system

Abstract

The system according to this embodiment aims to understand the mental health and engagement status of subordinates and to provide appropriate support. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, an adjustment unit, an enhancement unit, a facial expression analysis unit, and a report provision unit. The collection unit collects data from subordinates' mental health checks and engagement surveys. The analysis unit analyzes the data collected by the collection unit. The provision unit provides personalized advice to each subordinate based on the analysis results obtained by the analysis unit. The adjustment unit automates schedule adjustments, starting with subordinates with a high mental risk level, based on the advice provided by the provision unit. The enhancement unit enhances the content of 1-on-1 meetings based on the schedules adjusted by the adjustment unit. The facial expression analysis unit analyzes facial expressions during interviews based on the content enhanced by the enhancement unit. The report provision unit continuously analyzes emails, meeting minutes, meeting recordings, etc., based on the facial expression data analyzed by the facial expression analysis unit, and regularly reports the subordinate's mental state and challenges to the supervisor.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to grasp the mental health and engagement status of subordinates and provide appropriate support.

[0005] The system according to the embodiment aims to grasp the mental health and engagement status of subordinates and provide appropriate support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, an adjustment unit, an enhancement unit, a facial expression analysis unit, and a report provision unit. The collection unit collects data from subordinates' mental health checks and engagement surveys. The analysis unit analyzes the data collected by the collection unit. The provision unit provides personalized advice to each subordinate based on the analysis results obtained by the analysis unit. The adjustment unit automates schedule adjustments, starting with subordinates with the highest mental risk level, based on the advice provided by the provision unit. The enhancement unit enhances the content of 1-on-1 meetings based on the schedules adjusted by the adjustment unit. The facial expression analysis unit analyzes facial expressions during interviews based on the content enhanced by the enhancement unit. The report provision unit continuously analyzes emails, meeting minutes, meeting recordings, etc., based on the facial expression data analyzed by the facial expression analysis unit, and periodically reports the subordinate's mental state and challenges to their supervisor. [Effects of the Invention]

[0007] The system according to this embodiment can understand the mental health and engagement status of subordinates and provide appropriate support. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that subtly supports the relationship between a supervisor and a subordinate. This AI agent system collects and analyzes data from subordinates' mental health checks and engagement surveys, and provides personalized advice to each subordinate. Furthermore, it automates scheduling adjustments for subordinates with a high mental risk level, prioritizing those with the highest risk level, and enhances the content of 1-on-1 meetings. It also analyzes facial expressions during interviews to understand the subordinate's mental state. In addition, it continuously analyzes emails, meeting minutes, meeting recordings, etc., and regularly reports the subordinate's mental state and challenges to the supervisor. This allows supervisors to constantly understand the mental state of their subordinates and provide continuous mental support. For example, the AI ​​agent system collects data from subordinates' mental health checks and engagement surveys. This allows it to understand the subordinate's mental state and engagement status. Next, the AI ​​analyzes the collected data and generates personalized advice for each subordinate. For example, if a particular subordinate is experiencing stress, it provides that subordinate with advice on relaxation methods and stress management. Furthermore, it automates scheduling adjustments for subordinates with a high mental risk level, prioritizing those with the highest risk level. This allows supervisors to prioritize one-on-one meetings with subordinates at high mental health risk. During these meetings, the AI ​​enriches the conversation, supporting supervisors in effective communication with their subordinates. The AI ​​also analyzes facial expressions during the meeting to understand the subordinate's mental state. For example, if a subordinate appears anxious during the meeting, the AI ​​provides this information to the supervisor, enabling them to take appropriate action. Furthermore, the AI ​​continuously analyzes emails, meeting minutes, and meeting recordings, regularly reporting the subordinate's mental state and challenges to the supervisor. This allows supervisors to constantly monitor their subordinates' mental state and provide continuous mental support. In this way, the AI ​​agent system can subtly support the supervisor-subordinate relationship and improve the subordinate's mental health and engagement.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, an adjustment unit, an enhancement unit, a facial expression analysis unit, and a report provision unit. The collection unit collects data from subordinates' mental health checks and engagement surveys. For example, the collection unit automatically collects data from mental health checks and engagement surveys answered by subordinates. The collection unit can also collect data from subordinates' mental health checks and engagement surveys on a regular basis. Furthermore, the collection unit can collect data from subordinates' mental health checks and engagement surveys in real time. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the collected data and understand the mental state and engagement status of subordinates. The analysis unit can also analyze the collected data using statistical methods. Furthermore, the analysis unit can also analyze the collected data using machine learning algorithms. The provision unit provides personalized advice to individual subordinates based on the analysis results obtained by the analysis unit. For example, the provision unit provides advice to subordinates on relaxation methods and stress management based on the analysis results. Furthermore, the Service Department can provide advice to subordinates to improve engagement based on the analysis results. In addition, the Service Department can provide advice to subordinates to improve their mental health based on the analysis results. The Coordination Department automates scheduling, prioritizing subordinates with high mental health risk based on the advice provided by the Service Department. For example, the Coordination Department adjusts schedules to prioritize 1-on-1 meetings with subordinates at high mental health risk. The Coordination Department can also adjust schedules to reduce the workload of subordinates at high mental health risk. Furthermore, the Coordination Department can adjust schedules to encourage subordinates at high mental health risk to take leave. The Enhancement Department enhances the content of 1-on-1 meetings based on the schedules adjusted by the Coordination Department. For example, the Enhancement Department suggests topics that supervisors should discuss with their subordinates to enhance the content of 1-on-1 meetings.Furthermore, the Enhancement Department can suggest questions that supervisors should ask their subordinates to enhance the content of 1-on-1 meetings. The Enhancement Department can also suggest feedback that supervisors should provide to their subordinates to further enhance the content of 1-on-1 meetings. The Facial Expression Analysis Department analyzes facial expressions during meetings based on the enhanced content provided by the Enhancement Department. For example, the Facial Expression Analysis Department can capture the subordinate's facial expressions during the meeting with a camera and analyze them using AI. The Facial Expression Analysis Department can also analyze the subordinate's facial expressions in real time during the meeting. Furthermore, the Facial Expression Analysis Department can record the subordinate's facial expressions during the meeting and analyze them later. The Reporting Department continuously analyzes emails, meeting minutes, meeting recordings, etc., based on the facial expression data analyzed by the Facial Expression Analysis Department, and regularly reports the subordinate's mental state and challenges to the supervisor. For example, the Reporting Department notifies supervisors via email to regularly report on the subordinate's mental state and challenges. The Reporting Department can also provide supervisors with meeting minutes to regularly report on the subordinate's mental state and challenges. Furthermore, the reporting unit can also provide managers with meeting recordings to regularly report on the mental state and challenges of their subordinates. This allows the AI ​​agent system, according to this embodiment, to subtly support the relationship between managers and subordinates, thereby improving the mental health and engagement of subordinates.

[0030] The data collection unit collects data from employee mental health checks and engagement surveys. Specifically, it automatically collects data from mental health checks and engagement surveys completed by employees. This includes data entered by employees through online forms and applications. The data collection unit can collect this data regularly, for example, on a weekly or monthly cycle. Furthermore, the data collection unit can also collect employee mental health checks and engagement survey data in real time. With real-time collection, data is sent to the system the moment an employee completes the survey, making it immediately available for analysis. This allows the data collection unit to always have up-to-date information on employees' mental health and engagement. In addition, the data collection unit can use encryption technology to protect privacy during data collection, preventing employees' personal information from being leaked externally. Furthermore, the data collection unit can centrally manage employee data and link it with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, it uses AI to analyze the collected data and understand the mental state and engagement levels of its subordinates. The AI ​​uses natural language processing technology and machine learning algorithms to analyze the content of subordinates' responses and detect fluctuations in stress levels and motivation. The analysis department can also analyze the collected data using statistical methods. For example, it calculates the mean and standard deviation of the data to understand the overall mental health trends of subordinates. Furthermore, the analysis department can also analyze the collected data using machine learning algorithms. Machine learning algorithms build models based on past data and make predictions and classifications for newly collected data. This allows the analysis department to detect changes in subordinates' mental state and engagement early and take appropriate measures. In addition, the analysis department can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past mental health data, it can predict fluctuations in risk at specific times or events and plan future countermeasures. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0032] The service provider provides personalized advice to each subordinate based on the analysis results obtained by the analysis department. Specifically, they provide advice on relaxation methods and stress management based on the analysis results. For example, if a subordinate shows a high stress level, the service provider will suggest relaxation methods such as deep breathing, meditation, or light exercise. The service provider can also provide advice to subordinates to improve engagement based on the analysis results. For example, if a subordinate shows a low engagement score, the service provider will suggest team-building activities or career development opportunities. Furthermore, the service provider can also provide advice to subordinates to improve their mental health based on the analysis results. For example, if a subordinate is experiencing mental health problems, the service provider will suggest professional counseling or access to mental health resources. Because the service provider provides this advice individually and personalized to each subordinate, subordinates can receive the support that is best suited to them. In addition, the service provider can monitor the effectiveness of the advice and update the advice content as needed. In this way, the service provider can support the improvement of subordinates' mental health and engagement, and contribute to improving the workplace environment.

[0033] The scheduling department automates scheduling adjustments, prioritizing subordinates with the highest mental health risk based on advice provided by the service provider. Specifically, it adjusts schedules to prioritize one-on-one meetings with subordinates at high mental health risk. For example, if a subordinate has a high mental health risk, the scheduling department checks the supervisor's schedule and sets the earliest possible one-on-one meeting. The scheduling department can also adjust schedules to reduce the workload of subordinates at high mental health risk. For example, if a subordinate is experiencing excessive stress, the scheduling department adjusts the schedule to distribute the subordinate's work to other members. Furthermore, the scheduling department can adjust schedules to encourage subordinates at high mental health risk to take leave. For example, if a subordinate has been subjected to prolonged stress, the scheduling department suggests that the subordinate take leave and assigns their work to other members during the leave period. This allows the scheduling department to protect the mental health of subordinates and maintain work efficiency. In addition, the scheduling department can notify supervisors of the results of the scheduling adjustments and provide them with information to take appropriate action. This allows the scheduling department to reduce the mental health risk of subordinates and contribute to improving the workplace environment.

