system

Abstract

The system according to this embodiment aims to monitor the performance and adaptation status of seconded employees and provide support at the appropriate time. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects performance data and adaptation status data of seconded employees. The analysis unit analyzes the data collected by the collection unit and evaluates the performance and adaptation status of seconded employees. The provision unit provides support and mentoring at an appropriate time based on the evaluation results obtained by the analysis unit.

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to appropriately monitor the performance and adaptation status of secondees and provide timely support.

[0005] The system according to the embodiment aims to monitor the performance and adaptation status of secondees and provide support at an appropriate timing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects performance data and adaptation data of seconded employees. The analysis unit analyzes the data collected by the data collection unit and evaluates the performance and adaptation status of the seconded employees. The data provision unit provides support and mentoring at an appropriate time based on the evaluation results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the performance and adaptation status of seconded employees and provide support at the appropriate time. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides an employee monitoring system in which AI monitors the performance and adaptation status of seconded employees and provides support and mentoring at the appropriate time. The employee monitoring system collects performance data and adaptation status data of seconded employees, and the AI ​​analyzes and evaluates this data to provide support and mentoring at the appropriate time. For example, the employee monitoring system collects detailed data such as the employee's work results, communication status, and stress level. For example, as part of the employee's work results, it records the progress and achievement level of projects. As part of the communication status, it records the frequency and content of interactions with colleagues and superiors at the seconded company. Furthermore, as part of the stress level, it measures the employee's self-reporting and using biosensors. Next, the employee monitoring system uses AI to analyze the collected data. Based on the collected data, the AI ​​evaluates the employee's performance and adaptation status. For example, the AI ​​analyzes work results data to evaluate the employee's work achievement level. It also analyzes communication status data to evaluate the employee's adaptation to the company. Furthermore, it analyzes stress level data to evaluate the employee's stress level. Based on the evaluation results, the seconded employee monitoring system provides support and mentoring at the appropriate time. For example, if the AI ​​determines that a seconded employee's work performance is declining, it provides support for work improvement. If it determines that the employee's internal adaptation is low, it provides mentoring to improve communication skills. Furthermore, if it determines that the employee is experiencing high stress levels, it provides support for stress management. As a result, the seconded employee monitoring system is expected to improve the performance and adaptation of seconded employees. By receiving support and mentoring at the right time, seconded employees can improve work efficiency and reduce stress. For the host company, improved performance and adaptation of seconded employees can lead to project success and smoother team management. For example, by receiving support for work improvement, seconded employees can ensure smoother project progress and better results.Furthermore, receiving mentoring to improve communication skills can enhance relationships with colleagues and superiors at the seconded company, strengthening team collaboration. Additionally, receiving support for stress management helps maintain the seconded employee's health, leading to improved long-term performance. Thus, the seconded employee monitoring system can improve the seconded employee's performance and adaptation.

[0029] The seconded employee monitoring system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects performance data and adaptation status data of seconded employees. For example, the collection unit collects data such as the seconded employee's work results, communication status, and stress level. For example, as work results, the collection unit records the progress and achievement level of projects. The collection unit can also record the frequency and content of interactions with colleagues and superiors at the seconded company as communication status. Furthermore, the collection unit can measure stress levels using the seconded employee's self-report or biosensors. For example, the collection unit records the seconded employee's work results using a project management tool to understand the progress. The collection unit can also collect the seconded employee's communication status from email and chat logs and analyze the frequency and content of interactions. Furthermore, the collection unit can measure the seconded employee's stress level using biosensors to understand their stress state. The analysis unit analyzes the data collected by the collection unit and evaluates the seconded employee's performance and adaptation status. The analysis department, for example, analyzes work performance data to evaluate the seconded employee's work performance. The analysis department can also analyze communication status data to evaluate the seconded employee's internal adaptation. Furthermore, the analysis department can analyze stress level data to evaluate the seconded employee's stress level. For example, the analysis department evaluates the seconded employee's goal achievement based on work performance data. The analysis department can also evaluate the seconded employee's internal adaptation based on communication status data. Furthermore, the analysis department can also evaluate the seconded employee's stress level based on stress level data. The service department provides support and mentoring at appropriate times based on the evaluation results obtained by the analysis department. For example, the service department provides support for work improvement. The service department can also provide mentoring to improve communication skills. Furthermore, the service department can also provide support for stress management. For example, if the service department determines that the seconded employee's work performance is declining, it will provide support for work improvement.Furthermore, if the service provider determines that an employee's internal adaptation level is low, it can provide mentoring to improve their communication skills. Additionally, if the service provider determines that an employee's stress level is high, it can provide support for stress management. As a result, the employee monitoring system according to this embodiment can improve the employee's performance and adaptation.

[0030] The data collection unit collects performance and adaptation data for seconded employees. Specifically, it collects data from multiple perspectives, including seconded employees' work results, communication status, and stress levels. For example, to record project progress and achievement as part of seconded employees' work results, a project management tool is used. This tool automatically records task completion status and deadline compliance, providing data for quantitatively evaluating seconded employees' work performance. In addition, to understand seconded employees' communication status, email and chat logs are collected and the frequency and content of interactions are analyzed. This allows for evaluation of how actively seconded employees communicate and how the content of those communications affects their work. Furthermore, biosensors are used to measure seconded employees' stress levels. For example, sensors that measure heart rate and skin electrical activity are attached to seconded employees to monitor their stress levels in real time. This allows for early intervention if seconded employees are experiencing high stress levels. The data collection unit uses a cloud-based database to centrally manage this data and make it accessible to the analysis and provisioning units. This database is designed to securely and efficiently store the collected data and allow for quick access when needed. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, as the project approaches important milestones, the frequency of data collection can be increased to conduct more detailed performance evaluations. This allows the data collection unit to gain a multifaceted and real-time understanding of the performance and adaptation status of seconded personnel, thereby improving the overall system performance.

[0031] The analysis department analyzes data collected by the data collection department to evaluate the performance and adaptation of seconded employees. Specifically, it analyzes work performance data to assess the degree of seconded employees' work achievement. For example, it quantitatively evaluates the degree of seconded employees' goal achievement based on task completion status and deadline compliance status obtained from project management tools. It also analyzes communication status data to assess the degree of seconded employees' adaptation to the company. By analyzing email and chat logs, it is possible to evaluate how actively seconded employees communicate and how the content of their communication affects their work. Furthermore, it analyzes stress level data to assess the stress level of seconded employees. By analyzing heart rate and skin electrical activity data obtained from biosensors, it is possible to determine whether seconded employees are in a high-stress state. The analysis department integrates this data to evaluate the overall performance and adaptation of seconded employees. For example, it combines work performance data, communication status data, and stress level data to calculate the seconded employee's overall performance score. This score comprehensively evaluates the seconded employee's work achievement, adaptation to the company, and stress level, and serves as an indicator for accurately understanding the seconded employee's current situation. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term performance evaluations and trend analyses. For example, it can predict fluctuations in the performance of seconded employees based on past work performance data and formulate future countermeasures. It can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term performance management and anomaly detection, improving the reliability and security of the entire system.

