System

Abstract

To provide a system capable of predicting changes in employees' physical conditions and motivations at an early stage and implementing appropriate countermeasures.SOLUTION: The system according to embodiments comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects relevant information associated with employees. The analysis unit analyzes the information collected by the collection unit and predicts signs of changes in employees' physical conditions and motivations. The provision unit provides the prediction results obtained by the analysis unit to managers.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to predict changes in employees' physical condition or motivation at an early stage and take appropriate measures.

[0005] The system according to the embodiment aims to predict changes in employees' physical condition and motivation at an early stage and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects related information linked to employees. The analysis unit analyzes the information collected by the collection unit and predicts signs of changes in the employee's physical condition and motivation. The provision unit provides the results predicted by the analysis unit to a manager. Effect of the Invention

[0007] The system according to the embodiment can predict changes in employees' physical condition and motivation at an early stage and take appropriate measures. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system for predicting changes in an employee's physical condition and motivation. This system collects related information associated with an employee, analyzes the collected information, and predicts signs of changes in the employee's physical condition and motivation. The predicted results are disclosed to managers and used for sound human resource management. This optimizes management tasks such as increasing employee motivation and identifying the cause of illness, and can prevent unnecessary employee resignations. For example, information such as employees' working hours, work content, vacation status, health check results, work performance data, and frequency of internal communication is collected. For example, information that may affect physical condition and motivation, such as when an employee's working hours are long or when the employee has not taken vacation, is collected. Next, the collected information is analyzed. AI is used for the analysis, and signs of changes in the employee's physical condition and motivation are predicted. For example, it is predicted that there are signs of poor physical condition and decreased motivation for employees who have been working long hours or who have not taken vacation. This prediction is made by AI based on past data. The predicted results are disclosed to managers. Based on the prediction results, managers can take measures against changes in employees' physical condition and motivation. For example, an employee who has been working long hours can be instructed to take a vacation. Also, for employees whose motivation is declining, the work content can be reviewed and counseling can be provided. This system optimizes management tasks such as increasing employee motivation and identifying the cause of illness. For example, by having an employee who has been working long hours take appropriate vacations, it is possible to prevent poor health in advance. Also, for employees whose motivation is declining, the work content can be reviewed and counseling can be provided to restore motivation. Furthermore, this system can prevent unnecessary employee resignations. By taking measures against changes in employees' physical condition and motivation, employee satisfaction can be improved and resignations can be prevented. For example, by taking appropriate measures against employees who show signs of poor health or declining motivation, it is possible to prevent employees from resigning in advance.In this way, a system that predicts changes in employees' physical condition and motivation can improve employee satisfaction and prevent unnecessary resignations.

[0029] The employee management system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects related information associated with the employee. The related information associated with the employee includes, for example, working hours, work content, vacation acquisition status, medical examination results, work performance data, and frequency of internal communication, but is not limited to such examples. The collection unit, for example, acquires the employee's working hours from a time card system. The collection unit can also acquire the work content from a project management system. Furthermore, the collection unit can also acquire the vacation acquisition status from a personnel management system. For example, the collection unit acquires the employee's arrival time and departure time from the time card system and calculates the working hours. The project name and task progress status of the employee are acquired from the project management system. The number of days and type of vacation acquired by the employee are acquired from the personnel management system. The analysis unit analyzes the information collected by the collection unit and predicts signs of changes in the employee's physical condition and motivation. For example, machine learning and deep learning are used for the analysis, but are not limited to such examples. For example, the analysis unit uses a machine learning algorithm to predict signs of poor health based on the employee's working hours and vacation status. The analysis unit can also use a deep learning algorithm to predict signs of declining motivation based on work performance data. For example, the analysis unit trains a machine learning model based on past data to predict signs of poor health for employees. A deep learning model learns large amounts of data and has advanced predictive capabilities. The provision unit provides the results predicted by the analysis unit to the manager. For example, a dashboard or an alert is used for the provision, but is not limited to such examples. For example, the provision unit visually displays signs of changes in the employee's health and motivation to the manager through the dashboard. The provision unit can also notify the manager of important signs through an alert. For example, the provision unit displays graphs and charts on the dashboard to allow the manager to grasp changes in the employee's health and motivation at a glance. An alert immediately notifies the manager of important signs through email or pop-up notification.As a result, the employee management system according to the embodiment can predict changes in employees' physical condition and motivation and provide this information to managers, thereby supporting sound human resource management.

[0030] The collection unit collects related information associated with employees. Examples of related information associated with employees include, but are not limited to, working hours, work content, vacation acquisition status, health check results, business performance data, and frequency of internal communication. For example, the collection unit acquires the working hours of employees from a time card system. The collection unit can also acquire the work content from a project management system. Furthermore, the collection unit can acquire the vacation acquisition status from a personnel management system. For example, the collection unit acquires the employee's arrival time and departure time from the time card system and calculates the working hours. From the project management system, the collection unit acquires the project name and task progress status of the employee. From the personnel management system, the collection unit acquires the number of days and type of vacation acquired by the employee. This allows the collection unit to centrally collect detailed data such as the employee's working status, work content, and vacation acquisition status. Furthermore, the collection unit also collects health check results and business performance data. The health check results are acquired from a database of regularly conducted health checks to grasp the health status of employees. The business performance data is obtained from a database that records employees' work evaluations and goal achievement status, and the employee's work efficiency and results are evaluated. As for the frequency of internal communication, data is collected from internal chat tools and email systems to grasp the liveliness of communication between employees. This allows the collection department to build a foundation for collecting multifaceted information on employees and providing it to the analysis department and provision department. The collection department collects these data in real time and stores them in a central database. The frequency and accuracy of data collection can be adjusted according to the system settings, allowing for flexible responses according to specific situations and conditions. For example, when a specific project is underway, the frequency of collection of data related to that project can be increased. This allows the collection department to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The analysis unit analyzes the information collected by the collection unit and predicts signs of changes in the employee's physical condition and motivation. For example, machine learning and deep learning are used for the analysis, but are not limited to such examples. For example, the analysis unit uses a machine learning algorithm to predict signs of poor physical condition from the employee's working hours and vacation acquisition status. The analysis unit can also predict signs of declining motivation from work performance data using a deep learning algorithm. For example, the analysis unit trains a machine learning model based on past data to predict signs of poor physical condition in the employee. The deep learning model learns large amounts of data and has advanced predictive capabilities. Specifically, the analysis unit analyzes the fluctuations in the employee's working hours and vacation acquisition patterns to detect behavior that is different from the norm. For example, an employee who suddenly starts taking more vacations or an employee whose working hours become extremely long may show signs of poor physical condition or stress. In addition, the work performance data is analyzed to identify employees whose work efficiency or results are declining. As a result, the analysis unit can detect changes in the employee's physical condition and motivation early and provide information for taking appropriate measures. In addition, the analysis unit can analyze the frequency and content of internal communications to evaluate the relationships between employees and the atmosphere of the team. For example, if a particular employee hardly communicates with other employees, that employee may be isolated. This allows the analysis unit to grasp not only the physical condition and motivation of employees, but also the interpersonal relationships and team dynamics within the company. Based on these analysis results, the analysis unit predicts changes in employees' physical condition and motivation and generates information to provide to managers.

[0032] The providing unit provides the manager with the results predicted by the analysis unit. For example, a dashboard or an alert is used for the provision, but is not limited to such examples. For example, the providing unit visually displays to the manager signs of changes in the employee's physical condition and motivation through the dashboard. The providing unit can also notify the manager of important signs through an alert. For example, the providing unit displays graphs and charts on the dashboard so that the manager can grasp changes in the employee's physical condition and motivation at a glance. The alert immediately notifies the manager of important signs through email or pop-up notification. As a result, the employee management system according to the embodiment can predict changes in the employee's physical condition and motivation and provide them to the manager, thereby supporting sound human resource management. Specifically, the dashboard centrally displays the working hours, vacation status, business performance data, health check results, etc. for each employee, allowing the manager to quickly grasp the situation. For example, if an employee's working hours are increasing rapidly, the employee's name is highlighted to call attention to the employee. In addition, for employees whose business performance is declining, detailed data and possible causes and measures are displayed. An alert is automatically generated when a specific condition is met, and the manager is immediately notified. For example, if signs of poor health are detected in an employee, an alert is generated and the manager is notified via email or a pop-up notification. This allows the manager to respond quickly and take appropriate measures to maintain the health and motivation of employees. Furthermore, the provision department can collect feedback from managers and continuously improve the accuracy and usability of the system. This allows the provision department to provide effective information to managers and improve the efficiency and accuracy of employee management.

[0033] The collection unit can collect information on the employee's working hours, work content, vacation acquisition status, health check results, work performance data, and frequency of internal communication. For example, the collection unit acquires the employee's working hours from a time card system. The working hours include the arrival time, departure time, and break time. For example, the collection unit acquires the employee's arrival time and departure time from the time card system and calculates the working hours. The collection unit can also acquire the work content from a project management system. The work content includes the project name, the assigned task, and the progress of the work. For example, the collection unit acquires the project name and task progress status of the employee from the project management system. Furthermore, the collection unit can acquire the vacation acquisition status from the personnel management system. The vacation acquisition status includes the type of vacation, the number of days acquired, and the frequency of acquisition. For example, the collection unit acquires the number of days and type of vacation acquired by the employee from the personnel management system. The collection unit can also acquire the medical examination results from the health management system. The medical examination results include the diagnosis items, details of the diagnosis results, and the like. For example, the collection unit obtains the results of the employee's health checkup from a health management system, and analyzes the results for each diagnostic item. Furthermore, the collection unit can also obtain business performance data from a performance evaluation system. Business performance data includes performance evaluation, KPI, and goal achievement level. For example, the collection unit obtains employee performance evaluation data from the performance evaluation system, and analyzes the KPI and goal achievement level. Furthermore, the collection unit can also obtain the frequency of internal communication from a communication tool. The frequency of internal communication includes the number of times emails are sent and received, the number of times meetings are attended, and the like. For example, the collection unit obtains the number of times an employee sends and receives emails and the number of times an employee participates in meetings from a communication tool, and analyzes the frequency of communication. In this way, the collection unit can make more accurate predictions by collecting a variety of information about employees.

[0034] The analysis unit can analyze the collected data using machine learning and deep learning, and predict signs of changes in the employee's physical condition and motivation. For example, the analysis unit uses a machine learning algorithm to predict signs of poor physical condition based on the employee's working hours and vacation status. Regression analysis and classification algorithms are used for machine learning. For example, the analysis unit trains a machine learning model based on past data to predict signs of poor physical condition in employees. The analysis unit can also predict signs of decreased motivation from work performance data using a deep learning algorithm. For deep learning, neural networks and convolutional neural networks are used. For example, the analysis unit learns a large amount of data and builds a deep learning model with advanced predictive capabilities. Furthermore, the analysis unit can also analyze the collected data using a generation AI. For the generation AI, a text generation AI (for example, LLM) or a multimodal generation AI is used. For example, the analysis unit uses a text generation AI to predict changes in motivation based on the employee's work content and frequency of communication. A multimodal generation AI can handle multiple modalities such as images and voices as well as text. As a result, the analysis unit improves the accuracy of prediction by using machine learning or deep learning. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collected data to the generation AI and have the generation AI predict signs of changes in physical condition or motivation.