[0034] The Enhancement Department enhances the content of 1-on-1 meetings based on the schedule adjusted by the Coordination Department. Specifically, it proposes topics that managers should discuss with their subordinates to enhance the content of 1-on-1 meetings. For example, it may propose topics related to the subordinate's mental health or specific action plans to improve engagement. The Enhancement Department can also propose questions that managers should ask their subordinates to enhance the content of 1-on-1 meetings. For example, it may propose questions about the subordinate's current work situation and causes of stress to support a deeper understanding of the subordinate's situation. Furthermore, the Enhancement Department can also propose feedback that managers should provide to their subordinates to enhance the content of 1-on-1 meetings. For example, it may propose specific feedback on the subordinate's work performance and constructive advice on areas for improvement. In this way, the Enhancement Department can improve the quality of 1-on-1 meetings and facilitate smoother communication between managers and subordinates. In addition, the Enhancement Department can record the results of 1-on-1 meetings and provide information for use in the next meeting. In this way, the Enhancement Department can support continuous improvement of communication and contribute to improving the mental health and engagement of subordinates.

[0035] The Facial Expression Analysis Department analyzes facial expressions during interviews based on content enhanced by the Enhancement Department. Specifically, it captures the subordinate's facial expressions with a camera during the interview and analyzes them using AI. The AI ​​uses facial recognition technology and emotion analysis algorithms to estimate the subordinate's emotional state from their facial expressions. For example, if a subordinate is feeling stressed, the AI ​​detects features such as facial tension and frown lines and assesses the stress level. The Facial Expression Analysis Department can also analyze the subordinate's facial expressions in real time during interviews. Real-time analysis allows for immediate identification of changes in the subordinate's emotions during the interview and provides feedback to the supervisor. Furthermore, the Facial Expression Analysis Department can record the subordinate's facial expressions during interviews and analyze them later. Using the recorded data, a detailed analysis can be performed after the interview to understand the subordinate's emotional changes and trends. This allows the Facial Expression Analysis Department to more accurately understand the subordinate's mental state and support appropriate responses. In addition, the Facial Expression Analysis Department can share the analysis results with other departments to contribute to the subordinate's mental health care. For example, the analysis results can be shared with the Service Department and the Coordination Department to inform advice and schedule adjustments for the subordinate. This allows the facial expression analysis department to comprehensively support the mental health care of its subordinates and contribute to improving the workplace environment.

[0036] The reporting department continuously analyzes emails, meeting minutes, and meeting recordings based on facial expression data analyzed by the facial expression analysis department, and regularly reports on the mental state and challenges of subordinates to their supervisors. Specifically, it notifies supervisors via email to regularly report on the mental state and challenges of their subordinates. For example, it sends weekly or monthly reports summarizing the mental health status and engagement fluctuations of subordinates. The reporting department can also provide supervisors with meeting minutes to regularly report on the mental state and challenges of their subordinates. The minutes include the content of 1-on-1 meetings, feedback from subordinates, and future action plans. Furthermore, the reporting department can also provide supervisors with meeting recordings to regularly report on the mental state and challenges of their subordinates. By using meeting recordings, supervisors can examine the subordinates' facial expressions and statements in detail, gaining a deeper understanding. This allows the reporting department to provide supervisors with information to grasp the mental health and engagement status of their subordinates and take appropriate action. In addition, the reporting department can continuously update the report content to provide the latest information. This allows the reporting department to subtly support the relationship between superiors and subordinates, improving the mental health and engagement of subordinates.

[0037] The data collection unit can analyze the subordinate's past mental health data and select the optimal collection method. For example, the data collection unit can select the optimal question format based on the data from mental health checks that the subordinate has answered in the past. The data collection unit can also identify the time of day when the subordinate is most likely to answer the survey based on their past data and conduct the survey during that time. Furthermore, the data collection unit can analyze the subordinate's past data and adjust the collection method to match periods when stress levels are high. This improves the accuracy of data collection by selecting the optimal collection method based on past data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the subordinate's past mental health data into a generating AI and have the generating AI select the optimal collection method.

[0038] The data collection unit can filter data based on the employee's current work situation and stress level when collecting mental health checks and engagement surveys. For example, if an employee has a high workload, the data collection unit can provide a simplified mental health check. Furthermore, if an employee has a high stress level, the data collection unit can conduct a detailed engagement survey to identify the root cause of the problem. The data collection unit can also customize the survey questions according to the employee's work situation. This allows for the collection of appropriate data by filtering based on work situation and stress level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the employee's work situation and stress level into a generating AI and have the generating AI perform the filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of subordinates when collecting mental health checks and engagement surveys. For example, if a subordinate is on a business trip, the data collection unit will collect mental health data related to the environment at the business trip destination. Similarly, if a subordinate is working remotely, the data collection unit can collect engagement data related to their home environment. Furthermore, the data collection unit can adjust the content of the data collected according to the subordinate's work location. This allows for the collection of more accurate data by considering geographical location information and collecting highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subordinate's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0040] The data collection unit can analyze employees' social media activity and collect relevant data when collecting mental health checks and engagement surveys. For example, the data collection unit can analyze the content of employees' social media posts to detect signs of stress. The data collection unit can also understand the level of engagement from employees' social media activity. Furthermore, the data collection unit can customize the questions for mental health checks based on employees' social media data. This allows for a more accurate understanding of employees' mental state by analyzing social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employees' social media data into a generating AI and have the generating AI collect the relevant data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the mental health data during the analysis. For example, the analysis unit will perform a detailed analysis on data with high importance. Conversely, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the mental health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the mental health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of mental health data during analysis. For example, the analysis unit can apply a stress analysis algorithm to stress data. It can also apply an engagement analysis algorithm to engagement data. Furthermore, the analysis unit can apply the most suitable analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the mental health data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of mental health data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0043] The analysis unit can determine the priority of analysis based on the timing of mental health data collection. For example, the analysis unit may prioritize the analysis of recently collected data. It can also analyze current data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis according to the timing of data collection. This enables efficient analysis by determining the priority of analysis based on the timing of mental health data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of mental health data collection into a generating AI and have the generating AI determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the mental health data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the mental health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the mental health data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The advice delivery unit can adjust the level of detail of the advice based on the subordinate's mental risk level when providing advice. For example, the delivery unit can provide detailed advice to subordinates with a high mental risk level. It can also provide simpler advice to subordinates with a low mental risk level. Furthermore, the delivery unit can adjust the level of detail of the advice according to the mental risk level. This allows for the provision of appropriate advice by adjusting the level of detail of the advice based on the subordinate's mental risk level. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the subordinate's mental risk level into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0046] The service provider can apply different advice algorithms depending on the subordinate's work content when providing advice. For example, the service provider can provide sales-specific advice to subordinates in the sales department. It can also provide technology-specific advice to subordinates in the technology department. Furthermore, the service provider can apply the most appropriate advice algorithm depending on the subordinate's work content. This allows for the provision of appropriate advice by applying the most suitable advice algorithm according to the subordinate's work content. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the subordinate's work content into a generating AI and have the generating AI apply different advice algorithms.

[0047] The advice delivery unit can prioritize advice based on when the subordinate's mental health data is collected. For example, the unit can prioritize advice based on recently collected data. It can also provide advice based on current data while referring to past data. Furthermore, the unit can adjust the priority of advice according to when the data is collected. This enables efficient advice delivery by prioritizing advice based on when the subordinate's mental health data is collected. Some or all of the above processing in the advice delivery unit may be performed using AI, for example, or not using AI. For example, the advice delivery unit can input the timing of mental health data collection into a generating AI and have the generating AI determine the priority of advice.

[0048] The service provider can adjust the order of advice based on the relevance of the subordinate's mental health data when providing advice. For example, the service provider can prioritize advice based on highly relevant data. It can also postpone less relevant data. Furthermore, the service provider can adjust the order of advice according to the relevance of the data. This allows for efficient advice provision by adjusting the order of advice based on the relevance of the subordinate's mental health data. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the relevance of the mental health data into a generating AI and have the generating AI adjust the order of advice.

[0049] The scheduling unit can determine the priority of scheduling based on the mental risk level of its subordinates. For example, the scheduling unit may prioritize scheduling subordinates with a high mental risk level. It may also schedule subordinates with a moderate mental risk level next. Furthermore, it may schedule subordinates with a low mental risk level last. This allows for efficient scheduling by determining the priority of scheduling based on the mental risk level of the subordinates. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the mental risk levels of its subordinates into a generating AI and have the generating AI determine the priority of scheduling.

[0050] The scheduling unit can apply different scheduling algorithms depending on the subordinate's work content when scheduling. For example, the scheduling unit can adjust the schedule of a subordinate in the sales department so as not to disrupt sales activities. It can also adjust the schedule of a subordinate in the technical department in accordance with the progress of the project. Furthermore, the scheduling unit can apply the most suitable scheduling algorithm depending on the subordinate's work content. This enables efficient scheduling by applying the most suitable scheduling algorithm according to the subordinate's work content. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the subordinate's work content into a generating AI and have the generating AI execute the application of different scheduling algorithms.

[0051] The scheduling unit can determine scheduling priorities based on the timing of collecting subordinates' mental health data. For example, the scheduling unit can prioritize scheduling based on recently collected mental health data. The scheduling unit can also adjust schedules based on current data while referring to past data. Furthermore, the scheduling unit can determine scheduling priorities according to the timing of data collection. This enables efficient scheduling by determining scheduling priorities based on the timing of collecting subordinates' mental health data. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the timing of mental health data collection into a generating AI and have the generating AI determine the scheduling priorities.

