvement measures to reduce the risk, it is possible to reduce the turnover rate.

[0050] The analysis unit can analyze employees' career data and provide advice to improve career satisfaction. The analysis unit, for example, analyzes employees' career data and provides advice to improve career satisfaction. For example, if career satisfaction is declining, it can suggest a review of career paths. The analysis unit can also integrate career data with happiness data, and, if career satisfaction is declining, can suggest a review of career paths or skill development. In this way, by analyzing career data and providing advice to improve career satisfaction, it is possible to reduce employee turnover.

[0051] The analysis unit can analyze employee feedback data and propose measures to improve retention rates based on the feedback. The analysis unit, for example, analyzes employee feedback data and proposes measures to improve retention rates based on the feedback. For example, it can propose improvements to the work environment based on the feedback. The analysis unit can also integrate the feedback data with happiness data and propose improvements to the work environment or revisions to work content based on the feedback. In this way, by analyzing the feedback data and proposing measures to improve retention rates based on the feedback, it is possible to reduce the turnover rate.

[0052] The analysis unit can analyze employee benefit data and propose improvement measures for employee benefits. The analysis unit, for example, analyzes employee benefit data and proposes improvement measures for employee benefits. For example, if employee benefits are insufficient, it proposes enhancing employee benefits. The analysis unit can also integrate employee benefit data with happiness data and propose enhancing employee benefits when the benefits are insufficient. In this way, by analyzing employee benefit data and proposing improvement measures for employee benefits, it is possible to reduce employee turnover.

[0053] The analysis unit can analyze the employee's work environment data and propose improvements to the work environment. The analysis unit, for example, analyzes the employee's work environment data and proposes improvements to the work environment. For example, if the work environment is insufficient, it proposes improvements to the work environment. The analysis unit can also integrate the work environment data with the happiness data and propose improvements to the work environment when the work environment is insufficient. In this way, by analyzing the work environment data and proposing improvements to the work environment, it is possible to reduce the turnover rate.

[0054] The analysis unit can analyze market data and predict demand for sleep improvement programs. The analysis unit, for example, analyzes market data and predicts demand for sleep improvement programs. For example, future demand is predicted based on past market data. The analysis unit can also integrate market data and sleep data to predict demand for sleep improvement programs in a specific market. In this way, future demand can be predicted by analyzing market data and predicting demand for sleep improvement programs.

[0055] The analysis unit can analyze technological advances and propose sleep improvement measures that incorporate new technologies. The analysis unit, for example, analyzes technological advances and proposes sleep improvement measures that incorporate new technologies. For example, it can propose collecting sleep data using new sensor technology. The analysis unit can also integrate data on technological advances and sleep data and propose collecting sleep data using new sensor technology. In this way, by analyzing technological advances and proposing sleep improvement measures that incorporate new technologies, it is possible to predict future demand.

[0056] The analysis unit can analyze competitor data and propose strategies to enhance competitive advantage. For example, the analysis unit can analyze competitor data and propose strategies to enhance competitive advantage. For example, the analysis unit can propose strategies to enhance competitive advantage based on data on competitor products and services. The analysis unit can also integrate competitor data with market data and propose strategies to enhance competitive advantage based on data on competitor products and services. In this way, future demand can be predicted by analyzing competitor data and proposing strategies to enhance competitive advantage.

[0057] The analysis unit can analyze data from different industries to explore applicability in other industries. For example, the analysis unit analyzes data from different industries to explore applicability in other industries. For example, the analysis unit can analyze data from different industries to explore applicability in other industries based on data from different industries. For example, the analysis unit can analyze data from the medical industry to explore applicability in other industries for sleep improvement programs. The analysis unit can also integrate data from different industries with market data and explore applicability in other industries based on the data from different industries. In this way, by analyzing data from different industries and exploring applicability in other industries, future demand can be predicted.