[0035] The providing unit can provide the manager with the prediction result in the form of a dashboard or an alert. The providing unit visually displays signs of changes in the employee's physical condition and motivation to the manager through, for example, a dashboard. The dashboard includes graphs, charts, tables, and the like. For example, the providing unit displays graphs and charts on the dashboard to enable the manager to grasp changes in the employee's physical condition and motivation at a glance. The providing unit can also notify the manager of important signs through an alert. The alert includes email notifications, pop-up notifications, and the like. For example, the providing unit immediately notifies the manager of important signs through email notifications. The pop-up notifications are displayed on the manager's device to immediately notify the manager of important signs. In this way, the providing unit visually provides the prediction result, allowing the manager to respond quickly. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the prediction result to a generating AI and output it in the form of a dashboard or an alert.

[0036] The provision unit can propose specific measures based on the prediction results. For example, the provision unit proposes specific measures for changes in the employee's physical condition and motivation based on the prediction results. The specific measures include advice on health management and proposals for work improvement. For example, the provision unit recommends employees who have been working long hours to take a vacation. The provision unit can also propose a review of work content and counseling to employees whose motivation is declining. For example, the provision unit recommends employees who have been working long hours to take a vacation based on the employee's working hours data. Furthermore, the provision unit proposes a review of work content and counseling to employees whose motivation is declining based on the employee's work performance data. As a result, the provision unit proposes specific measures, allowing the manager to take appropriate action. Part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the prediction results to the generation AI and have the generation AI propose specific measures.

[0037] The collection unit can update the collected data in real time. For example, the collection unit updates the working time data of employees in real time. Real-time updates include seconds, minutes, hours, and the like. For example, the collection unit acquires the employee's arrival time and departure time from a time card system in real time and updates the working time. The collection unit can also update the work content data in real time. For example, the collection unit acquires the progress of the employee's tasks from a project management system in real time and updates the work content. Furthermore, the collection unit can also update the vacation acquisition status data in real time. For example, the collection unit acquires the employee's vacation acquisition status from a personnel management system in real time and updates the vacation data. As a result, the collection unit can make predictions based on the latest information by updating the data in real time. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the data acquired in real time to the generation AI and cause the generation AI to execute the data update.

[0038] The analysis unit can learn a prediction model based on past data. The analysis unit, for example, trains a machine learning model based on past data. The past data includes data from the past year, data from the past five years, etc. For example, the analysis unit trains a machine learning model that predicts signs of poor health based on data on employees' working hours from the past year. The analysis unit can also train a deep learning model that predicts changes in motivation based on work performance data from the past five years. For example, the analysis unit uses past data to build a model that predicts changes in employees' physical condition and motivation. As a result, the analysis unit learns based on past data, thereby improving the accuracy of the prediction model. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data to the generation AI and cause the generation AI to learn the prediction model.

[0039] The collection unit can analyze the employee's past work history and select the optimal information collection method. For example, the collection unit collects detailed data on work hours for employees who have worked long hours in the past. The optimal information collection method includes questionnaire surveys, use of sensor data, and the like. For example, the collection unit collects detailed data on work hours for employees who have worked long hours in the past based on the past work history. The collection unit can also focus on collecting data on vacation acquisition status for employees who have taken vacation frequently in the past. For example, the collection unit collects data on vacation acquisition status for employees who have taken vacation frequently based on the past work history. Furthermore, the collection unit can also collect detailed data on the work content for employees whose work performance has deteriorated in the past. For example, the collection unit collects detailed data on the work content for employees whose work performance has deteriorated based on the past work history. As a result, the collection unit can efficiently collect information by selecting the optimal information collection method based on the past work history. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past work history data into the generation AI and have the generation AI select the optimal information collection method.

[0040] The collection unit can perform filtering based on the employee's current project or field of interest when collecting information. For example, the collection unit preferentially collects data related to a project currently in progress. Filtering includes keyword filtering, category filtering, and the like. For example, the collection unit preferentially collects data related to a project currently in progress. The collection unit can also filter and collect data related to the employee's field of interest. For example, the collection unit filters and collects data related to the employee's field of interest. Furthermore, the collection unit can also collect data related to projects in which the employee has shown interest in the past. For example, the collection unit collects data related to projects in which the employee has shown interest in the past. In this way, the collection unit can collect highly relevant information by filtering information based on the current project or field of interest. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's project data and field of interest data to the generation AI and cause the generation AI to perform filtering.

[0041] The collection unit can preferentially collect data related to a specific health risk based on the employee's health check result. For example, the collection unit collects data related to blood pressure for employees who are at risk of high blood pressure in the health check. Health risks include abnormal blood pressure and abnormal heart rate. For example, the collection unit collects data related to blood pressure for employees who are at risk of high blood pressure in the health check. The collection unit can also collect data related to blood glucose levels for employees who are at risk of diabetes in the health check. For example, the collection unit collects data related to blood glucose levels for employees who are at risk of diabetes in the health check. Furthermore, the collection unit can also collect data related to heart rate for employees who are at risk of heart disease in the health check. For example, the collection unit collects data related to heart rate for employees who are at risk of heart disease in the health check. As a result, the collection unit preferentially collects data related to health risks, thereby improving health management. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input health check result data into the generation AI and cause the generation AI to collect data related to health risks.

[0042] The collection unit can monitor the employee's business performance data in real time and collect additional data when an abnormal value is detected. For example, when business performance suddenly drops, the collection unit collects additional data to identify the cause. Abnormal values ​​include deviation from standard deviation and exceeding a threshold. For example, when business performance suddenly drops, the collection unit collects additional data to identify the cause. In addition, when business performance suddenly improves, the collection unit can also collect additional data to identify the cause. For example, when business performance suddenly improves, the collection unit collects additional data to identify the cause. In addition, when business performance exceeds a certain standard, the collection unit can also collect detailed data. For example, when business performance exceeds a certain standard, the collection unit collects detailed data. As a result, the collection unit detects abnormalities in business performance and collects additional data, making it possible to detect problems early. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input business performance data into the generation AI and have the generation AI detect outliers and collect additional data.

[0043] When collecting information, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the employee. For example, when the employee is in the office, the collection unit collects data related to activities in the office. The geographical location information includes GPS data, address information, and the like. For example, when the employee is in the office, the collection unit collects data related to activities in the office. In addition, when the employee is on a business trip, the collection unit can also collect data related to activities at the business trip destination. For example, when the employee is on a business trip, the collection unit collects data related to activities at the business trip destination. In addition, when the employee is working remotely, the collection unit can also collect data related to remote work. For example, when the employee is working remotely, the collection unit collects data related to remote work. As a result, the collection unit can collect highly relevant information by preferentially collecting highly relevant data in consideration of the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the employee to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0044] The collection unit can analyze the employee's social media activities and collect related data when collecting information. The collection unit, for example, collects related data based on information shared by the employee on social media. Social media activities include the content of posts, the number of likes, the content of comments, and the like. For example, the collection unit collects related data based on information shared by the employee on social media. The collection unit can also collect data related to the employee's field of interest from the employee's social media activities. For example, the collection unit collects data related to the employee's field of interest from the employee's social media activities. Furthermore, the collection unit can also collect data related to stress and motivation from the employee's social media activities. For example, the collection unit collects data related to stress and motivation from the employee's social media activities. In this way, the collection unit can grasp the employee's field of interest and stress level by analyzing the social media activities. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the employee's social media data to the generation AI and cause the generation AI to collect related data.

[0045] The collection unit can collect a specific communication pattern preferentially based on the frequency of the employee's internal communication. For example, the collection unit collects detailed communication data for employees who frequently communicate internally. The communication patterns include the frequency of email exchanges and participation in meetings. For example, the collection unit collects detailed communication data for employees who frequently communicate internally. The collection unit can also collect detailed communication data for employees who do not communicate internally. For example, the collection unit collects detailed communication data for employees who do not communicate internally. Furthermore, the collection unit can also preferentially collect communication patterns related to a specific project. For example, the collection unit preferentially collects communication patterns related to a specific project. In this way, the collection unit improves the quality of communication by collecting data based on the frequency of internal communication. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the employee's communication data to the generation AI and cause the generation AI to collect the specific communication pattern.

[0046] The collection unit can focus on collecting post-vacation business performance data based on the employee's vacation status. The collection unit, for example, collects data on employees whose business performance has improved after vacation. The post-vacation business performance data includes performance evaluations before and after vacation, project progress, and the like. For example, the collection unit collects data on employees whose business performance has improved after vacation. The collection unit can also collect data on employees whose business performance has declined after vacation. For example, the collection unit collects data on employees whose business performance has declined after vacation. Furthermore, the collection unit can also collect detailed changes in business performance after vacation. For example, the collection unit collects detailed changes in business performance after vacation. In this way, the collection unit can evaluate the effect of vacation by collecting data based on the vacation status. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input vacation status data to the generation AI and cause the generation AI to collect post-vacation business performance data.

[0047] The analysis unit can predict a particular health risk based on the employee's health check results. For example, the analysis unit predicts high blood pressure for an employee who is found to be at risk for high blood pressure in the health check. Health risks include abnormal blood pressure and abnormal heart rate. For example, the analysis unit predicts high blood pressure for an employee who is found to be at risk for high blood pressure in the health check. The analysis unit can also predict diabetes for an employee who is found to be at risk for diabetes in the health check. For example, the analysis unit predicts diabetes for an employee who is found to be at risk for diabetes in the health check. Furthermore, the analysis unit can also predict heart disease for an employee who is found to be at risk for heart disease in the health check. For example, the analysis unit predicts heart disease for an employee who is found to be at risk for heart disease in the health check. This allows the analysis unit to predict health risks based on the health check results, enabling early health management. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health check result data to the generation AI and cause the generation AI to execute a prediction of health risks.

[0048] The analysis unit can predict changes in motivation based on the work performance data of employees. For example, the analysis unit predicts a decrease in motivation for an employee whose work performance has decreased. Changes in motivation include a decrease in work performance and a change in attendance rate. For example, the analysis unit predicts a decrease in motivation for an employee whose work performance has decreased. The analysis unit can also predict an increase in motivation for an employee whose work performance has improved. For example, the analysis unit predicts an increase in motivation for an employee whose work performance has improved. Furthermore, the analysis unit can predict changes in motivation for an employee whose work performance exceeds a certain standard. For example, the analysis unit predicts changes in motivation for an employee whose work performance has exceeded a certain standard. As a result, the analysis unit predicts changes in motivation based on work performance data, making it possible to take appropriate measures. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input work performance data to the generation AI and cause the generation AI to execute a prediction of changes in motivation.

