system

Abstract

The system according to this embodiment aims to analyze employee performance data and skill sets to enable effective goal setting and evaluation. [Solution] The system according to the embodiment comprises an analysis unit, a goal setting unit, a monitoring unit, and an evaluation unit. The analysis unit analyzes the employee's past performance data and skill set. The goal setting unit sets goals based on the analysis results obtained by the analysis unit. The monitoring unit monitors the employee's performance in real time based on the goals set by the goal setting unit. The evaluation unit evaluates the performance monitored by the monitoring unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the performance data and skill sets of employees have not been fully utilized effectively for goal setting and evaluation, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the performance data and skill sets of employees and perform effective goal setting and evaluation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a goal setting unit, a monitoring unit, and an evaluation unit. The analysis unit analyzes the employee's past performance data and skill set. The goal setting unit sets goals based on the analysis results obtained by the analysis unit. The monitoring unit monitors the employee's performance in real time based on the goals set by the goal setting unit. The evaluation unit evaluates the performance monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze employee performance data and skill sets to enable effective goal setting and evaluation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The HR process efficiency system according to an embodiment of the present invention is a system that improves the efficiency and quality of HR processes by using generative AI to set and evaluate HR goals. This system analyzes employees' past performance data and skill sets to set optimal goals for each individual employee. Next, it monitors and evaluates employee performance in real time based on the set goals. This allows for objective and efficient evaluation of employee performance. For example, the HR process efficiency system analyzes employees' past performance data and skill sets. In this process, it collects and analyzes detailed data such as the results of past projects and the status of skill acquisition. For example, by collecting data on the results of projects that employees have handled in the past, as well as the qualifications and skills they have acquired, and having the generative AI analyze this data, it is possible to understand the employee's strengths and weaknesses. Next, the generative AI sets optimal goals for each individual employee based on the analysis results. For example, it can set goals that leverage the employee's strengths or goals to overcome their weaknesses. This allows employees to work towards goals that are right for them, improving their motivation. Furthermore, it monitors and evaluates employee performance in real time based on the set goals. The generative AI monitors and evaluates employees' work progress and results in real time. For example, it allows for real-time monitoring and evaluation of how well employees are achieving their set goals. This enables objective and efficient assessment of employee performance, leading to increased efficiency and improved quality in HR processes. By utilizing generative AI, employee goal setting and evaluation are performed quickly and accurately, reducing the burden on HR personnel. Furthermore, objective evaluation of employee performance ensures fair assessment and improves employee motivation. For instance, when employees feel their efforts are fairly recognized, their motivation to work increases, leading to improved overall productivity.

[0029] The HR process efficiency system according to this embodiment comprises an analysis unit, a goal setting unit, a monitoring unit, and an evaluation unit. The analysis unit analyzes employees' past performance data and skill sets. For example, the analysis unit collects data on the results of past projects and acquired qualifications and skills of employees and analyzes it using generating AI. For example, the analysis unit analyzes the results of projects that employees have handled in the past to understand the employees' strengths and weaknesses. The analysis unit can also analyze data on the qualifications and skills acquired by employees to evaluate the employees' skill sets. The goal setting unit sets goals based on the analysis results obtained by the analysis unit. For example, the goal setting unit sets goals that leverage the employees' strengths and goals that overcome their weaknesses. For example, the goal setting unit sets a goal for employees to demonstrate leadership in a specific project by leveraging their strengths. The goal setting unit can also set a goal for employees to acquire specific skills in order to overcome their weaknesses. The monitoring unit monitors employee performance in real time based on the goals set by the goal setting unit. The monitoring unit, for example, monitors employees' work progress and results in real time and evaluates them using generative AI. For example, the monitoring unit grasps in real time the extent to which employees are achieving their set goals. The monitoring unit can also monitor employees' work progress in real time and evaluate it using generative AI. The evaluation unit evaluates the performance monitored by the monitoring unit. The evaluation unit, for example, evaluates employees' work progress and results and provides feedback on the evaluation results using generative AI. For example, the evaluation unit evaluates employees' work progress and provides feedback on the evaluation results using generative AI. The evaluation unit can also evaluate employees' results and provide feedback on the evaluation results using generative AI. As a result, the HR process efficiency system according to this embodiment achieves efficiency and quality improvement of HR processes by analyzing employees' past performance data and skill sets, setting goals, monitoring performance, and evaluating it. Some or all of the above-described processes in the analysis unit, goal setting unit, monitoring unit, and evaluation unit may be performed using generative AI or not.For example, the analysis unit can input employees' past performance data and skill sets into the generating AI and output the analysis results to the generating AI. The goal setting unit can input the analysis results obtained by the analysis unit into the generating AI and set goals in the generating AI. The monitoring unit can input the goals set by the goal setting unit into the generating AI and have the generating AI monitor employee performance. The evaluation unit can input the performance monitored by the monitoring unit into the generating AI and output the evaluation results to the generating AI.

[0030] The analytics department analyzes employees' past performance data and skill sets. For example, it collects data on the results of past projects, acquired qualifications, and skills, and analyzes it using generative AI. Specifically, it collects detailed deliverables, progress reports, and evaluation reports from projects that employees have previously worked on from a database and inputs this data into the generative AI. The generative AI uses natural language processing technology to analyze the text data and extract the success and failure factors of projects. Similarly, data on the qualifications and skills acquired by employees is also collected and analyzed by the generative AI. For example, it evaluates the types and levels of qualifications acquired by employees and their skill acquisition status to gain a detailed understanding of their skill sets. Based on this data, the generative AI can identify employees' strengths and weaknesses and propose optimal career paths and training plans for each individual employee. Furthermore, the analytics department can analyze employee performance data and skill sets over time to understand employee growth trends and performance fluctuations. This makes it possible to formulate strategies for employees' long-term career development and skill improvement. The analytics department can also provide dashboards and reports that visualize the analysis results of the generated AI, making them easily understandable for administrators and HR personnel. This allows the analytics department to efficiently and effectively analyze employee data and contribute to optimizing HR processes.

[0031] The goal-setting unit sets goals based on the analysis results obtained by the analysis unit. For example, the goal-setting unit sets goals that leverage an employee's strengths and goals that help them overcome their weaknesses. Specifically, it automatically generates individual goals based on the employee's strengths and weaknesses analyzed by the generation AI. For example, if an employee has excellent leadership skills, the goal-setting unit will set a goal for them to demonstrate leadership in a specific project, leveraging that strength. It can also set training goals to help an employee acquire specific skills if they lack them. The goal-setting unit uses the generation AI to adjust goals to align with the employee's career path and the organization's strategic goals. For example, it sets individual employee goals based on the organization's overall strategic goals, contributing to the achievement of the organization's goals. Furthermore, the goal-setting unit can collect employee feedback and evaluate the appropriateness and feasibility of the goals. Based on employee feedback, it flexibly adjusts goals to support goal achievement while maintaining employee motivation. The goal-setting unit can also visualize the set goals and provide dashboards and reports that employees and managers can easily review. This allows the goal-setting department to set appropriate goals that take into account the strengths and weaknesses of employees, thereby supporting employee growth and the achievement of organizational goals.