[0058] The analysis unit can analyze international market data and propose a strategy for global expansion. The analysis unit, for example, analyzes international market data and proposes a strategy for global expansion. For example, a strategy for global expansion is proposed based on the international market data. For example, the analysis unit can analyze market needs in a specific country or region and propose an optimal expansion strategy. The analysis unit can also integrate international market data and market data, analyze market needs in a specific country or region, and propose an optimal expansion strategy. In this way, by analyzing international market data and proposing a strategy for global expansion, future demand can be predicted.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The sleep improvement system may further include an environmental data collection unit. The environmental data collection unit collects data on employees' sleeping environments, and the analysis unit analyzes the environmental data to propose optimal sleeping environments. For example, the environmental data collection unit collects data on room temperature, humidity, lighting brightness, etc., and proposes optimal environmental conditions. The environmental data collection unit may also collect data on noise levels and air quality and analyze the impact of these factors on sleep. This allows the sleep environment of employees to be optimized and the quality of sleep to be improved.

[0061] The sleep improvement system can further include a gamification section. The gamification section provides game elements that allow employees to have fun while working to improve their sleep. For example, by achieving sleep goals, employees can earn points and receive rewards. The gamification section can also incorporate elements of competition between employees to increase motivation. This allows employees to actively work to improve their sleep, thereby improving the quality of their sleep.

[0062] The sleep improvement system may further include a nutrition advice unit. The nutrition advice unit analyzes the employee's dietary data and provides nutrition advice to improve sleep quality. For example, the nutrition advice unit may analyze the effects of specific nutrients on sleep and propose an optimal meal plan. The nutrition advice unit may also provide specific advice to improve the employee's eating habits. This makes it possible to utilize nutrition data to improve the employee's sleep quality.

[0063] The sleep improvement system may further include a fitness advice unit. The fitness advice unit analyzes the employee's exercise data and provides exercise advice to improve sleep quality. For example, it suggests the appropriate type and timing of exercise to maximize the effect of exercise on sleep. The fitness advice unit may also provide specific advice to improve the employee's exercise habits. In this way, the exercise data can be utilized to improve the employee's sleep quality.

[0064] The sleep improvement system may further include an entertainment provider. The entertainment provider provides entertainment content to help employees relax. For example, the entertainment provider may provide relaxing music, podcasts, meditation guides, etc. The entertainment provider may also suggest content based on the employee's hobbies and interests. This may promote employee relaxation through entertainment and improve the quality of sleep.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The data collection unit collects employee sleep data and lifestyle habit data. For example, employees record their sleep data using a smartwatch or smartphone, and the data is collected. The data collection unit can also collect lifestyle habit data such as employees' diet, exercise, smoking, and drinking habits. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it uses statistical analysis and machine learning algorithms to analyze the employee's sleep data and lifestyle habit data. It can also analyze the employee's emotional data and provide sleep improvement advice based on their emotional state. Step 3: The advice provider provides personalized advice based on the results of the analysis by the analysis unit. For example, it can suggest optimal sleep habits and improvement measures for employees. It can also suggest relaxation methods based on the employee's emotional state.

[0067] (Example 2) A sleep improvement system according to an embodiment of the present invention is a sleep improvement program provided to companies and their employees, and uses generative AI to provide personalized advice. This system solves the problems of reduced productivity and stress caused by lack of sleep, and provides business benefits to companies, such as increased productivity, improved employee happiness, and improved retention rates.

[0068] A sleep improvement system according to an embodiment includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects sleep data and lifestyle habit data of employees. For example, the data collection unit collects sleep data recorded by employees using smartwatches or smartphones. The data collection unit can also collect lifestyle habit data, such as employees' diet, exercise, smoking, and drinking habits. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the sleep data and lifestyle habit data of employees using statistical analysis or machine learning algorithms. The analysis unit can also analyze the employee's emotional data and provide sleep improvement advice based on the employee's emotional state. The advice provision unit provides personalized advice based on the results of the analysis by the analysis unit. For example, the advice provision unit can suggest optimal sleep habits and improvement measures to the employee. The advice provision unit can also suggest relaxation methods based on the employee's emotional state. As a result, the sleep improvement system according to an embodiment can resolve productivity declines and stress caused by lack of sleep by analyzing the employee's sleep data and lifestyle habit data and providing personalized advice.