[0052] The scheduling unit can adjust the order of scheduling based on the relevance of subordinates' mental health data. For example, the scheduling unit prioritizes scheduling based on highly relevant data. It can also postpone scheduling for less relevant data. Furthermore, the scheduling unit can adjust the order of scheduling according to the relevance of the data. This enables efficient scheduling by adjusting the order of scheduling based on the relevance of subordinates' mental health data. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the relevance of mental health data into a generating AI and have the generating AI perform the adjustment of the order of scheduling.

[0053] The enrichment unit can adjust the level of detail in 1-on-1 meetings based on the employee's mental health risk level. For example, the enrichment unit can provide detailed content to employees with a high mental health risk level, and simpler content to employees with a low mental health risk level. Furthermore, the enrichment unit can adjust the level of detail in the 1-on-1 meeting content according to the employee's mental health risk level. This allows the enrichment unit to provide appropriate content by adjusting the level of detail based on the employee's mental health risk level. Some or all of the above processing in the enrichment unit may be performed using AI, for example, or without AI. For example, the enrichment unit can input the employee's mental health risk level into a generating AI and have the generating AI adjust the level of detail in the content.

[0054] The enrichment unit can apply different enrichment algorithms depending on the subordinate's work content when enriching the content of 1-on-1 meetings. For example, the enrichment unit can provide sales-specific content to subordinates in the sales department. It can also provide technology-specific content to subordinates in the technology department. Furthermore, the enrichment unit can apply the optimal enrichment algorithm according to the subordinate's work content. This allows for the provision of appropriate content by applying the optimal enrichment algorithm according to the subordinate's work content. Some or all of the above processing in the enrichment unit may be performed using AI, for example, or without AI. For example, the enrichment unit can input the subordinate's work content into a generating AI and have the generating AI execute the application of different enrichment algorithms.

[0055] The content enhancement unit can prioritize the content of 1-on-1 meetings based on the timing of collecting subordinates' mental health data. For example, the enhancement unit can prioritize content enhancement based on recently collected data. It can also enhance content based on current data while referring to past data. Furthermore, the enhancement unit can determine the priority of 1-on-1 meeting content according to the timing of data collection. This enables efficient content delivery by prioritizing content based on the timing of collecting subordinates' mental health data. Some or all of the above processing in the enhancement unit may be performed using AI, for example, or without AI. For example, the enhancement unit can input the timing of mental health data collection into a generating AI and have the generating AI determine the content priority.

[0056] The enrichment unit can adjust the order of content based on the relevance of the subordinate's mental health data when enriching the content of 1-on-1 meetings. For example, the enrichment unit prioritizes enriching content based on highly relevant data. It can also postpone less relevant data. Furthermore, the enrichment unit can adjust the order of the content of the 1-on-1 meeting according to the relevance of the data. This allows for efficient content delivery by adjusting the order of content based on the relevance of the subordinate's mental health data. Some or all of the above processing in the enrichment unit may be performed using AI, for example, or without AI. For example, the enrichment unit can input the relevance of the mental health data into a generating AI and have the generating AI perform the adjustment of the order of content.

[0057] The facial expression analysis unit can adjust the level of detail of its analysis based on the subordinate's mental risk level during facial expression analysis. For example, the facial expression analysis unit will perform a detailed facial expression analysis for subordinates with a high mental risk level. It can also perform a simplified facial expression analysis for subordinates with a low mental risk level. Furthermore, the facial expression analysis unit can adjust the level of detail of its analysis according to the mental risk level. This allows for efficient facial expression analysis by adjusting the level of detail of the analysis based on the subordinate's mental risk level. Some or all of the above processing in the facial expression analysis unit may be performed using AI, for example, or without AI. For example, the facial expression analysis unit can input the subordinate's mental risk level into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0058] The facial expression analysis unit can apply different analysis algorithms depending on the subordinate's job duties during facial expression analysis. For example, the facial expression analysis unit can apply a facial expression analysis algorithm specialized for sales activities to subordinates in the sales department. It can also apply a facial expression analysis algorithm specialized for technical activities to subordinates in the technical department. Furthermore, the facial expression analysis unit can apply the most suitable analysis algorithm depending on the subordinate's job duties. This enables efficient facial expression analysis by applying the most suitable analysis algorithm according to the subordinate's job duties. Some or all of the above-described processes in the facial expression analysis unit may be performed using AI, for example, or without AI. For example, the facial expression analysis unit can input the subordinate's job duties into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0059] The facial expression analysis unit can determine the priority of analysis based on the timing of the collection of subordinates' mental health data during facial expression analysis. For example, the facial expression analysis unit can prioritize facial expression analysis based on recently collected mental health data. It can also perform facial expression analysis based on current data while referring to past data. Furthermore, the facial expression analysis unit can determine the priority of facial expression analysis according to the timing of data collection. This enables efficient facial expression analysis by determining the priority of analysis based on the timing of the collection of subordinates' mental health data. Some or all of the above processing in the facial expression analysis unit may be performed using AI, for example, or without AI. For example, the facial expression analysis unit can input the timing of mental health data collection into a generating AI and have the generating AI determine the priority of analysis.

[0060] The facial expression analysis unit can adjust the order of analysis based on the relevance of the subordinate's mental health data during facial expression analysis. For example, the facial expression analysis unit can prioritize facial expression analysis based on highly relevant data. It can also postpone analysis of less relevant data. Furthermore, the facial expression analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient facial expression analysis by adjusting the order of analysis based on the relevance of the subordinate's mental health data. Some or all of the above processing in the facial expression analysis unit may be performed using AI, for example, or without AI. For example, the facial expression analysis unit can input the relevance of the mental health data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0061] The report delivery unit can adjust the level of detail in a report based on the employee's mental risk level. For example, the report delivery unit can provide a detailed report to an employee with a high mental risk level, and a simpler report to an employee with a low mental risk level. Furthermore, the report delivery unit can adjust the level of detail in the report according to the mental risk level. This allows the delivery of an appropriate report by adjusting the level of detail based on the employee's mental risk level. Some or all of the above processing in the report delivery unit may be performed using AI, for example, or without AI. For example, the report delivery unit can input the employee's mental risk level into a generating AI and have the generating AI adjust the level of detail in the report.

[0062] The report delivery unit can apply different report algorithms depending on the subordinate's work content when providing reports. For example, the report delivery unit can provide a report specializing in sales activities to a subordinate in the sales department. It can also provide a report specializing in technical activities to a subordinate in the technical department. Furthermore, the report delivery unit can apply the most suitable report algorithm depending on the subordinate's work content. This allows for the provision of appropriate reports by applying the most suitable report algorithm according to the subordinate's work content. Some or all of the above processing in the report delivery unit may be performed using AI, for example, or without AI. For example, the report delivery unit can input the subordinate's work content into a generating AI and have the generating AI execute the application of different report algorithms.

[0063] The reporting department can prioritize reports based on when the subordinate's mental health data was collected. For example, the reporting department can prioritize reports based on recently collected data. It can also provide reports based on current data while referring to past data. Furthermore, the reporting department can prioritize reports according to when the data was collected. This enables efficient report delivery by prioritizing reports based on when the subordinate's mental health data was collected. Some or all of the above processes in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input the timing of mental health data collection into a generating AI and have the generating AI determine the report prioritization.

[0064] The reporting unit can adjust the order of reports based on the relevance of the subordinates' mental health data when providing reports. For example, the reporting unit can prioritize providing reports based on highly relevant data. It can also postpone providing reports on less relevant data. Furthermore, the reporting unit can adjust the order of reports according to the relevance of the data. This allows for efficient report provision by adjusting the order of reports based on the relevance of the subordinates' mental health data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the relevance of the mental health data into a generating AI and have the generating AI perform the adjustment of the report order.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The AI ​​agent system includes a data collection unit that analyzes the subordinate's past mental health data and selects the optimal data collection method. For example, the data collection unit selects the most suitable question format based on data from mental health checks the subordinate has previously answered. It can also identify the time of day when the subordinate is most likely to respond based on their past data and conduct the survey during that time. Furthermore, it can analyze the subordinate's past data and adjust the data collection method to match periods when stress levels are high. This improves the accuracy of data collection by selecting the optimal data collection method based on past data.

[0067] The AI ​​agent system includes a data collection unit that filters data based on the subordinate's current work situation and stress level when collecting mental health checks and engagement surveys. For example, if a subordinate has a high workload, the collection unit provides a simplified mental health check. If the subordinate's stress level is high, it can conduct a more detailed engagement survey to identify the root cause of the problem. Furthermore, the survey questions can be customized according to the subordinate's work situation. This allows for the collection of appropriate data by filtering based on work situation and stress level.

[0068] The AI ​​agent system includes a data collection unit that prioritizes the collection of highly relevant data, taking into account the geographical location of subordinates when collecting mental health checks and engagement surveys. For example, if a subordinate is on a business trip, the collection unit will collect mental health data related to the environment at their business trip destination. It can also collect engagement data related to their home environment if they are working remotely. Furthermore, it can adjust the content of the data collected according to the subordinate's work location. This allows for the collection of more accurate data by considering geographical location information and prioritizing the collection of highly relevant data.

[0069] The AI ​​agent system includes a data collection unit that analyzes employees' social media activity and gathers relevant data when collecting mental health checks and engagement surveys. For example, the data collection unit analyzes employees' social media posts to detect signs of stress. It can also understand employee engagement levels from their social media activity. Furthermore, it can customize mental health check questions based on employees' social media data. This allows for a more accurate understanding of employees' mental state by analyzing social media activity and collecting relevant data.

[0070] The AI ​​agent system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the mental health data during analysis. For example, the analysis unit performs detailed analysis on high-importance data, and simplified analysis on low-importance data. Furthermore, it can adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the mental health data.

