system

Abstract

The system according to this embodiment aims to provide objective and fair evaluations in personnel management. [Solution] The system according to the embodiment comprises an evaluation unit, a removal unit, a monitoring unit, and a feedback unit. The evaluation unit performs multidimensional evaluation. The removal unit removes bias based on the data obtained by the evaluation unit. The monitoring unit monitors performance based on the data from which bias has been removed by the removal unit. The feedback unit provides feedback based on the data monitored by the monitoring unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, human subjectivity and bias may enter into personnel management, and there is a risk of losing fairness and efficiency.

[0005] The system according to the embodiment aims to provide an objective and fair evaluation in personnel management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an evaluation unit, a removal unit, a monitoring unit, and a feedback unit. The evaluation unit performs multidimensional evaluation. The removal unit removes bias based on the data obtained by the evaluation unit. The monitoring unit monitors performance based on the data from which bias has been removed by the removal unit. The feedback unit provides feedback based on the data monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide objective and fair evaluations in personnel management. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls 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 3 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) The human resource management system according to an embodiment of the present invention is a system that uses AI to perform multidimensional evaluation and bias removal, providing objective and fair evaluations. This system achieves management efficiency through real-time performance monitoring and feedback. It also supports individual growth by setting personalized goals and making dynamic adjustments based on each employee's career aspirations and skills. Furthermore, it constantly monitors employee well-being through emotion analysis and stress monitoring functions, and responds immediately when problems arise. It also enables optimal personnel placement utilizing skill matching, maximizing the overall performance of the organization. For example, AI performs multidimensional evaluation and removes bias. Next, it achieves management efficiency through real-time performance monitoring and feedback. Furthermore, it supports individual growth by setting personalized goals and making dynamic adjustments based on each employee's career aspirations and skills. It constantly monitors employee well-being through emotion analysis and stress monitoring functions, and responds immediately when problems arise. It also enables optimal personnel placement utilizing skill matching, maximizing the overall performance of the organization. This provides fair and efficient future-oriented human resource management. We envision a future where companies and employees grow together by innovating human resource management with the power of AI. Eliminate human bias and create a fair workplace based on talent and effort. Maximize the potential of each employee and provide an environment where innovation thrives. Enhance corporate competitiveness, create an ideal workplace where employees feel fulfilled, and redefine the future of work. This will enable human resource management systems to evaluate employees objectively and fairly.

[0029] The human resource management system according to this embodiment comprises an evaluation unit, a filtering unit, a monitoring unit, and a feedback unit. The evaluation unit performs multidimensional evaluation. The evaluation unit can perform evaluations in dimensions such as technical skills, soft skills, and performance. For example, to evaluate technical skills, the evaluation unit evaluates an employee's technical knowledge and abilities. The evaluation unit can also evaluate an employee's communication skills and teamwork to evaluate soft skills. The evaluation unit can also evaluate an employee's performance indicators and results to evaluate performance. The filtering unit removes bias based on the data obtained by the evaluation unit. For example, to remove gender bias, the filtering unit removes information about gender from the evaluation data. The filtering unit can also remove information about age from the evaluation data to remove age bias. The filtering unit can also standardize the evaluation data to remove the evaluator's subjective bias. The monitoring unit monitors performance based on the data from which bias has been removed by the filtering unit. The monitoring unit monitors employee performance data in real time, for example, to monitor performance indicators. Furthermore, the monitoring unit can monitor employee behavioral data in real time to monitor behavioral indicators. The monitoring unit can also monitor employee feedback data in real time to monitor feedback. The feedback unit provides feedback based on the data monitored by the monitoring unit. For example, the feedback unit provides numerical feedback based on employee performance data to provide quantitative feedback. The feedback unit can also provide textual feedback based on employee behavioral data to provide qualitative feedback. Furthermore, the feedback unit can provide immediate feedback based on employee performance data to provide real-time feedback. This enables the human resource management system according to the embodiment to perform multidimensional evaluation, bias reduction, performance monitoring, and feedback provision.

[0030] The evaluation department conducts multidimensional evaluations. For example, it can evaluate employees across dimensions such as technical skills, soft skills, and performance. Specifically, in evaluating technical skills, it analyzes technical tests and project deliverables to assess employees' expertise and technical capabilities in detail. For instance, to evaluate programming skills, it conducts code reviews and tests to measure algorithmic understanding. In evaluating soft skills, it collects feedback from colleagues and supervisors and conducts 360-degree evaluations to assess employees' communication and teamwork skills. This allows for understanding how effectively employees communicate and how collaborative relationships function within teams. Furthermore, in performance evaluations, it uses KPIs (Key Performance Indicators) and OKRs (Objectives and Key Results) to evaluate employee performance indicators and outcomes. This allows for the quantification and evaluation of specific results, such as the extent to which employees are achieving set goals. The evaluation department integrates these multidimensional evaluations to generate comprehensive evaluation results. This clarifies employees' strengths and areas for improvement and provides foundational data for developing individual career development plans.

[0031] The bias removal unit removes bias based on the data obtained by the evaluation unit. Specifically, to remove gender bias, it removes information about gender from the evaluation data. For example, by anonymizing the evaluation data and processing it in a way that does not include information about gender, it prevents evaluators from unconsciously holding gender-based biases. It can also remove information about age from the evaluation data to remove age bias. This prevents preconceptions about age from influencing evaluations. Furthermore, to remove subjective bias of evaluators, the evaluation data is standardized. For example, to correct for variations in evaluation criteria among evaluators, evaluation scores are normalized using statistical methods. This ensures consistency in evaluations among evaluators and enables fair evaluations. The bias removal unit automates these bias removal processes, improving the reliability and fairness of evaluation data. This provides an environment where employees are evaluated fairly and enhances the overall reliability of the organization.

[0032] The monitoring unit monitors performance based on data from which bias has been removed by the bias removal unit. Specifically, it monitors employee performance data in real time to monitor performance indicators. For example, it regularly collects performance indicators such as sales, productivity, and project progress and displays them on a dashboard so that managers can quickly grasp the situation. It can also monitor employee behavioral data in real time to monitor behavioral indicators. For example, it collects behavioral data such as attendance time, work time, and break time to analyze employee work patterns. Furthermore, it can monitor employee feedback data in real time to monitor feedback. For example, it collects feedback from supervisors and colleagues and continuously tracks evaluations of employee performance. The monitoring unit integrates this data to comprehensively evaluate employee performance. This allows for real-time identification of employee strengths and areas for improvement, enabling the provision of appropriate support and guidance. In addition, the monitoring unit uses anomaly detection algorithms to detect unusual performance patterns and abnormal behavior early and respond quickly. This allows for continuous optimization of employee performance and improvement of overall organizational efficiency and productivity.

[0033] The Feedback Department provides feedback based on data monitored by the Monitoring Department. Specifically, to provide quantitative feedback, it provides numerical feedback based on employee performance data. For example, by providing feedback using specific numbers such as the degree of achievement of sales targets or the rate of productivity improvement, employees can objectively understand their own performance. In addition, to provide qualitative feedback, it can also provide written feedback based on employee behavior data. For example, it can provide specific advice on areas for improvement in communication skills or teamwork. Furthermore, to provide real-time feedback, the Feedback Department can also provide immediate feedback based on employee performance data. For example, by providing feedback at the appropriate time according to the progress of a project, employees can quickly grasp areas for improvement and modify their behavior. The Feedback Department customizes this feedback individually, providing specific advice tailored to the employee's needs and goals. This allows employees to obtain specific guidance for continuously improving their own performance. The Feedback Department also continuously evaluates the effectiveness of the feedback and improves the content and methods of feedback as needed. In this way, the Feedback Department can play a vital role in supporting employee growth and improving the overall performance of the organization.

[0034] The human resources management system includes a goal-setting unit that sets goals based on career aspirations and skills. The goal-setting unit can, for example, set short-term goals based on an employee's career aspirations. It can also set long-term goals based on an employee's career aspirations. Furthermore, it can set goals based on an employee's career path. This enables goal setting based on career aspirations and skills. Some or all of the above-described processes in the goal-setting unit may be performed using AI, for example, or without AI. For example, the goal-setting unit can input employee career aspiration data into a generating AI and have the generating AI perform goal setting.

[0035] The human resources management system includes an emotion analysis unit that performs emotion analysis. The emotion analysis unit can, for example, analyze employee facial expression data to perform facial expression analysis. It can also analyze employee voice data to perform voice analysis. It can also analyze employee text data to perform text analysis. This enables emotion analysis. Some or all of the above-described processes in the emotion analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion analysis unit can input employee facial expression data into a generative AI and have the generative AI perform emotion analysis.

[0036] The human resources management system includes a stress monitoring unit that performs stress monitoring. The stress monitoring unit can, for example, monitor employees' heart rate and skin electrical activity to monitor physiological indicators. It can also monitor employees' self-reported data to monitor psychological indicators. Furthermore, it can monitor employees' behavioral data to monitor behavioral indicators. This enables stress monitoring. Some or all of the above-described processes in the stress monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the stress monitoring unit can input employee physiological indicator data into a generative AI and have the generative AI perform stress monitoring.

[0037] The human resources management system includes a skill matching unit that performs skill matching. The skill matching unit can, for example, analyze employee skill data to evaluate the degree of match in skill sets. It can also analyze employee experience data to evaluate years of experience. Furthermore, it can analyze employee aptitude test data to evaluate aptitude test results. This enables skill matching. Some or all of the above-described processes in the skill matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the skill matching unit can input employee skill data into a generative AI and have the generative AI perform skill matching.