[0069] The data collection unit collects employee emotional data, and the analysis unit analyzes the emotional data to provide sleep improvement advice according to the employee's emotional state. The data collection unit, for example, collects employee emotional data. For example, the data collection unit may collect employee facial expression data using facial expression recognition technology. The data collection unit may also collect self-reported data from employees. The analysis unit, for example, analyzes the collected emotional data in real time and provides sleep improvement advice according to the employee's emotional state. For example, if stress is high, the analysis unit may suggest relaxation methods, and if emotions are stable, the analysis unit may provide specific advice to improve sleep quality. The analysis unit may also integrate the emotional data and sleep data to provide customized sleep improvement advice based on the employee's emotional state. As a result, providing sleep improvement advice according to the employee's emotional state can be expected to reduce stress and improve sleep quality.

[0070] The data collection unit collects the employee's past sleep data and current lifestyle habits, and the analysis unit compares the past sleep data with the current lifestyle habits to propose long-term improvement measures. The data collection unit, for example, collects the employee's past sleep data and current lifestyle habits. For example, the data collection unit collects data such as sleep duration and sleep quality over the past year. The data collection unit can also collect lifestyle habits such as the employee's current diet, exercise, smoking, and drinking habits. The analysis unit, for example, compares the collected past sleep data with the employee's current lifestyle habits to propose long-term improvement measures. For example, the analysis unit can identify the cause of declining sleep quality from the past data and propose a lifestyle review. The analysis unit can also adjust the employee's current lifestyle habits by referring to lifestyle habits from times when the employee had good sleep in the past. In this way, long-term improvement measures can be proposed by comparing the past data with the employee's current lifestyle habits.

[0071] The data collection unit collects the job details of employees, and the analysis unit can customize an optimal sleep schedule based on the job details. The data collection unit, for example, collects the job details of employees. For example, the data collection unit collects data such as the type of work, working hours, and workload of employees. The analysis unit customizes an optimal sleep schedule based on the collected job details. For example, it recommends daytime sleep for employees who work many night shifts, and nighttime sleep for employees who work many day shifts. The analysis unit can also suggest taking a short nap if a project deadline is approaching. In this way, an optimal sleep schedule can be customized based on the job details of employees.

[0072] The data collection unit collects employee dietary data, and the analysis unit can analyze the association between the dietary data and sleep data and provide advice. The data collection unit, for example, collects employee dietary data. For example, the data collection unit collects data such as the employee's dietary content, meal timing, and nutrient intake. The analysis unit, for example, analyzes the association between the collected dietary data and sleep data and provides advice. For example, the analysis unit analyzes the impact of a specific meal on sleep quality and suggests dietary improvement measures. The analysis unit can also analyze the impact of dinner time and content on sleep and suggest optimal meal times. In this way, by analyzing the association between dietary data and sleep data, it is possible to suggest dietary improvement measures.

[0073] The data collection unit collects the employee's exercise data, and the analysis unit can analyze the correlation between the exercise data and sleep data and provide advice. The data collection unit, for example, collects the employee's exercise data. For example, the data collection unit collects data such as the employee's type of exercise, exercise time, and exercise intensity. The analysis unit, for example, analyzes the correlation between the collected exercise data and sleep data and provides advice. For example, it analyzes the effect of exercise frequency and intensity on sleep and suggests exercise improvement measures. The analysis unit can also analyze the effect of exercise timing and type on sleep and suggest optimal exercise habits. In this way, by analyzing the correlation between exercise data and sleep data, it is possible to suggest exercise improvement measures.

[0074] The data collection unit collects employee emotional data, and the analysis unit analyzes the emotional data to suggest a relaxation method according to the employee's emotional state. The data collection unit, for example, collects employee emotional data. For example, the data collection unit may collect employee facial expression data using facial expression recognition technology. The data collection unit may also collect self-reported data from employees. The analysis unit, for example, analyzes the collected emotional data in real time and suggests a relaxation method according to the employee's emotional state. For example, if stress is high, deep breathing or meditation may be recommended, and if emotions are stable, the analysis unit may recommend using an aroma with a relaxing effect. The analysis unit may also integrate the emotional data and the effects of the relaxation method to suggest a customized relaxation method based on the employee's emotional state. This is expected to reduce employee stress by suggesting a relaxation method according to the employee's emotional state.