[0049] The analysis unit can predict the stress level based on the frequency of the employee's internal communication. The analysis unit, for example, predicts the stress level for an employee who has frequent internal communication. The stress level includes a self-reported stress score, a physiological index, and the like. For example, the analysis unit predicts the stress level for an employee who has frequent internal communication. The analysis unit can also predict the stress level for an employee who has little internal communication. For example, the analysis unit predicts the stress level for an employee who has little internal communication. Furthermore, the analysis unit can predict the stress level based on a communication pattern related to a specific project. For example, the analysis unit predicts the stress level based on a communication pattern related to a specific project. As a result, the analysis unit predicts the stress level based on the frequency of internal communication, thereby enabling appropriate stress management. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the internal communication data to the generation AI and cause the generation AI to execute the prediction of the stress level.

[0050] The analysis unit can predict changes in physical condition after a vacation based on the employee's vacation status. The analysis unit predicts changes in physical condition for an employee whose physical condition has improved after a vacation. The changes in physical condition include fatigue level and quality of sleep. For example, the analysis unit predicts changes in physical condition for an employee whose physical condition has improved after a vacation. The analysis unit can also predict changes in physical condition for an employee whose physical condition has deteriorated after a vacation. For example, the analysis unit predicts changes in physical condition for an employee whose physical condition has deteriorated after a vacation. Furthermore, the analysis unit can predict changes in physical condition after a vacation in detail. For example, the analysis unit predicts changes in physical condition after a vacation in detail. As a result, the analysis unit predicts changes in physical condition based on the vacation status, enabling appropriate health management. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input vacation status data to the generation AI and cause the generation AI to predict changes in physical condition.

[0051] The analysis unit can divide the analysis results by region taking into account the geographical location information of the employees. For example, the analysis unit provides analysis results related to activities in the office to employees in the office. The regions include countries, cities, offices, and the like. For example, the analysis unit provides analysis results related to activities in the office to employees in the office. The analysis unit can also provide analysis results related to activities at the business trip destination to employees on a business trip. For example, the analysis unit provides analysis results related to activities at the business trip destination to employees on a business trip. Furthermore, the analysis unit can also provide analysis results related to remote work to employees working remotely. For example, the analysis unit provides analysis results related to remote work to employees working remotely. As a result, the analysis unit can provide analysis results by region by dividing the analysis results by region taking into account the geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of employees to the generation AI and cause the generation AI to execute division of the analysis results by region.

[0052] The analysis unit can predict changes in emotions based on the employee's social media activities. The analysis unit predicts changes in emotions based on, for example, information shared by the employee on social media. Changes in emotions include changes from positive to negative, changes in stress levels, and the like. For example, the analysis unit predicts changes in emotions based on information shared by the employee on social media. The analysis unit can also predict changes in stress and motivation from the employee's social media activities. For example, the analysis unit predicts changes in stress and motivation from the employee's social media activities. Furthermore, the analysis unit can predict changes in emotions related to areas of interest from the employee's social media activities. For example, the analysis unit predicts changes in emotions related to areas of interest from the employee's social media activities. As a result, the analysis unit predicts changes in emotions based on social media activities, making it possible to take appropriate measures. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's social media data to the generation AI and cause the generation AI to execute a prediction of changes in emotions.

[0053] The analysis unit can analyze stress factors related to a specific task based on the employee's task content. The analysis unit, for example, analyzes stress factors related to the specific task content and predicts a stress level. Stress factors include an increase in the amount of work, interpersonal problems, and the like. For example, the analysis unit analyzes stress factors related to the specific task content and predicts a stress level. The analysis unit can also analyze stress factors associated with changes in task content and predict a stress level. For example, the analysis unit analyzes stress factors associated with changes in task content and predicts a stress level. Furthermore, the analysis unit can analyze stress factors associated with the complexity of the task content and predict a stress level. For example, the analysis unit analyzes stress factors associated with the complexity of the task content and predicts a stress level. As a result, the analysis unit can analyze stress factors based on the task content, thereby enabling appropriate stress management. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's task content data to the generation AI and cause the generation AI to execute an analysis of stress factors.

[0054] The analysis unit can predict changes in physical condition due to long working hours based on the working hours of the employee. The analysis unit, for example, predicts poor physical condition for an employee who continues to work long hours. Long working hours include working 8 hours or more a day, working 40 hours or more a week, etc. For example, the analysis unit predicts poor physical condition for an employee who continues to work long hours. The analysis unit can also predict a decrease in motivation for an employee who continues to work long hours. For example, the analysis unit predicts a decrease in motivation for an employee who continues to work long hours. Furthermore, the analysis unit can predict a stress level for an employee who continues to work long hours. For example, the analysis unit predicts a stress level for an employee who continues to work long hours. As a result, the analysis unit predicts changes in physical condition based on the working hours, thereby enabling appropriate health management. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the working hours data of the employee to the generation AI and cause the generation AI to execute a prediction of changes in physical condition due to long working hours.

[0055] The provision unit can propose specific health measures based on the employee's health check results. For example, the provision unit proposes specific health measures to employees who are at risk for high blood pressure in the health check. The specific health measures include recommendations for exercise and improvements in diet. For example, the provision unit proposes recommendations for exercise and improvements in diet to employees who are at risk for high blood pressure in the health check. The provision unit can also propose specific health measures to employees who are at risk for diabetes in the health check. For example, the provision unit proposes recommendations for exercise and improvements in diet to employees who are at risk for diabetes in the health check. Furthermore, the provision unit can also propose specific health measures to employees who are at risk for heart disease in the health check. For example, the provision unit proposes recommendations for exercise and improvements in diet to employees who are at risk for heart disease in the health check. As a result, the provision unit improves health management by proposing specific health measures based on the health check results. Part or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input health check result data to the generation AI and cause the generation AI to execute the proposal of specific health measures.

[0056] The provision unit can propose specific business improvement measures based on the business performance data of employees. The provision unit proposes specific business improvement measures for employees whose business performance has declined, for example. Specific business improvement measures include reviewing business processes and introducing tools. For example, the provision unit proposes reviewing business processes and introducing tools to employees whose business performance has declined. The provision unit can also propose specific business improvement measures for employees whose business performance has improved. For example, the provision unit proposes reviewing business processes and introducing tools to employees whose business performance has improved. Furthermore, the provision unit can also propose specific business improvement measures for employees whose business performance exceeds a certain standard. For example, the provision unit proposes reviewing business processes and introducing tools to employees whose business performance has exceeded a certain standard. As a result, the provision unit proposes specific business improvement measures based on the business performance data, thereby improving business efficiency. Part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input business performance data to the generation AI and cause the generation AI to execute the proposal of specific business improvement measures.

[0057] The provision department can propose specific communication improvement measures based on the frequency of the employee's internal communication. The provision department proposes specific communication improvement measures for employees who have frequent internal communication, for example. Specific communication improvement measures include holding regular meetings and strengthening feedback. For example, the provision department proposes holding regular meetings and strengthening feedback for employees who have frequent internal communication. The provision department can also propose specific communication improvement measures for employees who have little internal communication. For example, the provision department proposes holding regular meetings and strengthening feedback for employees who have little internal communication. Furthermore, the provision department can also propose specific communication improvement measures based on communication patterns related to a specific project. For example, the provision department proposes holding regular meetings and strengthening feedback based on communication patterns related to a specific project. As a result, the provision department proposes specific improvement measures based on the frequency of internal communication, thereby improving the quality of communication. Part or all of the above-mentioned processing in the provision department may be performed using, for example, AI, or may be performed without using AI. For example, the provision department can input internal communication data to the generation AI and cause the generation AI to execute the proposal of specific communication improvement measures.

[0058] The provision unit can make specific recommendations for taking vacation based on the employee's vacation acquisition status. The provision unit, for example, makes specific recommendations for taking vacation to employees who have been working long hours. Specific recommendations for taking vacation include taking regular vacations and taking refreshing vacations. For example, the provision unit recommends taking regular vacations and taking refreshing vacations to employees who have been working long hours. The provision unit can also make specific recommendations for taking vacation to employees who have not taken vacation. For example, the provision unit recommends taking regular vacations and taking refreshing vacations to employees who have not taken vacation. Furthermore, the provision unit can also make specific recommendations for taking vacation to employees whose work performance has deteriorated after vacation. For example, the provision unit recommends taking regular vacations and taking refreshing vacations to employees whose work performance has deteriorated after vacation. As a result, the provision unit makes specific recommendations for taking vacation based on the vacation acquisition status, making it possible to properly manage vacations. A part or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input vacation taking status data into the generation AI and cause the generation AI to make specific vacation taking recommendations.

[0059] The provision unit can propose measures for each region taking into account the geographical location information of the employees. For example, the provision unit proposes measures related to activities in the office to employees in the office. The measures for each region include responses to health risks specific to the region and business improvement measures for each region. For example, the provision unit proposes measures related to activities in the office to employees in the office. In addition, the provision unit can also propose measures related to activities at the business trip destination to employees on a business trip. For example, the provision unit proposes measures related to activities at the business trip destination to employees on a business trip. In addition, the provision unit can also propose measures related to remote work to employees working remotely. For example, the provision unit proposes measures related to remote work to employees working remotely. As a result, the provision unit can propose measures for each region by proposing measures for each region taking into account the geographical location information. Part or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input the geographical location information of employees to the generation AI and cause the generation AI to execute the proposal of measures for each region.

[0060] The provision unit can propose specific stress countermeasures based on the employee's social media activities. The provision unit proposes specific stress countermeasures based on, for example, information shared by the employee on social media. Specific stress countermeasures include recommending relaxation methods and providing counseling. For example, the provision unit proposes recommending relaxation methods and providing counseling based on information shared by the employee on social media. The provision unit can also propose countermeasures related to stress and motivation from the employee's social media activities. For example, the provision unit proposes countermeasures related to stress and motivation from the employee's social media activities. Furthermore, the provision unit can also propose stress countermeasures related to the employee's field of interest from the employee's social media activities. For example, the provision unit proposes stress countermeasures related to the employee's field of interest from the employee's social media activities. As a result, the provision unit proposes specific stress countermeasures based on the social media activities, thereby improving stress management. Part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the employee's social media data to the generation AI and cause the generation AI to execute the proposal of specific stress countermeasures.

[0061] The provision unit can propose improvement measures related to specific tasks based on the employee's work content. The provision unit, for example, proposes improvement measures related to specific task content. Specific task improvement measures include reviewing the work process and introducing tools. For example, the provision unit proposes improvement measures related to specific task content. The provision unit can also propose improvement measures associated with changes in task content. For example, the provision unit proposes improvement measures associated with changes in task content. Furthermore, the provision unit can also propose improvement measures related to the complexity of task content. For example, the provision unit proposes improvement measures related to the complexity of task content. As a result, the provision unit proposes specific improvement measures based on task content, thereby improving task efficiency. Part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the employee's task content data into the generation AI and cause the generation AI to execute the proposal of improvement measures related to specific tasks.