[0038] The goal-setting unit can set goals based on data from the evaluation unit. For example, the goal-setting unit can set SMART goals based on data from the evaluation unit. The goal-setting unit can also set OKRs based on data from the evaluation unit. Furthermore, the goal-setting unit can set KPIs based on data from the evaluation unit. This makes it possible to set goals based on data from the evaluation unit. Some or all of the above processes in the goal-setting unit may be performed using AI, for example, or without AI. For example, the goal-setting unit can input data from the evaluation unit into a generation AI and have the generation AI perform goal setting.

[0039] The emotion analysis unit can analyze emotions based on data from the monitoring unit. For example, the emotion analysis unit can perform facial expression analysis based on data from the monitoring unit. The emotion analysis unit can also perform voice analysis based on data from the monitoring unit. Furthermore, the emotion analysis unit can perform text analysis based on data from the monitoring unit. This makes it possible to perform emotion analysis based on data from the monitoring unit. Some or all of the above-described processes in the emotion analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the emotion analysis unit can input data from the monitoring unit into a generative AI and have the generative AI perform emotion analysis.

[0040] The stress monitoring unit can monitor stress based on data from the monitoring unit. For example, the stress monitoring unit can monitor physiological indicators based on data from the monitoring unit. It can also monitor psychological indicators based on data from the monitoring unit. Furthermore, the stress monitoring unit can monitor behavioral indicators based on data from the monitoring unit. This makes it possible to monitor stress based on data from the monitoring unit. Some or all of the above-described processing in the stress monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the stress monitoring unit can input data from the monitoring unit into a generative AI and have the generative AI perform stress monitoring.

[0041] The evaluation department can analyze employees' past performance data to improve the accuracy of evaluations. For example, it can reflect the success rate of past projects in current evaluations. It can also analyze past feedback and adjust evaluation criteria. Furthermore, it can reflect the growth of specific skill sets in evaluations based on past performance data. This improves the accuracy of evaluations by analyzing past performance data. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can input past performance data into a generating AI and have the generating AI perform the task of improving the accuracy of evaluations.

[0042] The evaluation department can conduct evaluations while considering the employee's skill set and career aspirations. For example, the evaluation department can set appropriate evaluation criteria based on the employee's skill set. It can also add evaluation items that align with the employee's career aspirations. Furthermore, the evaluation department can comprehensively evaluate the employee's skill set and career aspirations, conducting evaluations that look ahead to future growth. This allows for more appropriate evaluations by considering skill sets and career aspirations. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can input employee skill set and career aspirations data into a generating AI and have the generating AI perform the evaluation.

[0043] The evaluation unit can perform evaluations while taking into account the geographical location information of employees. For example, if an employee is working remotely, the evaluation unit can set evaluation criteria suitable for the remote environment. Similarly, if an employee is working in the office, the evaluation unit can set evaluation criteria suitable for the office environment. Furthermore, if an employee is on a business trip, the evaluation unit can set evaluation criteria that take into account the environment of the business trip location. This allows for more appropriate evaluations by considering geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input employee geographical location data into a generating AI and have the generating AI perform the evaluation.

[0044] The evaluation department can analyze employees' social media activities during evaluations and incorporate relevant data into the evaluation. For example, the evaluation department can reflect an employee's professional activities on social media in the evaluation. It can also reflect an employee's communication skills on social media in the evaluation. Furthermore, it can reflect an employee's leadership activities on social media in the evaluation. This allows for more appropriate evaluations by analyzing social media activities. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can input employee social media activity data into a generating AI and have the generating AI perform the evaluation.

[0045] The bias removal unit can optimize the algorithm by referring to past evaluation data during bias removal. For example, the bias removal unit can adjust the bias removal algorithm based on past evaluation data. The bias removal unit can also detect specific biases from past evaluation data and optimize the algorithm. Furthermore, the bias removal unit can analyze past evaluation data to improve the accuracy of the bias removal algorithm. As a result, the bias removal algorithm is optimized by referring to past evaluation data. Some or all of the above processing in the bias removal unit may be performed using AI, for example, or without AI. For example, the bias removal unit can input past evaluation data into a generating AI and have the generating AI perform algorithm optimization.

[0046] The bias removal unit can perform bias removal while considering the evaluator's attribute information. For example, the bias removal unit can adjust the bias removal algorithm by considering the evaluator's age and gender. The bias removal unit can also optimize the bias removal algorithm by considering the evaluator's work experience. Furthermore, the bias removal unit can adjust the bias removal algorithm by considering the evaluator's department. This makes it possible to perform more appropriate bias removal by considering the evaluator's attribute information. Some or all of the above processing in the bias removal unit may be performed using AI, for example, or without using AI. For example, the bias removal unit can input evaluator attribute information data into a generating AI and have the generating AI perform bias removal.

[0047] The bias removal unit can perform bias removal while considering the evaluator's geographical location information. For example, if the evaluator is working remotely, the bias removal unit can apply a bias removal algorithm suitable for the remote environment. If the evaluator is working in the office, the bias removal unit can apply a bias removal algorithm suitable for the office environment. Furthermore, if the evaluator is on a business trip, the bias removal unit can apply a bias removal algorithm that takes into account the environment of the business trip destination. This allows for more appropriate bias removal by considering geographical location information. Some or all of the above processing in the bias removal unit may be performed using AI, for example, or without AI. For example, the bias removal unit can input the evaluator's geographical location information data into a generating AI and have the generating AI perform bias removal.

[0048] The bias removal unit can analyze the evaluator's social media activities during bias removal and incorporate relevant data into the removal process. For example, the bias removal unit can incorporate the evaluator's professional activities on social media into the bias removal process. It can also incorporate the evaluator's communication skills on social media into the bias removal process. Furthermore, it can incorporate the evaluator's leadership activities on social media into the bias removal process. This allows for more appropriate bias removal by analyzing social media activities. Some or all of the above processing in the bias removal unit may be performed using AI, for example, or without AI. For example, the bias removal unit can input the evaluator's social media activity data into a generating AI and have the generating AI perform bias removal.

[0049] The monitoring unit can improve the accuracy of monitoring by referring to employees' past performance data during monitoring. For example, the monitoring unit can reflect the success rate of past projects in current monitoring. The monitoring unit can also analyze past feedback and adjust monitoring criteria. Furthermore, the monitoring unit can reflect the growth of specific skill sets in monitoring based on past performance data. This improves the accuracy of monitoring by referring to past performance data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input past performance data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0050] The monitoring unit can perform monitoring while considering the employee's skill set and career aspirations. For example, the monitoring unit can set appropriate monitoring criteria based on the employee's skill set. It can also add monitoring items that align with the employee's career aspirations. Furthermore, the monitoring unit can comprehensively monitor the employee's skill set and career aspirations, conducting monitoring with a view to future growth. This allows for more appropriate monitoring by considering skill sets and career aspirations. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input employee skill set and career aspiration data into a generating AI and have the generating AI perform the monitoring.

[0051] The monitoring unit can perform monitoring while taking into account the geographical location information of employees. For example, if an employee is working remotely, the monitoring unit can set monitoring criteria appropriate for the remote environment. Similarly, if an employee is working in the office, the monitoring unit can set monitoring criteria appropriate for the office environment. Furthermore, if an employee is on a business trip, the monitoring unit can set monitoring criteria that take into account the environment at the destination. This allows for more appropriate monitoring by considering geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input employee geographical location data into a generating AI and have the generating AI perform the monitoring.

[0052] The monitoring unit can analyze employees' social media activities during monitoring and reflect the relevant data in the monitoring process. For example, the monitoring unit can reflect employees' professional activities on social media in the monitoring. It can also reflect employees' communication skills on social media in the monitoring. Furthermore, the monitoring unit can reflect employees' leadership activities on social media in the monitoring. This allows for more appropriate monitoring by analyzing social media activities. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input employee social media activity data into a generating AI and have the generating AI perform the monitoring.

[0053] The feedback unit can improve the accuracy of feedback by referring to the employee's past performance data during the feedback process. For example, the feedback unit can reflect the success rate of past projects in current feedback. The feedback unit can also analyze past feedback content to improve its accuracy. Furthermore, the feedback unit can reflect the growth of specific skill sets in feedback based on past performance data. This improves the accuracy of feedback by referring to past performance data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input past performance data into a generating AI and have the generating AI perform the task of improving the accuracy of feedback.

[0054] The feedback unit can provide feedback while considering the employee's skill set and career aspirations. For example, the feedback unit can provide appropriate feedback based on the employee's skill set. It can also add feedback items that align with the employee's career aspirations. Furthermore, the feedback unit can provide comprehensive feedback that considers the employee's skill set and career aspirations, and provides feedback that looks ahead to future growth. This allows for more appropriate feedback by considering skill sets and career aspirations. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input employee skill set and career aspirations data into a generating AI and have the generating AI execute the feedback.

[0055] The feedback unit can provide feedback while considering the employee's geographical location. For example, if an employee is working remotely, the feedback unit can provide feedback appropriate for the remote environment. Similarly, if an employee is working in the office, the feedback unit can provide feedback appropriate for the office environment. Furthermore, if an employee is on a business trip, the feedback unit can provide feedback that takes into account the environment of their business trip destination. This allows for more appropriate feedback by considering geographical location. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the employee's geographical location data into a generating AI and have the generating AI execute the feedback.