[0075] The data collection unit collects the employee's sleep data in real time, and the analysis unit can instantly analyze the sleep data and provide feedback. The data collection unit, for example, collects the employee's sleep data in real time. For example, the data collection unit collects data such as the employee's heart rate and breathing rate in real time. The analysis unit, for example, instantly analyzes the collected sleep data and provides feedback. For example, the analysis unit analyzes the heart rate and breathing rate during sleep to evaluate the quality of sleep. The analysis unit can also issue an alert if an abnormality is detected during sleep. In this way, by collecting sleep data in real time and providing instant feedback, the quality of sleep can be improved.

[0076] The data collection unit collects employee lifestyle data over a long period of time, and the analysis unit can analyze the lifestyle data to identify trends. The data collection unit, for example, collects employee lifestyle data over a long period of time. For example, the data collection unit collects data on employees' diet, exercise, sleep, etc. over a period of several months to several years. The analysis unit, for example, analyzes the collected lifestyle data to identify trends. For example, the data on diet, exercise, and sleep can be integrated to suggest lifestyle improvement measures. The analysis unit can also analyze seasonal variations in lifestyle habits and suggest optimal lifestyle habits. In this way, by analyzing long-term data, lifestyle trends can be identified and improvement measures can be suggested.

[0077] The data collection unit anonymizes employee data, and the analysis unit analyzes the anonymized data to provide advice while protecting privacy. The data collection unit, for example, anonymizes employee data. For example, the data collection unit deletes personal identifying information and masks the data. The analysis unit analyzes the anonymized data to provide advice while protecting privacy. For example, the analysis unit collects data in a form that does not identify individuals and provides the analysis results. The analysis unit can also encrypt the data to protect personal information. By anonymizing the data, analysis can be performed while protecting privacy, creating an environment in which data can be provided with peace of mind.

[0078] The data collection unit collects employee health data, and the analysis unit can integrate and analyze the health data and sleep data. The data collection unit, for example, collects employee health data. For example, the data collection unit collects data such as the employee's heart rate, blood pressure, and body temperature. The analysis unit, for example, integrates and analyzes the collected health data and sleep data to evaluate the employee's overall health condition. For example, the analysis unit evaluates the quality of sleep based on the heart rate and blood pressure data. The analysis unit can also analyze the impact of fluctuations in heart rate and blood pressure on sleep and suggest measures to improve the health condition. In this way, by integrating and analyzing the health data and sleep data, the employee can evaluate the employee's overall health condition and suggest measures to improve it.

[0079] The data collection unit collects employee data by region, and the analysis unit analyzes the data to provide advice based on lifestyle habits specific to the region. The data collection unit, for example, collects employee data by region. For example, the data collection unit collects data by region, such as by city, prefecture, or country. The analysis unit, for example, analyzes the collected data to provide advice based on lifestyle habits specific to the region. For example, it proposes lifestyle improvement measures tailored to the climate and culture of each region. The analysis unit can also provide advice that takes into account the dietary and exercise habits of each region. As a result, by providing advice based on lifestyle habits specific to each region, more effective improvement measures can be proposed.

[0080] The data collection unit collects employee emotional data, and the analysis unit can analyze the association between the emotional data and sleep data and provide advice. The data collection unit, for example, collects employee emotional data. For example, the data collection unit may collect employee facial expression data using facial expression recognition technology. The data collection unit may also collect self-reported data from employees. The analysis unit, for example, analyzes the association between the collected emotional data and sleep data and provides advice. For example, the analysis unit may analyze the impact of emotional states on sleep quality and suggest sleep improvement measures based on the emotional states. The analysis unit may also recommend activities that have a relaxing effect during periods of emotional instability. In this way, by analyzing the association between emotional data and sleep data, customized sleep improvement measures can be suggested according to emotional states.

[0081] The analysis unit can analyze employees' work performance data and quantify the impact of sleep improvement on productivity. For example, the analysis unit can analyze employees' work performance data and quantify the impact of sleep improvement on productivity. For example, it can numerically show the change in work efficiency when sleep quality improves. The analysis unit can also quantify the improvement in concentration and judgment when sleep time increases. This makes it possible to quantify the impact of sleep improvement on productivity and propose specific improvement measures.