[0071] The AI ​​agent system includes an analysis unit that applies different analysis algorithms depending on the category of mental health data during analysis. For example, the analysis unit applies a stress analysis algorithm to stress data. It can also apply an engagement analysis algorithm to engagement data. Furthermore, it can apply the most suitable analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the category of mental health data.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The data collection unit collects data from employee mental health checks and engagement surveys. For example, it can automatically collect data from employee mental health checks and engagement surveys, either periodically or in real time. Step 2: The analysis department analyzes the data collected by the data collection department. For example, they use AI, statistical methods, and machine learning algorithms to understand the mental state and engagement level of their subordinates. Step 3: The service department provides personalized advice to each subordinate based on the analysis results obtained by the analysis department. For example, they may provide advice on relaxation methods, stress management, engagement improvement, and mental health improvement. Step 4: The scheduling department automatically adjusts schedules, prioritizing employees with a high mental health risk based on the advice provided by the delivery department. For example, it adjusts schedules to prioritize one-on-one meetings with employees at high mental health risk, thereby reducing their workload and encouraging them to take leave. Step 5: The Enhancement Department enhances the content of the 1-on-1 meetings based on the schedule adjusted by the Coordination Department. For example, they suggest topics that managers should discuss with their subordinates, questions to ask, and feedback to provide. Step 6: The facial expression analysis unit analyzes the facial expressions during the interview based on the content enhanced by the enhancement unit. For example, the employee's facial expressions during the interview are captured with a camera and analyzed in real time or later using AI. Step 7: The reporting department continuously analyzes emails, meeting minutes, meeting recordings, etc., based on the facial expression data analyzed by the facial expression analysis department, and regularly reports the mental state and challenges of subordinates to their superiors. For example, they may notify superiors via email or provide meeting minutes and meeting recordings.

[0074] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that subtly supports the relationship between a supervisor and a subordinate. This AI agent system collects and analyzes data from subordinates' mental health checks and engagement surveys, and provides personalized advice to each subordinate. Furthermore, it automates scheduling adjustments for subordinates with a high mental risk level, prioritizing those with the highest risk level, and enhances the content of 1-on-1 meetings. It also analyzes facial expressions during interviews to understand the subordinate's mental state. In addition, it continuously analyzes emails, meeting minutes, meeting recordings, etc., and regularly reports the subordinate's mental state and challenges to the supervisor. This allows supervisors to constantly understand the mental state of their subordinates and provide continuous mental support. For example, the AI ​​agent system collects data from subordinates' mental health checks and engagement surveys. This allows it to understand the subordinate's mental state and engagement status. Next, the AI ​​analyzes the collected data and generates personalized advice for each subordinate. For example, if a particular subordinate is experiencing stress, it provides that subordinate with advice on relaxation methods and stress management. Furthermore, it automates scheduling adjustments for subordinates with a high mental risk level, prioritizing those with the highest risk level. This allows supervisors to prioritize one-on-one meetings with subordinates at high mental health risk. During these meetings, the AI ​​enriches the conversation, supporting supervisors in effective communication with their subordinates. The AI ​​also analyzes facial expressions during the meeting to understand the subordinate's mental state. For example, if a subordinate appears anxious during the meeting, the AI ​​provides this information to the supervisor, enabling them to take appropriate action. Furthermore, the AI ​​continuously analyzes emails, meeting minutes, and meeting recordings, regularly reporting the subordinate's mental state and challenges to the supervisor. This allows supervisors to constantly monitor their subordinates' mental state and provide continuous mental support. In this way, the AI ​​agent system can subtly support the supervisor-subordinate relationship and improve the subordinate's mental health and engagement.

[0075] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, an adjustment unit, an enhancement unit, a facial expression analysis unit, and a report provision unit. The collection unit collects data from subordinates' mental health checks and engagement surveys. For example, the collection unit automatically collects data from mental health checks and engagement surveys answered by subordinates. The collection unit can also collect data from subordinates' mental health checks and engagement surveys on a regular basis. Furthermore, the collection unit can collect data from subordinates' mental health checks and engagement surveys in real time. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the collected data and understand the mental state and engagement status of subordinates. The analysis unit can also analyze the collected data using statistical methods. Furthermore, the analysis unit can also analyze the collected data using machine learning algorithms. The provision unit provides personalized advice to individual subordinates based on the analysis results obtained by the analysis unit. For example, the provision unit provides advice to subordinates on relaxation methods and stress management based on the analysis results. Furthermore, the Service Department can provide advice to subordinates to improve engagement based on the analysis results. In addition, the Service Department can provide advice to subordinates to improve their mental health based on the analysis results. The Coordination Department automates scheduling, prioritizing subordinates with high mental health risk based on the advice provided by the Service Department. For example, the Coordination Department adjusts schedules to prioritize 1-on-1 meetings with subordinates at high mental health risk. The Coordination Department can also adjust schedules to reduce the workload of subordinates at high mental health risk. Furthermore, the Coordination Department can adjust schedules to encourage subordinates at high mental health risk to take leave. The Enhancement Department enhances the content of 1-on-1 meetings based on the schedules adjusted by the Coordination Department. For example, the Enhancement Department suggests topics that supervisors should discuss with their subordinates to enhance the content of 1-on-1 meetings.Furthermore, the Enhancement Department can suggest questions that supervisors should ask their subordinates to enhance the content of 1-on-1 meetings. The Enhancement Department can also suggest feedback that supervisors should provide to their subordinates to further enhance the content of 1-on-1 meetings. The Facial Expression Analysis Department analyzes facial expressions during meetings based on the enhanced content provided by the Enhancement Department. For example, the Facial Expression Analysis Department can capture the subordinate's facial expressions during the meeting with a camera and analyze them using AI. The Facial Expression Analysis Department can also analyze the subordinate's facial expressions in real time during the meeting. Furthermore, the Facial Expression Analysis Department can record the subordinate's facial expressions during the meeting and analyze them later. The Reporting Department continuously analyzes emails, meeting minutes, meeting recordings, etc., based on the facial expression data analyzed by the Facial Expression Analysis Department, and regularly reports the subordinate's mental state and challenges to the supervisor. For example, the Reporting Department notifies supervisors via email to regularly report on the subordinate's mental state and challenges. The Reporting Department can also provide supervisors with meeting minutes to regularly report on the subordinate's mental state and challenges. Furthermore, the reporting unit can also provide managers with meeting recordings to regularly report on the mental state and challenges of their subordinates. This allows the AI ​​agent system, according to this embodiment, to subtly support the relationship between managers and subordinates, thereby improving the mental health and engagement of subordinates.

[0076] The data collection unit collects data from employee mental health checks and engagement surveys. Specifically, it automatically collects data from mental health checks and engagement surveys completed by employees. This includes data entered by employees through online forms and applications. The data collection unit can collect this data regularly, for example, on a weekly or monthly cycle. Furthermore, the data collection unit can also collect employee mental health checks and engagement survey data in real time. With real-time collection, data is sent to the system the moment an employee completes the survey, making it immediately available for analysis. This allows the data collection unit to always have up-to-date information on employees' mental health and engagement. In addition, the data collection unit can use encryption technology to protect privacy during data collection, preventing employees' personal information from being leaked externally. Furthermore, the data collection unit can centrally manage employee data and link it with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0077] The analysis department analyzes the data collected by the data collection department. Specifically, it uses AI to analyze the collected data and understand the mental state and engagement levels of its subordinates. The AI ​​uses natural language processing technology and machine learning algorithms to analyze the content of subordinates' responses and detect fluctuations in stress levels and motivation. The analysis department can also analyze the collected data using statistical methods. For example, it calculates the mean and standard deviation of the data to understand the overall mental health trends of subordinates. Furthermore, the analysis department can also analyze the collected data using machine learning algorithms. Machine learning algorithms build models based on past data and make predictions and classifications for newly collected data. This allows the analysis department to detect changes in subordinates' mental state and engagement early and take appropriate measures. In addition, the analysis department can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past mental health data, it can predict fluctuations in risk at specific times or events and plan future countermeasures. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0078] The service provider provides personalized advice to each subordinate based on the analysis results obtained by the analysis department. Specifically, they provide advice on relaxation methods and stress management based on the analysis results. For example, if a subordinate shows a high stress level, the service provider will suggest relaxation methods such as deep breathing, meditation, or light exercise. The service provider can also provide advice to subordinates to improve engagement based on the analysis results. For example, if a subordinate shows a low engagement score, the service provider will suggest team-building activities or career development opportunities. Furthermore, the service provider can also provide advice to subordinates to improve their mental health based on the analysis results. For example, if a subordinate is experiencing mental health problems, the service provider will suggest professional counseling or access to mental health resources. Because the service provider provides this advice individually and personalized to each subordinate, subordinates can receive the support that is best suited to them. In addition, the service provider can monitor the effectiveness of the advice and update the advice content as needed. In this way, the service provider can support the improvement of subordinates' mental health and engagement, and contribute to improving the workplace environment.

[0079] The scheduling department automates scheduling adjustments, prioritizing subordinates with the highest mental health risk based on advice provided by the service provider. Specifically, it adjusts schedules to prioritize one-on-one meetings with subordinates at high mental health risk. For example, if a subordinate has a high mental health risk, the scheduling department checks the supervisor's schedule and sets the earliest possible one-on-one meeting. The scheduling department can also adjust schedules to reduce the workload of subordinates at high mental health risk. For example, if a subordinate is experiencing excessive stress, the scheduling department adjusts the schedule to distribute the subordinate's work to other members. Furthermore, the scheduling department can adjust schedules to encourage subordinates at high mental health risk to take leave. For example, if a subordinate has been subjected to prolonged stress, the scheduling department suggests that the subordinate take leave and assigns their work to other members during the leave period. This allows the scheduling department to protect the mental health of subordinates and maintain work efficiency. In addition, the scheduling department can notify supervisors of the results of the scheduling adjustments and provide them with information to take appropriate action. This allows the scheduling department to reduce the mental health risk of subordinates and contribute to improving the workplace environment.