[0056] The feedback department can analyze employees' social media activity and incorporate relevant data into the feedback. For example, it can reflect an employee's professional activities on social media in the feedback. It can also reflect an employee's communication skills on social media in the feedback. Furthermore, it can reflect an employee's leadership activities on social media in the feedback. This allows for more appropriate feedback by analyzing social media activity. Some or all of the above processing in the feedback department may be performed using AI, for example, or not. For example, the feedback department can input employee social media activity data into a generating AI and have the generating AI generate the feedback.

[0057] The goal-setting unit can improve the accuracy of goals by referring to employees' past performance data when setting goals. For example, the goal-setting unit can reflect the success rate of past projects in current goal setting. It can also analyze past feedback to improve the accuracy of goal setting. Furthermore, the goal-setting unit can reflect the growth of specific skill sets in goal setting based on past performance data. In this way, the accuracy of goals is improved by referring to past performance data. Some or all of the above processes in the goal-setting unit may be performed using AI, for example, or not using AI. For example, the goal-setting unit can input past performance data into a generating AI and have the generating AI perform the task of improving the accuracy of goal setting.

[0058] The goal-setting unit can set goals while taking into account the geographical location information of employees. For example, if an employee is working remotely, the goal-setting unit can set goals that are appropriate for the remote environment. Similarly, if an employee is working in the office, the goal-setting unit can set goals that are appropriate for the office environment. Furthermore, if an employee is on a business trip, the goal-setting unit can set goals that take into account the environment of the destination. This allows for more appropriate goal setting by considering geographical location information. Some or all of the above-described processes in the goal-setting unit may be performed using AI, for example, or without AI. For example, the goal-setting unit can input employee geographical location data into a generating AI and have the generating AI perform goal setting.

[0059] The emotion analysis unit can improve the accuracy of its analysis by referring to the employee's past emotional data during the analysis process. For example, the emotion analysis unit can reflect past emotional data in the current emotional analysis. It can also detect specific emotional patterns from past emotional data to improve the accuracy of the analysis. Furthermore, the emotion analysis unit can analyze past emotional data to improve the accuracy of the emotion analysis algorithm. As a result, the accuracy of the analysis is improved by referring to past emotional data. Some or all of the above processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input past emotional data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0060] The emotion analysis unit can perform emotion analysis while considering the employee's geographical location information. For example, if an employee is working remotely, the emotion analysis unit can perform emotion analysis appropriate for the remote environment. Similarly, if an employee is working in the office, the emotion analysis unit can perform emotion analysis appropriate for the office environment. Furthermore, if an employee is on a business trip, the emotion analysis unit can perform emotion analysis considering the environment of the business trip destination. This allows for more appropriate emotion analysis by considering geographical location information. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the employee's geographical location data into a generating AI and have the generating AI perform the emotion analysis.

[0061] The stress monitoring unit can improve the accuracy of stress monitoring by referring to the employee's past stress data during stress monitoring. For example, the stress monitoring unit can reflect past stress data in the current stress monitoring. The stress monitoring unit can also detect specific stress patterns from past stress data and improve the accuracy of monitoring. Furthermore, the stress monitoring unit can analyze past stress data and improve the accuracy of the stress monitoring algorithm. As a result, the accuracy of monitoring is improved by referring to past stress data. Some or all of the above processes in the stress monitoring unit may be performed using AI, for example, or without using AI. For example, the stress monitoring unit can input past stress data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0062] The stress monitoring unit can perform stress monitoring while taking into account the employee's geographical location information. For example, if an employee is working remotely, the stress monitoring unit can perform stress monitoring appropriate for the remote environment. It can also perform stress monitoring appropriate for the office environment if the employee is working in the office. Furthermore, if an employee is on a business trip, the stress monitoring unit can perform stress monitoring that takes into account the environment of the business trip destination. This allows for more appropriate stress monitoring by considering geographical location information. Some or all of the above processing in the stress monitoring unit may be performed using AI, for example, or without AI. For example, the stress monitoring unit can input the employee's geographical location data into a generating AI and have the generating AI perform stress monitoring.

[0063] The skill matching unit can improve the accuracy of skill matching by referring to the employee's past skill data during the skill matching process. For example, the skill matching unit can reflect past skill data in the current skill matching. The skill matching unit can also detect specific skill sets from past skill data and improve the accuracy of matching. Furthermore, the skill matching unit can analyze past skill data and improve the accuracy of the skill matching algorithm. As a result, the accuracy of matching is improved by referring to past skill data. Some or all of the above processes in the skill matching unit may be performed using AI, for example, or without AI. For example, the skill matching unit can input past skill data into a generating AI and have the generating AI perform the matching accuracy improvement.

[0064] The skill matching unit can perform skill matching while considering the employee's career aspirations. For example, the skill matching unit can perform appropriate skill matching based on the employee's career aspirations. The skill matching unit can also prioritize matching with skill sets that align with the employee's career aspirations. Furthermore, the skill matching unit can comprehensively consider the employee's career aspirations and perform skill matching with a view to future growth. This allows for more appropriate skill matching by considering career aspirations. Some or all of the above processes in the skill matching unit may be performed using AI, for example, or without AI. For example, the skill matching unit can input employee career aspiration data into a generating AI and have the generating AI perform skill matching.

[0065] The skill matching unit can perform skill matching while considering the geographical location information of employees. For example, if an employee is working remotely, the skill matching unit can perform skill matching suitable for the remote environment. Similarly, if an employee is working in the office, the skill matching unit can perform skill matching suitable for the office environment. Furthermore, if an employee is on a business trip, the skill matching unit can perform skill matching considering the environment of the business trip destination. This allows for more appropriate skill matching by considering geographical location information. Some or all of the above processing in the skill matching unit may be performed using AI, for example, or without AI. For example, the skill matching unit can input employee geographical location data into a generating AI and have the generating AI perform skill matching.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] Human resource management systems can collect employee health data and adjust performance evaluation criteria based on their health status. For example, if an employee is diagnosed with high blood pressure during a health checkup, the evaluation criteria can be relaxed to prioritize health improvement. Conversely, if an employee is in good health, the standard evaluation criteria can be applied to accurately assess their performance. Furthermore, if an employee is in excellent health, the evaluation criteria can be tightened to provide feedback that encourages further growth. This allows for more appropriate evaluations by dynamically adjusting evaluation criteria based on employee health status.

[0068] Human resource management systems can set goals that take into account employees' hobbies and interests. For example, if an employee is interested in sports, they can set a goal to participate in a sports-related project. Similarly, if an employee is interested in art, they can set a goal to participate in a creative project. Furthermore, if an employee is interested in technology, they can set a goal to learn new technologies. This allows for goal setting that is more motivating by considering employees' hobbies and interests.

[0069] Human resource management systems can analyze employees' past project data to improve the accuracy of performance evaluations. For example, they can reflect the success rate of past projects in current evaluations. They can also analyze feedback from past projects and adjust evaluation criteria. Furthermore, they can reflect the growth of specific skill sets in evaluations based on past project data. In this way, analyzing past project data improves the accuracy of performance evaluations.

[0070] Human resource management systems can tailor feedback to employees' geographical locations. For example, if an employee is working remotely, feedback appropriate for the remote environment can be provided. Similarly, if an employee is working in the office, feedback appropriate for the office environment can be provided. Furthermore, if an employee is traveling, feedback that takes into account the environment of their destination can be provided. This allows for more appropriate feedback by considering geographical location.

[0071] Human resource management systems can analyze employees' social media activities and incorporate them into performance evaluations. For example, they can reflect an employee's professional activities on social media, their communication skills on social media, and even their leadership activities on social media. This allows for more accurate evaluations by analyzing social media activity.

[0072] Human resource management systems can perform stress monitoring while considering employees' geographical location. For example, if an employee is working remotely, stress monitoring can be optimized for the remote environment. Similarly, if an employee is working in the office, stress monitoring can be optimized for the office environment. Furthermore, if an employee is on a business trip, stress monitoring can be optimized for the environment at their destination. This allows for more appropriate stress monitoring by considering geographical location.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The evaluation department conducts multidimensional evaluations. The evaluation department can evaluate employees in dimensions such as technical skills, soft skills, and performance. For example, to evaluate technical skills, they can assess employees' technical knowledge and abilities; to evaluate soft skills, they can assess employees' communication skills and teamwork; and to evaluate performance, they can assess employees' performance indicators and results. Step 2: The removal unit removes bias based on the data obtained by the evaluation unit. For example, it removes gender information from the evaluation data to remove gender bias, removes age information from the evaluation data to remove age bias, and standardizes the evaluation data to remove the evaluator's subjective bias. Step 3: The monitoring unit monitors performance based on data from which bias has been removed by the elimination unit. For example, it monitors employee performance data in real time to monitor performance indicators, employee behavior data in real time to monitor behavioral indicators, and employee feedback data in real time to monitor feedback. Step 4: The feedback unit provides feedback based on the data monitored by the monitoring unit. For example, it provides numerical feedback based on employee performance data to provide quantitative feedback, textual feedback based on employee behavior data to provide qualitative feedback, and immediate feedback based on employee performance data to provide real-time feedback.