[0082] The analysis unit can analyze an employee's work schedule and suggest the optimal timing for taking a break. The analysis unit, for example, analyzes an employee's work schedule and suggests the optimal timing for taking a break. For example, it can recommend taking a break at a time when concentration levels are low. The analysis unit can also show that taking a short break after a long meeting improves work efficiency. This makes it possible to improve productivity by suggesting the optimal timing for taking a break.

[0083] The analysis unit can analyze the employee's concentration data and suggest sleeping habits to maintain concentration. The analysis unit, for example, analyzes the employee's concentration data and suggests sleeping habits to maintain concentration. For example, it suggests a sleep schedule tailored to the time periods when concentration is low. The analysis unit can also show that improving sleep quality will extend the duration of concentration. This makes it possible to improve productivity by suggesting sleeping habits to maintain concentration.

[0084] The analysis unit can analyze data for the entire employee team and propose measures to improve productivity for the entire team. For example, the analysis unit can analyze data for the entire employee team and propose measures to improve productivity for the entire team. For example, the analysis unit can propose optimal meeting times based on the sleep data of the entire team. The analysis unit can also propose schedule adjustments that take into account the sleep patterns of team members. In this way, by analyzing the data of the entire team, it is possible to propose measures to improve productivity for the entire team.

[0085] The analysis unit can analyze the work content of employees and provide customized advice to improve productivity. The analysis unit can, for example, analyze the work content of employees and provide customized advice to improve productivity. For example, the analysis unit can suggest relaxing breaks for creative work. The analysis unit can also recommend regular stretching for employees who do a lot of desk work. In this way, work efficiency can be improved by providing customized advice to improve productivity according to the work content.

[0086] The analysis unit can analyze employees' emotional data and propose productivity improvement measures according to their emotional state. For example, the analysis unit can analyze employees' emotional data and propose productivity improvement measures according to their emotional state. For example, if stress is high, it can propose relaxation techniques, and if emotions are stable, it can give advice on how to improve concentration. The analysis unit can also integrate emotional data with work performance data and propose customized productivity improvement measures based on the employee's emotional state. In this way, by proposing productivity improvement measures according to the employee's emotional state, it is expected that employees will experience reduced stress and increased productivity.

[0087] The analysis unit can analyze employees' stress levels and suggest sleep improvement measures to reduce stress. The analysis unit, for example, analyzes employees' stress levels and suggests sleep improvement measures to reduce stress. For example, if stress is high, it can suggest a sleeping environment that has a relaxing effect. The analysis unit can also integrate stress levels and sleep data to show that sleep quality declines during periods of high stress and suggest stress reduction measures. In this way, by analyzing stress levels and suggesting sleep improvement measures to reduce stress, it is possible to improve employees' mental health.

[0088] The analysis unit can analyze employees' mental health data and suggest sleeping habits to improve their mental health. For example, the analysis unit can analyze employees' mental health data and suggest sleeping habits to improve their mental health. For example, if mental health is declining, the analysis unit can suggest a sleeping environment that has a relaxing effect. The analysis unit can also integrate mental health data and sleep data to indicate that sleep quality declines when mental health is declining and suggest measures to improve mental health. In this way, by analyzing mental health data and suggesting sleeping habits to improve mental health, it is possible to improve employees' mental health.

[0089] The analysis unit can analyze employee happiness data and provide a specific action plan to improve happiness. For example, the analysis unit can analyze employee happiness data and provide a specific action plan to improve happiness. For example, if happiness is declining, the analysis unit can suggest activities that have a relaxing effect. The analysis unit can also integrate happiness data with sleep data, indicate that sleep quality declines when happiness levels decline, and suggest measures to improve happiness. In this way, employee happiness can be improved by analyzing happiness data and providing a specific action plan.

[0090] The analysis unit can analyze the employee's family data and propose measures to improve the happiness of the entire family. For example, the analysis unit can analyze the employee's family data and propose measures to improve the happiness of the entire family. For example, it can propose an environment where the whole family can relax. The analysis unit can also integrate the family data and happiness data and propose activities that have a relaxing effect when the happiness of the whole family is declining. In this way, by analyzing the family data and proposing measures to improve the happiness of the whole family, it is possible to improve the mental health of the whole family.