[0062] The provision unit can propose specific adjustments to working hours based on the working hours of the employee. For example, the provision unit proposes specific adjustments to working hours to an employee who has been working long hours. Specific adjustments to working hours include the introduction of flextime and reduction of overtime hours. For example, the provision unit proposes the introduction of flextime and reduction of overtime hours to an employee who has been working long hours. The provision unit can also propose specific adjustments to working hours to an employee whose working hours are irregular. For example, the provision unit proposes the introduction of flextime and reduction of overtime hours to an employee whose working hours are irregular. Furthermore, the provision unit can also propose specific adjustments to working hours to an employee whose working hours exceed a certain standard. For example, the provision unit proposes the introduction of flextime and reduction of overtime hours to an employee whose working hours exceed a certain standard. As a result, the provision unit proposes specific adjustments based on the working hours, enabling appropriate work management. A part or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision department can input employee working hour data into the generation AI and have the generation AI execute specific adjustments to working hours.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The employee management system further includes a feedback collection unit. The feedback collection unit can collect feedback from employees and provide it to the analysis unit. For example, the feedback collection unit collects employee opinions and thoughts through regular surveys. The feedback collection unit can also provide a platform where employees can freely post their opinions. Furthermore, the feedback collection unit can analyze employee feedback and identify employee satisfaction levels and areas for improvement. In this way, the employee management system can utilize employee feedback to achieve more effective human resource management.

[0065] The collection department can collect digital activity data of employees. For example, the collection department can collect the amount of time employees spend using a computer and the frequency of application use. The collection department can also collect employees' internet browsing history and the number of times they send and receive emails. Furthermore, the collection department can analyze employees' digital activity data to evaluate their work efficiency and concentration. In this way, the collection department can use employees' digital activity data to identify areas for improvement in work.

[0066] The analysis unit can predict long-term health risks based on the employee's health data. For example, the analysis unit can analyze the employee's health checkup results and daily health data to predict the future risk of illness. The analysis unit can also analyze the employee's lifestyle data to evaluate health risks. Furthermore, the analysis unit can analyze the employee's genetic information to predict genetic health risks. In this way, the analysis unit can utilize the employee's health data to support long-term health management.

[0067] The provision department can propose career paths for employees based on the prediction results. For example, the provision department can propose appropriate career paths based on the work performance data and skill data of employees. The provision department can also propose career paths taking into consideration the interests and concerns of employees. Furthermore, the provision department can also propose necessary skills and experience based on the career goals of employees. In this way, the provision department can support the career growth of employees.

[0068] The provision department can suggest team building activities for employees based on the prediction results. For example, the provision department can suggest appropriate team building activities based on communication data and motivation data of employees. The provision department can also suggest team building activities taking into consideration the interests and concerns of employees. Furthermore, the provision department can evaluate the team dynamics of employees and measure the effectiveness of team building activities. This allows the provision department to improve teamwork among employees.

[0069] The process flow of the first embodiment will be briefly described below.

[0070] Step 1: The collection department collects relevant information related to employees. The relevant information related to employees includes working hours, work content, vacation status, health check results, work performance data, frequency of internal communication, etc. For example, the collection department obtains working hours from a time card system, work content from a project management system, and vacation status from a human resources management system. Step 2: The analysis unit analyzes the information collected by the collection unit and predicts signs of changes in employees' physical condition and motivation. Machine learning and deep learning are used for the analysis; for example, signs of poor physical condition can be predicted from working hours and vacation status, and signs of declining motivation can be predicted from work performance data. Step 3: The provision unit provides the results predicted by the analysis unit to the manager. For example, dashboards and alerts are used to visually display the results through a dashboard and notify important signs through alerts.

[0071] (Example 2) The system according to the embodiment of the present invention is a system for predicting changes in an employee's physical condition and motivation. This system collects related information associated with an employee, analyzes the collected information, and predicts signs of changes in the employee's physical condition and motivation. The predicted results are disclosed to managers and used for sound human resource management. This optimizes management tasks such as increasing employee motivation and identifying the cause of illness, and can prevent unnecessary employee resignations. For example, information such as employees' working hours, work content, vacation status, health check results, work performance data, and frequency of internal communication is collected. For example, information that may affect physical condition and motivation, such as when an employee's working hours are long or when the employee has not taken vacation, is collected. Next, the collected information is analyzed. AI is used for the analysis, and signs of changes in the employee's physical condition and motivation are predicted. For example, it is predicted that there are signs of poor physical condition and decreased motivation for employees who have been working long hours or who have not taken vacation. This prediction is made by AI based on past data. The predicted results are disclosed to managers. Based on the prediction results, managers can take measures against changes in employees' physical condition and motivation. For example, an employee who has been working long hours can be instructed to take a vacation. Also, for employees whose motivation is declining, the work content can be reviewed and counseling can be provided. This system optimizes management tasks such as increasing employee motivation and identifying the cause of illness. For example, by having an employee who has been working long hours take appropriate vacations, it is possible to prevent poor health in advance. Also, for employees whose motivation is declining, the work content can be reviewed and counseling can be provided to restore motivation. Furthermore, this system can prevent unnecessary employee resignations. By taking measures against changes in employees' physical condition and motivation, employee satisfaction can be improved and resignations can be prevented. For example, by taking appropriate measures against employees who show signs of poor health or declining motivation, it is possible to prevent employees from resigning in advance.In this way, a system that predicts changes in employees' physical condition and motivation can improve employee satisfaction and prevent unnecessary resignations.

[0072] The employee management system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects related information associated with the employee. The related information associated with the employee includes, for example, working hours, work content, vacation acquisition status, medical examination results, work performance data, and frequency of internal communication, but is not limited to such examples. The collection unit, for example, acquires the employee's working hours from a time card system. The collection unit can also acquire the work content from a project management system. Furthermore, the collection unit can also acquire the vacation acquisition status from a personnel management system. For example, the collection unit acquires the employee's arrival time and departure time from the time card system and calculates the working hours. The project name and task progress status of the employee are acquired from the project management system. The number of days and type of vacation acquired by the employee are acquired from the personnel management system. The analysis unit analyzes the information collected by the collection unit and predicts signs of changes in the employee's physical condition and motivation. For example, machine learning and deep learning are used for the analysis, but are not limited to such examples. For example, the analysis unit uses a machine learning algorithm to predict signs of poor health based on the employee's working hours and vacation status. The analysis unit can also use a deep learning algorithm to predict signs of declining motivation based on work performance data. For example, the analysis unit trains a machine learning model based on past data to predict signs of poor health for employees. A deep learning model learns large amounts of data and has advanced predictive capabilities. The provision unit provides the results predicted by the analysis unit to the manager. For example, a dashboard or an alert is used for the provision, but is not limited to such examples. For example, the provision unit visually displays signs of changes in the employee's health and motivation to the manager through the dashboard. The provision unit can also notify the manager of important signs through an alert. For example, the provision unit displays graphs and charts on the dashboard to allow the manager to grasp changes in the employee's health and motivation at a glance. An alert immediately notifies the manager of important signs through email or pop-up notification.As a result, the employee management system according to the embodiment can predict changes in employees' physical condition and motivation and provide this information to managers, thereby supporting sound human resource management.

[0073] The collection unit collects related information associated with employees. Examples of related information associated with employees include, but are not limited to, working hours, work content, vacation acquisition status, health check results, business performance data, and frequency of internal communication. For example, the collection unit acquires the working hours of employees from a time card system. The collection unit can also acquire the work content from a project management system. Furthermore, the collection unit can acquire the vacation acquisition status from a personnel management system. For example, the collection unit acquires the employee's arrival time and departure time from the time card system and calculates the working hours. From the project management system, the collection unit acquires the project name and task progress status of the employee. From the personnel management system, the collection unit acquires the number of days and type of vacation acquired by the employee. This allows the collection unit to centrally collect detailed data such as the employee's working status, work content, and vacation acquisition status. Furthermore, the collection unit also collects health check results and business performance data. The health check results are acquired from a database of regularly conducted health checks to grasp the health status of employees. The business performance data is obtained from a database that records employees' work evaluations and goal achievement status, and the employee's work efficiency and results are evaluated. As for the frequency of internal communication, data is collected from internal chat tools and email systems to grasp the liveliness of communication between employees. This allows the collection department to build a foundation for collecting multifaceted information on employees and providing it to the analysis department and provision department. The collection department collects these data in real time and stores them in a central database. The frequency and accuracy of data collection can be adjusted according to the system settings, allowing for flexible responses according to specific situations and conditions. For example, when a specific project is underway, the frequency of collection of data related to that project can be increased. This allows the collection department to collect data efficiently and effectively and improve the performance of the entire system.

[0074] The analysis unit analyzes the information collected by the collection unit and predicts signs of changes in the employee's physical condition and motivation. For example, machine learning and deep learning are used for the analysis, but are not limited to such examples. For example, the analysis unit uses a machine learning algorithm to predict signs of poor physical condition from the employee's working hours and vacation acquisition status. The analysis unit can also predict signs of declining motivation from work performance data using a deep learning algorithm. For example, the analysis unit trains a machine learning model based on past data to predict signs of poor physical condition in the employee. The deep learning model learns large amounts of data and has advanced predictive capabilities. Specifically, the analysis unit analyzes the fluctuations in the employee's working hours and vacation acquisition patterns to detect behavior that is different from the norm. For example, an employee who suddenly starts taking more vacations or an employee whose working hours become extremely long may show signs of poor physical condition or stress. In addition, the work performance data is analyzed to identify employees whose work efficiency or results are declining. As a result, the analysis unit can detect changes in the employee's physical condition and motivation early and provide information for taking appropriate measures. In addition, the analysis unit can analyze the frequency and content of internal communications to evaluate the relationships between employees and the atmosphere of the team. For example, if a particular employee hardly communicates with other employees, that employee may be isolated. This allows the analysis unit to grasp not only the physical condition and motivation of employees, but also the interpersonal relationships and team dynamics within the company. Based on these analysis results, the analysis unit predicts changes in employees' physical condition and motivation and generates information to provide to managers.

[0075] The providing unit provides the manager with the results predicted by the analysis unit. For example, a dashboard or an alert is used for the provision, but is not limited to such examples. For example, the providing unit visually displays to the manager signs of changes in the employee's physical condition and motivation through the dashboard. The providing unit can also notify the manager of important signs through an alert. For example, the providing unit displays graphs and charts on the dashboard so that the manager can grasp changes in the employee's physical condition and motivation at a glance. The alert immediately notifies the manager of important signs through email or pop-up notification. As a result, the employee management system according to the embodiment can predict changes in the employee's physical condition and motivation and provide them to the manager, thereby supporting sound human resource management. Specifically, the dashboard centrally displays the working hours, vacation status, business performance data, health check results, etc. for each employee, allowing the manager to quickly grasp the situation. For example, if an employee's working hours are increasing rapidly, the employee's name is highlighted to call attention to the employee. In addition, for employees whose business performance is declining, detailed data and possible causes and measures are displayed. An alert is automatically generated when a specific condition is met, and the manager is immediately notified. For example, if signs of poor health are detected in an employee, an alert is generated and the manager is notified via email or a pop-up notification. This allows the manager to respond quickly and take appropriate measures to maintain the health and motivation of employees. Furthermore, the provision department can collect feedback from managers and continuously improve the accuracy and usability of the system. This allows the provision department to provide effective information to managers and improve the efficiency and accuracy of employee management.