[0075] (Example of form 2) The human resource management system according to an embodiment of the present invention is a system that uses AI to perform multidimensional evaluation and bias removal, providing objective and fair evaluations. This system achieves management efficiency through real-time performance monitoring and feedback. It also supports individual growth by setting personalized goals and making dynamic adjustments based on each employee's career aspirations and skills. Furthermore, it constantly monitors employee well-being through emotion analysis and stress monitoring functions, and responds immediately when problems arise. It also enables optimal personnel placement utilizing skill matching, maximizing the overall performance of the organization. For example, AI performs multidimensional evaluation and removes bias. Next, it achieves management efficiency through real-time performance monitoring and feedback. Furthermore, it supports individual growth by setting personalized goals and making dynamic adjustments based on each employee's career aspirations and skills. It constantly monitors employee well-being through emotion analysis and stress monitoring functions, and responds immediately when problems arise. It also enables optimal personnel placement utilizing skill matching, maximizing the overall performance of the organization. This provides fair and efficient future-oriented human resource management. We envision a future where companies and employees grow together by innovating human resource management with the power of AI. Eliminate human bias and create a fair workplace based on talent and effort. Maximize the potential of each employee and provide an environment where innovation thrives. Enhance corporate competitiveness, create an ideal workplace where employees feel fulfilled, and redefine the future of work. This will enable human resource management systems to evaluate employees objectively and fairly.

[0076] The human resource management system according to this embodiment comprises an evaluation unit, a filtering unit, a monitoring unit, and a feedback unit. The evaluation unit performs multidimensional evaluation. The evaluation unit can perform evaluations in dimensions such as technical skills, soft skills, and performance. For example, to evaluate technical skills, the evaluation unit evaluates an employee's technical knowledge and abilities. The evaluation unit can also evaluate an employee's communication skills and teamwork to evaluate soft skills. The evaluation unit can also evaluate an employee's performance indicators and results to evaluate performance. The filtering unit removes bias based on the data obtained by the evaluation unit. For example, to remove gender bias, the filtering unit removes information about gender from the evaluation data. The filtering unit can also remove information about age from the evaluation data to remove age bias. The filtering unit can also standardize the evaluation data to remove the evaluator's subjective bias. The monitoring unit monitors performance based on the data from which bias has been removed by the filtering unit. The monitoring unit monitors employee performance data in real time, for example, to monitor performance indicators. Furthermore, the monitoring unit can monitor employee behavioral data in real time to monitor behavioral indicators. The monitoring unit can also monitor employee feedback data in real time to monitor feedback. The feedback unit provides feedback based on the data monitored by the monitoring unit. For example, the feedback unit provides numerical feedback based on employee performance data to provide quantitative feedback. The feedback unit can also provide textual feedback based on employee behavioral data to provide qualitative feedback. Furthermore, the feedback unit can provide immediate feedback based on employee performance data to provide real-time feedback. This enables the human resource management system according to the embodiment to perform multidimensional evaluation, bias reduction, performance monitoring, and feedback provision.

[0077] The evaluation department conducts multidimensional evaluations. For example, it can evaluate employees across dimensions such as technical skills, soft skills, and performance. Specifically, in evaluating technical skills, it analyzes technical tests and project deliverables to assess employees' expertise and technical capabilities in detail. For instance, to evaluate programming skills, it conducts code reviews and tests to measure algorithmic understanding. In evaluating soft skills, it collects feedback from colleagues and supervisors and conducts 360-degree evaluations to assess employees' communication and teamwork skills. This allows for understanding how effectively employees communicate and how collaborative relationships function within teams. Furthermore, in performance evaluations, it uses KPIs (Key Performance Indicators) and OKRs (Objectives and Key Results) to evaluate employee performance indicators and outcomes. This allows for the quantification and evaluation of specific results, such as the extent to which employees are achieving set goals. The evaluation department integrates these multidimensional evaluations to generate comprehensive evaluation results. This clarifies employees' strengths and areas for improvement and provides foundational data for developing individual career development plans.

[0078] The bias removal unit removes bias based on the data obtained by the evaluation unit. Specifically, to remove gender bias, it removes information about gender from the evaluation data. For example, by anonymizing the evaluation data and processing it in a way that does not include information about gender, it prevents evaluators from unconsciously holding gender-based biases. It can also remove information about age from the evaluation data to remove age bias. This prevents preconceptions about age from influencing evaluations. Furthermore, to remove subjective bias of evaluators, the evaluation data is standardized. For example, to correct for variations in evaluation criteria among evaluators, evaluation scores are normalized using statistical methods. This ensures consistency in evaluations among evaluators and enables fair evaluations. The bias removal unit automates these bias removal processes, improving the reliability and fairness of evaluation data. This provides an environment where employees are evaluated fairly and enhances the overall reliability of the organization.

[0079] The monitoring unit monitors performance based on data from which bias has been removed by the bias removal unit. Specifically, it monitors employee performance data in real time to monitor performance indicators. For example, it regularly collects performance indicators such as sales, productivity, and project progress and displays them on a dashboard so that managers can quickly grasp the situation. It can also monitor employee behavioral data in real time to monitor behavioral indicators. For example, it collects behavioral data such as attendance time, work time, and break time to analyze employee work patterns. Furthermore, it can monitor employee feedback data in real time to monitor feedback. For example, it collects feedback from supervisors and colleagues and continuously tracks evaluations of employee performance. The monitoring unit integrates this data to comprehensively evaluate employee performance. This allows for real-time identification of employee strengths and areas for improvement, enabling the provision of appropriate support and guidance. In addition, the monitoring unit uses anomaly detection algorithms to detect unusual performance patterns and abnormal behavior early and respond quickly. This allows for continuous optimization of employee performance and improvement of overall organizational efficiency and productivity.

[0080] The Feedback Department provides feedback based on data monitored by the Monitoring Department. Specifically, to provide quantitative feedback, it provides numerical feedback based on employee performance data. For example, by providing feedback using specific numbers such as the degree of achievement of sales targets or the rate of productivity improvement, employees can objectively understand their own performance. In addition, to provide qualitative feedback, it can also provide written feedback based on employee behavior data. For example, it can provide specific advice on areas for improvement in communication skills or teamwork. Furthermore, to provide real-time feedback, the Feedback Department can also provide immediate feedback based on employee performance data. For example, by providing feedback at the appropriate time according to the progress of a project, employees can quickly grasp areas for improvement and modify their behavior. The Feedback Department customizes this feedback individually, providing specific advice tailored to the employee's needs and goals. This allows employees to obtain specific guidance for continuously improving their own performance. The Feedback Department also continuously evaluates the effectiveness of the feedback and improves the content and methods of feedback as needed. In this way, the Feedback Department can play a vital role in supporting employee growth and improving the overall performance of the organization.

[0081] The human resources management system includes a goal-setting unit that sets goals based on career aspirations and skills. The goal-setting unit can, for example, set short-term goals based on an employee's career aspirations. It can also set long-term goals based on an employee's career aspirations. Furthermore, it can set goals based on an employee's career path. This enables goal setting based on career aspirations and skills. Some or all of the above-described processes in the goal-setting unit may be performed using AI, for example, or without AI. For example, the goal-setting unit can input employee career aspiration data into a generating AI and have the generating AI perform goal setting.

[0082] The human resources management system includes an emotion analysis unit that performs emotion analysis. The emotion analysis unit can, for example, analyze employee facial expression data to perform facial expression analysis. It can also analyze employee voice data to perform voice analysis. It can also analyze employee text data to perform text analysis. This enables emotion analysis. Some or all of the above-described processes in the emotion analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion analysis unit can input employee facial expression data into a generative AI and have the generative AI perform emotion analysis.

[0083] The human resources management system includes a stress monitoring unit that performs stress monitoring. The stress monitoring unit can, for example, monitor employees' heart rate and skin electrical activity to monitor physiological indicators. It can also monitor employees' self-reported data to monitor psychological indicators. Furthermore, it can monitor employees' behavioral data to monitor behavioral indicators. This enables stress monitoring. Some or all of the above-described processes in the stress monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the stress monitoring unit can input employee physiological indicator data into a generative AI and have the generative AI perform stress monitoring.

[0084] The human resources management system includes a skill matching unit that performs skill matching. The skill matching unit can, for example, analyze employee skill data to evaluate the degree of match in skill sets. It can also analyze employee experience data to evaluate years of experience. Furthermore, it can analyze employee aptitude test data to evaluate aptitude test results. This enables skill matching. Some or all of the above-described processes in the skill matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the skill matching unit can input employee skill data into a generative AI and have the generative AI perform skill matching.

[0085] The goal-setting unit can set goals based on data from the evaluation unit. For example, the goal-setting unit can set SMART goals based on data from the evaluation unit. The goal-setting unit can also set OKRs based on data from the evaluation unit. Furthermore, the goal-setting unit can set KPIs based on data from the evaluation unit. This makes it possible to set goals based on data from the evaluation unit. Some or all of the above processes in the goal-setting unit may be performed using AI, for example, or without AI. For example, the goal-setting unit can input data from the evaluation unit into a generation AI and have the generation AI perform goal setting.

[0086] The emotion analysis unit can analyze emotions based on data from the monitoring unit. For example, the emotion analysis unit can perform facial expression analysis based on data from the monitoring unit. The emotion analysis unit can also perform voice analysis based on data from the monitoring unit. Furthermore, the emotion analysis unit can perform text analysis based on data from the monitoring unit. This makes it possible to perform emotion analysis based on data from the monitoring unit. Some or all of the above-described processes in the emotion analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the emotion analysis unit can input data from the monitoring unit into a generative AI and have the generative AI perform emotion analysis.

[0087] The stress monitoring unit can monitor stress based on data from the monitoring unit. For example, the stress monitoring unit can monitor physiological indicators based on data from the monitoring unit. It can also monitor psychological indicators based on data from the monitoring unit. Furthermore, the stress monitoring unit can monitor behavioral indicators based on data from the monitoring unit. This makes it possible to monitor stress based on data from the monitoring unit. Some or all of the above-described processing in the stress monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the stress monitoring unit can input data from the monitoring unit into a generative AI and have the generative AI perform stress monitoring.