[0080] The Enhancement Department enhances the content of 1-on-1 meetings based on the schedule adjusted by the Coordination Department. Specifically, it proposes topics that managers should discuss with their subordinates to enhance the content of 1-on-1 meetings. For example, it may propose topics related to the subordinate's mental health or specific action plans to improve engagement. The Enhancement Department can also propose questions that managers should ask their subordinates to enhance the content of 1-on-1 meetings. For example, it may propose questions about the subordinate's current work situation and causes of stress to support a deeper understanding of the subordinate's situation. Furthermore, the Enhancement Department can also propose feedback that managers should provide to their subordinates to enhance the content of 1-on-1 meetings. For example, it may propose specific feedback on the subordinate's work performance and constructive advice on areas for improvement. In this way, the Enhancement Department can improve the quality of 1-on-1 meetings and facilitate smoother communication between managers and subordinates. In addition, the Enhancement Department can record the results of 1-on-1 meetings and provide information for use in the next meeting. In this way, the Enhancement Department can support continuous improvement of communication and contribute to improving the mental health and engagement of subordinates.

[0081] The Facial Expression Analysis Department analyzes facial expressions during interviews based on content enhanced by the Enhancement Department. Specifically, it captures the subordinate's facial expressions with a camera during the interview and analyzes them using AI. The AI ​​uses facial recognition technology and emotion analysis algorithms to estimate the subordinate's emotional state from their facial expressions. For example, if a subordinate is feeling stressed, the AI ​​detects features such as facial tension and frown lines and assesses the stress level. The Facial Expression Analysis Department can also analyze the subordinate's facial expressions in real time during interviews. Real-time analysis allows for immediate identification of changes in the subordinate's emotions during the interview and provides feedback to the supervisor. Furthermore, the Facial Expression Analysis Department can record the subordinate's facial expressions during interviews and analyze them later. Using the recorded data, a detailed analysis can be performed after the interview to understand the subordinate's emotional changes and trends. This allows the Facial Expression Analysis Department to more accurately understand the subordinate's mental state and support appropriate responses. In addition, the Facial Expression Analysis Department can share the analysis results with other departments to contribute to the subordinate's mental health care. For example, the analysis results can be shared with the Service Department and the Coordination Department to inform advice and schedule adjustments for the subordinate. This allows the facial expression analysis department to comprehensively support the mental health care of its subordinates and contribute to improving the workplace environment.

[0082] The reporting department continuously analyzes emails, meeting minutes, and meeting recordings based on facial expression data analyzed by the facial expression analysis department, and regularly reports on the mental state and challenges of subordinates to their supervisors. Specifically, it notifies supervisors via email to regularly report on the mental state and challenges of their subordinates. For example, it sends weekly or monthly reports summarizing the mental health status and engagement fluctuations of subordinates. The reporting department can also provide supervisors with meeting minutes to regularly report on the mental state and challenges of their subordinates. The minutes include the content of 1-on-1 meetings, feedback from subordinates, and future action plans. Furthermore, the reporting department can also provide supervisors with meeting recordings to regularly report on the mental state and challenges of their subordinates. By using meeting recordings, supervisors can examine the subordinates' facial expressions and statements in detail, gaining a deeper understanding. This allows the reporting department to provide supervisors with information to grasp the mental health and engagement status of their subordinates and take appropriate action. In addition, the reporting department can continuously update the report content to provide the latest information. This allows the reporting department to subtly support the relationship between superiors and subordinates, improving the mental health and engagement of subordinates.

[0083] The data collection unit can estimate the emotions of its subordinates and adjust the timing of mental health checks and engagement surveys based on the estimated emotions. For example, if a subordinate is stressed, the data collection unit can increase the frequency of mental health checks to detect problems early. It can also adjust the timing of engagement surveys if a subordinate is relaxed to collect more accurate data. Furthermore, if a subordinate is busy, the data collection unit can provide surveys that can be completed quickly during work breaks. This allows for the collection of more accurate data by adjusting the timing of data collection based on the subordinate's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input subordinate emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The data collection unit can analyze the subordinate's past mental health data and select the optimal collection method. For example, the data collection unit can select the optimal question format based on the data from mental health checks that the subordinate has answered in the past. The data collection unit can also identify the time of day when the subordinate is most likely to answer the survey based on their past data and conduct the survey during that time. Furthermore, the data collection unit can analyze the subordinate's past data and adjust the collection method to match periods when stress levels are high. This improves the accuracy of data collection by selecting the optimal collection method based on past data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the subordinate's past mental health data into a generating AI and have the generating AI select the optimal collection method.

[0085] The data collection unit can filter data based on the employee's current work situation and stress level when collecting mental health checks and engagement surveys. For example, if an employee has a high workload, the data collection unit can provide a simplified mental health check. Furthermore, if an employee has a high stress level, the data collection unit can conduct a detailed engagement survey to identify the root cause of the problem. The data collection unit can also customize the survey questions according to the employee's work situation. This allows for the collection of appropriate data by filtering based on work situation and stress level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the employee's work situation and stress level into a generating AI and have the generating AI perform the filtering.

[0086] The data collection unit can estimate the emotions of its subordinates and determine the priority of data to collect based on the estimated emotions. For example, if a subordinate is stressed, the data collection unit may prioritize collecting mental health data. Alternatively, if a subordinate is relaxed, the data collection unit may prioritize collecting engagement data. Furthermore, the data collection unit can adjust the types of data collected according to the subordinate's emotions. This allows for the priority collection of important data based on the subordinate's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input subordinate emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0087] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of subordinates when collecting mental health checks and engagement surveys. For example, if a subordinate is on a business trip, the data collection unit will collect mental health data related to the environment at the business trip destination. Similarly, if a subordinate is working remotely, the data collection unit can collect engagement data related to their home environment. Furthermore, the data collection unit can adjust the content of the data collected according to the subordinate's work location. This allows for the collection of more accurate data by considering geographical location information and collecting highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subordinate's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0088] The data collection unit can analyze employees' social media activity and collect relevant data when collecting mental health checks and engagement surveys. For example, the data collection unit can analyze the content of employees' social media posts to detect signs of stress. The data collection unit can also understand the level of engagement from employees' social media activity. Furthermore, the data collection unit can customize the questions for mental health checks based on employees' social media data. This allows for a more accurate understanding of employees' mental state by analyzing social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employees' social media data into a generating AI and have the generating AI collect the relevant data.

[0089] The analysis unit can estimate the emotions of its subordinates and adjust the presentation of the analysis based on the estimated emotions. For example, if a subordinate is stressed, the analysis unit can provide a simple and easy-to-understand analysis result. Conversely, if a subordinate is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, the analysis unit can adjust the presentation of the analysis result according to the subordinate's emotions. This allows the analysis to be presented in a way that is easy for the subordinate to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input subordinate emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0090] The analysis unit can adjust the level of detail of the analysis based on the importance of the mental health data during the analysis. For example, the analysis unit will perform a detailed analysis on data with high importance. Conversely, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the mental health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the mental health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0091] The analysis unit can apply different analysis algorithms depending on the category of mental health data during analysis. For example, the analysis unit can apply a stress analysis algorithm to stress data. It can also apply an engagement analysis algorithm to engagement data. Furthermore, the analysis unit can apply the most suitable analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the mental health data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of mental health data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0092] The analysis unit can estimate the emotions of its subordinates and adjust the length of the analysis based on the estimated emotions. For example, if a subordinate is stressed, the analysis unit will provide a short, concise analysis. Conversely, if a subordinate is relaxed, the analysis unit can provide a detailed analysis. Furthermore, the analysis unit can adjust the length of the analysis according to the subordinate's emotions. By adjusting the length of the analysis based on the subordinate's emotions, the analysis unit can provide results that are easy for the subordinate to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input subordinate emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0093] The analysis unit can determine the priority of analysis based on the timing of mental health data collection. For example, the analysis unit may prioritize the analysis of recently collected data. It can also analyze current data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis according to the timing of data collection. This enables efficient analysis by determining the priority of analysis based on the timing of mental health data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of mental health data collection into a generating AI and have the generating AI determine the priority of analysis.

[0094] The analysis unit can adjust the order of analysis based on the relevance of the mental health data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the mental health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the mental health data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0095] The service provider can estimate the emotions of its subordinates and adjust the way it expresses advice based on those emotions. For example, if a subordinate is stressed, the service provider will provide simple and easy-to-understand advice. If a subordinate is relaxed, the service provider can also provide detailed advice. Furthermore, the service provider can adjust the way it expresses advice according to the subordinate's emotions. This allows the service provider to provide advice that is easy for subordinates to understand by adjusting the way it expresses advice based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input subordinate emotion data into the generative AI and have the generative AI adjust the way it expresses advice.

[0096] The advice delivery unit can adjust the level of detail of the advice based on the subordinate's mental risk level when providing advice. For example, the delivery unit can provide detailed advice to subordinates with a high mental risk level. It can also provide simpler advice to subordinates with a low mental risk level. Furthermore, the delivery unit can adjust the level of detail of the advice according to the mental risk level. This allows for the provision of appropriate advice by adjusting the level of detail of the advice based on the subordinate's mental risk level. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the subordinate's mental risk level into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0097] The service provider can apply different advice algorithms depending on the subordinate's work content when providing advice. For example, the service provider can provide sales-specific advice to subordinates in the sales department. It can also provide technology-specific advice to subordinates in the technology department. Furthermore, the service provider can apply the most appropriate advice algorithm depending on the subordinate's work content. This allows for the provision of appropriate advice by applying the most suitable advice algorithm according to the subordinate's work content. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the subordinate's work content into a generating AI and have the generating AI apply different advice algorithms.

[0098] The service provider can estimate the emotions of its subordinates and adjust the length of the advice based on the estimated emotions. For example, if a subordinate is feeling stressed, the service provider will provide short, concise advice. If a subordinate is relaxed, the service provider can also provide detailed advice. Furthermore, the service provider can adjust the length of the advice according to the subordinate's emotions. This allows the service provider to provide advice that is easy for subordinates to understand by adjusting the length of the advice based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input subordinate emotion data into the generative AI and have the generative AI adjust the length of the advice.