[0091] The analysis unit can analyze employees' hobbies and interests and suggest relaxation methods. For example, the analysis unit can analyze employees' hobbies and interests and suggest relaxation methods. For example, it can suggest activities related to hobbies. For example, it can recommend listening to music that has a relaxing effect to an employee who likes music. The analysis unit can also integrate hobby and interest data with happiness data, analyze the impact of hobby-related activities on happiness, and suggest optimal relaxation methods. In this way, by analyzing hobbies and interests and suggesting relaxation methods, it is possible to improve employee happiness.

[0092] The analysis unit can analyze the employee's emotional data and propose happiness improvement measures according to the employee's emotional state. The analysis unit, for example, analyzes the employee's emotional data and proposes happiness improvement measures according to the employee's emotional state. For example, if stress is high, the analysis unit can propose relaxation methods, and if emotions are stable, the analysis unit can perform activities to increase happiness. The analysis unit can also integrate the emotional data and happiness data to propose customized happiness improvement measures based on the employee's emotional state. This makes it possible to improve employee happiness by proposing happiness improvement measures according to the employee's emotional state.

[0093] The analysis unit can analyze employee turnover risk and propose sleep improvement measures to reduce the risk. The analysis unit, for example, analyzes employee turnover risk and proposes sleep improvement measures to reduce the risk. For example, it can propose a relaxing sleep environment for employees at high turnover risk. The analysis unit can also integrate turnover risk data with sleep data, show that sleep quality declines during periods of high turnover risk, and propose risk reduction measures. In this way, by analyzing turnover risk and proposing sleep improvement measures to reduce the risk, it is possible to reduce the turnover rate.

[0094] The analysis unit can analyze employees' career data and provide advice to improve career satisfaction. The analysis unit, for example, analyzes employees' career data and provides advice to improve career satisfaction. For example, if career satisfaction is declining, it can suggest a review of career paths. The analysis unit can also integrate career data with happiness data, and, if career satisfaction is declining, can suggest a review of career paths or skill development. In this way, by analyzing career data and providing advice to improve career satisfaction, it is possible to reduce employee turnover.

[0095] The analysis unit can analyze employee feedback data and propose measures to improve retention rates based on the feedback. The analysis unit, for example, analyzes employee feedback data and proposes measures to improve retention rates based on the feedback. For example, it can propose improvements to the work environment based on the feedback. The analysis unit can also integrate the feedback data with happiness data and propose improvements to the work environment or revisions to work content based on the feedback. In this way, by analyzing the feedback data and proposing measures to improve retention rates based on the feedback, it is possible to reduce the turnover rate.

[0096] The analysis unit can analyze employee benefit data and propose improvement measures for employee benefits. The analysis unit, for example, analyzes employee benefit data and proposes improvement measures for employee benefits. For example, if employee benefits are insufficient, it proposes enhancing employee benefits. The analysis unit can also integrate employee benefit data with happiness data and propose enhancing employee benefits when the benefits are insufficient. In this way, by analyzing employee benefit data and proposing improvement measures for employee benefits, it is possible to reduce employee turnover.

[0097] The analysis unit can analyze the employee's work environment data and propose improvements to the work environment. The analysis unit, for example, analyzes the employee's work environment data and proposes improvements to the work environment. For example, if the work environment is insufficient, it proposes improvements to the work environment. The analysis unit can also integrate the work environment data with the happiness data and propose improvements to the work environment when the work environment is insufficient. In this way, by analyzing the work environment data and proposing improvements to the work environment, it is possible to reduce the turnover rate.

[0098] The analysis unit can analyze the employee's emotional data and propose measures to improve the retention rate according to their emotional state. For example, the analysis unit can analyze the employee's emotional data and propose measures to improve the retention rate according to their emotional state. For example, if stress levels are high, the analysis unit can propose relaxation techniques, and if emotions are stable, the analysis unit can perform activities to increase the retention rate. The analysis unit can also integrate the emotional data and retention rate data to propose customized measures to improve the retention rate based on the employee's emotional state. This makes it possible to reduce turnover by proposing measures to improve the retention rate according to the employee's emotional state.