[0088] The evaluation unit can estimate an employee's emotions and dynamically adjust evaluation criteria based on those estimated emotions. For example, if an employee is stressed, the evaluation unit may relax the evaluation criteria and prioritize stress reduction. Conversely, if an employee is relaxed, the evaluation unit can apply normal evaluation criteria to accurately assess performance. If an employee is agitated, the evaluation unit may tighten the evaluation criteria and provide feedback to improve focus. This allows for more appropriate evaluations by dynamically adjusting evaluation criteria based on employee emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using or without generative AI. For example, the evaluation unit can input employee emotion data into a generative AI and have the generative AI adjust the evaluation criteria.

[0089] The evaluation department can analyze employees' past performance data to improve the accuracy of evaluations. For example, it can reflect the success rate of past projects in current evaluations. It can also analyze past feedback and adjust evaluation criteria. Furthermore, it can reflect the growth of specific skill sets in evaluations based on past performance data. This improves the accuracy of evaluations by analyzing past performance data. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can input past performance data into a generating AI and have the generating AI perform the task of improving the accuracy of evaluations.

[0090] The evaluation department can conduct evaluations while considering the employee's skill set and career aspirations. For example, the evaluation department can set appropriate evaluation criteria based on the employee's skill set. It can also add evaluation items that align with the employee's career aspirations. Furthermore, the evaluation department can comprehensively evaluate the employee's skill set and career aspirations, conducting evaluations that look ahead to future growth. This allows for more appropriate evaluations by considering skill sets and career aspirations. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can input employee skill set and career aspirations data into a generating AI and have the generating AI perform the evaluation.

[0091] The evaluation unit can estimate an employee's emotions and determine evaluation priorities based on those estimated emotions. For example, if an employee is stressed, the evaluation unit may set a lower evaluation priority and prioritize stress reduction. If an employee is relaxed, the evaluation unit may perform the evaluation with normal priorities. If an employee is excited, the evaluation unit may set a higher evaluation priority and provide feedback to improve their concentration. This allows for more appropriate evaluations by determining evaluation priorities based on employee emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using or without a generative AI. For example, the evaluation unit can input employee emotion data into a generative AI and have the generative AI perform evaluation priority determination.

[0092] The evaluation unit can perform evaluations while taking into account the geographical location information of employees. For example, if an employee is working remotely, the evaluation unit can set evaluation criteria suitable for the remote environment. Similarly, if an employee is working in the office, the evaluation unit can set evaluation criteria suitable for the office environment. Furthermore, if an employee is on a business trip, the evaluation unit can set evaluation criteria that take into account the environment of the business trip location. This allows for more appropriate evaluations by considering geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input employee geographical location data into a generating AI and have the generating AI perform the evaluation.

[0093] The evaluation department can analyze employees' social media activities during evaluations and incorporate relevant data into the evaluation. For example, the evaluation department can reflect an employee's professional activities on social media in the evaluation. It can also reflect an employee's communication skills on social media in the evaluation. Furthermore, it can reflect an employee's leadership activities on social media in the evaluation. This allows for more appropriate evaluations by analyzing social media activities. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can input employee social media activity data into a generating AI and have the generating AI perform the evaluation.

[0094] The bias removal unit can estimate an employee's emotions and adjust the bias removal algorithm based on the estimated emotions. For example, if an employee is stressed, the bias removal unit will relax the bias removal algorithm and prioritize stress reduction. Alternatively, if an employee is relaxed, the unit can apply the normal bias removal algorithm. Furthermore, if an employee is agitated, the unit can tighten the bias removal algorithm and provide feedback to enhance concentration. This allows for more appropriate bias removal by adjusting the bias removal algorithm based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the bias removal unit may be performed using or without a generative AI. For example, the bias removal unit can input employee emotion data into a generative AI and have the generative AI perform the bias removal algorithm adjustment.

[0095] The bias removal unit can optimize the algorithm by referring to past evaluation data during bias removal. For example, the bias removal unit can adjust the bias removal algorithm based on past evaluation data. The bias removal unit can also detect specific biases from past evaluation data and optimize the algorithm. Furthermore, the bias removal unit can analyze past evaluation data to improve the accuracy of the bias removal algorithm. As a result, the bias removal algorithm is optimized by referring to past evaluation data. Some or all of the above processing in the bias removal unit may be performed using AI, for example, or without AI. For example, the bias removal unit can input past evaluation data into a generating AI and have the generating AI perform algorithm optimization.

[0096] The bias removal unit can perform bias removal while considering the evaluator's attribute information. For example, the bias removal unit can adjust the bias removal algorithm by considering the evaluator's age and gender. The bias removal unit can also optimize the bias removal algorithm by considering the evaluator's work experience. Furthermore, the bias removal unit can adjust the bias removal algorithm by considering the evaluator's department. This makes it possible to perform more appropriate bias removal by considering the evaluator's attribute information. Some or all of the above processing in the bias removal unit may be performed using AI, for example, or without using AI. For example, the bias removal unit can input evaluator attribute information data into a generating AI and have the generating AI perform bias removal.

[0097] The bias removal unit can estimate an employee's emotions and determine the priority of bias removal based on the estimated emotions. For example, if an employee is stressed, the bias removal unit may set a lower priority for bias removal and prioritize stress reduction. If an employee is relaxed, the bias removal unit can perform bias removal with the normal priority. If an employee is excited, the bias removal unit may set a higher priority for bias removal and provide feedback to enhance concentration. This allows for more appropriate bias removal by determining the priority of bias removal based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the bias removal unit may be performed using a generative AI, or not. For example, the bias removal unit can input employee emotion data into a generative AI and have the generative AI perform the bias removal priority determination.

[0098] The bias removal unit can perform bias removal while considering the evaluator's geographical location information. For example, if the evaluator is working remotely, the bias removal unit can apply a bias removal algorithm suitable for the remote environment. If the evaluator is working in the office, the bias removal unit can apply a bias removal algorithm suitable for the office environment. Furthermore, if the evaluator is on a business trip, the bias removal unit can apply a bias removal algorithm that takes into account the environment of the business trip destination. This allows for more appropriate bias removal by considering geographical location information. Some or all of the above processing in the bias removal unit may be performed using AI, for example, or without AI. For example, the bias removal unit can input the evaluator's geographical location information data into a generating AI and have the generating AI perform bias removal.

[0099] The bias removal unit can analyze the evaluator's social media activities during bias removal and incorporate relevant data into the removal process. For example, the bias removal unit can incorporate the evaluator's professional activities on social media into the bias removal process. It can also incorporate the evaluator's communication skills on social media into the bias removal process. Furthermore, it can incorporate the evaluator's leadership activities on social media into the bias removal process. This allows for more appropriate bias removal by analyzing social media activities. Some or all of the above processing in the bias removal unit may be performed using AI, for example, or without AI. For example, the bias removal unit can input the evaluator's social media activity data into a generating AI and have the generating AI perform bias removal.

[0100] The monitoring unit can estimate an employee's emotions and adjust monitoring criteria based on the estimated emotions. For example, if an employee is stressed, the monitoring unit may relax the monitoring criteria and prioritize stress reduction. It can also apply normal monitoring criteria if the employee is relaxed. Furthermore, if an employee is agitated, the monitoring unit may tighten the monitoring criteria and provide feedback to improve concentration. This allows for more appropriate monitoring by adjusting monitoring criteria based on employee 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 monitoring unit may be performed using, for example, generative AI, or not. For example, the monitoring unit can input employee emotion data into a generative AI and have the generative AI adjust the monitoring criteria.

[0101] The monitoring unit can improve the accuracy of monitoring by referring to employees' past performance data during monitoring. For example, the monitoring unit can reflect the success rate of past projects in current monitoring. The monitoring unit can also analyze past feedback and adjust monitoring criteria. Furthermore, the monitoring unit can reflect the growth of specific skill sets in monitoring based on past performance data. This improves the accuracy of monitoring by referring to past performance data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input past performance data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0102] The monitoring unit can perform monitoring while considering the employee's skill set and career aspirations. For example, the monitoring unit can set appropriate monitoring criteria based on the employee's skill set. It can also add monitoring items that align with the employee's career aspirations. Furthermore, the monitoring unit can comprehensively monitor the employee's skill set and career aspirations, conducting monitoring with a view to future growth. This allows for more appropriate monitoring by considering skill sets and career aspirations. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input employee skill set and career aspiration data into a generating AI and have the generating AI perform the monitoring.

[0103] The monitoring unit can estimate an employee's emotions and determine monitoring priorities based on those estimates. For example, if an employee is stressed, the monitoring unit can set a lower monitoring priority and prioritize stress reduction. Conversely, if an employee is relaxed, the monitoring unit can perform monitoring at the normal priority level. Furthermore, if an employee is agitated, the monitoring unit can set a higher monitoring priority and provide feedback to enhance their concentration. This allows for more appropriate monitoring by determining monitoring priorities based on employee 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-described processes in the monitoring unit may be performed using, for example, generative AI, or not. For example, the monitoring unit can input employee emotion data into a generative AI and have the generative AI determine monitoring priorities.