[0099] The advice delivery unit can prioritize advice based on when the subordinate's mental health data is collected. For example, the unit can prioritize advice based on recently collected data. It can also provide advice based on current data while referring to past data. Furthermore, the unit can adjust the priority of advice according to when the data is collected. This enables efficient advice delivery by prioritizing advice based on when the subordinate's mental health data is collected. Some or all of the above processing in the advice delivery unit may be performed using AI, for example, or not using AI. For example, the advice delivery unit can input the timing of mental health data collection into a generating AI and have the generating AI determine the priority of advice.

[0100] The service provider can adjust the order of advice based on the relevance of the subordinate's mental health data when providing advice. For example, the service provider can prioritize advice based on highly relevant data. It can also postpone less relevant data. Furthermore, the service provider can adjust the order of advice according to the relevance of the data. This allows for efficient advice provision by adjusting the order of advice based on the relevance of the subordinate's mental health data. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the relevance of the mental health data into a generating AI and have the generating AI adjust the order of advice.

[0101] The adjustment unit can estimate the emotions of its subordinates and adjust the scheduling method based on the estimated emotions. For example, if a subordinate is feeling stressed, the adjustment unit will adjust the schedule more gradually. Conversely, if a subordinate is relaxed, the adjustment unit can adjust the schedule more efficiently. Furthermore, the adjustment unit can adjust the scheduling method according to the subordinate's emotions. This allows the adjustment unit to provide a schedule that is appropriate for the subordinate by adjusting the scheduling method based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit can input subordinate emotion data into the generative AI and have the generative AI perform the adjustment of the scheduling method.

[0102] The scheduling unit can determine the priority of scheduling based on the mental risk level of its subordinates. For example, the scheduling unit may prioritize scheduling subordinates with a high mental risk level. It may also schedule subordinates with a moderate mental risk level next. Furthermore, it may schedule subordinates with a low mental risk level last. This allows for efficient scheduling by determining the priority of scheduling based on the mental risk level of the subordinates. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the mental risk levels of its subordinates into a generating AI and have the generating AI determine the priority of scheduling.

[0103] The scheduling unit can apply different scheduling algorithms depending on the subordinate's work content when scheduling. For example, the scheduling unit can adjust the schedule of a subordinate in the sales department so as not to disrupt sales activities. It can also adjust the schedule of a subordinate in the technical department in accordance with the progress of the project. Furthermore, the scheduling unit can apply the most suitable scheduling algorithm depending on the subordinate's work content. This enables efficient scheduling by applying the most suitable scheduling algorithm according to the subordinate's work content. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the subordinate's work content into a generating AI and have the generating AI execute the application of different scheduling algorithms.

[0104] The adjustment unit can estimate the emotions of its subordinates and adjust the frequency of schedule adjustments based on the estimated emotions. For example, if a subordinate is feeling stressed, the adjustment unit can increase the frequency of schedule adjustments. Conversely, if a subordinate is relaxed, the adjustment unit can decrease the frequency of schedule adjustments. Furthermore, the adjustment unit can adjust the frequency of schedule adjustments according to the subordinate's emotions. This allows the system to provide an appropriate schedule for the subordinate by adjusting the frequency of schedule adjustments based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input subordinate emotion data into a generative AI and have the generative AI perform the adjustment of the schedule adjustment frequency.

[0105] The scheduling unit can determine scheduling priorities based on the timing of collecting subordinates' mental health data. For example, the scheduling unit can prioritize scheduling based on recently collected mental health data. The scheduling unit can also adjust schedules based on current data while referring to past data. Furthermore, the scheduling unit can determine scheduling priorities according to the timing of data collection. This enables efficient scheduling by determining scheduling priorities based on the timing of collecting subordinates' mental health data. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the timing of mental health data collection into a generating AI and have the generating AI determine the scheduling priorities.

[0106] The scheduling unit can adjust the order of scheduling based on the relevance of subordinates' mental health data. For example, the scheduling unit prioritizes scheduling based on highly relevant data. It can also postpone scheduling for less relevant data. Furthermore, the scheduling unit can adjust the order of scheduling according to the relevance of the data. This enables efficient scheduling by adjusting the order of scheduling based on the relevance of subordinates' mental health data. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the relevance of mental health data into a generating AI and have the generating AI perform the adjustment of the order of scheduling.

[0107] The enrichment unit can estimate the emotions of its subordinates and adjust the content of 1-on-1 meetings based on the estimated emotions. For example, if a subordinate is feeling stressed, the enrichment unit can provide relaxing topics. If a subordinate is relaxed, the enrichment unit can also provide detailed topics related to work. Furthermore, the enrichment unit can adjust the content of 1-on-1 meetings according to the subordinate's emotions. This allows the enrichment unit to provide content that is appropriate for the subordinate by adjusting the content of 1-on-1 meetings based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the enrichment unit may be performed using AI, for example, or without AI. For example, the enrichment unit can input subordinate emotion data into a generative AI and have the generative AI adjust the content of the 1-on-1 meeting.

[0108] The enrichment unit can adjust the level of detail in 1-on-1 meetings based on the employee's mental health risk level. For example, the enrichment unit can provide detailed content to employees with a high mental health risk level, and simpler content to employees with a low mental health risk level. Furthermore, the enrichment unit can adjust the level of detail in the 1-on-1 meeting content according to the employee's mental health risk level. This allows the enrichment unit to provide appropriate content by adjusting the level of detail based on the employee's mental health risk level. Some or all of the above processing in the enrichment unit may be performed using AI, for example, or without AI. For example, the enrichment unit can input the employee's mental health risk level into a generating AI and have the generating AI adjust the level of detail in the content.

[0109] The enrichment unit can apply different enrichment algorithms depending on the subordinate's work content when enriching the content of 1-on-1 meetings. For example, the enrichment unit can provide sales-specific content to subordinates in the sales department. It can also provide technology-specific content to subordinates in the technology department. Furthermore, the enrichment unit can apply the optimal enrichment algorithm according to the subordinate's work content. This allows for the provision of appropriate content by applying the optimal enrichment algorithm according to the subordinate's work content. Some or all of the above processing in the enrichment unit may be performed using AI, for example, or without AI. For example, the enrichment unit can input the subordinate's work content into a generating AI and have the generating AI execute the application of different enrichment algorithms.

[0110] The enrichment unit can estimate the subordinate's emotions and adjust the length of the 1-on-1 meeting content based on the estimated emotions. For example, if the subordinate is stressed, the enrichment unit will provide short, concise content. Conversely, if the subordinate is relaxed, the enrichment unit can provide detailed content. Furthermore, the enrichment unit can adjust the length of the 1-on-1 meeting content according to the subordinate's emotions. This allows the enrichment unit to provide content that is appropriate for the subordinate by adjusting the length of the 1-on-1 meeting content based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the enrichment unit may be performed using AI, for example, or without AI. For example, the enrichment unit can input the subordinate's emotion data into the generative AI and have the generative AI adjust the length of the 1-on-1 meeting content.

[0111] The content enhancement unit can prioritize the content of 1-on-1 meetings based on the timing of collecting subordinates' mental health data. For example, the enhancement unit can prioritize content enhancement based on recently collected data. It can also enhance content based on current data while referring to past data. Furthermore, the enhancement unit can determine the priority of 1-on-1 meeting content according to the timing of data collection. This enables efficient content delivery by prioritizing content based on the timing of collecting subordinates' mental health data. Some or all of the above processing in the enhancement unit may be performed using AI, for example, or without AI. For example, the enhancement unit can input the timing of mental health data collection into a generating AI and have the generating AI determine the content priority.

[0112] The enrichment unit can adjust the order of content based on the relevance of the subordinate's mental health data when enriching the content of 1-on-1 meetings. For example, the enrichment unit prioritizes enriching content based on highly relevant data. It can also postpone less relevant data. Furthermore, the enrichment unit can adjust the order of the content of the 1-on-1 meeting according to the relevance of the data. This allows for efficient content delivery by adjusting the order of content based on the relevance of the subordinate's mental health data. Some or all of the above processing in the enrichment unit may be performed using AI, for example, or without AI. For example, the enrichment unit can input the relevance of the mental health data into a generating AI and have the generating AI perform the adjustment of the order of content.

[0113] The facial expression analysis unit can estimate the emotions of its subordinates and adjust the facial expression analysis method based on the estimated emotions. For example, if a subordinate is feeling stressed, the facial expression analysis unit will perform a detailed facial expression analysis. It can also perform a simpler facial expression analysis if the subordinate is relaxed. Furthermore, the facial expression analysis unit can adjust the facial expression analysis method according to the subordinate's emotions. This allows for an accurate understanding of the subordinate's mental state by adjusting the facial expression analysis method based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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-described processes in the facial expression analysis unit may be performed using AI, or not. For example, the facial expression analysis unit can input the subordinate's emotional data into the generative AI and have the generative AI adjust the facial expression analysis method.

[0114] The facial expression analysis unit can adjust the level of detail of its analysis based on the subordinate's mental risk level during facial expression analysis. For example, the facial expression analysis unit will perform a detailed facial expression analysis for subordinates with a high mental risk level. It can also perform a simplified facial expression analysis for subordinates with a low mental risk level. Furthermore, the facial expression analysis unit can adjust the level of detail of its analysis according to the mental risk level. This allows for efficient facial expression analysis by adjusting the level of detail of the analysis based on the subordinate's mental risk level. Some or all of the above processing in the facial expression analysis unit may be performed using AI, for example, or without AI. For example, the facial expression analysis unit can input the subordinate's mental risk level into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0115] The facial expression analysis unit can apply different analysis algorithms depending on the subordinate's job duties during facial expression analysis. For example, the facial expression analysis unit can apply a facial expression analysis algorithm specialized for sales activities to subordinates in the sales department. It can also apply a facial expression analysis algorithm specialized for technical activities to subordinates in the technical department. Furthermore, the facial expression analysis unit can apply the most suitable analysis algorithm depending on the subordinate's job duties. This enables efficient facial expression analysis by applying the most suitable analysis algorithm according to the subordinate's job duties. Some or all of the above-described processes in the facial expression analysis unit may be performed using AI, for example, or without AI. For example, the facial expression analysis unit can input the subordinate's job duties into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0116] The facial expression analysis unit can estimate the emotions of its subordinates and adjust the frequency of facial expression analysis based on the estimated emotions. For example, if a subordinate is stressed, the facial expression analysis unit can increase the frequency of facial expression analysis. Conversely, if a subordinate is relaxed, the facial expression analysis unit can decrease the frequency of facial expression analysis. Furthermore, the facial expression analysis unit can adjust the frequency of facial expression analysis according to the subordinate's emotions. This allows for an accurate understanding of the subordinate's mental state by adjusting the frequency of facial expression analysis based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the facial expression analysis unit may be performed using AI, or not using AI. For example, the facial expression analysis unit can input the subordinate's emotion data into the generative AI and have the generative AI adjust the frequency of facial expression analysis.