[0032] The Monitoring Department monitors employee performance in real time based on goals set by the Goal Setting Department. For example, the Monitoring Department monitors employee work progress and results in real time and evaluates them using generative AI. Specifically, it collects progress reports and deliverables entered by employees in their daily work and inputs them into the generative AI. The generative AI analyzes this data to understand in real time how well employees are achieving their set goals. For example, it can evaluate project progress, task completion, and the quality of deliverables to quantitatively assess employee performance. Based on the generative AI's analysis results, the Monitoring Department can also provide dashboards and reports that visualize employee performance, making it easy for managers and HR personnel to review. Furthermore, the Monitoring Department can analyze employee performance data over time to understand performance fluctuations and trends. This makes it possible to formulate strategies for long-term performance improvement and career development for employees. The Monitoring Department collects employee feedback and can continuously improve the accuracy and appropriateness of performance evaluations. Based on employee feedback, it flexibly adjusts evaluation criteria and monitoring methods to support performance improvement while maintaining employee motivation. This allows the monitoring department to accurately monitor employee performance in real time, contributing to improved overall organizational performance.

[0033] The evaluation department assesses performance monitored by the monitoring department. For example, the evaluation department evaluates employees' work progress and results, and uses generative AI to provide feedback on the evaluation results. Specifically, it inputs performance data provided by the monitoring department into the generative AI to evaluate employees' work progress and results in detail. The generative AI quantitatively evaluates employee performance and provides feedback on the evaluation results. For example, it evaluates employees' task completion, the quality of deliverables, and project progress, clarifying employees' strengths and areas for improvement. Based on the generative AI's evaluation results, the evaluation department provides specific feedback to employees. For example, it provides specific advice on the next steps that leverage the employee's strengths, and on skills and behaviors that need improvement. Furthermore, the evaluation department can collect employee feedback and continuously improve the accuracy and appropriateness of the evaluation process. Based on employee feedback, it flexibly adjusts evaluation criteria and feedback methods to support performance improvement while maintaining employee motivation. The evaluation department can also provide dashboards and reports to visualize evaluation results and make them easily accessible to employees and managers. This allows the evaluation department to accurately evaluate employee performance and provide specific feedback, supporting employee growth and the achievement of organizational goals.

[0034] The data collection unit collects employee skill data. For example, the data collection unit collects employee qualifications and training history. For example, the data collection unit collects employee qualifications and analyzes them using a generative AI. The data collection unit can also collect employee training history and analyze it using a generative AI. For example, the data collection unit collects the history of training courses taken by employees and analyzes it using a generative AI. By collecting employee skill data, the analysis unit can perform more accurate analysis. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input employee qualifications and training history into a generative AI and output the analysis results to the generative AI.

[0035] The evaluation unit includes a feedback unit that provides feedback on the evaluation results. For example, the evaluation unit evaluates the work progress and results of employees and provides feedback on the evaluation results using a generating AI. The evaluation unit can also evaluate the results of employees and provide feedback on the evaluation results using a generating AI. This allows employees to understand and improve their performance by providing feedback on the evaluation results. Some or all of the above processing in the feedback unit may be performed using a generating AI or not. For example, the feedback unit can input the evaluation results obtained by the evaluation unit into a generating AI and output the feedback content to the generating AI.

[0036] The analysis unit analyzes data on employees' past project results and acquired qualifications and skills. For example, the analysis unit analyzes the results of projects employees have previously worked on to understand their strengths and weaknesses. The analysis unit can also analyze data on employees' acquired qualifications and skills to evaluate their skill sets. This allows the analysis unit to understand employees' strengths and weaknesses by analyzing data on employees' past project results and acquired qualifications and skills. Some or all of the above processing in the analysis unit may be performed using or without a generating AI. For example, the analysis unit can input data on employees' past project results and acquired qualifications and skills into a generating AI and output the analysis results to the generating AI.

[0037] The goal-setting unit sets goals that leverage employees' strengths and goals that overcome their weaknesses. For example, the goal-setting unit can set a goal for an employee to demonstrate leadership in a specific project by leveraging their strengths. It can also set a goal for an employee to acquire specific skills in order to overcome their weaknesses. This improves employee motivation by setting goals that leverage employees' strengths and goals that overcome their weaknesses. Some or all of the above processes in the goal-setting unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the goal-setting unit can input employees' strengths and weaknesses into a generative AI and have the generative AI set goals.

[0038] The monitoring unit monitors employees' work progress and results in real time. For example, the monitoring unit monitors employees' work progress and results in real time and evaluates them using generative AI. For example, the monitoring unit can grasp in real time the extent to which employees are achieving their set goals. The monitoring unit can also monitor employees' work progress in real time and evaluate it using generative AI. This allows for an objective and efficient evaluation of employee performance by monitoring employees' work progress and results in real time. Some or all of the above processes in the monitoring unit may be performed using generative AI, or they may not be performed using generative AI. For example, the monitoring unit can input employees' work progress and results into the generative AI and output evaluation results to the generative AI.

[0039] The analysis unit considers project difficulty and team composition when analyzing employees' past performance data. For example, the analysis unit uses a generative AI to evaluate the difficulty of past projects and analyze employee performance. The analysis unit can also use a generative AI to consider team composition and analyze employee roles and contributions. Furthermore, the analysis unit can use a generative AI to analyze employees' skill sets based on project success rates. This allows for more accurate performance evaluation by considering project difficulty and team composition. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without one. For example, the analysis unit can input employees' past performance data into a generative AI and have the generative AI output analysis results that consider project difficulty and team composition.

[0040] The analysis unit evaluates the speed of skill acquisition and learning methods when analyzing an employee's skill set. For example, the analysis unit can use a generative AI to analyze an employee's skill acquisition speed and propose an appropriate training program. The analysis unit can also use a generative AI to evaluate an employee's learning methods and provide optimal learning resources. Furthermore, the analysis unit can use a generative AI to analyze an employee's skill set and propose a future career path. This allows for the proposal of an appropriate training program by evaluating the speed of skill acquisition and learning methods. Some or all of the above processing in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input an employee's skill set into a generative AI and have the generative AI output the results of evaluating the speed of skill acquisition and learning methods.

[0041] The analysis unit analyzes an employee's past performance data, comparing it to other employees and performing a relative evaluation. For example, the analysis unit can use a generating AI to compare an employee's past performance data with other employees and perform a relative evaluation. The analysis unit can also use a generating AI to compare an employee's skill set with other employees and perform a relative evaluation. Furthermore, the analysis unit can use a generating AI to compare an employee's project results with other employees and perform a relative evaluation. This makes relative evaluation possible by comparing with other employees. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input an employee's past performance data into a generating AI and have the generating AI output a relative evaluation result based on a comparison with other employees.

[0042] The analysis unit evaluates employees' skill sets while considering industry standards and market trends. For example, the analysis unit uses a generating AI to compare and evaluate employees' skill sets against industry standards. The analysis unit can also have the generating AI consider market trends when evaluating employees' skill sets. Furthermore, the analysis unit can have the generating AI compare and evaluate employees' skill sets against those of other companies. This allows for more appropriate skill evaluation by considering industry standards and market trends. Some or all of the above processing in the analysis unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the analysis unit can input employees' skill sets into the generating AI and have the generating AI output evaluation results that consider industry standards and market trends.