[0104] The monitoring unit can perform monitoring while taking into account the geographical location information of employees. For example, if an employee is working remotely, the monitoring unit can set monitoring criteria appropriate for the remote environment. Similarly, if an employee is working in the office, the monitoring unit can set monitoring criteria appropriate for the office environment. Furthermore, if an employee is on a business trip, the monitoring unit can set monitoring criteria that take into account the environment at the destination. This allows for more appropriate monitoring by considering geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input employee geographical location data into a generating AI and have the generating AI perform the monitoring.

[0105] The monitoring unit can analyze employees' social media activities during monitoring and reflect the relevant data in the monitoring process. For example, the monitoring unit can reflect employees' professional activities on social media in the monitoring. It can also reflect employees' communication skills on social media in the monitoring. Furthermore, the monitoring unit can reflect employees' leadership activities on social media in the monitoring. This allows for more appropriate monitoring by analyzing social media activities. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input employee social media activity data into a generating AI and have the generating AI perform the monitoring.

[0106] The feedback unit can estimate an employee's emotions and adjust the way feedback is expressed based on those emotions. For example, if an employee is stressed, the feedback unit can provide feedback in gentle terms. If an employee is relaxed, the feedback unit can provide detailed feedback. If an employee is excited, the feedback unit can provide feedback that includes many words of encouragement. By adjusting the way feedback is expressed based on the employee's emotions, more appropriate feedback becomes possible. Emotion estimation is achieved using an emotion estimation function, such as 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 feedback unit may be performed using or without a generative AI. For example, the feedback unit can input employee emotion data into a generative AI and have the generative AI adjust the way feedback is expressed.

[0107] The feedback unit can improve the accuracy of feedback by referring to the employee's past performance data during the feedback process. For example, the feedback unit can reflect the success rate of past projects in current feedback. The feedback unit can also analyze past feedback content to improve its accuracy. Furthermore, the feedback unit can reflect the growth of specific skill sets in feedback based on past performance data. This improves the accuracy of feedback by referring to past performance data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input past performance data into a generating AI and have the generating AI perform the task of improving the accuracy of feedback.

[0108] The feedback unit can provide feedback while considering the employee's skill set and career aspirations. For example, the feedback unit can provide appropriate feedback based on the employee's skill set. It can also add feedback items that align with the employee's career aspirations. Furthermore, the feedback unit can provide comprehensive feedback that considers the employee's skill set and career aspirations, and provides feedback that looks ahead to future growth. This allows for more appropriate feedback by considering skill sets and career aspirations. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input employee skill set and career aspirations data into a generating AI and have the generating AI execute the feedback.

[0109] The feedback unit can estimate an employee's emotions and determine the priority of feedback based on those emotions. For example, if an employee is stressed, the feedback unit may set a lower priority for feedback, prioritizing stress reduction. If an employee is relaxed, the feedback unit can provide feedback with the normal priority. If an employee is agitated, the feedback unit may set a higher priority for feedback, providing feedback to improve concentration. This allows for more appropriate feedback by determining the priority of feedback based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using, for example, generative AI, or not using generative AI. For example, the feedback unit can input employee emotion data into a generative AI and have the generative AI perform the task of determining the priority of feedback.

[0110] The feedback unit can provide feedback while considering the employee's geographical location. For example, if an employee is working remotely, the feedback unit can provide feedback appropriate for the remote environment. Similarly, if an employee is working in the office, the feedback unit can provide feedback appropriate for the office environment. Furthermore, if an employee is on a business trip, the feedback unit can provide feedback that takes into account the environment of their business trip destination. This allows for more appropriate feedback by considering geographical location. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the employee's geographical location data into a generating AI and have the generating AI execute the feedback.

[0111] The feedback department can analyze employees' social media activity and incorporate relevant data into the feedback. For example, it can reflect an employee's professional activities on social media in the feedback. It can also reflect an employee's communication skills on social media in the feedback. Furthermore, it can reflect an employee's leadership activities on social media in the feedback. This allows for more appropriate feedback by analyzing social media activity. Some or all of the above processing in the feedback department may be performed using AI, for example, or not. For example, the feedback department can input employee social media activity data into a generating AI and have the generating AI generate the feedback.

[0112] The goal-setting unit can estimate an employee's emotions and adjust goal-setting criteria based on those emotions. For example, if an employee is stressed, the goal-setting unit may relax the goal-setting criteria and prioritize stress reduction. Alternatively, if an employee is relaxed, the goal-setting unit can apply normal goal-setting criteria. Furthermore, if an employee is agitated, the goal-setting unit may tighten the goal-setting criteria and set goals to improve concentration. This allows for more appropriate goal setting by adjusting goal-setting criteria based on employee 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-described processes in the goal-setting unit may be performed using, for example, a generative AI, or not. For example, the goal-setting unit can input employee emotion data into a generative AI and have the generative AI adjust the goal-setting criteria.

[0113] The goal-setting unit can improve the accuracy of goals by referring to employees' past performance data when setting goals. For example, the goal-setting unit can reflect the success rate of past projects in current goal setting. It can also analyze past feedback to improve the accuracy of goal setting. Furthermore, the goal-setting unit can reflect the growth of specific skill sets in goal setting based on past performance data. In this way, the accuracy of goals is improved by referring to past performance data. Some or all of the above processes in the goal-setting unit may be performed using AI, for example, or not using AI. For example, the goal-setting unit can input past performance data into a generating AI and have the generating AI perform the task of improving the accuracy of goal setting.

[0114] The goal-setting unit can estimate an employee's emotions and determine the priority of goal setting based on those emotions. For example, if an employee is stressed, the goal-setting unit may set a lower priority for goal setting and prioritize stress reduction. If an employee is relaxed, the goal-setting unit can set goals with normal priorities. If an employee is excited, the goal-setting unit may set a higher priority for goal setting and set goals to improve concentration. This allows for more appropriate goal setting by determining the priority of goal setting based on the employee's 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 goal-setting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the goal-setting unit can input employee emotion data into a generative AI and have the generative AI perform goal setting priority determination.

[0115] The goal-setting unit can set goals while taking into account the geographical location information of employees. For example, if an employee is working remotely, the goal-setting unit can set goals that are appropriate for the remote environment. Similarly, if an employee is working in the office, the goal-setting unit can set goals that are appropriate for the office environment. Furthermore, if an employee is on a business trip, the goal-setting unit can set goals that take into account the environment of the destination. This allows for more appropriate goal setting by considering geographical location information. Some or all of the above-described processes in the goal-setting unit may be performed using AI, for example, or without AI. For example, the goal-setting unit can input employee geographical location data into a generating AI and have the generating AI perform goal setting.

[0116] The emotion analysis unit can estimate an employee's emotions and adjust the analysis criteria based on the estimated emotions. For example, if an employee is stressed, the emotion analysis unit may relax the analysis criteria and prioritize stress reduction. It can also apply the normal analysis criteria if the employee is relaxed. Furthermore, if an employee is agitated, the emotion analysis unit may tighten the analysis criteria and provide feedback to improve concentration. This allows for more appropriate analysis by adjusting the analysis criteria based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the emotion analysis unit may be performed using or without a generative AI. For example, the emotion analysis unit can input employee emotion data into a generative AI and have the generative AI adjust the analysis criteria.

[0117] The emotion analysis unit can improve the accuracy of its analysis by referring to the employee's past emotional data during the analysis process. For example, the emotion analysis unit can reflect past emotional data in the current emotional analysis. It can also detect specific emotional patterns from past emotional data to improve the accuracy of the analysis. Furthermore, the emotion analysis unit can analyze past emotional data to improve the accuracy of the emotion analysis algorithm. As a result, the accuracy of the analysis is improved by referring to past emotional data. Some or all of the above processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input past emotional data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0118] The emotion analysis unit can estimate an employee's emotions and determine the priority of analysis based on the estimated emotions. For example, if an employee is stressed, the emotion analysis unit can set a low priority for emotion analysis and prioritize stress reduction. If an employee is relaxed, the emotion analysis unit can perform emotion analysis with the normal priority. Furthermore, if an employee is agitated, the emotion analysis unit can set a high priority for emotion analysis and provide feedback to enhance concentration. This allows for more appropriate analysis by determining the priority of analysis based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the emotion analysis unit may be performed using, for example, a generative AI, or not. For example, the emotion analysis unit can input employee emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0119] The emotion analysis unit can perform emotion analysis while considering the employee's geographical location information. For example, if an employee is working remotely, the emotion analysis unit can perform emotion analysis appropriate for the remote environment. Similarly, if an employee is working in the office, the emotion analysis unit can perform emotion analysis appropriate for the office environment. Furthermore, if an employee is on a business trip, the emotion analysis unit can perform emotion analysis considering the environment of the business trip destination. This allows for more appropriate emotion analysis by considering geographical location information. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the employee's geographical location data into a generating AI and have the generating AI perform the emotion analysis.

[0120] The stress monitoring unit can estimate an employee's emotions and adjust stress monitoring criteria based on those estimated emotions. For example, if an employee is feeling stressed, the stress monitoring unit can relax the stress monitoring criteria and prioritize stress reduction. It can also apply normal stress monitoring criteria if the employee is relaxed. Furthermore, if an employee is agitated, the stress monitoring unit can tighten the stress monitoring criteria and provide feedback to improve concentration. This allows for more appropriate stress monitoring by adjusting stress monitoring criteria based on 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-described processes in the stress monitoring unit may be performed using, for example, generative AI, or not. For example, the stress monitoring unit can input employee emotion data into a generative AI and have the generative AI adjust the stress monitoring criteria.