[0076] The collection unit can collect information on the employee's working hours, work content, vacation acquisition status, health check results, work performance data, and frequency of internal communication. For example, the collection unit acquires the employee's working hours from a time card system. The working hours include the arrival time, departure time, and break time. For example, the collection unit acquires the employee's arrival time and departure time from the time card system and calculates the working hours. The collection unit can also acquire the work content from a project management system. The work content includes the project name, the assigned task, and the progress of the work. For example, the collection unit acquires the project name and task progress status of the employee from the project management system. Furthermore, the collection unit can acquire the vacation acquisition status from the personnel management system. The vacation acquisition status includes the type of vacation, the number of days acquired, and the frequency of acquisition. For example, the collection unit acquires the number of days and type of vacation acquired by the employee from the personnel management system. The collection unit can also acquire the medical examination results from the health management system. The medical examination results include the diagnosis items, details of the diagnosis results, and the like. For example, the collection unit obtains the results of the employee's health checkup from a health management system, and analyzes the results for each diagnostic item. Furthermore, the collection unit can also obtain business performance data from a performance evaluation system. Business performance data includes performance evaluation, KPI, and goal achievement level. For example, the collection unit obtains employee performance evaluation data from the performance evaluation system, and analyzes the KPI and goal achievement level. Furthermore, the collection unit can also obtain the frequency of internal communication from a communication tool. The frequency of internal communication includes the number of times emails are sent and received, the number of times meetings are attended, and the like. For example, the collection unit obtains the number of times an employee sends and receives emails and the number of times an employee participates in meetings from a communication tool, and analyzes the frequency of communication. In this way, the collection unit can make more accurate predictions by collecting a variety of information about employees.

[0077] The analysis unit can analyze the collected data using machine learning and deep learning, and predict signs of changes in the employee's physical condition and motivation. For example, the analysis unit uses a machine learning algorithm to predict signs of poor physical condition based on the employee's working hours and vacation status. Regression analysis and classification algorithms are used for machine learning. For example, the analysis unit trains a machine learning model based on past data to predict signs of poor physical condition in employees. The analysis unit can also predict signs of decreased motivation from work performance data using a deep learning algorithm. For deep learning, neural networks and convolutional neural networks are used. For example, the analysis unit learns a large amount of data and builds a deep learning model with advanced predictive capabilities. Furthermore, the analysis unit can also analyze the collected data using a generation AI. For the generation AI, a text generation AI (for example, LLM) or a multimodal generation AI is used. For example, the analysis unit uses a text generation AI to predict changes in motivation based on the employee's work content and frequency of communication. A multimodal generation AI can handle multiple modalities such as images and voices as well as text. As a result, the analysis unit improves the accuracy of prediction by using machine learning or deep learning. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collected data to the generation AI and have the generation AI predict signs of changes in physical condition or motivation.

[0078] The providing unit can provide the manager with the prediction result in the form of a dashboard or an alert. The providing unit visually displays signs of changes in the employee's physical condition and motivation to the manager through, for example, a dashboard. The dashboard includes graphs, charts, tables, and the like. For example, the providing unit displays graphs and charts on the dashboard to enable the manager to grasp changes in the employee's physical condition and motivation at a glance. The providing unit can also notify the manager of important signs through an alert. The alert includes email notifications, pop-up notifications, and the like. For example, the providing unit immediately notifies the manager of important signs through email notifications. The pop-up notifications are displayed on the manager's device to immediately notify the manager of important signs. In this way, the providing unit visually provides the prediction result, allowing the manager to respond quickly. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the prediction result to a generating AI and output it in the form of a dashboard or an alert.

[0079] The provision unit can propose specific measures based on the prediction results. For example, the provision unit proposes specific measures for changes in the employee's physical condition and motivation based on the prediction results. The specific measures include advice on health management and proposals for work improvement. For example, the provision unit recommends employees who have been working long hours to take a vacation. The provision unit can also propose a review of work content and counseling to employees whose motivation is declining. For example, the provision unit recommends employees who have been working long hours to take a vacation based on the employee's working hours data. Furthermore, the provision unit proposes a review of work content and counseling to employees whose motivation is declining based on the employee's work performance data. As a result, the provision unit proposes specific measures, allowing the manager to take appropriate action. Part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the prediction results to the generation AI and have the generation AI propose specific measures.

[0080] The collection unit can update the collected data in real time. For example, the collection unit updates the working time data of employees in real time. Real-time updates include seconds, minutes, hours, and the like. For example, the collection unit acquires the employee's arrival time and departure time from a time card system in real time and updates the working time. The collection unit can also update the work content data in real time. For example, the collection unit acquires the progress of the employee's tasks from a project management system in real time and updates the work content. Furthermore, the collection unit can also update the vacation acquisition status data in real time. For example, the collection unit acquires the employee's vacation acquisition status from a personnel management system in real time and updates the vacation data. As a result, the collection unit can make predictions based on the latest information by updating the data in real time. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the data acquired in real time to the generation AI and cause the generation AI to execute the data update.

[0081] The analysis unit can learn a prediction model based on past data. The analysis unit, for example, trains a machine learning model based on past data. The past data includes data from the past year, data from the past five years, etc. For example, the analysis unit trains a machine learning model that predicts signs of poor health based on data on employees' working hours from the past year. The analysis unit can also train a deep learning model that predicts changes in motivation based on work performance data from the past five years. For example, the analysis unit uses past data to build a model that predicts changes in employees' physical condition and motivation. As a result, the analysis unit learns based on past data, thereby improving the accuracy of the prediction model. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data to the generation AI and cause the generation AI to learn the prediction model.

[0082] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the emotions. For example, when the user is feeling stressed, the collection unit reduces the frequency of information collection to reduce the burden. Facial expression recognition, voice analysis, text analysis, etc. are used to estimate the emotions. For example, the collection unit captures the user's facial expression with a camera and estimates the stress level using an emotion estimation algorithm. The collection unit can also record the user's voice with a microphone and estimate the emotion using voice analysis technology. Furthermore, when the user is relaxed, the collection unit increases the frequency of information collection and collects detailed data. For example, when the collection unit determines that the user is relaxed based on the user's facial expression data, it increases the frequency of information collection. Furthermore, when the user is in a hurry, the collection unit prioritizes collecting only important information. For example, when the collection unit determines that the user is in a hurry based on the user's voice data, it prioritizes collecting important information. As a result, the collection unit reduces the burden by adjusting the timing of information collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to adjust the timing of information collection.

[0083] The collection unit can analyze the employee's past work history and select the optimal information collection method. For example, the collection unit collects detailed data on work hours for employees who have worked long hours in the past. The optimal information collection method includes questionnaire surveys, use of sensor data, and the like. For example, the collection unit collects detailed data on work hours for employees who have worked long hours in the past based on the past work history. The collection unit can also focus on collecting data on vacation acquisition status for employees who have taken vacation frequently in the past. For example, the collection unit collects data on vacation acquisition status for employees who have taken vacation frequently based on the past work history. Furthermore, the collection unit can also collect detailed data on the work content for employees whose work performance has deteriorated in the past. For example, the collection unit collects detailed data on the work content for employees whose work performance has deteriorated based on the past work history. As a result, the collection unit can efficiently collect information by selecting the optimal information collection method based on the past work history. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past work history data into the generation AI and have the generation AI select the optimal information collection method.

[0084] The collection unit can perform filtering based on the employee's current project or field of interest when collecting information. For example, the collection unit preferentially collects data related to a project currently in progress. Filtering includes keyword filtering, category filtering, and the like. For example, the collection unit preferentially collects data related to a project currently in progress. The collection unit can also filter and collect data related to the employee's field of interest. For example, the collection unit filters and collects data related to the employee's field of interest. Furthermore, the collection unit can also collect data related to projects in which the employee has shown interest in the past. For example, the collection unit collects data related to projects in which the employee has shown interest in the past. In this way, the collection unit can collect highly relevant information by filtering information based on the current project or field of interest. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's project data and field of interest data to the generation AI and cause the generation AI to perform filtering.

[0085] The collection unit can preferentially collect data related to a specific health risk based on the employee's health check result. For example, the collection unit collects data related to blood pressure for employees who are at risk of high blood pressure in the health check. Health risks include abnormal blood pressure and abnormal heart rate. For example, the collection unit collects data related to blood pressure for employees who are at risk of high blood pressure in the health check. The collection unit can also collect data related to blood glucose levels for employees who are at risk of diabetes in the health check. For example, the collection unit collects data related to blood glucose levels for employees who are at risk of diabetes in the health check. Furthermore, the collection unit can also collect data related to heart rate for employees who are at risk of heart disease in the health check. For example, the collection unit collects data related to heart rate for employees who are at risk of heart disease in the health check. As a result, the collection unit preferentially collects data related to health risks, thereby improving health management. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input health check result data into the generation AI and cause the generation AI to collect data related to health risks.

[0086] The collection unit can monitor the employee's business performance data in real time and collect additional data when an abnormal value is detected. For example, when business performance suddenly drops, the collection unit collects additional data to identify the cause. Abnormal values ​​include deviation from standard deviation and exceeding a threshold. For example, when business performance suddenly drops, the collection unit collects additional data to identify the cause. In addition, when business performance suddenly improves, the collection unit can also collect additional data to identify the cause. For example, when business performance suddenly improves, the collection unit collects additional data to identify the cause. In addition, when business performance exceeds a certain standard, the collection unit can also collect detailed data. For example, when business performance exceeds a certain standard, the collection unit collects detailed data. As a result, the collection unit detects abnormalities in business performance and collects additional data, making it possible to detect problems early. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input business performance data into the generation AI and have the generation AI detect outliers and collect additional data.

[0087] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the emotions. For example, when the user is feeling stressed, the collection unit preferentially collects information related to stress. Facial expression recognition, voice analysis, text analysis, and the like are used to estimate emotions. For example, the collection unit captures the user's facial expression with a camera and estimates the stress level using an emotion estimation algorithm. The collection unit can also record the user's voice with a microphone and estimate emotions using voice analysis technology. Furthermore, when the user is relaxed, the collection unit preferentially collects information related to relaxation. For example, when the collection unit determines that the user is relaxed based on the user's facial expression data, it preferentially collects information related to relaxation. Furthermore, when the user is in a hurry, the collection unit preferentially collects important information. For example, when the collection unit determines that the user is in a hurry based on the user's voice data, it preferentially collects important information. As a result, the collection unit determines the priority of information according to the user's emotions, thereby enabling efficient information collection. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processes in the collection unit may be performed, for example, using an AI or may be performed without using an AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to determine the priority order of information.