[0032] The service provider department will provide support and mentoring at the appropriate time based on the evaluation results obtained by the analysis department. Specifically, it will provide support for business improvement. For example, if it is determined that an expatriate's work performance is declining, it will provide specific advice and resources for business improvement. This includes reviewing business processes, introducing tools for efficiency, and consulting with experts. It can also provide mentoring to improve communication skills. If it is determined that an expatriate's internal adaptation is low, it will provide training and mentoring to improve communication skills. This includes basic communication skills, effective dialogue methods, and team-building techniques. Furthermore, it can also provide support for stress management. If it is determined that an expatriate is experiencing high stress levels, it will provide specific advice and resources for stress management. This includes relaxation techniques for stress reduction, counseling by mental health professionals, and workshops for stress management. To provide this support and mentoring at the appropriate time, the service provider department will receive evaluation results from the analysis department in real time and respond quickly. In addition, the service provider department can collect feedback from expatriates and continuously improve the content of support and mentoring. For example, feedback is collected on how seconded employees felt about the support and mentoring they received, and how effective it was, and this feedback is then used to improve future support and mentoring. This allows the service provider to offer optimal support and mentoring to seconded employees, improve their performance, and enhance their adaptation.

[0033] The data collection unit can collect data such as the work performance, communication status, and stress levels of seconded employees. For example, the data collection unit can record the work performance of seconded employees using project management tools and understand their progress. It can also collect data on the communication status of seconded employees from email and chat logs and analyze the frequency and content of interactions. Furthermore, the data collection unit can measure the stress levels of seconded employees using biosensors to understand their stress levels. This allows for more accurate evaluation by collecting detailed data on seconded employees. 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 the work performance data of seconded employees into AI, which can then analyze the data to understand their progress.

[0034] The analysis unit can evaluate the seconded employee's work performance based on the collected data. For example, the analysis unit can evaluate the seconded employee's achievement of goals based on work performance data. The analysis unit can also analyze the progress of projects and evaluate the seconded employee's work performance. Furthermore, the analysis unit can quantitatively evaluate work results and calculate the work performance level. This allows for accurate evaluation of the seconded employee's work performance, enabling the provision of appropriate support. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected work performance data into AI, which can then analyze the data and evaluate the work performance level.

[0035] The analysis unit can evaluate the internal fit of seconded employees based on the collected data. For example, the analysis unit can evaluate the internal fit of seconded employees based on communication status data. The analysis unit can also analyze the frequency of seconded employees' interactions with colleagues and superiors to evaluate their internal fit. Furthermore, the analysis unit can analyze the quality of seconded employees' communication to evaluate their internal fit. This allows for accurate evaluation of seconded employees' internal fit and enables the provision of appropriate mentoring. 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 collected communication status data into AI, which can then analyze the data to evaluate the internal fit.

[0036] The analysis unit can evaluate the stress level of seconded employees based on the collected data. For example, the analysis unit can evaluate the stress level of seconded employees based on stress level data. The analysis unit can also analyze the stress level of seconded employees' self-reports and biosensor data. Furthermore, the analysis unit can analyze fluctuations in stress levels to evaluate the stress level of seconded employees. This allows for accurate evaluation of the stress level of seconded employees, enabling the provision of appropriate support. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected stress level data into an AI, which can then analyze the data and evaluate the stress level.

[0037] The service provider can provide support for business improvement based on the evaluation results. For example, if the service provider determines that an employee's performance has declined, it will provide support for business improvement. The service provider can also propose specific improvement measures. Furthermore, the service provider can provide expert consulting. By providing support for business improvement, the performance of seconded employees can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the evaluation results into AI, and the AI ​​can propose specific support for business improvement.

[0038] The service provider can provide mentoring to improve communication skills based on the evaluation results. For example, if the service provider determines that an employee's ability to adapt to the company is low, it will provide mentoring to improve their communication skills. The service provider can also provide training to improve communication skills. Furthermore, the service provider can provide individual guidance from a mentor. In this way, by providing mentoring to improve communication skills, the employee's ability to adapt to the company can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the evaluation results into AI, and the AI ​​can propose specific mentoring content to improve communication skills.

[0039] The service provider can provide support for stress management based on the evaluation results. For example, if the service provider determines that an employee on secondment is experiencing high stress levels, it will provide support for stress management. The service provider can also provide relaxation techniques. Furthermore, the service provider can provide counseling sessions. By providing support for stress management, the service provider can maintain the employee's health and improve their performance. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the evaluation results into an AI, which can then suggest specific support measures for stress management.

[0040] The data collection unit can analyze the seconded employee's past performance data and select the optimal data collection method. For example, the data collection unit can refer to data collection methods used during periods when the seconded employee demonstrated high performance in the past. It can also avoid data collection methods used during periods when the seconded employee demonstrated low performance in the past. Furthermore, the data collection unit can determine the optimal data collection timing based on the seconded employee's past performance data. This enables efficient data collection by selecting the optimal data collection method based on past performance 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 past performance data into AI, which can then select the optimal data collection method.

[0041] The data collection unit can filter data based on the seconded employee's current projects and work content during data collection. For example, the collection unit can collect only data related to the seconded employee's current ongoing projects. The collection unit can also prioritize the collection of necessary data according to the seconded employee's work content. Furthermore, the collection unit can adjust the types of data collected based on the progress of the seconded employee's projects. This allows for efficient collection of necessary data by filtering data based on current projects and work content. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input data related to current projects and work content into an AI, which can then perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the seconded employee during data collection. For example, if the seconded employee is in a specific region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if the seconded employee is on the move, the data collection unit can prioritize the collection of data related to their destination. Additionally, if the seconded employee is in a specific office, the data collection unit can prioritize the collection of data related to that office. This allows for the efficient collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the seconded employee's geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze the social media activities of seconded employees and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by seconded employees on social media. The data collection unit can also prioritize the collection of work-related data from seconded employees' social media activities. Furthermore, the data collection unit can analyze the content of seconded employees' social media posts and collect necessary data. This allows for the efficient collection of work-related data by analyzing social media activities and collecting data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input seconded employees' social media activity data into AI, which can then collect relevant data.

[0044] The analysis unit can evaluate the degree of work achievement based on the seconded employee's work performance data during the analysis. For example, the analysis unit can analyze the seconded employee's project progress and evaluate the degree of work achievement. The analysis unit can also quantitatively evaluate the seconded employee's work results and calculate the degree of work achievement. Furthermore, the analysis unit can analyze the seconded employee's work goal achievement rate and evaluate the degree of work achievement. In this way, by evaluating the degree of work achievement based on work performance data, the performance of seconded employees can be accurately grasped. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the seconded employee's work performance data into AI, and the AI ​​can evaluate the degree of work achievement.

[0045] The analysis unit can evaluate the degree of internal adaptation of seconded employees based on their communication data during analysis. For example, the analysis unit can analyze the frequency of interactions between seconded employees and their colleagues and superiors to evaluate their internal adaptation. The analysis unit can also analyze the quality of seconded employees' communication to evaluate their internal adaptation. Furthermore, the analysis unit can analyze the extent of seconded employees' internal networks to evaluate their internal adaptation. This allows for an accurate understanding of seconded employees' adaptation status by evaluating their internal adaptation based on communication data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input seconded employees' communication data into AI, which can then evaluate their internal adaptation.