[0121] The stress monitoring unit can improve the accuracy of stress monitoring by referring to the employee's past stress data during stress monitoring. For example, the stress monitoring unit can reflect past stress data in the current stress monitoring. The stress monitoring unit can also detect specific stress patterns from past stress data and improve the accuracy of monitoring. Furthermore, the stress monitoring unit can analyze past stress data and improve the accuracy of the stress monitoring algorithm. As a result, the accuracy of monitoring is improved by referring to past stress data. Some or all of the above processes in the stress monitoring unit may be performed using AI, for example, or without using AI. For example, the stress monitoring unit can input past stress data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0122] The stress monitoring unit can estimate an employee's emotions and determine the priority of stress monitoring based on the estimated emotions. For example, if an employee is feeling stressed, the stress monitoring unit can set a lower priority for stress monitoring and prioritize stress reduction. The stress monitoring unit can also perform stress monitoring at the normal priority level if the employee is relaxed. Furthermore, if an employee is agitated, the stress monitoring unit can set a higher priority for stress monitoring and provide feedback to improve concentration. This allows for more appropriate stress monitoring by determining the priority of stress monitoring based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the stress monitoring unit may be performed using, for example, a generative AI, or not. For example, the stress monitoring unit can input employee emotion data into a generative AI and have the generative AI perform stress monitoring priority determination.

[0123] The stress monitoring unit can perform stress monitoring while taking into account the employee's geographical location information. For example, if an employee is working remotely, the stress monitoring unit can perform stress monitoring appropriate for the remote environment. It can also perform stress monitoring appropriate for the office environment if the employee is working in the office. Furthermore, if an employee is on a business trip, the stress monitoring unit can perform stress monitoring that takes into account the environment of the business trip destination. This allows for more appropriate stress monitoring by considering geographical location information. Some or all of the above processing in the stress monitoring unit may be performed using AI, for example, or without AI. For example, the stress monitoring unit can input the employee's geographical location data into a generating AI and have the generating AI perform stress monitoring.

[0124] The skill matching unit can estimate an employee's emotions and adjust the skill matching criteria based on those emotions. For example, if an employee is stressed, the skill matching unit may relax the skill matching criteria and prioritize stress reduction. It can also apply the normal skill matching criteria if the employee is relaxed. Furthermore, if an employee is excited, the skill matching unit may tighten the skill matching criteria to facilitate matching that enhances concentration. This allows for more appropriate skill matching by adjusting the skill matching criteria based on 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-described processes in the skill matching unit may be performed using, for example, a generative AI, or not. For example, the skill matching unit can input employee emotion data into a generative AI and have the generative AI adjust the skill matching criteria.

[0125] The skill matching unit can improve the accuracy of skill matching by referring to the employee's past skill data during the skill matching process. For example, the skill matching unit can reflect past skill data in the current skill matching. The skill matching unit can also detect specific skill sets from past skill data and improve the accuracy of matching. Furthermore, the skill matching unit can analyze past skill data and improve the accuracy of the skill matching algorithm. As a result, the accuracy of matching is improved by referring to past skill data. Some or all of the above processes in the skill matching unit may be performed using AI, for example, or without AI. For example, the skill matching unit can input past skill data into a generating AI and have the generating AI perform the matching accuracy improvement.

[0126] The skill matching unit can perform skill matching while considering the employee's career aspirations. For example, the skill matching unit can perform appropriate skill matching based on the employee's career aspirations. The skill matching unit can also prioritize matching with skill sets that align with the employee's career aspirations. Furthermore, the skill matching unit can comprehensively consider the employee's career aspirations and perform skill matching with a view to future growth. This allows for more appropriate skill matching by considering career aspirations. Some or all of the above processes in the skill matching unit may be performed using AI, for example, or without AI. For example, the skill matching unit can input employee career aspiration data into a generating AI and have the generating AI perform skill matching.

[0127] The skill matching unit can estimate an employee's emotions and determine the priority of skill matching based on the estimated emotions. For example, if an employee is stressed, the skill matching unit can set a lower priority for skill matching, prioritizing stress reduction. If an employee is relaxed, the skill matching unit can perform skill matching with the normal priority. If an employee is excited, the skill matching unit can set a higher priority for skill matching, performing matching to enhance concentration. This allows for more appropriate skill matching by determining the priority of skill matching based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 skill matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the skill matching unit can input employee emotion data into a generative AI and have the generative AI perform the skill matching priority determination.

[0128] The skill matching unit can perform skill matching while considering the geographical location information of employees. For example, if an employee is working remotely, the skill matching unit can perform skill matching suitable for the remote environment. Similarly, if an employee is working in the office, the skill matching unit can perform skill matching suitable for the office environment. Furthermore, if an employee is on a business trip, the skill matching unit can perform skill matching considering the environment of the business trip destination. This allows for more appropriate skill matching by considering geographical location information. Some or all of the above processing in the skill matching unit may be performed using AI, for example, or without AI. For example, the skill matching unit can input employee geographical location data into a generating AI and have the generating AI perform skill matching.

[0129] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0130] Human resource management systems can collect employee health data and adjust performance evaluation criteria based on their health status. For example, if an employee is diagnosed with high blood pressure during a health checkup, the evaluation criteria can be relaxed to prioritize health improvement. Conversely, if an employee is in good health, the standard evaluation criteria can be applied to accurately assess their performance. Furthermore, if an employee is in excellent health, the evaluation criteria can be tightened to provide feedback that encourages further growth. This allows for more appropriate evaluations by dynamically adjusting evaluation criteria based on employee health status.

[0131] Human resource management systems can set goals that take into account employees' hobbies and interests. For example, if an employee is interested in sports, they can set a goal to participate in a sports-related project. Similarly, if an employee is interested in art, they can set a goal to participate in a creative project. Furthermore, if an employee is interested in technology, they can set a goal to learn new technologies. This allows for goal setting that is more motivating by considering employees' hobbies and interests.

[0132] Human resource management systems can estimate employees' emotions and adjust the timing of feedback based on those estimates. For example, if an employee is stressed, the timing of feedback can be delayed to prioritize stress reduction. Conversely, if an employee is relaxed, feedback can be provided at the usual time. Furthermore, if an employee is agitated, feedback can be provided earlier to enhance their focus. By adjusting the timing of feedback based on employees' emotions, more appropriate feedback can be provided.

[0133] Human resource management systems can analyze employees' past project data to improve the accuracy of performance evaluations. For example, they can reflect the success rate of past projects in current evaluations. They can also analyze feedback from past projects and adjust evaluation criteria. Furthermore, they can reflect the growth of specific skill sets in evaluations based on past project data. In this way, analyzing past project data improves the accuracy of performance evaluations.

[0134] Human resource management systems can estimate employees' emotions and adjust the frequency of stress monitoring based on those estimates. For example, if an employee is feeling stressed, the frequency of stress monitoring can be increased to prioritize stress reduction. Conversely, if an employee is relaxed, stress monitoring can be performed at the normal frequency. Furthermore, if an employee is agitated, the frequency of stress monitoring can be decreased, and feedback can be provided to improve focus. This allows for more effective stress management by adjusting the frequency of stress monitoring based on employees' emotions.

[0135] Human resource management systems can tailor feedback to employees' geographical locations. For example, if an employee is working remotely, feedback appropriate for the remote environment can be provided. Similarly, if an employee is working in the office, feedback appropriate for the office environment can be provided. Furthermore, if an employee is traveling, feedback that takes into account the environment of their destination can be provided. This allows for more appropriate feedback by considering geographical location.

[0136] Human resource management systems can estimate employees' emotions and dynamically adjust skill matching results based on those estimates. For example, if an employee is stressed, skill matching prioritizes stress reduction. If an employee is relaxed, standard skill matching can be performed. Furthermore, if an employee is agitated, skill matching can be performed to enhance their concentration. This allows for more appropriate talent placement by dynamically adjusting skill matching results based on employees' emotions.

[0137] Human resource management systems can analyze employees' social media activities and incorporate them into performance evaluations. For example, they can reflect an employee's professional activities on social media, their communication skills on social media, and even their leadership activities on social media. This allows for more accurate evaluations by analyzing social media activity.

[0138] Human resource management systems can estimate employees' emotions and prioritize goal setting based on those emotions. For example, if an employee is stressed, goal setting can be given a lower priority, prioritizing stress reduction. If an employee is relaxed, goal setting can be done with the usual priority. Furthermore, if an employee is excited, goal setting can be given a higher priority, setting goals to improve their concentration. This allows for more appropriate goal setting by prioritizing goal setting based on employees' emotions.

[0139] Human resource management systems can perform stress monitoring while considering employees' geographical location. For example, if an employee is working remotely, stress monitoring can be optimized for the remote environment. Similarly, if an employee is working in the office, stress monitoring can be optimized for the office environment. Furthermore, if an employee is on a business trip, stress monitoring can be optimized for the environment at their destination. This allows for more appropriate stress monitoring by considering geographical location.

[0140] The following briefly describes the processing flow for example form 2.