[0099] The analysis unit can analyze market data and predict demand for sleep improvement programs. The analysis unit, for example, analyzes market data and predicts demand for sleep improvement programs. For example, future demand is predicted based on past market data. The analysis unit can also integrate market data and sleep data to predict demand for sleep improvement programs in a specific market. In this way, future demand can be predicted by analyzing market data and predicting demand for sleep improvement programs.

[0100] The analysis unit can analyze technological advances and propose sleep improvement measures that incorporate new technologies. The analysis unit, for example, analyzes technological advances and proposes sleep improvement measures that incorporate new technologies. For example, it can propose collecting sleep data using new sensor technology. The analysis unit can also integrate data on technological advances and sleep data and propose collecting sleep data using new sensor technology. In this way, by analyzing technological advances and proposing sleep improvement measures that incorporate new technologies, it is possible to predict future demand.

[0101] The analysis unit can analyze competitor data and propose strategies to enhance competitive advantage. For example, the analysis unit can analyze competitor data and propose strategies to enhance competitive advantage. For example, the analysis unit can propose strategies to enhance competitive advantage based on data on competitor products and services. The analysis unit can also integrate competitor data with market data and propose strategies to enhance competitive advantage based on data on competitor products and services. In this way, future demand can be predicted by analyzing competitor data and proposing strategies to enhance competitive advantage.

[0102] The analysis unit can analyze data from different industries to explore applicability in other industries. For example, the analysis unit analyzes data from different industries to explore applicability in other industries. For example, the analysis unit can analyze data from different industries to explore applicability in other industries based on data from different industries. For example, the analysis unit can analyze data from the medical industry to explore applicability in other industries for sleep improvement programs. The analysis unit can also integrate data from different industries with market data and explore applicability in other industries based on the data from different industries. In this way, by analyzing data from different industries and exploring applicability in other industries, future demand can be predicted.

[0103] The analysis unit can analyze international market data and propose a strategy for global expansion. The analysis unit, for example, analyzes international market data and proposes a strategy for global expansion. For example, a strategy for global expansion is proposed based on the international market data. For example, the analysis unit can analyze market needs in a specific country or region and propose an optimal expansion strategy. The analysis unit can also integrate international market data and market data, analyze market needs in a specific country or region, and propose an optimal expansion strategy. In this way, by analyzing international market data and proposing a strategy for global expansion, future demand can be predicted.

[0104] The analysis unit can use the emotion estimation function to perform market demand prediction based on the user's emotional state. The analysis unit, for example, uses the emotion estimation function to perform market demand prediction based on the user's emotional state. For example, the analysis unit analyzes the impact of the user's emotional state on market demand and performs demand prediction. The analysis unit can also integrate the user's emotional data and market data, analyze the impact of the user's emotional state on market demand, and perform demand prediction. As a result, future demand can be predicted by using the emotion estimation function to perform market demand prediction based on the user's emotional state.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The sleep improvement system can further include a voice recognition unit. The voice recognition unit collects voice data from employees, and the analysis unit can analyze the voice data to estimate stress levels and emotional states. For example, the voice recognition unit can determine the degree of stress and fatigue from the tone of voice and speaking rate and suggest appropriate relaxation methods. The voice recognition unit can also provide music or meditation guides to help employees relax. This makes it possible to use voice data to understand the emotional state of employees and provide more effective sleep improvement measures.

[0107] The sleep improvement system may further include an environmental data collection unit. The environmental data collection unit collects data on employees' sleeping environments, and the analysis unit analyzes the environmental data to propose optimal sleeping environments. For example, the environmental data collection unit collects data on room temperature, humidity, lighting brightness, etc., and proposes optimal environmental conditions. The environmental data collection unit may also collect data on noise levels and air quality and analyze the impact of these factors on sleep. This allows the sleep environment of employees to be optimized and the quality of sleep to be improved.

[0108] The sleep improvement system can further include a biofeedback unit. The biofeedback unit collects physiological data from employees in real time, and the analysis unit can analyze the physiological data to evaluate their stress level and relaxation level. For example, it measures heart rate variability and electrodermal activity and suggests relaxation methods if stress levels are high. The biofeedback unit can also provide breathing techniques and meditation guides to help employees relax. This makes it possible to use physiological data to understand employees' stress levels and provide appropriate relaxation methods.