[0046] The analysis unit can perform analysis while considering the geographical distribution of seconded employees. For example, if seconded employees are in a specific region, the analysis unit will consider the characteristics of that region. Furthermore, if seconded employees are dispersed across multiple regions, the analysis unit can consider the characteristics of each region. In addition, the analysis unit can display the analysis results separately for each region based on the geographical distribution of seconded employees. This allows for analysis results that reflect the characteristics of each region by considering geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input geographical distribution data of seconded employees into an AI, which can then perform the analysis while considering the characteristics of each region.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the seconded employee during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to literature related to the seconded employee's work. It can also improve the accuracy of its analysis by referring to literature related to the seconded employee's past work results. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to the latest research results in the seconded employee's field of work. By improving the accuracy of the analysis by referring to relevant literature, more accurate analysis results can be obtained. 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 seconded employee's relevant literature data into AI, which can then improve the accuracy of the analysis.

[0048] The service provider can provide support for improving the work performance of seconded employees if their performance declines at the time of service provision. For example, if an seconded employee's performance declines, the service provider can propose specific improvement measures. The service provider can also provide expert consulting. Furthermore, the service provider can propose a review of business processes. By providing appropriate support when performance declines, the performance of seconded employees can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on seconded employees' work performance into AI, which can then propose specific improvement measures.

[0049] The service provider can provide mentoring to improve communication skills if the seconded employee has a low level of internal adaptation at the time of provision. For example, if the seconded employee has a low level of internal adaptation, the service provider can provide training to improve communication skills. The service provider can also provide individual guidance by a mentor. Furthermore, the service provider can encourage participation in internal networking events. In this way, by providing appropriate mentoring when internal adaptation is low, the seconded employee's internal adaptation can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the seconded employee's internal adaptation data into AI, and the AI ​​can suggest specific mentoring content to improve communication skills.

[0050] The service provider can provide optimal support and mentoring by considering the geographical location of the seconded employee at the time of delivery. For example, if the seconded employee is in a specific region, the service provider can provide support related to that region. Furthermore, if the seconded employee is on the move, the service provider can provide support related to their destination. Additionally, if the seconded employee is in a specific office, the service provider can provide support related to that office. This allows for more appropriate support by considering geographical location. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the seconded employee's geographical location into an AI, which can then provide optimal support and mentoring.

[0051] The service provider can analyze the social media activities of seconded employees at the time of service provision and propose means of support and mentoring. For example, the service provider can propose appropriate support means based on the seconded employee's social media activities. The service provider can also analyze the content of the seconded employee's social media posts and propose necessary mentoring means. Furthermore, the service provider can propose the optimal support means based on the frequency of the seconded employee's social media activity. By analyzing social media activities and proposing means of support and mentoring, more effective support becomes possible. 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 seconded employee's social media activity data into AI, and the AI ​​can propose means of support and mentoring.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The seconded employee monitoring system can monitor not only the performance and adaptation of seconded employees, but also their health status. For example, the data collection unit can collect records of seconded employees' sleep patterns and meals. The data collection unit can also measure seconded employees' exercise levels and heart rate using biosensors. The analysis unit can analyze the collected health data and evaluate the seconded employees' health status. For example, the analysis unit can analyze seconded employees' sleep data and evaluate the quality of their sleep. The analysis unit can also analyze seconded employees' meal data and evaluate nutritional balance. Furthermore, the analysis unit can analyze seconded employees' exercise data and evaluate whether they are getting too little or too much exercise. The service unit can provide support for health management based on the evaluation results. For example, if the service unit determines that a seconded employee's sleep quality is poor, it can provide advice on improving sleep. Also, if the service unit determines that a seconded employee's nutritional balance is unbalanced, it can provide advice on improving their diet. Furthermore, if the service unit determines that a seconded employee is not getting enough exercise, it can propose an exercise program. This means that the seconded employee monitoring system can monitor the health status of seconded employees and provide appropriate support, which is expected to improve their performance and adaptation.

[0054] The seconded employee monitoring system can monitor not only the performance and adaptation of seconded employees, but also their learning progress. For example, the data collection unit can collect records of training and workshops attended by seconded employees. The data collection unit can also collect records of seconded employees' learning progress and test results. The analysis unit can analyze the collected learning data and evaluate the seconded employees' learning progress. For example, the analysis unit can analyze seconded employees' training records and evaluate their learning progress. The analysis unit can also analyze seconded employees' test results and evaluate their learning achievement. Based on the evaluation results, the provision unit can provide support for learning assistance. For example, if the provision unit determines that a seconded employee's learning progress is behind, it can suggest additional training. The provision unit can also provide review materials if it determines that a seconded employee's test results are low. Furthermore, if the provision unit determines that a seconded employee's motivation to learn is low, it can send a message explaining the importance of learning. In this way, the seconded employee monitoring system can improve the learning effectiveness of seconded employees by monitoring their learning progress and providing appropriate support.

[0055] The seconded employee monitoring system not only monitors the performance and adaptation of seconded employees but can also support their career development. For example, the data collection unit can collect seconded employees' career goals and skill sets. The data collection unit can also collect seconded employees' past work experience and training history. The analysis unit can analyze the collected career data and evaluate the progress of seconded employees' career development. For example, the analysis unit can analyze seconded employees' career goals and evaluate their achievement. The analysis unit can also analyze seconded employees' skill sets and evaluate their skill acquisition status. Based on the evaluation results, the service provision unit can provide support for career development. For example, if the service provision unit determines that a seconded employee's career goals have not been achieved, it can provide advice on how to achieve those goals. Furthermore, if the service provision unit determines that a seconded employee's skill set is insufficient, it can propose training to improve those skills. In addition, if the service provision unit determines that a seconded employee's career development is lagging behind, it can provide career consulting. In this way, the seconded employee monitoring system can support seconded employees' career development and promote their career growth by providing appropriate support.

[0056] The seconded employee monitoring system not only monitors the performance and adaptation of seconded employees but can also provide support to improve their teamwork. For example, the data collection unit can collect data on seconded employees' roles and contributions within the team. The data collection unit can also collect data on seconded employees' communication with team members. The analysis unit can analyze the collected team data and evaluate the seconded employees' teamwork. For example, the analysis unit can analyze seconded employees' role data to evaluate their contributions within the team. The analysis unit can also analyze seconded employees' communication data to evaluate the quality of communication within the team. Based on the evaluation results, the service provider unit can provide support to improve teamwork. For example, if the service provider unit determines that a seconded employee's contribution is low, it can propose a review of their role. If the service provider unit determines that the quality of seconded employees' communication is low, it can propose training to improve their communication skills. Furthermore, if the service provider unit determines that the seconded employees' teamwork is lacking, it can propose team-building activities. In this way, the seconded employee monitoring system can improve the overall team performance by providing support to improve seconded employees' teamwork.