[0043] The goal-setting unit considers the employee's career path and future goals when setting goals. For example, the goal-setting unit can use a generating AI to consider the employee's career path and set appropriate goals. The goal-setting unit can also use a generating AI to consider the employee's future goals and set appropriate goals. Furthermore, the goal-setting unit can use a generating AI to consider the employee's skill set and set goals aligned with their career path. This allows employees to work towards their goals from a long-term perspective by considering their career path and future goals. Some or all of the above processes in the goal-setting unit may be performed using a generating AI or not. For example, the goal-setting unit can input the employee's career path and future goals into a generating AI and have the generating AI set the goals.

[0044] The goal-setting unit sets goals while considering the roles and responsibilities of employees within their teams. For example, the goal-setting unit can use a generating AI to consider the roles of employees within their teams and set appropriate goals. The goal-setting unit can also use a generating AI to consider the responsibilities of employees and set appropriate goals. Furthermore, the goal-setting unit can use a generating AI to consider the contributions of employees within their teams and set appropriate goals. This improves the overall team performance by considering the roles and responsibilities of employees within their teams. Some or all of the above processes in the goal-setting unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the goal-setting unit can input the roles and responsibilities of employees within their teams into a generating AI and have the generating AI set goals.

[0045] The goal-setting unit sets goals while considering the employee's past goal achievement rate. For example, the goal-setting unit uses a generating AI to consider the employee's past goal achievement rate and set appropriate goals. The goal-setting unit can also use a generating AI to analyze the employee's past goal achievement rate and set achievable goals. Furthermore, the goal-setting unit can use a generating AI to set challenging goals based on the employee's past goal achievement rate. In this way, achievable goals can be set by considering the employee's past goal achievement rate. Some or all of the above processes in the goal-setting unit may be performed using a generating AI or not. For example, the goal-setting unit can input the employee's past goal achievement rate into a generating AI and have the generating AI set the goals.

[0046] The goal-setting unit sets goals while taking into account the employee's personal interests and concerns. For example, the goal-setting unit may use a generating AI to consider the employee's personal interests and set appropriate goals. Alternatively, the goal-setting unit may use a generating AI to consider the employee's interests and set appropriate goals. Furthermore, the goal-setting unit may use a generating AI to consider the employee's hobbies and special skills and set appropriate goals. By considering the employee's personal interests and concerns, employees can work towards their goals with greater motivation. Some or all of the above-described processes in the goal-setting unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the goal-setting unit may input the employee's personal interests and concerns into a generating AI and have the generating AI set goals.

[0047] The monitoring unit considers the employee's work environment and working conditions during monitoring. For example, the monitoring unit's generating AI considers the employee's work environment and selects an appropriate monitoring method. The monitoring unit can also have the generating AI consider the employee's working conditions and select an appropriate monitoring method. Furthermore, the monitoring unit can have the generating AI analyze the employee's work environment and working conditions and propose the optimal monitoring method. This allows for more appropriate monitoring by considering the employee's work environment and working conditions. Some or all of the above processing in the monitoring unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the monitoring unit can input the employee's work environment and working conditions into the generating AI and have the generating AI select a monitoring method.

[0048] The monitoring unit considers not only the progress of individual employees but also the progress of the entire team during monitoring. For example, the monitoring unit can use a generating AI to monitor the progress of individual employees and consider the progress of the entire team. Alternatively, the monitoring unit can use a generating AI to monitor the progress of the entire team and consider the progress of individual employees. Furthermore, the monitoring unit can use a generating AI to compare the progress of individual employees with the progress of the entire team and propose the optimal monitoring method. This allows for the optimization of the overall team performance by considering not only the progress of individual employees but also the progress of the entire team. Some or all of the above-described processes in the monitoring unit may be performed using a generating AI or not. For example, the monitoring unit can input the progress of individual employees and the progress of the entire team into a generating AI and output the monitoring results to the generating AI.

[0049] The monitoring unit evaluates employees while considering their non-work activities and health status during monitoring. For example, the monitoring unit's generating AI can consider employees' non-work activities and select an appropriate monitoring method. The monitoring unit can also have the generating AI consider employees' health status and select an appropriate monitoring method. Furthermore, the monitoring unit can have the generating AI analyze employees' non-work activities and health status and propose the optimal monitoring method. This allows for a more comprehensive evaluation by considering employees' non-work activities and health status. Some or all of the above processing in the monitoring unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the monitoring unit can input employees' non-work activities and health status into the generating AI and output evaluation results to the generating AI.

[0050] The monitoring unit evaluates employees' work progress by comparing it with other projects and teams during monitoring. For example, the monitoring unit can use a generating AI to compare and evaluate employees' work progress with other projects. The monitoring unit can also use a generating AI to compare and evaluate employees' work progress with other teams. Furthermore, the monitoring unit can use a generating AI to compare employees' work progress with other projects and teams and propose the optimal evaluation method. This enables relative evaluation by comparing with other projects and teams. Some or all of the above processing in the monitoring unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the monitoring unit can input employees' work progress into a generating AI and have the generating AI output evaluation results compared with other projects and teams.

[0051] The evaluation unit considers the employee's self-assessment and feedback from colleagues when conducting evaluations. For example, the evaluation unit may use a generating AI to consider the employee's self-assessment and perform the evaluation. The evaluation unit may also use a generating AI to consider feedback from colleagues and perform the evaluation. Furthermore, the evaluation unit may use a generating AI to integrate the employee's self-assessment and feedback from colleagues to perform a comprehensive evaluation. This allows for a more comprehensive evaluation by considering the employee's self-assessment and feedback from colleagues. Some or all of the above processes in the evaluation unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the evaluation unit may input the employee's self-assessment and feedback from colleagues into a generating AI and have the generating AI output the evaluation results.

[0052] The evaluation unit considers the employee's long-term growth and career path when conducting evaluations. For example, the evaluation unit may use a generating AI to consider the employee's long-term growth when performing evaluations. The evaluation unit may also use a generating AI to consider the employee's career path when performing evaluations. Furthermore, the evaluation unit may use a generating AI to integrate the employee's long-term growth and career path to perform a comprehensive evaluation. This allows employees to aim for long-term growth by considering their long-term growth and career path. Some or all of the above processes in the evaluation unit may be performed using a generating AI, or they may be performed without using a generating AI. For example, the evaluation unit may input the employee's long-term growth and career path into a generating AI and have the generating AI output the evaluation results.

[0053] The evaluation unit considers the employee's past evaluation results and areas for improvement when conducting evaluations. For example, the evaluation unit may use a generating AI to consider the employee's past evaluation results when performing the evaluation. The evaluation unit may also use a generating AI to consider the employee's past areas for improvement when performing the evaluation. Furthermore, the evaluation unit may use a generating AI to integrate the employee's past evaluation results and areas for improvement to perform a comprehensive evaluation. This allows for a more accurate evaluation by considering the employee's past evaluation results and areas for improvement. Some or all of the above processes in the evaluation unit may be performed using a generating AI, or they may be performed without using a generating AI. For example, the evaluation unit may input the employee's past evaluation results and areas for improvement into a generating AI and have the generating AI output the evaluation results.