[0117] The facial expression analysis unit can determine the priority of analysis based on the timing of the collection of subordinates' mental health data during facial expression analysis. For example, the facial expression analysis unit can prioritize facial expression analysis based on recently collected mental health data. It can also perform facial expression analysis based on current data while referring to past data. Furthermore, the facial expression analysis unit can determine the priority of facial expression analysis according to the timing of data collection. This enables efficient facial expression analysis by determining the priority of analysis based on the timing of the collection of subordinates' mental health data. Some or all of the above processing in the facial expression analysis unit may be performed using AI, for example, or without AI. For example, the facial expression analysis unit can input the timing of mental health data collection into a generating AI and have the generating AI determine the priority of analysis.

[0118] The facial expression analysis unit can adjust the order of analysis based on the relevance of the subordinate's mental health data during facial expression analysis. For example, the facial expression analysis unit can prioritize facial expression analysis based on highly relevant data. It can also postpone analysis of less relevant data. Furthermore, the facial expression analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient facial expression analysis by adjusting the order of analysis based on the relevance of the subordinate's mental health data. Some or all of the above processing in the facial expression analysis unit may be performed using AI, for example, or without AI. For example, the facial expression analysis unit can input the relevance of the mental health data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0119] The reporting unit can estimate the emotions of its subordinates and adjust the way the report is presented based on those estimated emotions. For example, if a subordinate is stressed, the reporting unit will provide a simple and easy-to-understand report. Conversely, if a subordinate is relaxed, the reporting unit can provide a detailed report. Furthermore, the reporting unit can adjust the way the report is presented according to the subordinate's emotions. This allows the reporting unit to provide reports that are easy for subordinates to understand by adjusting the way the report is presented based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI, or not using AI. For example, the reporting unit can input subordinate emotion data into a generative AI and have the generative AI adjust the way the report is presented.

[0120] The report delivery unit can adjust the level of detail in a report based on the employee's mental risk level. For example, the report delivery unit can provide a detailed report to an employee with a high mental risk level, and a simpler report to an employee with a low mental risk level. Furthermore, the report delivery unit can adjust the level of detail in the report according to the mental risk level. This allows the delivery of an appropriate report by adjusting the level of detail based on the employee's mental risk level. Some or all of the above processing in the report delivery unit may be performed using AI, for example, or without AI. For example, the report delivery unit can input the employee's mental risk level into a generating AI and have the generating AI adjust the level of detail in the report.

[0121] The report delivery unit can apply different report algorithms depending on the subordinate's work content when providing reports. For example, the report delivery unit can provide a report specializing in sales activities to a subordinate in the sales department. It can also provide a report specializing in technical activities to a subordinate in the technical department. Furthermore, the report delivery unit can apply the most suitable report algorithm depending on the subordinate's work content. This allows for the provision of appropriate reports by applying the most suitable report algorithm according to the subordinate's work content. Some or all of the above processing in the report delivery unit may be performed using AI, for example, or without AI. For example, the report delivery unit can input the subordinate's work content into a generating AI and have the generating AI execute the application of different report algorithms.

[0122] The reporting unit can estimate the emotions of its subordinates and adjust the length of the report based on the estimated emotions. For example, if a subordinate is stressed, the reporting unit will provide a short, concise report. Conversely, if a subordinate is relaxed, the reporting unit can provide a detailed report. Furthermore, the reporting unit can adjust the length of the report according to the subordinate's emotions. This allows the reporting unit to provide reports that are easy for subordinates to understand by adjusting the length based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input subordinate emotion data into a generative AI and have the generative AI adjust the length of the report.

[0123] The reporting department can prioritize reports based on when the subordinate's mental health data was collected. For example, the reporting department can prioritize reports based on recently collected data. It can also provide reports based on current data while referring to past data. Furthermore, the reporting department can prioritize reports according to when the data was collected. This enables efficient report delivery by prioritizing reports based on when the subordinate's mental health data was collected. Some or all of the above processes in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input the timing of mental health data collection into a generating AI and have the generating AI determine the report prioritization.

[0124] The reporting unit can adjust the order of reports based on the relevance of the subordinates' mental health data when providing reports. For example, the reporting unit can prioritize providing reports based on highly relevant data. It can also postpone providing reports on less relevant data. Furthermore, the reporting unit can adjust the order of reports according to the relevance of the data. This allows for efficient report provision by adjusting the order of reports based on the relevance of the subordinates' mental health data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the relevance of the mental health data into a generating AI and have the generating AI perform the adjustment of the report order.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The AI ​​agent system includes a data collection unit that gathers data from employee mental health checks and engagement surveys. This unit estimates the employee's emotions and adjusts the timing of mental health checks and engagement surveys based on the estimated emotions. For example, if an employee is stressed, the frequency of mental health checks can be increased to detect problems early. Similarly, if an employee is relaxed, the timing of engagement surveys can be adjusted to collect more accurate data. Furthermore, if an employee is busy, a survey that can be completed quickly during work breaks can be provided. This allows for the collection of more accurate data by adjusting the timing of data collection based on the employee's emotions.

[0127] The AI ​​agent system includes a data collection unit that analyzes the subordinate's past mental health data and selects the optimal data collection method. For example, the data collection unit selects the most suitable question format based on data from mental health checks the subordinate has previously answered. It can also identify the time of day when the subordinate is most likely to respond based on their past data and conduct the survey during that time. Furthermore, it can analyze the subordinate's past data and adjust the data collection method to match periods when stress levels are high. This improves the accuracy of data collection by selecting the optimal data collection method based on past data.

[0128] The AI ​​agent system includes a data collection unit that filters data based on the subordinate's current work situation and stress level when collecting mental health checks and engagement surveys. For example, if a subordinate has a high workload, the collection unit provides a simplified mental health check. If the subordinate's stress level is high, it can conduct a more detailed engagement survey to identify the root cause of the problem. Furthermore, the survey questions can be customized according to the subordinate's work situation. This allows for the collection of appropriate data by filtering based on work situation and stress level.

[0129] The AI ​​agent system includes a data collection unit that estimates the emotions of its subordinates and determines the priority of data to collect based on those emotions. For example, if a subordinate is stressed, the data collection unit will prioritize collecting mental health data. If a subordinate is relaxed, it can also prioritize collecting engagement data. Furthermore, it can adjust the types of data collected according to the subordinate's emotions. This allows for the priority collection of important data by determining the data to collect based on the subordinate's emotions.

[0130] The AI ​​agent system includes a data collection unit that prioritizes the collection of highly relevant data, taking into account the geographical location of subordinates when collecting mental health checks and engagement surveys. For example, if a subordinate is on a business trip, the collection unit will collect mental health data related to the environment at their business trip destination. It can also collect engagement data related to their home environment if they are working remotely. Furthermore, it can adjust the content of the data collected according to the subordinate's work location. This allows for the collection of more accurate data by considering geographical location information and prioritizing the collection of highly relevant data.

[0131] The AI ​​agent system includes a data collection unit that analyzes employees' social media activity and gathers relevant data when collecting mental health checks and engagement surveys. For example, the data collection unit analyzes employees' social media posts to detect signs of stress. It can also understand employee engagement levels from their social media activity. Furthermore, it can customize mental health check questions based on employees' social media data. This allows for a more accurate understanding of employees' mental state by analyzing social media activity and collecting relevant data.

[0132] The AI ​​agent system includes an analysis unit that estimates the emotions of its subordinates and adjusts the presentation of the analysis based on the estimated emotions. For example, if a subordinate is feeling stressed, the analysis unit provides a simple and easy-to-understand analysis result. If the subordinate is relaxed, it can also provide a detailed analysis result. Furthermore, it can adjust the presentation of the analysis result according to the subordinate's emotions. This allows the system to provide analysis results that are easy for subordinates to understand by adjusting the presentation of the analysis based on their emotions.

[0133] The AI ​​agent system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the mental health data during analysis. For example, the analysis unit performs detailed analysis on high-importance data, and simplified analysis on low-importance data. Furthermore, it can adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the mental health data.

[0134] The AI ​​agent system includes an analysis unit that applies different analysis algorithms depending on the category of mental health data during analysis. For example, the analysis unit applies a stress analysis algorithm to stress data. It can also apply an engagement analysis algorithm to engagement data. Furthermore, it can apply the most suitable analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the category of mental health data.