[0088] When collecting information, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the employee. For example, when the employee is in the office, the collection unit collects data related to activities in the office. The geographical location information includes GPS data, address information, and the like. For example, when the employee is in the office, the collection unit collects data related to activities in the office. In addition, when the employee is on a business trip, the collection unit can also collect data related to activities at the business trip destination. For example, when the employee is on a business trip, the collection unit collects data related to activities at the business trip destination. In addition, when the employee is working remotely, the collection unit can also collect data related to remote work. For example, when the employee is working remotely, the collection unit collects data related to remote work. As a result, the collection unit can collect highly relevant information by preferentially collecting highly relevant data in consideration of the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the employee to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0089] The collection unit can analyze the employee's social media activities and collect related data when collecting information. The collection unit, for example, collects related data based on information shared by the employee on social media. Social media activities include the content of posts, the number of likes, the content of comments, and the like. For example, the collection unit collects related data based on information shared by the employee on social media. The collection unit can also collect data related to the employee's field of interest from the employee's social media activities. For example, the collection unit collects data related to the employee's field of interest from the employee's social media activities. Furthermore, the collection unit can also collect data related to stress and motivation from the employee's social media activities. For example, the collection unit collects data related to stress and motivation from the employee's social media activities. In this way, the collection unit can grasp the employee's field of interest and stress level by analyzing the social media activities. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the employee's social media data to the generation AI and cause the generation AI to collect related data.

[0090] The collection unit can collect a specific communication pattern preferentially based on the frequency of the employee's internal communication. For example, the collection unit collects detailed communication data for employees who frequently communicate internally. The communication patterns include the frequency of email exchanges and participation in meetings. For example, the collection unit collects detailed communication data for employees who frequently communicate internally. The collection unit can also collect detailed communication data for employees who do not communicate internally. For example, the collection unit collects detailed communication data for employees who do not communicate internally. Furthermore, the collection unit can also preferentially collect communication patterns related to a specific project. For example, the collection unit preferentially collects communication patterns related to a specific project. In this way, the collection unit improves the quality of communication by collecting data based on the frequency of internal communication. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's communication data to the generation AI and cause the generation AI to collect the specific communication pattern.

[0091] The collection unit can focus on collecting post-vacation business performance data based on the employee's vacation status. The collection unit, for example, collects data on employees whose business performance has improved after vacation. The post-vacation business performance data includes performance evaluations before and after vacation, project progress, and the like. For example, the collection unit collects data on employees whose business performance has improved after vacation. The collection unit can also collect data on employees whose business performance has declined after vacation. For example, the collection unit collects data on employees whose business performance has declined after vacation. Furthermore, the collection unit can also collect detailed changes in business performance after vacation. For example, the collection unit collects detailed changes in business performance after vacation. In this way, the collection unit can evaluate the effect of vacation by collecting data based on the vacation status. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input vacation status data to the generation AI and cause the generation AI to collect post-vacation business performance data.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the emotions. For example, when the user is feeling stressed, the analysis unit analyzes with emphasis on data related to stress. Facial expression recognition, voice analysis, text analysis, etc. are used to estimate emotions. For example, the analysis unit captures the user's facial expression with a camera and estimates the stress level using an emotion estimation algorithm. The analysis unit can also record the user's voice with a microphone and estimate emotions using voice analysis technology. Furthermore, when the user is relaxed, the analysis unit analyzes with emphasis on data related to relaxation. For example, when the analysis unit determines that the user is relaxed based on the user's facial expression data, it analyzes with emphasis on data related to relaxation. Furthermore, when the user is in a hurry, the analysis unit prioritizes analysis of important data. For example, when the analysis unit determines that the user is in a hurry based on the user's voice data, it analyzes important data with priority. As a result, the analysis unit adjusts the analysis algorithm according to the user's emotions, thereby improving the accuracy of the analysis. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0093] The analysis unit can predict a particular health risk based on the employee's health check results. For example, the analysis unit predicts high blood pressure for an employee who is found to be at risk for high blood pressure in the health check. Health risks include abnormal blood pressure and abnormal heart rate. For example, the analysis unit predicts high blood pressure for an employee who is found to be at risk for high blood pressure in the health check. The analysis unit can also predict diabetes for an employee who is found to be at risk for diabetes in the health check. For example, the analysis unit predicts diabetes for an employee who is found to be at risk for diabetes in the health check. Furthermore, the analysis unit can also predict heart disease for an employee who is found to be at risk for heart disease in the health check. For example, the analysis unit predicts heart disease for an employee who is found to be at risk for heart disease in the health check. This allows the analysis unit to predict health risks based on the health check results, enabling early health management. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health check result data to the generation AI and cause the generation AI to execute a prediction of health risks.

[0094] The analysis unit can predict changes in motivation based on the work performance data of employees. For example, the analysis unit predicts a decrease in motivation for an employee whose work performance has decreased. Changes in motivation include a decrease in work performance and a change in attendance rate. For example, the analysis unit predicts a decrease in motivation for an employee whose work performance has decreased. The analysis unit can also predict an increase in motivation for an employee whose work performance has improved. For example, the analysis unit predicts an increase in motivation for an employee whose work performance has improved. Furthermore, the analysis unit can predict changes in motivation for an employee whose work performance exceeds a certain standard. For example, the analysis unit predicts changes in motivation for an employee whose work performance has exceeded a certain standard. As a result, the analysis unit predicts changes in motivation based on work performance data, making it possible to take appropriate measures. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input work performance data to the generation AI and cause the generation AI to execute a prediction of changes in motivation.

[0095] The analysis unit can predict the stress level based on the frequency of the employee's internal communication. The analysis unit, for example, predicts the stress level for an employee who has frequent internal communication. The stress level includes a self-reported stress score, a physiological index, and the like. For example, the analysis unit predicts the stress level for an employee who has frequent internal communication. The analysis unit can also predict the stress level for an employee who has little internal communication. For example, the analysis unit predicts the stress level for an employee who has little internal communication. Furthermore, the analysis unit can predict the stress level based on a communication pattern related to a specific project. For example, the analysis unit predicts the stress level based on a communication pattern related to a specific project. As a result, the analysis unit predicts the stress level based on the frequency of internal communication, thereby enabling appropriate stress management. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the internal communication data to the generation AI and cause the generation AI to execute the prediction of the stress level.

[0096] The analysis unit can predict changes in physical condition after a vacation based on the employee's vacation status. The analysis unit predicts changes in physical condition for an employee whose physical condition has improved after a vacation. The changes in physical condition include fatigue level and quality of sleep. For example, the analysis unit predicts changes in physical condition for an employee whose physical condition has improved after a vacation. The analysis unit can also predict changes in physical condition for an employee whose physical condition has deteriorated after a vacation. For example, the analysis unit predicts changes in physical condition for an employee whose physical condition has deteriorated after a vacation. Furthermore, the analysis unit can predict changes in physical condition after a vacation in detail. For example, the analysis unit predicts changes in physical condition after a vacation in detail. As a result, the analysis unit predicts changes in physical condition based on the vacation status, enabling appropriate health management. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input vacation status data to the generation AI and cause the generation AI to predict changes in physical condition.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the emotions. For example, when the user is feeling stressed, the analysis unit provides a simple and highly visible display method. Facial expression recognition, voice analysis, text analysis, etc. are used to estimate emotions. For example, the analysis unit captures the user's facial expression with a camera and estimates the stress level using an emotion estimation algorithm. The analysis unit can also record the user's voice with a microphone and estimate the emotion using voice analysis technology. Furthermore, when the user is relaxed, the analysis unit provides a display method including detailed information. For example, when the analysis unit determines that the user is relaxed based on the user's facial expression data, it provides a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit provides a display method that focuses on the main points. For example, when the analysis unit determines that the user is in a hurry based on the user's voice data, it provides a display method that focuses on the main points. As a result, the analysis unit adjusts the display method of the analysis results according to the user's emotions, thereby improving visibility. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis result.

[0098] The analysis unit can divide the analysis results by region taking into account the geographical location information of the employees. For example, the analysis unit provides analysis results related to activities in the office to employees in the office. The regions include countries, cities, offices, and the like. For example, the analysis unit provides analysis results related to activities in the office to employees in the office. The analysis unit can also provide analysis results related to activities at the business trip destination to employees on a business trip. For example, the analysis unit provides analysis results related to activities at the business trip destination to employees on a business trip. Furthermore, the analysis unit can also provide analysis results related to remote work to employees working remotely. For example, the analysis unit provides analysis results related to remote work to employees working remotely. As a result, the analysis unit can provide analysis results by region by dividing the analysis results by region taking into account the geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of employees to the generation AI and cause the generation AI to execute division of the analysis results by region.

[0099] The analysis unit can predict changes in emotions based on the employee's social media activities. The analysis unit predicts changes in emotions based on, for example, information shared by the employee on social media. Changes in emotions include changes from positive to negative, changes in stress levels, and the like. For example, the analysis unit predicts changes in emotions based on information shared by the employee on social media. The analysis unit can also predict changes in stress and motivation from the employee's social media activities. For example, the analysis unit predicts changes in stress and motivation from the employee's social media activities. Furthermore, the analysis unit can predict changes in emotions related to areas of interest from the employee's social media activities. For example, the analysis unit predicts changes in emotions related to areas of interest from the employee's social media activities. As a result, the analysis unit predicts changes in emotions based on social media activities, making it possible to take appropriate measures. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's social media data to the generation AI and cause the generation AI to execute a prediction of changes in emotions.

[0100] The analysis unit can analyze stress factors related to a specific task based on the employee's task content. The analysis unit, for example, analyzes stress factors related to the specific task content and predicts a stress level. Stress factors include an increase in the amount of work, interpersonal problems, and the like. For example, the analysis unit analyzes stress factors related to the specific task content and predicts a stress level. The analysis unit can also analyze stress factors associated with changes in task content and predict a stress level. For example, the analysis unit analyzes stress factors associated with changes in task content and predicts a stress level. Furthermore, the analysis unit can analyze stress factors associated with the complexity of the task content and predict a stress level. For example, the analysis unit analyzes stress factors associated with the complexity of the task content and predicts a stress level. As a result, the analysis unit can analyze stress factors based on the task content, thereby enabling appropriate stress management. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's task content data to the generation AI and cause the generation AI to execute an analysis of stress factors.