[0057] The seconded employee monitoring system not only monitors the performance and adaptation of seconded employees but can also provide support to improve their leadership skills. For example, the data collection unit can collect data on the seconded employee's leadership behavior and feedback from team members. The data collection unit can also collect self-assessment data on the seconded employee's leadership. The analysis unit can analyze the collected leadership data and evaluate the seconded employee's leadership skills. For example, the analysis unit can analyze the seconded employee's leadership behavior data and evaluate the effectiveness of their leadership. The analysis unit can also analyze feedback from team members and evaluate areas for improvement in leadership. Based on the evaluation results, the service provider unit can provide support to improve leadership skills. For example, if the service provider unit determines that the seconded employee's leadership skills are lacking, it can propose leadership training. Furthermore, if the service provider unit determines that the seconded employee's leadership behavior needs improvement, it can propose specific improvement measures. In addition, the service provider unit can provide mentoring to improve the seconded employee's leadership skills. In this way, the seconded employee monitoring system can strengthen the seconded employee's leadership capabilities by providing support to improve their leadership skills.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects performance data and adaptation data for seconded employees. Specifically, it collects data such as the seconded employees' work results, communication status, and stress levels. For example, it records project progress and achievements, collects communication status from email and chat logs, and measures stress levels using biosensors. Step 2: The analysis department analyzes the data collected by the data collection department to evaluate the performance and adaptation status of the seconded employees. Specifically, it analyzes work performance data to evaluate work achievement, analyzes communication status data to evaluate internal adaptation, and analyzes stress level data to evaluate stress levels. Step 3: The service provider will provide support and mentoring at the appropriate time based on the evaluation results obtained by the analysis department. Specifically, they will provide support for business process improvement, mentoring for improving communication skills, and support for stress management.

[0060] (Example of form 2) An embodiment of the present invention provides an employee monitoring system in which AI monitors the performance and adaptation status of seconded employees and provides support and mentoring at the appropriate time. The employee monitoring system collects performance data and adaptation status data of seconded employees, and the AI ​​analyzes and evaluates this data to provide support and mentoring at the appropriate time. For example, the employee monitoring system collects detailed data such as the employee's work results, communication status, and stress level. For example, as part of the employee's work results, it records the progress and achievement level of projects. As part of the communication status, it records the frequency and content of interactions with colleagues and superiors at the seconded company. Furthermore, as part of the stress level, it measures the employee's self-reporting and using biosensors. Next, the employee monitoring system uses AI to analyze the collected data. Based on the collected data, the AI ​​evaluates the employee's performance and adaptation status. For example, the AI ​​analyzes work results data to evaluate the employee's work achievement level. It also analyzes communication status data to evaluate the employee's adaptation to the company. Furthermore, it analyzes stress level data to evaluate the employee's stress level. Based on the evaluation results, the seconded employee monitoring system provides support and mentoring at the appropriate time. For example, if the AI ​​determines that a seconded employee's work performance is declining, it provides support for work improvement. If it determines that the employee's internal adaptation is low, it provides mentoring to improve communication skills. Furthermore, if it determines that the employee is experiencing high stress levels, it provides support for stress management. As a result, the seconded employee monitoring system is expected to improve the performance and adaptation of seconded employees. By receiving support and mentoring at the right time, seconded employees can improve work efficiency and reduce stress. For the host company, improved performance and adaptation of seconded employees can lead to project success and smoother team management. For example, by receiving support for work improvement, seconded employees can ensure smoother project progress and better results.Furthermore, receiving mentoring to improve communication skills can enhance relationships with colleagues and superiors at the seconded company, strengthening team collaboration. Additionally, receiving support for stress management helps maintain the seconded employee's health, leading to improved long-term performance. Thus, the seconded employee monitoring system can improve the seconded employee's performance and adaptation.

[0061] The seconded employee monitoring system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects performance data and adaptation status data of seconded employees. For example, the collection unit collects data such as the seconded employee's work results, communication status, and stress level. For example, as work results, the collection unit records the progress and achievement level of projects. The collection unit can also record the frequency and content of interactions with colleagues and superiors at the seconded company as communication status. Furthermore, the collection unit can measure stress levels using the seconded employee's self-report or biosensors. For example, the collection unit records the seconded employee's work results using a project management tool to understand the progress. The collection unit can also collect the seconded employee's communication status from email and chat logs and analyze the frequency and content of interactions. Furthermore, the collection unit can measure the seconded employee's stress level using biosensors to understand their stress state. The analysis unit analyzes the data collected by the collection unit and evaluates the seconded employee's performance and adaptation status. The analysis department, for example, analyzes work performance data to evaluate the seconded employee's work performance. The analysis department can also analyze communication status data to evaluate the seconded employee's internal adaptation. Furthermore, the analysis department can analyze stress level data to evaluate the seconded employee's stress level. For example, the analysis department evaluates the seconded employee's goal achievement based on work performance data. The analysis department can also evaluate the seconded employee's internal adaptation based on communication status data. Furthermore, the analysis department can also evaluate the seconded employee's stress level based on stress level data. The service department provides support and mentoring at appropriate times based on the evaluation results obtained by the analysis department. For example, the service department provides support for work improvement. The service department can also provide mentoring to improve communication skills. Furthermore, the service department can also provide support for stress management. For example, if the service department determines that the seconded employee's work performance is declining, it will provide support for work improvement.Furthermore, if the service provider determines that an employee's internal adaptation level is low, it can provide mentoring to improve their communication skills. Additionally, if the service provider determines that an employee's stress level is high, it can provide support for stress management. As a result, the employee monitoring system according to this embodiment can improve the employee's performance and adaptation.

[0062] The data collection unit collects performance and adaptation data for seconded employees. Specifically, it collects data from multiple perspectives, including seconded employees' work results, communication status, and stress levels. For example, to record project progress and achievement as part of seconded employees' work results, a project management tool is used. This tool automatically records task completion status and deadline compliance, providing data for quantitatively evaluating seconded employees' work performance. In addition, to understand seconded employees' communication status, email and chat logs are collected and the frequency and content of interactions are analyzed. This allows for evaluation of how actively seconded employees communicate and how the content of those communications affects their work. Furthermore, biosensors are used to measure seconded employees' stress levels. For example, sensors that measure heart rate and skin electrical activity are attached to seconded employees to monitor their stress levels in real time. This allows for early intervention if seconded employees are experiencing high stress levels. The data collection unit uses a cloud-based database to centrally manage this data and make it accessible to the analysis and provisioning units. This database is designed to securely and efficiently store the collected data and allow for quick access when needed. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, as the project approaches important milestones, the frequency of data collection can be increased to conduct more detailed performance evaluations. This allows the data collection unit to gain a multifaceted and real-time understanding of the performance and adaptation status of seconded personnel, thereby improving the overall system performance.