[0109] The sleep improvement system can further include a gamification section. The gamification section provides game elements that allow employees to have fun while working to improve their sleep. For example, by achieving sleep goals, employees can earn points and receive rewards. The gamification section can also incorporate elements of competition between employees to increase motivation. This allows employees to actively work to improve their sleep, thereby improving the quality of their sleep.

[0110] The sleep improvement system can also be equipped with a virtual reality (VR) unit, which provides a virtual environment for employees to relax. For example, experiencing a virtual environment that combines natural scenery and relaxing music can reduce stress. The VR unit can also provide meditation and deep breathing guides to help employees relax. This makes it possible to use VR technology to promote employee relaxation and improve the quality of their sleep.

[0111] The sleep improvement system may further include a nutrition advice unit. The nutrition advice unit analyzes the employee's dietary data and provides nutrition advice to improve sleep quality. For example, the nutrition advice unit may analyze the effects of specific nutrients on sleep and propose an optimal meal plan. The nutrition advice unit may also provide specific advice to improve the employee's eating habits. This makes it possible to utilize nutrition data to improve the employee's sleep quality.

[0112] The sleep improvement system may further include a fitness advice unit. The fitness advice unit analyzes the employee's exercise data and provides exercise advice to improve sleep quality. For example, it suggests the appropriate type and timing of exercise to maximize the effect of exercise on sleep. The fitness advice unit may also provide specific advice to improve the employee's exercise habits. In this way, the exercise data can be utilized to improve the employee's sleep quality.

[0113] The sleep improvement system can further include a social support section. The social support section provides a platform for employees to support each other. For example, employees can share information about improving sleep and send encouraging messages to each other. The social support section can also provide online counseling by experts, creating an environment where employees can discuss their individual concerns. This can promote sleep improvement among employees through social support.

[0114] The sleep improvement system can further include a personalized reminder unit. The personalized reminder unit provides individual reminders based on the employee's lifestyle and sleep data. For example, it notifies the employee of bedtime and wake-up time, supporting regular lifestyle habits. The personalized reminder unit can also adjust the content of reminders according to the employee's emotional state. This makes it possible to support employees in improving their sleep through individual reminders.

[0115] The sleep improvement system may further include an entertainment provider. The entertainment provider provides entertainment content to help employees relax. For example, the entertainment provider may provide relaxing music, podcasts, meditation guides, etc. The entertainment provider may also suggest content based on the employee's hobbies and interests. This may promote employee relaxation through entertainment and improve the quality of sleep.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The data collection unit collects employee sleep data and lifestyle habit data. For example, employees record their sleep data using a smartwatch or smartphone, and the data is collected. The data collection unit can also collect lifestyle habit data such as employees' diet, exercise, smoking, and drinking habits. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it uses statistical analysis and machine learning algorithms to analyze the employee's sleep data and lifestyle habit data. It can also analyze the employee's emotional data and provide sleep improvement advice based on their emotional state. Step 3: The advice provider provides personalized advice based on the results of the analysis by the analysis unit. For example, it can suggest optimal sleep habits and improvement measures for employees. It can also suggest relaxation methods based on the employee's emotional state.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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]

[0185] 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 sleep data and lifestyle habit data of employees; an analysis unit that analyzes the data collected by the data collection unit; an advice providing unit that provides individual advice based on the results of the analysis by the analysis unit; A system characterized by:

2. The data collection unit Collect employee meal data and The analysis unit Analyzing the correlation between the dietary data and the sleep data and providing advice 2. The system of claim 1.

3. The data collection unit Collect employee sleep data in real time, The analysis unit Analyze the sleep data in real time and provide feedback 2. The system of claim 1.

4. The analysis unit Analyze employee performance data, Quantifying the impact of improved sleep on productivity 2. The system of claim 1.

5. The analysis unit Analyze employee stress levels, Suggest ways to improve sleep to reduce stress 2. The system of claim 1.

6. The data collection unit Collect employee sentiment data, The analysis unit Analyzing the emotional data and providing advice on improving sleep according to the emotional state 2. The system of claim 1.

Images