[0101] The analysis unit can predict changes in physical condition due to long working hours based on the working hours of the employee. The analysis unit, for example, predicts poor physical condition for an employee who continues to work long hours. Long working hours include working 8 hours or more a day, working 40 hours or more a week, etc. For example, the analysis unit predicts poor physical condition for an employee who continues to work long hours. The analysis unit can also predict a decrease in motivation for an employee who continues to work long hours. For example, the analysis unit predicts a decrease in motivation for an employee who continues to work long hours. Furthermore, the analysis unit can predict a stress level for an employee who continues to work long hours. For example, the analysis unit predicts a stress level for an employee who continues to work long hours. As a result, the analysis unit predicts changes in physical condition based on the working hours, thereby enabling appropriate health management. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the working hours data of the employee to the generation AI and cause the generation AI to execute a prediction of changes in physical condition due to long working hours.

[0102] The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the emotion. For example, when the user is feeling stressed, the providing unit provides information related to stress with priority. Facial expression recognition, voice analysis, text analysis, etc. are used for estimating the emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the stress level using an emotion estimation algorithm. The providing unit can also record the user's voice with a microphone and estimate the emotion using voice analysis technology. Furthermore, when the user is relaxed, the providing unit provides information related to relaxation with priority. For example, when the providing unit determines that the user is relaxed based on the user's facial expression data, it provides information related to relaxation with priority. Furthermore, when the user is in a hurry, the providing unit provides important information with priority. For example, when the providing unit determines that the user is in a hurry based on the user's voice data, it provides important information with priority. As a result, the providing unit determines the priority of information according to the user's emotion, thereby enabling efficient information provision. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generation AI and cause the generation AI to determine the priority order of information.

[0103] The provision unit can propose specific health measures based on the employee's health check results. The provision unit proposes specific health measures for employees who are at risk for high blood pressure in the health check. The specific health measures include recommendations for exercise and improvements in diet. For example, the provision unit proposes recommendations for exercise and improvements in diet for employees who are at risk for high blood pressure in the health check. The provision unit can also propose specific health measures for employees who are at risk for diabetes in the health check. For example, the provision unit proposes recommendations for exercise and improvements in diet for employees who are at risk for diabetes in the health check. The provision unit can also propose specific health measures for employees who are at risk for heart disease in the health check. For example, the provision unit proposes recommendations for exercise and improvements in diet for employees who are at risk for heart disease in the health check. As a result, the provision unit proposes specific health measures based on the health check results, thereby improving health management. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input health check result data to the generation AI and cause the generation AI to execute the proposal of specific health measures.

[0104] The provision unit can propose specific business improvement measures based on the business performance data of employees. The provision unit proposes specific business improvement measures for employees whose business performance has declined, for example. Specific business improvement measures include reviewing business processes and introducing tools. For example, the provision unit proposes reviewing business processes and introducing tools to employees whose business performance has declined. The provision unit can also propose specific business improvement measures for employees whose business performance has improved. For example, the provision unit proposes reviewing business processes and introducing tools to employees whose business performance has improved. Furthermore, the provision unit can also propose specific business improvement measures for employees whose business performance exceeds a certain standard. For example, the provision unit proposes reviewing business processes and introducing tools to employees whose business performance has exceeded a certain standard. As a result, the provision unit proposes specific business improvement measures based on the business performance data, thereby improving business efficiency. Part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input business performance data to the generation AI and cause the generation AI to execute the proposal of specific business improvement measures.

[0105] The provision department can propose specific communication improvement measures based on the frequency of the employee's internal communication. The provision department proposes specific communication improvement measures for employees who have frequent internal communication, for example. Specific communication improvement measures include holding regular meetings and strengthening feedback. For example, the provision department proposes holding regular meetings and strengthening feedback for employees who have frequent internal communication. The provision department can also propose specific communication improvement measures for employees who have little internal communication. For example, the provision department proposes holding regular meetings and strengthening feedback for employees who have little internal communication. Furthermore, the provision department can also propose specific communication improvement measures based on communication patterns related to a specific project. For example, the provision department proposes holding regular meetings and strengthening feedback based on communication patterns related to a specific project. As a result, the provision department proposes specific improvement measures based on the frequency of internal communication, thereby improving the quality of communication. Part or all of the above-mentioned processing in the provision department may be performed using, for example, AI, or may be performed without using AI. For example, the provision department can input internal communication data to the generation AI and cause the generation AI to execute the proposal of specific communication improvement measures.

[0106] The provision unit can make specific recommendations for taking vacation based on the employee's vacation acquisition status. The provision unit, for example, makes specific recommendations for taking vacation to employees who have been working long hours. Specific recommendations for taking vacation include taking regular vacations and taking refreshing vacations. For example, the provision unit recommends taking regular vacations and taking refreshing vacations to employees who have been working long hours. The provision unit can also make specific recommendations for taking vacation to employees who have not taken vacation. For example, the provision unit recommends taking regular vacations and taking refreshing vacations to employees who have not taken vacation. Furthermore, the provision unit can also make specific recommendations for taking vacation to employees whose work performance has deteriorated after vacation. For example, the provision unit recommends taking regular vacations and taking refreshing vacations to employees whose work performance has deteriorated after vacation. As a result, the provision unit makes specific recommendations for taking vacation based on the vacation acquisition status, making it possible to properly manage vacations. A part or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input vacation taking status data into the generation AI and cause the generation AI to make specific vacation taking recommendations.

[0107] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the emotion. For example, when the user is feeling stressed, the providing unit provides a simple and highly visible display method. Facial expression recognition, voice analysis, text analysis, etc. are used to estimate the emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the stress level using an emotion estimation algorithm. The providing unit can also record the user's voice with a microphone and estimate the emotion using voice analysis technology. Furthermore, when the user is relaxed, the providing unit provides a display method including detailed information. For example, when the providing unit determines that the user is relaxed based on the user's facial expression data, the providing unit provides a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit provides a display method that focuses on the main points. For example, when the providing unit determines that the user is in a hurry based on the user's voice data, the providing unit provides a display method that focuses on the main points. As a result, the providing unit adjusts the display method of information according to the user's emotion, thereby improving visibility. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generation AI and cause the generation AI to adjust the method of displaying information.

[0108] The provision unit can propose measures for each region taking into account the geographical location information of the employees. For example, the provision unit proposes measures related to activities in the office to employees in the office. The measures for each region include responses to health risks specific to the region and business improvement measures for each region. For example, the provision unit proposes measures related to activities in the office to employees in the office. In addition, the provision unit can also propose measures related to activities at the business trip destination to employees on a business trip. For example, the provision unit proposes measures related to activities at the business trip destination to employees on a business trip. In addition, the provision unit can also propose measures related to remote work to employees working remotely. For example, the provision unit proposes measures related to remote work to employees working remotely. As a result, the provision unit can propose measures for each region by proposing measures for each region taking into account the geographical location information. Part or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input the geographical location information of employees to the generation AI and cause the generation AI to execute the proposal of measures for each region.

[0109] The provision unit can propose specific stress countermeasures based on the employee's social media activities. The provision unit proposes specific stress countermeasures based on, for example, information shared by the employee on social media. Specific stress countermeasures include recommending relaxation methods and providing counseling. For example, the provision unit proposes recommending relaxation methods and providing counseling based on information shared by the employee on social media. The provision unit can also propose countermeasures related to stress and motivation from the employee's social media activities. For example, the provision unit proposes countermeasures related to stress and motivation from the employee's social media activities. Furthermore, the provision unit can also propose stress countermeasures related to the employee's field of interest from the employee's social media activities. For example, the provision unit proposes stress countermeasures related to the employee's field of interest from the employee's social media activities. As a result, the provision unit proposes specific stress countermeasures based on the social media activities, thereby improving stress management. Part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the employee's social media data to the generation AI and cause the generation AI to execute the proposal of specific stress countermeasures.

[0110] The provision unit can propose improvement measures related to specific tasks based on the employee's work content. The provision unit, for example, proposes improvement measures related to specific task content. Specific task improvement measures include reviewing the work process and introducing tools. For example, the provision unit proposes improvement measures related to specific task content. The provision unit can also propose improvement measures associated with changes in task content. For example, the provision unit proposes improvement measures associated with changes in task content. Furthermore, the provision unit can also propose improvement measures related to the complexity of task content. For example, the provision unit proposes improvement measures related to the complexity of task content. As a result, the provision unit proposes specific improvement measures based on task content, thereby improving task efficiency. Part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the employee's task content data into the generation AI and cause the generation AI to execute the proposal of improvement measures related to specific tasks.

[0111] The provision unit can propose specific adjustments to working hours based on the working hours of the employee. For example, the provision unit proposes specific adjustments to working hours to an employee who has been working long hours. Specific adjustments to working hours include the introduction of flextime and reduction of overtime hours. For example, the provision unit proposes the introduction of flextime and reduction of overtime hours to an employee who has been working long hours. The provision unit can also propose specific adjustments to working hours to an employee whose working hours are irregular. For example, the provision unit proposes the introduction of flextime and reduction of overtime hours to an employee whose working hours are irregular. Furthermore, the provision unit can also propose specific adjustments to working hours to an employee whose working hours exceed a certain standard. For example, the provision unit proposes the introduction of flextime and reduction of overtime hours to an employee whose working hours exceed a certain standard. As a result, the provision unit proposes specific adjustments based on the working hours, enabling appropriate work management. A part or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision department can input employee working hour data into the generation AI and have the generation AI execute specific adjustments to working hours.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The employee management system further includes a feedback collection unit. The feedback collection unit can collect feedback from employees and provide it to the analysis unit. For example, the feedback collection unit collects employee opinions and thoughts through regular surveys. The feedback collection unit can also provide a platform where employees can freely post their opinions. Furthermore, the feedback collection unit can analyze employee feedback and identify employee satisfaction levels and areas for improvement. In this way, the employee management system can utilize employee feedback to achieve more effective human resource management.

[0114] The collection department can collect digital activity data of employees. For example, the collection department can collect the amount of time employees spend using a computer and the frequency of application use. The collection department can also collect employees' internet browsing history and the number of times they send and receive emails. Furthermore, the collection department can analyze employees' digital activity data to evaluate their work efficiency and concentration. In this way, the collection department can use employees' digital activity data to identify areas for improvement in work.

[0115] The analysis unit can predict long-term health risks based on the employee's health data. For example, the analysis unit can analyze the employee's health checkup results and daily health data to predict the future risk of illness. The analysis unit can also analyze the employee's lifestyle data to evaluate health risks. Furthermore, the analysis unit can analyze the employee's genetic information to predict genetic health risks. In this way, the analysis unit can utilize the employee's health data to support long-term health management.

[0116] The provision department can propose career paths for employees based on the prediction results. For example, the provision department can propose appropriate career paths based on the work performance data and skill data of employees. The provision department can also propose career paths taking into consideration the interests and concerns of employees. Furthermore, the provision department can also propose necessary skills and experience based on the career goals of employees. In this way, the provision department can support the career growth of employees.

[0117] The provision department can suggest team building activities for employees based on the prediction results. For example, the provision department can suggest appropriate team building activities based on communication data and motivation data of employees. The provision department can also suggest team building activities taking into consideration the interests and concerns of employees. Furthermore, the provision department can evaluate the team dynamics of employees and measure the effectiveness of team building activities. This allows the provision department to improve teamwork among employees.