[0063] The analysis department analyzes data collected by the data collection department to evaluate the performance and adaptation of seconded employees. Specifically, it analyzes work performance data to assess the degree of seconded employees' work achievement. For example, it quantitatively evaluates the degree of seconded employees' goal achievement based on task completion status and deadline compliance status obtained from project management tools. It also analyzes communication status data to assess the degree of seconded employees' adaptation to the company. By analyzing email and chat logs, it is possible to evaluate how actively seconded employees communicate and how the content of their communication affects their work. Furthermore, it analyzes stress level data to assess the stress level of seconded employees. By analyzing heart rate and skin electrical activity data obtained from biosensors, it is possible to determine whether seconded employees are in a high-stress state. The analysis department integrates this data to evaluate the overall performance and adaptation of seconded employees. For example, it combines work performance data, communication status data, and stress level data to calculate the seconded employee's overall performance score. This score comprehensively evaluates the seconded employee's work achievement, adaptation to the company, and stress level, and serves as an indicator for accurately understanding the seconded employee's current situation. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term performance evaluations and trend analyses. For example, it can predict fluctuations in the performance of seconded employees based on past work performance data and formulate future countermeasures. It can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term performance management and anomaly detection, improving the reliability and security of the entire system.

[0064] The service provider department will provide support and mentoring at the appropriate time based on the evaluation results obtained by the analysis department. Specifically, it will provide support for business improvement. For example, if it is determined that an expatriate's work performance is declining, it will provide specific advice and resources for business improvement. This includes reviewing business processes, introducing tools for efficiency, and consulting with experts. It can also provide mentoring to improve communication skills. If it is determined that an expatriate's internal adaptation is low, it will provide training and mentoring to improve communication skills. This includes basic communication skills, effective dialogue methods, and team-building techniques. Furthermore, it can also provide support for stress management. If it is determined that an expatriate is experiencing high stress levels, it will provide specific advice and resources for stress management. This includes relaxation techniques for stress reduction, counseling by mental health professionals, and workshops for stress management. To provide this support and mentoring at the appropriate time, the service provider department will receive evaluation results from the analysis department in real time and respond quickly. In addition, the service provider department can collect feedback from expatriates and continuously improve the content of support and mentoring. For example, feedback is collected on how seconded employees felt about the support and mentoring they received, and how effective it was, and this feedback is then used to improve future support and mentoring. This allows the service provider to offer optimal support and mentoring to seconded employees, improve their performance, and enhance their adaptation.

[0065] The data collection unit can collect data such as the work performance, communication status, and stress levels of seconded employees. For example, the data collection unit can record the work performance of seconded employees using project management tools and understand their progress. It can also collect data on the communication status of seconded employees from email and chat logs and analyze the frequency and content of interactions. Furthermore, the data collection unit can measure the stress levels of seconded employees using biosensors to understand their stress levels. This allows for more accurate evaluation by collecting detailed data on seconded employees. 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 the work performance data of seconded employees into AI, which can then analyze the data to understand their progress.

[0066] The analysis unit can evaluate the seconded employee's work performance based on the collected data. For example, the analysis unit can evaluate the seconded employee's achievement of goals based on work performance data. The analysis unit can also analyze the progress of projects and evaluate the seconded employee's work performance. Furthermore, the analysis unit can quantitatively evaluate work results and calculate the work performance level. This allows for accurate evaluation of the seconded employee's work performance, enabling the provision of appropriate support. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected work performance data into AI, which can then analyze the data and evaluate the work performance level.

[0067] The analysis unit can evaluate the internal fit of seconded employees based on the collected data. For example, the analysis unit can evaluate the internal fit of seconded employees based on communication status data. The analysis unit can also analyze the frequency of seconded employees' interactions with colleagues and superiors to evaluate their internal fit. Furthermore, the analysis unit can analyze the quality of seconded employees' communication to evaluate their internal fit. This allows for accurate evaluation of seconded employees' internal fit and enables the provision of appropriate mentoring. 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 collected communication status data into AI, which can then analyze the data to evaluate the internal fit.

[0068] The analysis unit can evaluate the stress level of seconded employees based on the collected data. For example, the analysis unit can evaluate the stress level of seconded employees based on stress level data. The analysis unit can also analyze the stress level of seconded employees' self-reports and biosensor data. Furthermore, the analysis unit can analyze fluctuations in stress levels to evaluate the stress level of seconded employees. This allows for accurate evaluation of the stress level of seconded employees, enabling the provision of appropriate support. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected stress level data into an AI, which can then analyze the data and evaluate the stress level.

[0069] The service provider can provide support for business improvement based on the evaluation results. For example, if the service provider determines that an employee's performance has declined, it will provide support for business improvement. The service provider can also propose specific improvement measures. Furthermore, the service provider can provide expert consulting. By providing support for business improvement, the performance of seconded employees can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the evaluation results into AI, and the AI ​​can propose specific support for business improvement.

[0070] The service provider can provide mentoring to improve communication skills based on the evaluation results. For example, if the service provider determines that an employee's ability to adapt to the company is low, it will provide mentoring to improve their communication skills. The service provider can also provide training to improve communication skills. Furthermore, the service provider can provide individual guidance from a mentor. In this way, by providing mentoring to improve communication skills, the employee's ability to adapt to the company can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the evaluation results into AI, and the AI ​​can propose specific mentoring content to improve communication skills.

[0071] The service provider can provide support for stress management based on the evaluation results. For example, if the service provider determines that an employee on secondment is experiencing high stress levels, it will provide support for stress management. The service provider can also provide relaxation techniques. Furthermore, the service provider can provide counseling sessions. By providing support for stress management, the service provider can maintain the employee's health and improve their performance. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the evaluation results into an AI, which can then suggest specific support measures for stress management.

[0072] The data collection unit can estimate the emotions of seconded employees and adjust the frequency of data collection based on the estimated emotions. For example, if a seconded employee is stressed, the data collection unit can reduce the frequency of data collection to alleviate their burden. Conversely, if a seconded employee is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if a seconded employee is in a hurry, the data collection unit can minimize the frequency of data collection to allow them to concentrate on their work. This reduces the burden on seconded employees and allows for the collection of detailed data by adjusting the frequency of data collection according to 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input seconded employees' emotion data into an AI, which can then adjust the frequency of data collection.

[0073] The data collection unit can analyze the seconded employee's past performance data and select the optimal data collection method. For example, the data collection unit can refer to data collection methods used during periods when the seconded employee demonstrated high performance in the past. It can also avoid data collection methods used during periods when the seconded employee demonstrated low performance in the past. Furthermore, the data collection unit can determine the optimal data collection timing based on the seconded employee's past performance data. This enables efficient data collection by selecting the optimal data collection method based on past performance 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 past performance data into AI, which can then select the optimal data collection method.

[0074] The data collection unit can filter data based on the seconded employee's current projects and work content during data collection. For example, the collection unit can collect only data related to the seconded employee's current ongoing projects. The collection unit can also prioritize the collection of necessary data according to the seconded employee's work content. Furthermore, the collection unit can adjust the types of data collected based on the progress of the seconded employee's projects. This allows for efficient collection of necessary data by filtering data based on current projects and work content. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input data related to current projects and work content into an AI, which can then perform the filtering.