[0141] Step 1: The evaluation department conducts multidimensional evaluations. The evaluation department can evaluate employees in dimensions such as technical skills, soft skills, and performance. For example, to evaluate technical skills, they can assess employees' technical knowledge and abilities; to evaluate soft skills, they can assess employees' communication skills and teamwork; and to evaluate performance, they can assess employees' performance indicators and results. Step 2: The removal unit removes bias based on the data obtained by the evaluation unit. For example, it removes gender information from the evaluation data to remove gender bias, removes age information from the evaluation data to remove age bias, and standardizes the evaluation data to remove the evaluator's subjective bias. Step 3: The monitoring unit monitors performance based on data from which bias has been removed by the elimination unit. For example, it monitors employee performance data in real time to monitor performance indicators, employee behavior data in real time to monitor behavioral indicators, and employee feedback data in real time to monitor feedback. Step 4: The feedback unit provides feedback based on the data monitored by the monitoring unit. For example, it provides numerical feedback based on employee performance data to provide quantitative feedback, textual feedback based on employee behavior data to provide qualitative feedback, and immediate feedback based on employee performance data to provide real-time feedback.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the evaluation unit, elimination unit, monitoring unit, feedback unit, goal setting unit, emotion analysis unit, stress monitoring unit, skill matching unit, and emotion estimation function, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the smart device 14 and evaluates the employee's technical and soft skills. The elimination unit is implemented by the specific processing unit 290 of the data processing unit 12 and removes bias from the evaluation data. The monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the employee's performance in real time. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback to the employee. The goal setting unit is implemented by the control unit 46A of the smart device 14 and sets goals based on the employee's career aspirations. The emotion analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the employee's facial expressions and voice data. The stress monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the employee's physiological indicators. The skill matching unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes employee skill data. The emotion estimation function is implemented by the specific processing unit 290 of the data processing device 12 and dynamically adjusts the evaluation criteria based on the employee's emotions. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0146] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the evaluation unit, elimination unit, monitoring unit, feedback unit, goal setting unit, emotion analysis unit, stress monitoring unit, skill matching unit, and emotion estimation function, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the smart glasses 214 and evaluates the employee's technical and soft skills. The elimination unit is implemented by the specific processing unit 290 of the data processing unit 12 and removes bias from the evaluation data. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the employee's performance in real time. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback to the employee. The goal setting unit is implemented by the control unit 46A of the smart glasses 214 and sets goals based on the employee's career aspirations. The emotion analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the employee's facial expressions and voice data. The stress monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the employee's physiological indicators. The skill matching unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the employee's skill data. The emotion estimation function is implemented by the specific processing unit 290 of the data processing device 12 and dynamically adjusts the evaluation criteria based on the employee's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the evaluation unit, elimination unit, monitoring unit, feedback unit, goal setting unit, emotion analysis unit, stress monitoring unit, skill matching unit, and emotion estimation function, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the headset terminal 314 and evaluates the employee's technical and soft skills. The elimination unit is implemented by the specific processing unit 290 of the data processing unit 12 and removes bias from the evaluation data. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the employee's performance in real time. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback to the employee. The goal setting unit is implemented by the control unit 46A of the headset terminal 314 and sets goals based on the employee's career aspirations. The emotion analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the employee's facial expressions and voice data. The stress monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the employee's physiological indicators. The skill matching unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the employee's skill data. The emotion estimation function is implemented by the specific processing unit 290 of the data processing device 12 and dynamically adjusts the evaluation criteria based on the employee's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] Each of the multiple elements described above, including the evaluation unit, removal unit, monitoring unit, feedback unit, goal setting unit, emotion analysis unit, stress monitoring unit, skill matching unit, and emotion estimation function, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the robot 414 and evaluates the technical and soft skills of employees. The removal unit is implemented by the specific processing unit 290 of the data processing unit 12 and removes bias from the evaluation data. The monitoring unit is implemented by the control unit 46A of the robot 414 and monitors employee performance in real time. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback to employees. The goal setting unit is implemented by the control unit 46A of the robot 414 and sets goals based on the employee's career aspirations. The emotion analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the employee's facial expressions and voice data. The stress monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the employee's physiological indicators. The skill matching unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes employee skill data. The emotion estimation function is implemented by the specific processing unit 290 of the data processing device 12 and dynamically adjusts the evaluation criteria based on the employee's emotions. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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."

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] (Note 1) An evaluation unit that performs multidimensional evaluation, A bias removal unit removes bias based on the data obtained by the evaluation unit, A monitoring unit monitors performance based on data from which bias has been removed by the aforementioned removal unit, A feedback unit provides feedback based on the data monitored by the monitoring unit, Equipped with A system characterized by the following features. (Note 2) It includes a goal-setting department that sets goals based on career aspirations and skills. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with an emotion analysis unit that performs emotion analysis. The system described in Appendix 1, characterized by the features described herein. (Note 4) Equipped with a stress monitoring unit for stress monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 5) It is equipped with a skill matching department that performs skill matching. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned target setting unit, Set goals based on data from the evaluation department. The system described in Appendix 2, characterized by the features described herein. (Note 7) The aforementioned emotion analysis unit, We analyze emotions based on data from the monitoring department. The system described in Appendix 3, characterized by the features described herein. (Note 8) The stress monitoring unit is Stress is monitored based on data from the monitoring department. The system described in Appendix 4, characterized by the features described herein. (Note 9) The evaluation unit described above, It estimates employee emotions and dynamically adjusts evaluation criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The evaluation unit described above, Analyze past employee performance data to improve the accuracy of evaluations. The system described in Appendix 1, characterized by the features described herein. (Note 11) The evaluation unit described above, During the evaluation process, we will take into account the employee's skill set and career aspirations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit described above, The system estimates employee emotions and determines evaluation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit described above, During the evaluation process, the geographical location of employees will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit described above, During performance reviews, we analyze employees' social media activity and incorporate relevant data into the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The removal section is, We estimate employee emotions and adjust the bias removal algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The removal section is, During bias removal, the algorithm is optimized by referring to past evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The removal section is, When removing bias, the attribute information of the evaluators is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The removal section is, Estimate employees' emotions and determine bias removal priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The removal section is, When removing bias, the geographical location of the evaluator is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The removal section is, During bias removal, analyze the evaluators' social media activity and incorporate relevant data into the removal process. The system described in Appendix 1, characterized by the features described herein. (Note 21) The monitoring unit, Estimate employees' emotions and adjust monitoring criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The monitoring unit, During monitoring, we improve the accuracy of monitoring by referring to employees' past performance data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The monitoring unit, During monitoring, we will take into account the employee's skill set and career aspirations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The monitoring unit, Estimate employee emotions and prioritize monitoring based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, During monitoring, the monitoring process takes into account the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, analyze employees' social media activity and incorporate relevant data into the monitoring process. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is The system estimates employees' emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When providing feedback, we refer to employees' past performance data to improve the accuracy of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, consider the employee's skill set and career aspirations. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is Estimate employees' emotions and prioritize feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is When providing feedback, consider the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, analyze employees' social media activity and incorporate relevant data into the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned target setting unit, Estimate employees' emotions and adjust goal-setting criteria based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned target setting unit, When setting goals, refer to employees' past performance data to improve the accuracy of those goals. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned target setting unit, Estimate employees' emotions and prioritize goal setting based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned target setting unit, When setting goals, take into account the geographical location of employees. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned emotion analysis unit, We estimate the emotions of employees and adjust the analysis criteria based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned emotion analysis unit, When performing sentiment analysis, we improve the accuracy of the analysis by referring to the employee's past sentiment data. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned emotion analysis unit, The system estimates employee emotions and determines analysis priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned emotion analysis unit, When performing sentiment analysis, the analysis takes into account the geographical location information of the employees. The system described in Appendix 3, characterized by the features described herein. (Note 41) The stress monitoring unit is Estimate employees' emotions and adjust stress monitoring criteria based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The stress monitoring unit is When monitoring stress, referencing employees' past stress data can improve the accuracy of the monitoring process. The system described in Appendix 4, characterized by the features described herein. (Note 43) The stress monitoring unit is Estimate employees' emotions and prioritize stress monitoring based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The stress monitoring unit is When conducting stress monitoring, the monitoring should take into account the geographical location of employees. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned skill matching unit, Estimate employees' emotions and adjust skill matching criteria based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 46) The aforementioned skill matching unit, When matching skills, we improve the accuracy of matching by referring to employees' past skill data. The system described in Appendix 5, characterized by the features described herein. (Note 47) The aforementioned skill matching unit, When matching skills, we take into account the employee's career aspirations. The system described in Appendix 5, characterized by the features described herein. (Note 48) The aforementioned skill matching unit, The system estimates employee sentiment and prioritizes skill matching based on that sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 49) The aforementioned skill matching unit, When matching skills, the geographical location of employees is taken into consideration. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0214] 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. An evaluation unit that performs multidimensional evaluation, A bias removal unit removes bias based on the data obtained by the evaluation unit, A monitoring unit monitors performance based on data from which bias has been removed by the aforementioned removal unit, A feedback unit provides feedback based on the data monitored by the monitoring unit, Equipped with A system characterized by the following features.

2. It includes a goal-setting department that sets goals based on career aspirations and skills. The system according to feature 1.

3. It is equipped with an emotion analysis unit that performs emotion analysis. The system according to feature 1.

4. Equipped with a stress monitoring unit for stress monitoring. The system according to feature 1.

5. It is equipped with a skill matching department that performs skill matching. The system according to feature 1.

6. The aforementioned target setting unit, The target is set based on the data from the aforementioned evaluation unit. The system according to feature 2.

7. The aforementioned emotion analysis unit, We analyze emotions based on data from the monitoring department. The system according to claim 3.

8. The stress monitoring unit is Stress is monitored based on data from the monitoring department. The system according to feature 4.

9. The evaluation unit, It estimates employee emotions and dynamically adjusts evaluation criteria based on those estimated emotions. The system according to feature 1.

10. The evaluation unit, Analyze past employee performance data to improve the accuracy of evaluations. The system according to feature 1.

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