[0054] The evaluation department considers employees' extra-work activities and contributions when conducting evaluations. For example, the evaluation department may use a generating AI to consider employees' extra-work activities and perform evaluations. The evaluation department may also use a generating AI to consider employees' contributions and perform evaluations. Furthermore, the evaluation department may use a generating AI to integrate employees' extra-work activities and contributions to perform a comprehensive evaluation. This allows for a more comprehensive evaluation by considering employees' extra-work activities and contributions. Some or all of the above processes in the evaluation department may be performed using a generating AI, or they may be performed without a generating AI. For example, the evaluation department may input employees' extra-work activities and contributions into a generating AI and have the generating AI output evaluation results.

[0055] The data collection unit selects data collection methods while considering employee privacy. For example, the data collection unit may use a generating AI to select anonymized data collection methods while considering employee privacy. The data collection unit may also use a generating AI to collect only the minimum necessary data in order to protect employee privacy. Furthermore, the data collection unit may use a generating AI to respect employee privacy and obtain consent for data collection. This ensures that employees can provide data with peace of mind by considering their privacy. Some or all of the above processing in the data collection unit may be performed using a generating AI or not. For example, the data collection unit may input data related to employee privacy into a generating AI and have the generating AI select a data collection method.

[0056] The data collection unit also collects data on employees' extra-work activities and health status during data collection. For example, the data collection unit can use a generative AI to collect data on employees' extra-work activities and analyze its correlation with work performance. The data collection unit can also use a generative AI to collect data on employees' health status and analyze its correlation with work performance. Furthermore, the data collection unit can use a generative AI to integrate data on employees' extra-work activities and health status, enabling comprehensive data collection. This allows for more comprehensive data collection by collecting data on employees' extra-work activities and health status. Some or all of the above-described processes in the data collection unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the data collection unit can input data on employees' extra-work activities and health status into a generative AI and output the data collection results to the generative AI.

[0057] The feedback unit provides optimal feedback by referring to the employee's past feedback history. For example, the feedback unit's generating AI can refer to the employee's past feedback history and provide appropriate feedback. The feedback unit can also have the generating AI analyze the employee's past feedback history and provide feedback that includes areas for improvement. Furthermore, the feedback unit can have the generating AI suggest the optimal feedback method based on the employee's past feedback history. This makes it possible to provide more appropriate feedback by referring to the employee's past feedback history. Some or all of the above processes in the feedback unit may be performed using the generating AI or not. For example, the feedback unit can input the employee's past feedback history into the generating AI and have the generating AI adjust the feedback content.

[0058] The feedback unit provides feedback while considering the employee's extra-work activities and health status. For example, the feedback unit can use a generating AI to consider the employee's extra-work activities and provide appropriate feedback. The feedback unit can also use a generating AI to consider the employee's health status and provide appropriate feedback. Furthermore, the feedback unit can use a generating AI to analyze the employee's extra-work activities and health status and propose optimal feedback. This allows for more comprehensive feedback by considering the employee's extra-work activities and health status. Some or all of the above processing in the feedback unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the feedback unit can input data on the employee's extra-work activities and health status into a generating AI and have the generating AI adjust the feedback content.

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

[0060] The analytics department can take into account employees' personal interests and preferences when analyzing their past performance data and skill sets. For example, if an employee has a strong interest in a particular field, the analytics department can prioritize analyzing projects and tasks related to that field. Similarly, if an employee wants to acquire new skills, the analytics department can focus on analyzing data related to those skills. Furthermore, if an employee is pursuing a specific career path, the analytics department can analyze data aligned with that path to support their growth. This allows for more motivating goal setting by considering employees' personal interests and preferences.

[0061] The data collection department can consider employee self-assessments and feedback from colleagues when collecting employee skill data. For example, if an employee rates a particular skill highly in their self-assessment, the department can prioritize collecting data related to that skill. Similarly, if a particular skill is highly rated in feedback from colleagues, the department can focus on collecting data related to that skill. Furthermore, the department can collect data on skills that employees feel need improvement in their self-assessments and use this information to propose training programs. By considering employee self-assessments and feedback from colleagues, the department can collect more accurate skill data.

[0062] The analysis unit can consider project difficulty and team composition when analyzing employees' past performance data. For example, the generating AI can evaluate the difficulty of past projects and analyze employee performance. The generating AI can also consider team composition and analyze employee roles and contributions. Furthermore, the generating AI can analyze employees' skill sets based on project success rates. This allows for more accurate performance evaluation by considering project difficulty and team composition.

[0063] The monitoring unit can consider the employee's work environment and working conditions when monitoring employee work progress and results in real time. For example, the generating AI can consider the employee's work environment and select an appropriate monitoring method. The generating AI can also consider the employee's working conditions and select an appropriate monitoring method. Furthermore, the generating AI can analyze the employee's work environment and working conditions and propose the optimal monitoring method. This allows for more appropriate monitoring by considering the employee's work environment and working conditions.

[0064] The goal-setting unit can consider employees' career paths and future goals when setting goals. For example, the generating AI can consider an employee's career path and set appropriate goals. It can also consider an employee's future goals and set appropriate goals. Furthermore, the generating AI can consider an employee's skill set and set goals aligned with their career path. This allows employees to work towards their goals with a long-term perspective by considering their career paths and future goals.

[0065] The evaluation unit can consider employees' self-assessments and feedback from colleagues during the evaluation process. For example, a generating AI can consider an employee's self-assessment when performing the evaluation. It can also consider feedback from colleagues when performing the evaluation. Furthermore, the generating AI can integrate the employee's self-assessment and feedback from colleagues to perform a comprehensive evaluation. This allows for a more comprehensive evaluation by considering both employee self-assessments and feedback from colleagues.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The analysis unit analyzes employees' past performance data and skill sets. For example, it collects data on employees' past project results, acquired qualifications, and skills, and analyzes it using generative AI. This allows the system to understand employees' strengths and weaknesses and evaluate their skill sets. Step 2: The goal-setting unit sets goals based on the analysis results obtained by the analysis unit. For example, it sets goals that leverage employees' strengths or goals to overcome their weaknesses. This allows for the setting of goals that enable employees to demonstrate leadership by leveraging their strengths or goals that enable them to acquire specific skills. Step 3: The monitoring unit monitors employee performance in real time based on the goals set by the goal-setting unit. For example, it monitors employees' work progress and results in real time and evaluates them using generated AI. This allows for real-time understanding of how well employees are achieving their set goals. Step 4: The evaluation unit evaluates the performance monitored by the monitoring unit. For example, it evaluates employees' work progress and results and provides feedback on the evaluation results using generated AI. This allows for appropriate evaluation and feedback on employees' work progress and results.

[0068] (Example of form 2) The HR process efficiency system according to an embodiment of the present invention is a system that improves the efficiency and quality of HR processes by using generative AI to set and evaluate HR goals. This system analyzes employees' past performance data and skill sets to set optimal goals for each individual employee. Next, it monitors and evaluates employee performance in real time based on the set goals. This allows for objective and efficient evaluation of employee performance. For example, the HR process efficiency system analyzes employees' past performance data and skill sets. In this process, it collects and analyzes detailed data such as the results of past projects and the status of skill acquisition. For example, by collecting data on the results of projects that employees have handled in the past, as well as the qualifications and skills they have acquired, and having the generative AI analyze this data, it is possible to understand the employee's strengths and weaknesses. Next, the generative AI sets optimal goals for each individual employee based on the analysis results. For example, it can set goals that leverage the employee's strengths or goals to overcome their weaknesses. This allows employees to work towards goals that are right for them, improving their motivation. Furthermore, it monitors and evaluates employee performance in real time based on the set goals. The generative AI monitors and evaluates employees' work progress and results in real time. For example, it allows for real-time monitoring and evaluation of how well employees are achieving their set goals. This enables objective and efficient assessment of employee performance, leading to increased efficiency and improved quality in HR processes. By utilizing generative AI, employee goal setting and evaluation are performed quickly and accurately, reducing the burden on HR personnel. Furthermore, objective evaluation of employee performance ensures fair assessment and improves employee motivation. For instance, when employees feel their efforts are fairly recognized, their motivation to work increases, leading to improved overall productivity.