[0075] The data collection unit can estimate the emotions of seconded employees and determine the priority of data to collect based on the estimated emotions. For example, if an employee is stressed, the data collection unit will prioritize collecting stress-related data. It can also prioritize collecting work performance data if the employee is relaxed. Furthermore, if the employee is in a hurry, the data collection unit can prioritize collecting communication status data. This allows for the priority collection of important data by determining the priority of data to collect according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 employee emotion data into an AI, which can then determine the priority of data to collect.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the seconded employee during data collection. For example, if the seconded employee is in a specific region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if the seconded employee is on the move, the data collection unit can prioritize the collection of data related to their destination. Additionally, if the seconded employee is in a specific office, the data collection unit can prioritize the collection of data related to that office. This allows for the efficient collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the seconded employee's geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze the social media activities of seconded employees and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by seconded employees on social media. The data collection unit can also prioritize the collection of work-related data from seconded employees' social media activities. Furthermore, the data collection unit can analyze the content of seconded employees' social media posts and collect necessary data. This allows for the efficient collection of work-related data by analyzing social media activities and collecting data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input seconded employees' social media activity data into AI, which can then collect relevant data.

[0078] The analysis unit can estimate the emotions of seconded employees and adjust the analysis algorithm based on the estimated emotions. For example, if the seconded employee is stressed, the analysis unit can use an analysis algorithm that focuses on stress reduction. If the seconded employee is relaxed, the analysis unit can also use an analysis algorithm that focuses on work performance. Furthermore, if the seconded employee is in a hurry, the analysis unit can use an algorithm that performs rapid analysis. By adjusting the analysis algorithm according to the emotions of the seconded employee, more appropriate analysis results can be obtained. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the seconded employee's emotion data into the AI, and the AI ​​can adjust the analysis algorithm.

[0079] The analysis unit can evaluate the degree of work achievement based on the seconded employee's work performance data during the analysis. For example, the analysis unit can analyze the seconded employee's project progress and evaluate the degree of work achievement. The analysis unit can also quantitatively evaluate the seconded employee's work results and calculate the degree of work achievement. Furthermore, the analysis unit can analyze the seconded employee's work goal achievement rate and evaluate the degree of work achievement. In this way, by evaluating the degree of work achievement based on work performance data, the performance of seconded employees can be accurately grasped. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the seconded employee's work performance data into AI, and the AI ​​can evaluate the degree of work achievement.

[0080] The analysis unit can evaluate the degree of internal adaptation of seconded employees based on their communication data during analysis. For example, the analysis unit can analyze the frequency of interactions between seconded employees and their colleagues and superiors to evaluate their internal adaptation. The analysis unit can also analyze the quality of seconded employees' communication to evaluate their internal adaptation. Furthermore, the analysis unit can analyze the extent of seconded employees' internal networks to evaluate their internal adaptation. This allows for an accurate understanding of seconded employees' adaptation status by evaluating their internal adaptation based on communication data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input seconded employees' communication data into AI, which can then evaluate their internal adaptation.

[0081] The analysis unit can estimate the emotions of the seconded employee and adjust the display method of the analysis results based on the estimated emotions of the seconded employee. For example, if the seconded employee is feeling stressed, the analysis unit can provide a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the seconded employee is relaxed. Furthermore, if the seconded employee is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the emotions of the seconded employee, a highly visible display is possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the seconded employee's emotion data into the AI, and the AI ​​can adjust the display method of the analysis results.

[0082] The analysis unit can perform analysis while considering the geographical distribution of seconded employees. For example, if seconded employees are in a specific region, the analysis unit will consider the characteristics of that region. Furthermore, if seconded employees are dispersed across multiple regions, the analysis unit can consider the characteristics of each region. In addition, the analysis unit can display the analysis results separately for each region based on the geographical distribution of seconded employees. This allows for analysis results that reflect the characteristics of each region by considering geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input geographical distribution data of seconded employees into an AI, which can then perform the analysis while considering the characteristics of each region.

[0083] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the seconded employee during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to literature related to the seconded employee's work. It can also improve the accuracy of its analysis by referring to literature related to the seconded employee's past work results. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to the latest research results in the seconded employee's field of work. By improving the accuracy of the analysis by referring to relevant literature, more accurate analysis results can be obtained. 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 seconded employee's relevant literature data into AI, which can then improve the accuracy of the analysis.

[0084] The service provider can estimate the emotions of seconded employees and adjust the method of providing support and mentoring based on the estimated emotions. For example, if a seconded employee is feeling stressed, the service provider can provide support in a relaxing environment. If the seconded employee is relaxed, the service provider can also provide detailed mentoring. Furthermore, if the seconded employee is in a hurry, the service provider can provide quick and concise support. This allows for more effective support by adjusting the method of providing support and mentoring according to the seconded employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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. For example, the service provider can input the seconded employee's emotion data into an AI, which can then adjust the method of providing support and mentoring.

[0085] The service provider can provide support for improving the work performance of seconded employees if their performance declines at the time of service provision. For example, if an seconded employee's performance declines, the service provider can propose specific improvement measures. The service provider can also provide expert consulting. Furthermore, the service provider can propose a review of business processes. By providing appropriate support when performance declines, the performance of seconded employees can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on seconded employees' work performance into AI, which can then propose specific improvement measures.

[0086] The service provider can provide mentoring to improve communication skills if the seconded employee has a low level of internal adaptation at the time of provision. For example, if the seconded employee has a low level of internal adaptation, the service provider can provide training to improve communication skills. The service provider can also provide individual guidance by a mentor. Furthermore, the service provider can encourage participation in internal networking events. In this way, by providing appropriate mentoring when internal adaptation is low, the seconded employee's internal adaptation can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the seconded employee's internal adaptation data into AI, and the AI ​​can suggest specific mentoring content to improve communication skills.

[0087] The support department can estimate the emotions of seconded employees and determine the priority of support and mentoring based on the estimated emotions. For example, if a seconded employee is feeling stressed, the support department can prioritize stress management support. If a seconded employee is relaxed, the support department can also prioritize work improvement support. Furthermore, if a seconded employee is in a hurry, the support department can prioritize rapid support. This allows for more effective support by prioritizing support and mentoring according to the seconded employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support department may be performed using AI or not. For example, the support department can input seconded employee emotion data into an AI, which can then determine the priority of support and mentoring.

[0088] The service provider can provide optimal support and mentoring by considering the geographical location of the seconded employee at the time of delivery. For example, if the seconded employee is in a specific region, the service provider can provide support related to that region. Furthermore, if the seconded employee is on the move, the service provider can provide support related to their destination. Additionally, if the seconded employee is in a specific office, the service provider can provide support related to that office. This allows for more appropriate support by considering geographical location. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the seconded employee's geographical location into an AI, which can then provide optimal support and mentoring.