[0135] The AI ​​agent system includes an analysis unit that estimates the emotions of its subordinates and adjusts the length of the analysis based on the estimated emotions. For example, if a subordinate is feeling stressed, the analysis unit will provide a short, concise analysis. If the subordinate is relaxed, it can also provide a detailed analysis. Furthermore, it can adjust the length of the analysis according to the subordinate's emotions. This allows the system to provide analysis results that are easy for subordinates to understand by adjusting the length of the analysis based on their emotions.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The data collection unit collects data from employee mental health checks and engagement surveys. For example, it can automatically collect data from employee mental health checks and engagement surveys, either periodically or in real time. Step 2: The analysis department analyzes the data collected by the data collection department. For example, they use AI, statistical methods, and machine learning algorithms to understand the mental state and engagement level of their subordinates. Step 3: The service department provides personalized advice to each subordinate based on the analysis results obtained by the analysis department. For example, they may provide advice on relaxation methods, stress management, engagement improvement, and mental health improvement. Step 4: The scheduling department automatically adjusts schedules, prioritizing employees with a high mental health risk based on the advice provided by the delivery department. For example, it adjusts schedules to prioritize one-on-one meetings with employees at high mental health risk, thereby reducing their workload and encouraging them to take leave. Step 5: The Enhancement Department enhances the content of the 1-on-1 meetings based on the schedule adjusted by the Coordination Department. For example, they suggest topics that managers should discuss with their subordinates, questions to ask, and feedback to provide. Step 6: The facial expression analysis unit analyzes the facial expressions during the interview based on the content enhanced by the enhancement unit. For example, the employee's facial expressions during the interview are captured with a camera and analyzed in real time or later using AI. Step 7: The reporting department continuously analyzes emails, meeting minutes, meeting recordings, etc., based on the facial expression data analyzed by the facial expression analysis department, and regularly reports the mental state and challenges of subordinates to their superiors. For example, they may notify superiors via email or provide meeting minutes and meeting recordings.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0140] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, adjustment unit, enhancement unit, facial expression analysis unit, and report provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects data from subordinates' mental health checks and engagement surveys. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and provides personalized advice to each subordinate based on the analysis results. The adjustment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automates schedule adjustments, starting with subordinates with a high mental risk level. The enhancement unit is implemented by, for example, the control unit 46A of the smart device 14 and enhances the content of 1-on-1 meetings. The facial expression analysis unit takes pictures of facial expressions during interviews using the camera 42 of the smart device 14 and analyzes them using AI. The report provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and periodically reports the mental state and challenges of subordinates to their superiors based on the facial expression data analyzed by the facial expression analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, adjustment unit, enhancement unit, facial expression analysis unit, and report provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data from subordinates' mental health checks and engagement surveys. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides personalized advice to each subordinate based on the analysis results. The adjustment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and automates schedule adjustments, starting with subordinates with a high mental risk level. The enhancement unit is implemented, for example, by the control unit 46A of the smart glasses 214 and enhances the content of 1-on-1 meetings. The facial expression analysis unit takes pictures of facial expressions during interviews using the camera 42 of the smart glasses 214 and analyzes them using AI. The report provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and periodically reports the mental state and challenges of subordinates to their superiors based on the facial expression data analyzed by the facial expression analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, adjustment unit, enhancement unit, facial expression analysis unit, and report provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects data from subordinates' mental health checks and engagement surveys. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides personalized advice to each subordinate based on the analysis results. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automates schedule adjustments, starting with subordinates with a high mental risk level. The enhancement unit is implemented by, for example, the control unit 46A of the headset terminal 314 and enhances the content of 1-on-1 meetings. The facial expression analysis unit, for example, uses the camera 42 of the headset terminal 314 to capture facial expressions during an interview and analyzes them using AI. The report provision unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and periodically reports the subordinate's mental state and challenges to the supervisor based on the facial expression data analyzed by the facial expression analysis unit. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0176] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0177] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0178] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0180] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0181] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0182] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0183] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0184] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0185] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0186] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0187] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0189] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0190] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, adjustment unit, enhancement unit, facial expression analysis unit, and report provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects data from subordinates' mental health checks and engagement surveys. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and provides personalized advice to each subordinate based on the analysis results. The adjustment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automates schedule adjustments, starting with subordinates with a high mental risk level. The enhancement unit is implemented by, for example, the control unit 46A of the robot 414 and enhances the content of 1-on-1 meetings. The facial expression analysis unit takes pictures of facial expressions during interviews using the camera 42 of the robot 414 and analyzes them using AI. The report provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and periodically reports the mental state and challenges of subordinates to their superiors based on the facial expression data analyzed by the facial expression analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0191] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0192] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0193] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0194] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0195] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0196] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0197] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0198] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0199] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0200] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0201] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0202] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0203] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0204] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0205] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0206] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0207] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0208] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0209] (Note 1) The data collection department collects data from employee mental health checks and engagement surveys, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit provides personalized advice to each subordinate based on the analysis results obtained by the aforementioned analysis unit, Based on the advice provided by the aforementioned provision unit, the adjustment unit automatically adjusts the schedules of subordinates in order of their mental risk level, An enhancement unit enhances the content of the 1on1 meeting based on the schedule adjusted by the aforementioned adjustment unit, A facial expression analysis unit analyzes facial expressions during an interview based on the content enhanced by the aforementioned enhancement unit, The system includes a reporting unit that continuously analyzes emails, meeting minutes, meeting recordings, etc., based on facial expression data analyzed by the aforementioned facial expression analysis unit, and periodically reports the mental state and challenges of subordinates to their superiors. A system characterized by the following features. (Note 2) The aforementioned collection unit is Estimate the emotions of subordinates and adjust the timing of mental health checks and engagement surveys based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze your subordinates' past mental health data and select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting mental health checks and engagement surveys, filter the data based on the employee's current work situation and stress level. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Estimate the emotions of your subordinates and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting mental health checks and engagement surveys, prioritize the collection of highly relevant data by considering the geographical location of subordinates. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When conducting mental health checks or engagement surveys, analyze employees' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is Estimate the emotions of subordinates and adjust the way the analysis is presented based on the estimated emotions of subordinates. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the mental health data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of mental health data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is Estimate the emotions of your subordinates and adjust the length of the analysis based on the estimated emotions of your subordinates. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the mental health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of mental health data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, Estimate the emotions of your subordinates and adjust the way you express advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the subordinate's mental health risk level. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the subordinate's work content. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, Estimate the emotions of your subordinates and adjust the length of your advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing advice, prioritize the advice based on when the employee's mental health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, adjust the order of advice based on the relevance of the subordinate's mental health data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The adjustment unit is, Estimate the emotions of your subordinates and adjust the scheduling method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The adjustment unit is, When scheduling, prioritize adjustments based on the mental health risk level of your subordinates. The system described in Appendix 1, characterized by the features described herein. (Note 22) The adjustment unit is, When scheduling, apply different scheduling algorithms depending on the subordinate's work content. The system described in Appendix 1, characterized by the features described herein. (Note 23) The adjustment unit is, Estimate the emotions of your subordinates and adjust the frequency of schedule adjustments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The adjustment unit is, When scheduling, prioritize adjustments based on when you collect your subordinates' mental health data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, When scheduling, adjust the order of adjustments based on the relevance of subordinates' mental health data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned enhancement unit is Estimate the emotions of your subordinates and adjust the content of 1-on-1 meetings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned enhancement unit is When enriching the content of 1-on-1 meetings, adjust the level of detail based on the employee's mental health risk level. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned enhancement unit is When enriching the content of 1-on-1 meetings, different enrichment algorithms are applied depending on the subordinate's work content. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned enhancement unit is Estimate the emotions of your subordinates and adjust the length of the 1-on-1 meeting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned enhancement unit is When improving the content of 1-on-1 meetings, prioritize the content based on when you collect your subordinate's mental health data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned enhancement unit is When enriching the content of 1-on-1 meetings, adjust the order of the content based on the relevance of the subordinate's mental health data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned facial expression analysis unit, Estimate the emotions of your subordinates and adjust the facial expression analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned facial expression analysis unit, When analyzing facial expressions, adjust the level of detail based on the subordinate's mental risk level. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned facial expression analysis unit, When analyzing facial expressions, different analysis algorithms are applied depending on the subordinate's job duties. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned facial expression analysis unit, The system estimates the emotions of its subordinates and adjusts the frequency of facial expression analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned facial expression analysis unit, When performing facial expression analysis, prioritize the analysis based on when the subordinate's mental health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned facial expression analysis unit, When performing facial expression analysis, adjust the order of analysis based on the relevance of the subordinate's mental health data. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned report provision department, Estimate the emotions of your subordinates and adjust the way you express your report based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned report provision department, When providing reports, adjust the level of detail in the report based on the mental health risk level of the subordinates. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned report provision department, When providing reports, different reporting algorithms are applied depending on the subordinate's work content. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned report provision department, Estimate the emotions of your subordinates and adjust the length of the report based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned report provision department, When providing reports, prioritize reports based on when the subordinate's mental health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned report provision department, When providing reports, adjust the order of reports based on the relevance of the subordinates' mental health data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects data from employee mental health checks and engagement surveys, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit provides personalized advice to each subordinate based on the analysis results obtained by the aforementioned analysis unit, Based on the advice provided by the aforementioned provision unit, the adjustment unit automates schedule adjustments, starting with subordinates with a high mental risk level. An enhancement unit enhances the content of the 1on1 meeting based on the schedule adjusted by the aforementioned adjustment unit, A facial expression analysis unit analyzes facial expressions during an interview based on the content enhanced by the aforementioned enhancement unit, The system includes a reporting unit that continuously analyzes emails, meeting minutes, meeting recordings, etc., based on facial expression data analyzed by the aforementioned facial expression analysis unit, and periodically reports the mental state and challenges of subordinates to their superiors. A system characterized by the following features.

2. The aforementioned collection unit is Estimate the emotions of subordinates and adjust the timing of mental health checks and engagement surveys based on those estimates. The system according to feature 1.

3. The aforementioned collection unit is Analyze your subordinates' past mental health data and select the most suitable data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting mental health checks and engagement surveys, filter the data based on the employee's current work situation and stress level. The system according to feature 1.

5. The aforementioned collection unit is Estimate the emotions of your subordinates and prioritize the data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting mental health checks and engagement surveys, prioritize the collection of highly relevant data by considering the geographical location of subordinates. The system according to feature 1.

7. The aforementioned collection unit is When conducting mental health checks or engagement surveys, analyze employees' social media activity and collect relevant data. The system according to feature 1.

8. The aforementioned analysis unit is Estimate the emotions of subordinates and adjust the way the analysis is presented based on the estimated emotions of subordinates. The system according to feature 1.

9. The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the mental health data. The system according to feature 1.

10. The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of mental health data. The system according to feature 1.

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