[0069] The HR process efficiency system according to this embodiment comprises an analysis unit, a goal setting unit, a monitoring unit, and an evaluation unit. The analysis unit analyzes employees' past performance data and skill sets. For example, the analysis unit collects data on the results of past projects and acquired qualifications and skills of employees and analyzes it using generating AI. For example, the analysis unit analyzes the results of projects that employees have handled in the past to understand the employees' strengths and weaknesses. The analysis unit can also analyze data on the qualifications and skills acquired by employees to evaluate the employees' skill sets. The goal setting unit sets goals based on the analysis results obtained by the analysis unit. For example, the goal setting unit sets goals that leverage the employees' strengths and goals that overcome their weaknesses. For example, the goal setting unit sets a goal for employees to demonstrate leadership in a specific project by leveraging their strengths. The goal setting unit can also set a goal for employees to acquire specific skills in order to overcome their weaknesses. The monitoring unit monitors employee performance in real time based on the goals set by the goal setting unit. The monitoring unit, for example, monitors employees' work progress and results in real time and evaluates them using generative AI. For example, the monitoring unit grasps in real time the extent to which employees are achieving their set goals. The monitoring unit can also monitor employees' work progress in real time and evaluate it using generative AI. The evaluation unit evaluates the performance monitored by the monitoring unit. The evaluation unit, for example, evaluates employees' work progress and results and provides feedback on the evaluation results using generative AI. For example, the evaluation unit evaluates employees' work progress and provides feedback on the evaluation results using generative AI. The evaluation unit can also evaluate employees' results and provide feedback on the evaluation results using generative AI. As a result, the HR process efficiency system according to this embodiment achieves efficiency and quality improvement of HR processes by analyzing employees' past performance data and skill sets, setting goals, monitoring performance, and evaluating it. Some or all of the above-described processes in the analysis unit, goal setting unit, monitoring unit, and evaluation unit may be performed using generative AI or not.For example, the analysis unit can input employees' past performance data and skill sets into the generating AI and output the analysis results to the generating AI. The goal setting unit can input the analysis results obtained by the analysis unit into the generating AI and set goals in the generating AI. The monitoring unit can input the goals set by the goal setting unit into the generating AI and have the generating AI monitor employee performance. The evaluation unit can input the performance monitored by the monitoring unit into the generating AI and output the evaluation results to the generating AI.

[0070] The analytics department analyzes employees' past performance data and skill sets. For example, it collects data on the results of past projects, acquired qualifications, and skills, and analyzes it using generative AI. Specifically, it collects detailed deliverables, progress reports, and evaluation reports from projects that employees have previously worked on from a database and inputs this data into the generative AI. The generative AI uses natural language processing technology to analyze the text data and extract the success and failure factors of projects. Similarly, data on the qualifications and skills acquired by employees is also collected and analyzed by the generative AI. For example, it evaluates the types and levels of qualifications acquired by employees and their skill acquisition status to gain a detailed understanding of their skill sets. Based on this data, the generative AI can identify employees' strengths and weaknesses and propose optimal career paths and training plans for each individual employee. Furthermore, the analytics department can analyze employee performance data and skill sets over time to understand employee growth trends and performance fluctuations. This makes it possible to formulate strategies for employees' long-term career development and skill improvement. The analytics department can also provide dashboards and reports that visualize the analysis results of the generated AI, making them easily understandable for administrators and HR personnel. This allows the analytics department to efficiently and effectively analyze employee data and contribute to optimizing HR processes.

[0071] The goal-setting unit sets goals based on the analysis results obtained by the analysis unit. For example, the goal-setting unit sets goals that leverage an employee's strengths and goals that help them overcome their weaknesses. Specifically, it automatically generates individual goals based on the employee's strengths and weaknesses analyzed by the generation AI. For example, if an employee has excellent leadership skills, the goal-setting unit will set a goal for them to demonstrate leadership in a specific project, leveraging that strength. It can also set training goals to help an employee acquire specific skills if they lack them. The goal-setting unit uses the generation AI to adjust goals to align with the employee's career path and the organization's strategic goals. For example, it sets individual employee goals based on the organization's overall strategic goals, contributing to the achievement of the organization's goals. Furthermore, the goal-setting unit can collect employee feedback and evaluate the appropriateness and feasibility of the goals. Based on employee feedback, it flexibly adjusts goals to support goal achievement while maintaining employee motivation. The goal-setting unit can also visualize the set goals and provide dashboards and reports that employees and managers can easily review. This allows the goal-setting department to set appropriate goals that take into account the strengths and weaknesses of employees, thereby supporting employee growth and the achievement of organizational goals.

[0072] The Monitoring Department monitors employee performance in real time based on goals set by the Goal Setting Department. For example, the Monitoring Department monitors employee work progress and results in real time and evaluates them using generative AI. Specifically, it collects progress reports and deliverables entered by employees in their daily work and inputs them into the generative AI. The generative AI analyzes this data to understand in real time how well employees are achieving their set goals. For example, it can evaluate project progress, task completion, and the quality of deliverables to quantitatively assess employee performance. Based on the generative AI's analysis results, the Monitoring Department can also provide dashboards and reports that visualize employee performance, making it easy for managers and HR personnel to review. Furthermore, the Monitoring Department can analyze employee performance data over time to understand performance fluctuations and trends. This makes it possible to formulate strategies for long-term performance improvement and career development for employees. The Monitoring Department collects employee feedback and can continuously improve the accuracy and appropriateness of performance evaluations. Based on employee feedback, it flexibly adjusts evaluation criteria and monitoring methods to support performance improvement while maintaining employee motivation. This allows the monitoring department to accurately monitor employee performance in real time, contributing to improved overall organizational performance.

[0073] The evaluation department assesses performance monitored by the monitoring department. For example, the evaluation department evaluates employees' work progress and results, and uses generative AI to provide feedback on the evaluation results. Specifically, it inputs performance data provided by the monitoring department into the generative AI to evaluate employees' work progress and results in detail. The generative AI quantitatively evaluates employee performance and provides feedback on the evaluation results. For example, it evaluates employees' task completion, the quality of deliverables, and project progress, clarifying employees' strengths and areas for improvement. Based on the generative AI's evaluation results, the evaluation department provides specific feedback to employees. For example, it provides specific advice on the next steps that leverage the employee's strengths, and on skills and behaviors that need improvement. Furthermore, the evaluation department can collect employee feedback and continuously improve the accuracy and appropriateness of the evaluation process. Based on employee feedback, it flexibly adjusts evaluation criteria and feedback methods to support performance improvement while maintaining employee motivation. The evaluation department can also provide dashboards and reports to visualize evaluation results and make them easily accessible to employees and managers. This allows the evaluation department to accurately evaluate employee performance and provide specific feedback, supporting employee growth and the achievement of organizational goals.