[0089] The service provider can analyze the social media activities of seconded employees at the time of service provision and propose means of support and mentoring. For example, the service provider can propose appropriate support means based on the seconded employee's social media activities. The service provider can also analyze the content of the seconded employee's social media posts and propose necessary mentoring means. Furthermore, the service provider can propose the optimal support means based on the frequency of the seconded employee's social media activity. By analyzing social media activities and proposing means of support and mentoring, more effective support becomes possible. 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 seconded employee's social media activity data into AI, and the AI ​​can propose means of support and mentoring.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The seconded employee monitoring system can monitor not only the performance and adaptation of seconded employees, but also their health status. For example, the data collection unit can collect records of seconded employees' sleep patterns and meals. The data collection unit can also measure seconded employees' exercise levels and heart rate using biosensors. The analysis unit can analyze the collected health data and evaluate the seconded employees' health status. For example, the analysis unit can analyze seconded employees' sleep data and evaluate the quality of their sleep. The analysis unit can also analyze seconded employees' meal data and evaluate nutritional balance. Furthermore, the analysis unit can analyze seconded employees' exercise data and evaluate whether they are getting too little or too much exercise. The service unit can provide support for health management based on the evaluation results. For example, if the service unit determines that a seconded employee's sleep quality is poor, it can provide advice on improving sleep. Also, if the service unit determines that a seconded employee's nutritional balance is unbalanced, it can provide advice on improving their diet. Furthermore, if the service unit determines that a seconded employee is not getting enough exercise, it can propose an exercise program. This means that the seconded employee monitoring system can monitor the health status of seconded employees and provide appropriate support, which is expected to improve their performance and adaptation.

[0092] The seconded employee monitoring system can estimate the emotions of seconded employees and provide feedback to improve their motivation based on the estimated emotions. For example, the collection unit can analyze the seconded employee's facial expressions and tone of voice to estimate their emotions. The analysis unit can analyze the collected emotional data and evaluate the seconded employee's motivation level. For example, the analysis unit can analyze the seconded employee's facial expression data to evaluate whether they are tired. The analysis unit can also analyze the seconded employee's tone of voice to evaluate whether they are stressed. The provision unit can provide feedback to improve motivation based on the evaluation results. For example, if the provision unit determines that the seconded employee is tired, it can advise them to take a break. If the provision unit determines that the seconded employee is stressed, it can also suggest relaxation techniques. Furthermore, if the provision unit determines that the seconded employee's motivation is low, it can send an encouraging message. In this way, the seconded employee monitoring system can monitor the emotions of seconded employees and provide appropriate feedback, thereby improving their motivation.

[0093] The seconded employee monitoring system can monitor not only the performance and adaptation of seconded employees, but also their learning progress. For example, the data collection unit can collect records of training and workshops attended by seconded employees. The data collection unit can also collect records of seconded employees' learning progress and test results. The analysis unit can analyze the collected learning data and evaluate the seconded employees' learning progress. For example, the analysis unit can analyze seconded employees' training records and evaluate their learning progress. The analysis unit can also analyze seconded employees' test results and evaluate their learning achievement. Based on the evaluation results, the provision unit can provide support for learning assistance. For example, if the provision unit determines that a seconded employee's learning progress is behind, it can suggest additional training. The provision unit can also provide review materials if it determines that a seconded employee's test results are low. Furthermore, if the provision unit determines that a seconded employee's motivation to learn is low, it can send a message explaining the importance of learning. In this way, the seconded employee monitoring system can improve the learning effectiveness of seconded employees by monitoring their learning progress and providing appropriate support.

[0094] The seconded employee monitoring system can estimate the emotions of seconded employees and provide advice to adjust their communication style based on the estimated emotions. For example, the collection unit can analyze the content of seconded employees' emails and chats to estimate their emotions. The analysis unit can analyze the collected emotion data and evaluate the seconded employees' communication style. For example, the analysis unit can analyze the content of seconded employees' emails to evaluate what emotions they are communicating with. The analysis unit can also analyze the content of seconded employees' chats to evaluate what tone they are communicating in. Based on the evaluation results, the provision unit can provide advice to adjust the communication style. For example, if the provision unit determines that a seconded employee is feeling stressed, it can advise them to communicate in a relaxed tone. If the provision unit determines that a seconded employee is feeling angry, it can advise them to communicate in a calm tone. Furthermore, if the provision unit determines that a seconded employee is feeling joy, it can advise them to share that emotion. In this way, the seconded employee monitoring system can improve seconded employees' communication skills by monitoring their emotions and providing appropriate advice.

[0095] The seconded employee monitoring system not only monitors the performance and adaptation of seconded employees but can also support their career development. For example, the data collection unit can collect seconded employees' career goals and skill sets. The data collection unit can also collect seconded employees' past work experience and training history. The analysis unit can analyze the collected career data and evaluate the progress of seconded employees' career development. For example, the analysis unit can analyze seconded employees' career goals and evaluate their achievement. The analysis unit can also analyze seconded employees' skill sets and evaluate their skill acquisition status. Based on the evaluation results, the service provision unit can provide support for career development. For example, if the service provision unit determines that a seconded employee's career goals have not been achieved, it can provide advice on how to achieve those goals. Furthermore, if the service provision unit determines that a seconded employee's skill set is insufficient, it can propose training to improve those skills. In addition, if the service provision unit determines that a seconded employee's career development is lagging behind, it can provide career consulting. In this way, the seconded employee monitoring system can support seconded employees' career development and promote their career growth by providing appropriate support.

[0096] The seconded employee monitoring system can estimate the emotions of seconded employees and support their stress management based on those estimated emotions. For example, the data collection unit can analyze data obtained from the seconded employee's biosensors to estimate their emotions. The analysis unit can analyze the collected emotional data and evaluate the seconded employee's stress level. For example, the analysis unit can analyze the seconded employee's heart rate and skin electrical activity to evaluate their stress level. The analysis unit can also analyze the seconded employee's breathing patterns to evaluate signs of stress. The provision unit can provide support for stress management based on the evaluation results. For example, if the provision unit determines that the seconded employee's stress level is high, it can suggest relaxation techniques. The provision unit can also suggest stress-reducing activities if it determines that the seconded employee shows signs of stress. Furthermore, if the provision unit determines that the seconded employee needs stress management, it can provide counseling sessions. In this way, the seconded employee monitoring system can support the stress management of seconded employees by monitoring their emotions and providing appropriate support.

[0097] The seconded employee monitoring system not only monitors the performance and adaptation of seconded employees but can also provide support to improve their teamwork. For example, the data collection unit can collect data on seconded employees' roles and contributions within the team. The data collection unit can also collect data on seconded employees' communication with team members. The analysis unit can analyze the collected team data and evaluate the seconded employees' teamwork. For example, the analysis unit can analyze seconded employees' role data to evaluate their contributions within the team. The analysis unit can also analyze seconded employees' communication data to evaluate the quality of communication within the team. Based on the evaluation results, the service provider unit can provide support to improve teamwork. For example, if the service provider unit determines that a seconded employee's contribution is low, it can propose a review of their role. If the service provider unit determines that the quality of seconded employees' communication is low, it can propose training to improve their communication skills. Furthermore, if the service provider unit determines that the seconded employees' teamwork is lacking, it can propose team-building activities. In this way, the seconded employee monitoring system can improve the overall team performance by providing support to improve seconded employees' teamwork.