[0118] The collection unit can estimate the user's emotion and adjust the type of data to be collected based on the emotion. For example, when the user is feeling stressed, the collection unit prioritizes collecting data related to stress. Also, when the user is relaxed, the collection unit can also prioritize collecting data related to relaxation. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting important data. This allows the collection unit to improve the efficiency of data collection according to the user's emotion.

[0119] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis result based on the emotions. For example, when the user is feeling stressed, the analysis unit provides a notification method that is simple and highly visible. When the user is relaxed, the analysis unit can also provide a notification method that includes detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a notification method that focuses on the main points. In this way, the analysis unit can improve visibility by adjusting the notification method of the analysis result according to the user's emotions.

[0120] The providing unit can estimate the user's emotion and adjust the format of the information to be provided based on the emotion. For example, when the user is feeling stressed, the providing unit can provide information in a simple and easy-to-understand format. When the user is relaxed, the providing unit can also provide information in a format including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide information in a format that focuses on the main points. In this way, the providing unit can adjust the format of information according to the user's emotion, thereby improving the ease of receiving the information.

[0121] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the emotions. For example, the providing unit can delay the timing of providing information when the user is feeling stressed. Also, the providing unit can advance the timing of providing information when the user is relaxed. Furthermore, the providing unit can immediately provide important information when the user is in a hurry. In this way, the providing unit can adjust the timing of providing information according to the user's emotions, thereby improving the ease of receiving information.

[0122] The providing unit can estimate the user's emotions and adjust the content of the information to be provided based on the emotions. For example, when the user is feeling stressed, the providing unit can provide information that helps reduce stress. Also, when the user is relaxed, the providing unit can provide information to maintain relaxation. Furthermore, when the user is in a hurry, the providing unit can provide important information with priority. In this way, the providing unit can adjust the content of the information according to the user's emotions, thereby improving the usefulness of the information.

[0123] The process flow of the second embodiment will be briefly described below.

[0124] Step 1: The collection department collects relevant information related to employees. The relevant information related to employees includes working hours, work content, vacation status, health check results, work performance data, frequency of internal communication, etc. For example, the collection department obtains working hours from a time card system, work content from a project management system, and vacation status from a human resources management system. Step 2: The analysis unit analyzes the information collected by the collection unit and predicts signs of changes in employees' physical condition and motivation. Machine learning and deep learning are used for the analysis; for example, signs of poor physical condition can be predicted from working hours and vacation status, and signs of declining motivation can be predicted from work performance data. Step 3: The provision unit provides the results predicted by the analysis unit to the manager. For example, dashboards and alerts are used to visually display the results through a dashboard and notify important signs through alerts.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0127] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information such as the working hours and work contents of employees using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts signs of changes in the employee's physical condition and motivation by analyzing the collected information. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the prediction result to the manager. The collection unit is also realized, for example, by the specific processing unit 290 of the data processing device 12, and can collect information such as the working hours and work contents of employees. The analysis unit is also realized, for example, by the control unit 46A of the smart device 14, and can predict signs of changes in the employee's physical condition and motivation by analyzing the collected information. The provision unit is also realized, for example, by the specific processing unit 290 of the data processing device 12, and can provide the prediction result to the manager. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] Fig. 4 shows an example of 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.

[0137] 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.

[0138] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information such as the working hours and work contents of employees using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts signs of changes in the employee's physical condition and motivation by analyzing the collected information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the prediction result to the manager. The collection unit is also realized, for example, by the specific processing unit 290 of the data processing device 12, and can collect information such as the working hours and work contents of employees. The analysis unit is also realized, for example, by the control unit 46A of the smart glasses 214, and can predict signs of changes in the employee's physical condition and motivation by analyzing the collected information. The provision unit is also realized, for example, by the specific processing unit 290 of the data processing device 12, and can provide the prediction result to the manager. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0148] 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.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0151] 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 performed in a secure state.

[0152] 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.

[0153] 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.

[0154] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0155] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0157] 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 voice indicating a user input for 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.

[0158] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0159] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects information such as the working hours and work contents of employees using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts signs of changes in the employee's physical condition and motivation by analyzing the collected information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the prediction result to the manager. The collection unit is also realized, for example, by the specific processing unit 290 of the data processing device 12, and can collect information such as the working hours and work contents of employees. The analysis unit is also realized, for example, by the control unit 46A of the headset type terminal 314, and can predict signs of changes in the employee's physical condition and motivation by analyzing the collected information. The provision unit is also realized, for example, by the specific processing unit 290 of the data processing device 12, and can provide the prediction result to the manager. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a 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.

[0163] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0164] 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.

[0165] 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 the voice according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0167] 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 performed in a secure state.

[0168] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0169] 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.

[0170] 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.

[0171] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0172] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0173] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0174] 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 a voice indicating a user input for 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.

[0175] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0176] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0177] Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information such as the working hours and work contents of employees using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts signs of changes in the employee's physical condition and motivation by analyzing the collected information. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the prediction result to the manager. The collection unit is also realized, for example, by the specific processing unit 290 of the data processing device 12, and can collect information such as the working hours and work contents of employees. The analysis unit is also realized, for example, by the control unit 46A of the robot 414, and can predict signs of changes in the employee's physical condition and motivation by analyzing the collected information. The provision unit is also realized, for example, by the specific processing unit 290 of the data processing device 12, and can provide the prediction result to the manager. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0178] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0179] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0180] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0181] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0182] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0183] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0185] 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.

[0186] 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 Universal Serial Bus (USB) 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.

[0187] In addition, 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 upon request from the data processing device 12.

[0188] 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.

[0189] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0190] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0191] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0192] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The above specific processes are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0193] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0194] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0195] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0196] (Appendix 1) A collection department that collects relevant information linked to employees; an analysis unit that analyzes the information collected by the collection unit and predicts signs of changes in employee physical condition and motivation; A providing unit that provides a result predicted by the analysis unit to a manager. A system characterized by: (Appendix 2) The collecting unit includes: Collect information on employee working hours, job content, vacation status, health check results, work performance data, and frequency of internal communications 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze collected data using machine learning and deep learning to predict signs of changes in employees' physical condition and motivation. 2. The system of claim 1, (Appendix 4) The providing unit is Prediction results are presented to managers via dashboards or alerts 2. The system of claim 1, (Appendix 5) The providing unit is Propose specific measures based on prediction results 2. The system of claim 1, (Appendix 6) The collecting unit includes: Update collected data in real time 2. The system of claim 1, (Appendix 7) The analysis unit is Train predictive models based on past data 2. The system of claim 1, (Appendix 8) The collecting unit includes: Estimate user emotions and adjust information gathering timing based on emotions 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyze employees' past work history and select the most appropriate method of collecting information 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting information, filter it based on employees' current projects and areas of interest 2. The system of claim 1, (Appendix 11) The collecting unit includes: Prioritize the collection of data related to specific health risks based on employee health check results 2. The system of claim 1, (Appendix 12) The collecting unit includes: Monitor employee performance data in real time and collect additional data when anomalies are detected 2. The system of claim 1, (Appendix 13) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on emotions 2. The system of claim 1, (Appendix 14) The collecting unit includes: When collecting information, prioritize collecting the most relevant data by taking into account the geographic location of employees. 2. The system of claim 1, (Appendix 15) The collecting unit includes: When collecting information, analyze employees' social media activities and collect relevant data. 2. The system of claim 1, (Appendix 16) The collecting unit includes: Prioritize collection of specific communication patterns based on the frequency of employees' internal communications 2. The system of claim 1, (Appendix 17) The collecting unit includes: Focus on collecting data on employees' post-vacation work performance based on their vacation status. 2. The system of claim 1, (Appendix 18) The analysis unit is Infer user emotions and adjust analysis algorithms based on emotions 2. The system of claim 1, (Appendix 19) The analysis unit is Predicting specific health risks based on employee health check results 2. The system of claim 1, (Appendix 20) The analysis unit is Predicting changes in employee motivation based on work performance data 2. The system of claim 1, (Appendix 21) The analysis unit is Predicting employees' stress levels based on frequency of internal communication 2. The system of claim 1, (Appendix 22) The analysis unit is Predicting changes in health after vacation based on employee vacation status 2. The system of claim 1, (Appendix 23) The analysis unit is Infer user emotions and adjust how analysis results are displayed based on those emotions 2. The system of claim 1, (Appendix 24) The analysis unit is Split the analysis results by region, taking into account employees' geographic location 2. The system of claim 1, (Appendix 25) The analysis unit is Predict changes in employee sentiment based on social media activity 2. The system of claim 1, (Appendix 26) The analysis unit is Analyze specific work-related stress factors based on employees' work content 2. The system of claim 1, (Appendix 27) The analysis unit is Predicting changes in health caused by long working hours based on employees' working hours 2. The system of claim 1, (Appendix 28) The providing unit is Estimate user emotions and prioritize information based on emotions 2. The system of claim 1, (Appendix 29) The providing unit is Propose specific health measures based on employee health checkup results 2. The system of claim 1, (Appendix 30) The providing unit is Propose specific work improvement measures based on employee work performance data 2. The system of claim 1, (Appendix 31) The providing unit is Propose specific communication improvement measures based on the frequency of employees' internal communication 2. The system of claim 1, (Appendix 32) The providing unit is Make specific vacation recommendations based on employees' vacation usage status 2. The system of claim 1, (Appendix 33) The providing unit is Infer user emotions and adjust the way information is presented based on those emotions 2. The system of claim 1, (Appendix 34) The providing unit is Consider the geographic location of employees to suggest localized measures 2. The system of claim 1, (Appendix 35) The providing unit is Propose specific stress management measures based on employees' social media activity 2. The system of claim 1, (Appendix 36) The providing unit is Suggest specific work-related improvements based on employee job descriptions 2. The system of claim 1, (Appendix 37) The providing unit is Propose specific work hour adjustments based on employee working hours 2. The system of claim 1, [Explanation of symbols]

[0197] 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 collection department that collects relevant information linked to employees; an analysis unit that analyzes the information collected by the collection unit and predicts signs of changes in employee physical condition and motivation; A providing unit that provides a result predicted by the analysis unit to a manager. A system characterized by:

2. The collecting unit includes: Collect information on employee working hours, job content, vacation status, health check results, work performance data, and frequency of internal communications 2. The system of claim 1 .

3. The analysis unit is Analyze collected data using machine learning and deep learning to predict signs of changes in employees' physical condition and motivation.

2. The system of claim 1 .

4. The providing unit is Prediction results are presented to managers via dashboards or alerts 2. The system of claim 1 .

5. The providing unit is Propose specific measures based on prediction results 2. The system of claim 1 .

6. The collecting unit includes: Update collected data in real time 2. The system of claim 1 .

7. The analysis unit is Train predictive models based on past data 2. The system of claim 1 .

8. The collecting unit includes: Estimate user emotions and adjust information gathering timing based on emotions 2. The system of claim 1 .

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