[0074] The data collection unit collects employee skill data. For example, the data collection unit collects employee qualifications and training history. For example, the data collection unit collects employee qualifications and analyzes them using a generative AI. The data collection unit can also collect employee training history and analyze it using a generative AI. For example, the data collection unit collects the history of training courses taken by employees and analyzes it using a generative AI. By collecting employee skill data, the analysis unit can perform more accurate analysis. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input employee qualifications and training history into a generative AI and output the analysis results to the generative AI.

[0075] The evaluation unit includes a feedback unit that provides feedback on the evaluation results. For example, the evaluation unit evaluates the work progress and results of employees and provides feedback on the evaluation results using a generating AI. The evaluation unit can also evaluate the results of employees and provide feedback on the evaluation results using a generating AI. This allows employees to understand and improve their performance by providing feedback on the evaluation results. Some or all of the above processing in the feedback unit may be performed using a generating AI or not. For example, the feedback unit can input the evaluation results obtained by the evaluation unit into a generating AI and output the feedback content to the generating AI.

[0076] The analysis unit analyzes data on employees' past project results and acquired qualifications and skills. For example, the analysis unit analyzes the results of projects employees have previously worked on to understand their strengths and weaknesses. The analysis unit can also analyze data on employees' acquired qualifications and skills to evaluate their skill sets. This allows the analysis unit to understand employees' strengths and weaknesses by analyzing data on employees' past project results and acquired qualifications and skills. Some or all of the above processing in the analysis unit may be performed using or without a generating AI. For example, the analysis unit can input data on employees' past project results and acquired qualifications and skills into a generating AI and output the analysis results to the generating AI.

[0077] The goal-setting unit sets goals that leverage employees' strengths and goals that overcome their weaknesses. For example, the goal-setting unit can set a goal for an employee to demonstrate leadership in a specific project by leveraging their strengths. It can also set a goal for an employee to acquire specific skills in order to overcome their weaknesses. This improves employee motivation by setting goals that leverage employees' strengths and goals that overcome their weaknesses. Some or all of the above processes in the goal-setting unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the goal-setting unit can input employees' strengths and weaknesses into a generative AI and have the generative AI set goals.

[0078] The monitoring unit monitors employees' work progress and results in real time. For example, the monitoring unit monitors employees' work progress and results in real time and evaluates them using generative AI. For example, the monitoring unit can grasp in real time the extent to which employees are achieving their set goals. The monitoring unit can also monitor employees' work progress in real time and evaluate it using generative AI. This allows for an objective and efficient evaluation of employee performance by monitoring employees' work progress and results in real time. Some or all of the above processes in the monitoring unit may be performed using generative AI, or they may not be performed using generative AI. For example, the monitoring unit can input employees' work progress and results into the generative AI and output evaluation results to the generative AI.

[0079] The analysis unit estimates employees' emotions and determines analysis priorities based on the estimated emotions. For example, if an employee is stressed, the analysis unit's generative AI will prioritize analyzing tasks that help them relax and reduce stress. The analysis unit can also prioritize analyzing challenging projects if an employee is highly motivated. Furthermore, if an employee is tired, the analysis unit can prioritize analyzing simple tasks. This allows for stress reduction and increased motivation by prioritizing analysis 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-described processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input employee emotion data into a generative AI and have the generative AI determine the analysis priorities.

[0080] The analysis unit considers project difficulty and team composition when analyzing employees' past performance data. For example, the analysis unit uses a generative AI to evaluate the difficulty of past projects and analyze employee performance. The analysis unit can also use a generative AI to consider team composition and analyze employee roles and contributions. Furthermore, the analysis unit can use a generative AI to analyze employees' skill sets based on project success rates. This allows for more accurate performance evaluation by considering project difficulty and team composition. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without one. For example, the analysis unit can input employees' past performance data into a generative AI and have the generative AI output analysis results that consider project difficulty and team composition.

[0081] The analysis unit evaluates the speed of skill acquisition and learning methods when analyzing an employee's skill set. For example, the analysis unit can use a generative AI to analyze an employee's skill acquisition speed and propose an appropriate training program. The analysis unit can also use a generative AI to evaluate an employee's learning methods and provide optimal learning resources. Furthermore, the analysis unit can use a generative AI to analyze an employee's skill set and propose a future career path. This allows for the proposal of an appropriate training program by evaluating the speed of skill acquisition and learning methods. Some or all of the above processing in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input an employee's skill set into a generative AI and have the generative AI output the results of evaluating the speed of skill acquisition and learning methods.

[0082] The analysis unit estimates the employee's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if an employee is stressed, the generation AI provides a simple and easy-to-read display method. If an employee is relaxed, the generation AI can also provide detailed analysis results. Furthermore, if an employee is in a hurry, the generation AI can provide a concise display method. By adjusting the display method of the analysis results based on the employee's emotions, the results can be provided in a way that is easy for employees to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input employee emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0083] The analysis unit analyzes an employee's past performance data, comparing it to other employees and performing a relative evaluation. For example, the analysis unit can use a generating AI to compare an employee's past performance data with other employees and perform a relative evaluation. The analysis unit can also use a generating AI to compare an employee's skill set with other employees and perform a relative evaluation. Furthermore, the analysis unit can use a generating AI to compare an employee's project results with other employees and perform a relative evaluation. This makes relative evaluation possible by comparing with other employees. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input an employee's past performance data into a generating AI and have the generating AI output a relative evaluation result based on a comparison with other employees.

[0084] The analysis unit evaluates employees' skill sets while considering industry standards and market trends. For example, the analysis unit uses a generating AI to compare and evaluate employees' skill sets against industry standards. The analysis unit can also have the generating AI consider market trends when evaluating employees' skill sets. Furthermore, the analysis unit can have the generating AI compare and evaluate employees' skill sets against those of other companies. This allows for more appropriate skill evaluation by considering industry standards and market trends. Some or all of the above processing in the analysis unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the analysis unit can input employees' skill sets into the generating AI and have the generating AI output evaluation results that consider industry standards and market trends.

[0085] The goal-setting unit estimates employees' emotions and adjusts the difficulty of goals based on the estimated emotions. For example, if an employee is stressed, the goal-setting unit's generating AI may lower the difficulty of the goal. Conversely, if an employee is relaxed, the goal-setting unit may also have the generating AI increase the difficulty of the goal. Furthermore, if an employee is highly motivated, the goal-setting unit may have the generating AI set challenging goals. By adjusting the difficulty of goals based on employees' emotions, it is possible to reduce employee stress and improve motivation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the goal-setting unit may be performed using or without a generating AI. For example, the goal-setting unit can input employee emotion data into a generating AI and have the generating AI adjust the difficulty of the goals.

[0086] The goal-setting unit considers the employee's career path and future goals when setting goals. For example, the goal-setting unit can use a generating AI to consider the employee's career path and set appropriate goals. The goal-setting unit can also use a generating AI to consider the employee's future goals and set appropriate goals. Furthermore, the goal-setting unit can use a generating AI to consider the employee's skill set and set goals aligned with their career path. This allows employees to work towards their goals from a long-term perspective by considering their career path and future goals. Some or all of the above processes in the goal-setting unit may be performed using a generating AI or not. For example, the goal-setting unit can input the employee's career path and future goals into a generating AI and have the generating AI set the goals.