[0098] The seconded employee monitoring system can estimate the emotions of seconded employees and adjust the feedback based on those estimated emotions. For example, the collection unit can analyze the seconded employee's facial expressions and tone of voice to estimate their emotions. The analysis unit can analyze the collected emotion data and evaluate the seconded employee's emotional state. For example, the analysis unit can analyze the seconded employee's facial expression data to evaluate what emotions the seconded employee is experiencing. The analysis unit can also analyze the seconded employee's tone of voice to evaluate what emotions the seconded employee is expressing. The delivery unit can adjust the content and method of feedback based on the evaluation results. For example, if the delivery unit determines that the seconded employee is feeling stressed, it can provide feedback in a gentle tone. If the delivery unit determines that the seconded employee is relaxed, it can provide detailed feedback. Furthermore, if the delivery unit determines that the seconded employee is in a hurry, it can provide concise feedback. In this way, the seconded employee monitoring system can monitor the emotions of seconded employees and provide appropriate feedback, enabling feedback in a form that is easily accepted by seconded employees.

[0099] The seconded employee monitoring system not only monitors the performance and adaptation of seconded employees but can also provide support to improve their leadership skills. For example, the data collection unit can collect data on the seconded employee's leadership behavior and feedback from team members. The data collection unit can also collect self-assessment data on the seconded employee's leadership. The analysis unit can analyze the collected leadership data and evaluate the seconded employee's leadership skills. For example, the analysis unit can analyze the seconded employee's leadership behavior data and evaluate the effectiveness of their leadership. The analysis unit can also analyze feedback from team members and evaluate areas for improvement in leadership. Based on the evaluation results, the service provider unit can provide support to improve leadership skills. For example, if the service provider unit determines that the seconded employee's leadership skills are lacking, it can propose leadership training. Furthermore, if the service provider unit determines that the seconded employee's leadership behavior needs improvement, it can propose specific improvement measures. In addition, the service provider unit can provide mentoring to improve the seconded employee's leadership skills. In this way, the seconded employee monitoring system can strengthen the seconded employee's leadership capabilities by providing support to improve their leadership skills.

[0100] The seconded employee monitoring system can estimate the emotions of seconded employees and support their mental health based on those estimated emotions. For example, the collection unit can analyze data obtained from the seconded employee's self-reports and biosensors to estimate their emotions. The analysis unit can analyze the collected emotional data and evaluate the seconded employee's mental health status. For example, the analysis unit can analyze the seconded employee's self-report data to evaluate their mental health status. The analysis unit can also analyze biosensor data to evaluate signs of stress and anxiety. The provision unit can provide mental health support based on the evaluation results. For example, if the provision unit determines that the seconded employee's mental health status is deteriorating, it can propose a counseling session. The provision unit can also determine that the seconded employee is experiencing stress and propose activities to reduce stress. Furthermore, the provision unit can also provide relaxation techniques to maintain the seconded employee's mental health. In this way, the seconded employee monitoring system can monitor the emotions of seconded employees and provide appropriate support to maintain their mental health and help improve their performance.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection unit collects performance data and adaptation data for seconded employees. Specifically, it collects data such as the seconded employees' work results, communication status, and stress levels. For example, it records project progress and achievements, collects communication status from email and chat logs, and measures stress levels using biosensors. Step 2: The analysis department analyzes the data collected by the data collection department to evaluate the performance and adaptation status of the seconded employees. Specifically, it analyzes work performance data to evaluate work achievement, analyzes communication status data to evaluate internal adaptation, and analyzes stress level data to evaluate stress levels. Step 3: The service provider will provide support and mentoring at the appropriate time based on the evaluation results obtained by the analysis department. Specifically, they will provide support for business process improvement, mentoring for improving communication skills, and support for stress management.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects performance data and adaptation data of the seconded employee using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to evaluate the seconded employee's performance and adaptation status. The provision unit is implemented in the control unit 46A of the smart device 14, and provides support and mentoring at an appropriate time based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects performance data and adaptation data of the seconded employee using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to evaluate the seconded employee's performance and adaptation status. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides support and mentoring at an appropriate time based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects performance data and adaptation data of the seconded employee using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to evaluate the seconded employee's performance and adaptation status. The provision unit is implemented in the control unit 46A of the headset terminal 314, and provides support and mentoring at an appropriate time based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects performance data and adaptation data of the dispatched worker using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to evaluate the dispatched worker's performance and adaptation status. The provision unit is implemented in, for example, the control unit 46A of the robot 414, which provides support and mentoring at an appropriate time based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) The data collection department collects performance data and adaptation data for seconded employees, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the performance and adaptation status of the seconded employee, The system includes a provisioning unit that provides support and mentoring at an appropriate time based on the evaluation results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on the work performance, communication status, and stress levels of seconded employees. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The performance of seconded employees will be evaluated based on the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Based on the collected data, we will evaluate the degree of internal adaptation of seconded employees. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The stress levels of seconded employees are evaluated based on the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide support for business improvement based on the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Based on the evaluation results, we provide mentoring to improve communication skills. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, We provide support for stress management based on the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the emotions of seconded employees and adjusts the frequency of data collection based on the estimated emotions of the seconded employees. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the past performance data of seconded employees and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, filtering is performed based on the seconded employee's current project and job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We estimate the emotions of seconded employees and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data, the geographical location information of seconded employees is taken into consideration to prioritize the collection of highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, analyze the social media activities of seconded employees and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the emotions of seconded employees and adjusts the analysis algorithm based on the estimated emotions of the seconded employees. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the degree of work achievement is evaluated based on the work performance data of seconded employees. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the degree of internal adaptation of seconded employees is evaluated based on their communication data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system estimates the emotions of seconded employees and adjusts the display method of the analysis results based on the estimated emotions of the seconded employees. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the geographical distribution of seconded employees will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, we refer to relevant literature related to the seconded individuals to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the emotions of seconded employees and adjusts the way support and mentoring are provided based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing services, if the seconded employee's performance is declining, support will be provided to improve their work performance. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing services, if the seconded employee has difficulty adapting to the company, mentoring will be provided to improve their communication skills. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the emotions of seconded employees and prioritizes support and mentoring based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing services, we will take into account the geographical location of the seconded employee to provide the most appropriate support and mentoring. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing support, we analyze the social media activities of seconded employees and propose methods for support and mentoring. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0175] 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 performance data and adaptation data for seconded employees, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the performance and adaptation status of the seconded employee, The system includes a provisioning unit that provides support and mentoring at an appropriate time based on the evaluation results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on the work performance, communication status, and stress levels of seconded employees. The system according to feature 1.

3. The aforementioned analysis unit, The performance of seconded employees will be evaluated based on the collected data. The system according to feature 1.

4. The aforementioned analysis unit, Based on the collected data, we will evaluate the degree of internal adaptation of seconded employees. The system according to feature 1.

5. The aforementioned analysis unit, The stress levels of seconded employees are evaluated based on the collected data. The system according to feature 1.

6. The aforementioned supply unit is, We provide support for business improvement based on the evaluation results. The system according to feature 1.

7. The aforementioned supply unit is, Based on the evaluation results, we provide mentoring to improve communication skills. The system according to feature 1.

8. The aforementioned supply unit is, We provide support for stress management based on the evaluation results. The system according to feature 1.

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