[0087] The goal-setting unit sets goals while considering the roles and responsibilities of employees within their teams. For example, the goal-setting unit can use a generating AI to consider the roles of employees within their teams and set appropriate goals. The goal-setting unit can also use a generating AI to consider the responsibilities of employees and set appropriate goals. Furthermore, the goal-setting unit can use a generating AI to consider the contributions of employees within their teams and set appropriate goals. This improves the overall team performance by considering the roles and responsibilities of employees within their teams. Some or all of the above processes in the goal-setting unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the goal-setting unit can input the roles and responsibilities of employees within their teams into a generating AI and have the generating AI set goals.

[0088] The goal-setting unit estimates employees' emotions and adjusts goal deadlines based on these estimates. For example, if an employee is stressed, the goal-setting unit's generating AI may extend the goal deadline. Conversely, if an employee is relaxed, the goal-setting unit may shorten the goal deadline. Furthermore, if an employee is in a hurry, the goal-setting unit may adjust the goal deadline using the generating AI. By adjusting goal deadlines based on employee emotions, it is possible to reduce employee stress and increase the likelihood of achieving goals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the goal-setting unit may be performed using or without a generating AI. For example, the goal-setting unit can input employee emotion data into a generating AI and have the generating AI adjust the goal deadline.

[0089] The goal-setting unit sets goals while considering the employee's past goal achievement rate. For example, the goal-setting unit uses a generating AI to consider the employee's past goal achievement rate and set appropriate goals. The goal-setting unit can also use a generating AI to analyze the employee's past goal achievement rate and set achievable goals. Furthermore, the goal-setting unit can use a generating AI to set challenging goals based on the employee's past goal achievement rate. In this way, achievable goals can be set by considering the employee's past goal achievement rate. Some or all of the above processes in the goal-setting unit may be performed using a generating AI or not. For example, the goal-setting unit can input the employee's past goal achievement rate into a generating AI and have the generating AI set the goals.

[0090] The goal-setting unit sets goals while taking into account the employee's personal interests and concerns. For example, the goal-setting unit may use a generating AI to consider the employee's personal interests and set appropriate goals. Alternatively, the goal-setting unit may use a generating AI to consider the employee's interests and set appropriate goals. Furthermore, the goal-setting unit may use a generating AI to consider the employee's hobbies and special skills and set appropriate goals. By considering the employee's personal interests and concerns, employees can work towards their goals with greater motivation. Some or all of the above-described processes in the goal-setting unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the goal-setting unit may input the employee's personal interests and concerns into a generating AI and have the generating AI set goals.

[0091] The monitoring unit estimates the employee's emotions and adjusts the monitoring frequency based on the estimated emotions. For example, if an employee is stressed, the monitoring unit's generative AI may reduce the monitoring frequency. The monitoring unit can also increase the monitoring frequency if the employee is relaxed. Furthermore, if an employee is in a hurry, the monitoring unit can adjust the monitoring frequency using the generative AI. This allows for reduced employee stress and optimized performance by adjusting the monitoring frequency based on employee emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using or without the generative AI. For example, the monitoring unit can input employee emotion data into the generative AI and have the generative AI adjust the monitoring frequency.

[0092] The monitoring unit considers the employee's work environment and working conditions during monitoring. For example, the monitoring unit's generating AI considers the employee's work environment and selects an appropriate monitoring method. The monitoring unit can also have the generating AI consider the employee's working conditions and select an appropriate monitoring method. Furthermore, the monitoring unit can have the generating AI analyze the employee's work environment and working conditions and propose the optimal monitoring method. This allows for more appropriate monitoring by considering the employee's work environment and working conditions. Some or all of the above processing in the monitoring unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the monitoring unit can input the employee's work environment and working conditions into the generating AI and have the generating AI select a monitoring method.

[0093] The monitoring unit considers not only the progress of individual employees but also the progress of the entire team during monitoring. For example, the monitoring unit can use a generating AI to monitor the progress of individual employees and consider the progress of the entire team. Alternatively, the monitoring unit can use a generating AI to monitor the progress of the entire team and consider the progress of individual employees. Furthermore, the monitoring unit can use a generating AI to compare the progress of individual employees with the progress of the entire team and propose the optimal monitoring method. This allows for the optimization of the overall team performance by considering not only the progress of individual employees but also the progress of the entire team. Some or all of the above-described processes in the monitoring unit may be performed using a generating AI or not. For example, the monitoring unit can input the progress of individual employees and the progress of the entire team into a generating AI and output the monitoring results to the generating AI.

[0094] The monitoring unit estimates the employee's emotions and adjusts the display method of the monitoring results based on the estimated emotions. For example, if an employee is stressed, the monitoring unit's generating AI provides a simple and easy-to-read display method. If an employee is relaxed, the generating AI can also provide detailed monitoring results. Furthermore, if an employee is in a hurry, the generating AI can provide a concise display method. This allows the system to provide results in an easily understandable format by adjusting the display method of monitoring results based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using or without a generating AI. For example, the monitoring unit can input employee emotion data into a generating AI and have the generating AI adjust the display method of the monitoring results.

[0095] The monitoring unit evaluates employees while considering their non-work activities and health status during monitoring. For example, the monitoring unit's generating AI can consider employees' non-work activities and select an appropriate monitoring method. The monitoring unit can also have the generating AI consider employees' health status and select an appropriate monitoring method. Furthermore, the monitoring unit can have the generating AI analyze employees' non-work activities and health status and propose the optimal monitoring method. This allows for a more comprehensive evaluation by considering employees' non-work activities and health status. Some or all of the above processing in the monitoring unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the monitoring unit can input employees' non-work activities and health status into the generating AI and output evaluation results to the generating AI.

[0096] The monitoring unit evaluates employees' work progress by comparing it with other projects and teams during monitoring. For example, the monitoring unit can use a generating AI to compare and evaluate employees' work progress with other projects. The monitoring unit can also use a generating AI to compare and evaluate employees' work progress with other teams. Furthermore, the monitoring unit can use a generating AI to compare employees' work progress with other projects and teams and propose the optimal evaluation method. This enables relative evaluation by comparing with other projects and teams. Some or all of the above processing in the monitoring unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the monitoring unit can input employees' work progress into a generating AI and have the generating AI output evaluation results compared with other projects and teams.

[0097] The evaluation unit estimates employees' emotions and adjusts evaluation criteria based on the estimated emotions. For example, if an employee is stressed, the evaluation unit may use a generative AI to relax the evaluation criteria. Conversely, if an employee is relaxed, the evaluation unit may use the generative AI to make the evaluation criteria stricter. Furthermore, if an employee is highly motivated, the evaluation unit may use the generative AI to set challenging evaluation criteria. This allows for reducing employee stress and improving motivation by adjusting evaluation criteria based on employee emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using or without a generative AI. For example, the evaluation unit may input employee emotion data into a generative AI and have the generative AI adjust the evaluation criteria.

[0098] The evaluation unit considers the employee's self-assessment and feedback from colleagues when conducting evaluations. For example, the evaluation unit may use a generating AI to consider the employee's self-assessment and perform the evaluation. The evaluation unit may also use a generating AI to consider feedback from colleagues and perform the evaluation. Furthermore, the evaluation unit may use a generating AI to integrate the employee's self-assessment and feedback from colleagues to perform a comprehensive evaluation. This allows for a more comprehensive evaluation by considering the employee's self-assessment and feedback from colleagues. Some or all of the above processes in the evaluation unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the evaluation unit may input the employee's self-assessment and feedback from colleagues into a generating AI and have the generating AI output the evaluation results.

[0099] The evaluation unit considers the employee's long-term growth and career path when conducting evaluations. For example, the evaluation unit may use a generating AI to consider the employee's long-term growth when performing evaluations. The evaluation unit may also use a generating AI to consider the employee's career path when performing evaluations. Furthermore, the evaluation unit may use a generating AI to integrate the employee's long-term growth and career path to perform a comprehensive evaluation. This allows employees to aim for long-term growth by considering their long-term growth and career path. Some or all of the above processes in the evaluation unit may be performed using a generating AI, or they may be performed without using a generating AI. For example, the evaluation unit may input the employee's long-term growth and career path into a generating AI and have the generating AI output the evaluation results.

[0100] The evaluation unit estimates the employee's emotions and adjusts the feedback method of the evaluation results based on the estimated emotions. For example, if an employee is stressed, the evaluation unit can use a generative AI to provide feedback in gentle language. If an employee is relaxed, the evaluation unit can also use the generative AI to provide detailed feedback. Furthermore, if an employee is in a hurry, the evaluation unit can use the generative AI to provide concise feedback. In this way, by adjusting the feedback method based on the employee's emotions, feedback can be provided in a way that is easily accepted by the employee. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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 adjust the feedback method.

[0101] The evaluation unit considers the employee's past evaluation results and areas for improvement when conducting evaluations. For example, the evaluation unit may use a generating AI to consider the employee's past evaluation results when performing the evaluation. The evaluation unit may also use a generating AI to consider the employee's past areas for improvement when performing the evaluation. Furthermore, the evaluation unit may use a generating AI to integrate the employee's past evaluation results and areas for improvement to perform a comprehensive evaluation. This allows for a more accurate evaluation by considering the employee's past evaluation results and areas for improvement. Some or all of the above processes in the evaluation unit may be performed using a generating AI, or they may be performed without using a generating AI. For example, the evaluation unit may input the employee's past evaluation results and areas for improvement into a generating AI and have the generating AI output the evaluation results.

[0102] The evaluation department considers employees' extra-work activities and contributions when conducting evaluations. For example, the evaluation department may use a generating AI to consider employees' extra-work activities and perform evaluations. The evaluation department may also use a generating AI to consider employees' contributions and perform evaluations. Furthermore, the evaluation department may use a generating AI to integrate employees' extra-work activities and contributions to perform a comprehensive evaluation. This allows for a more comprehensive evaluation by considering employees' extra-work activities and contributions. Some or all of the above processes in the evaluation department may be performed using a generating AI, or they may be performed without a generating AI. For example, the evaluation department may input employees' extra-work activities and contributions into a generating AI and have the generating AI output evaluation results.

[0103] The data collection unit estimates the emotions of employees and adjusts the timing of data collection based on the estimated emotions. For example, if an employee is stressed, the data collection unit's generative AI will delay the timing of data collection. Conversely, if an employee is relaxed, the data collection unit's generative AI can also advance the timing of data collection. Furthermore, if an employee is in a hurry, the data collection unit's generative AI can also adjust the timing of data collection. By adjusting the timing of data collection based on employee emotions, it is possible to reduce employee stress and improve the efficiency of data collection. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without the generative AI. For example, the data collection unit can input employee emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0104] The data collection unit selects data collection methods while considering employee privacy. For example, the data collection unit may use a generating AI to select anonymized data collection methods while considering employee privacy. The data collection unit may also use a generating AI to collect only the minimum necessary data in order to protect employee privacy. Furthermore, the data collection unit may use a generating AI to respect employee privacy and obtain consent for data collection. This ensures that employees can provide data with peace of mind by considering their privacy. Some or all of the above processing in the data collection unit may be performed using a generating AI or not. For example, the data collection unit may input data related to employee privacy into a generating AI and have the generating AI select a data collection method.

[0105] The data collection unit estimates the employee's emotions and adjusts the type of data collected based on the estimated emotions. For example, if an employee is stressed, the data collection unit's generative AI will prioritize collecting stress-related data. Similarly, if an employee is relaxed, the generative AI can prioritize collecting work-related data. Furthermore, if an employee is in a hurry, the generative AI can collect only the minimum necessary data. This allows for more appropriate data collection by adjusting the type of data collected 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 data collection unit may be performed using or without a generative AI. For example, the data collection unit can input employee emotion data into a generative AI and have the generative AI adjust the type of data to collect.

[0106] The data collection unit also collects data on employees' extra-work activities and health status during data collection. For example, the data collection unit can use a generative AI to collect data on employees' extra-work activities and analyze its correlation with work performance. The data collection unit can also use a generative AI to collect data on employees' health status and analyze its correlation with work performance. Furthermore, the data collection unit can use a generative AI to integrate data on employees' extra-work activities and health status, enabling comprehensive data collection. This allows for more comprehensive data collection by collecting data on employees' extra-work activities and health status. Some or all of the above-described processes in the data collection unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the data collection unit can input data on employees' extra-work activities and health status into a generative AI and output the data collection results to the generative AI.

[0107] The feedback unit estimates the employee's emotions and adjusts the content of the feedback based on the estimated emotions. For example, if an employee is stressed, the feedback unit's generating AI can provide feedback in gentle language. If the employee is relaxed, the feedback unit's generating AI can provide detailed feedback. Furthermore, if the employee is in a hurry, the feedback unit's generating AI can provide concise feedback. This allows feedback to be provided in a way that is easily accepted by the employee by adjusting the content of the feedback based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using or without a generating AI. For example, the feedback unit can input employee emotion data into a generating AI and have the generating AI adjust the content of the feedback.

[0108] The feedback unit provides optimal feedback by referring to the employee's past feedback history. For example, the feedback unit's generating AI can refer to the employee's past feedback history and provide appropriate feedback. The feedback unit can also have the generating AI analyze the employee's past feedback history and provide feedback that includes areas for improvement. Furthermore, the feedback unit can have the generating AI suggest the optimal feedback method based on the employee's past feedback history. This makes it possible to provide more appropriate feedback by referring to the employee's past feedback history. Some or all of the above processes in the feedback unit may be performed using the generating AI or not. For example, the feedback unit can input the employee's past feedback history into the generating AI and have the generating AI adjust the feedback content.

[0109] The feedback unit estimates the employee's emotions and adjusts the timing of feedback based on the estimated emotions. For example, if an employee is stressed, the feedback unit's generative AI may delay the timing of the feedback. Conversely, if an employee is relaxed, the feedback unit's generative AI may advance the timing of the feedback. Furthermore, if an employee is in a hurry, the feedback unit's generative AI may adjust the timing of the feedback. This allows feedback to be provided at a time that is easily accepted by the employee by adjusting the timing of feedback based on their 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 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 timing of the feedback.

[0110] The feedback unit provides feedback while considering the employee's extra-work activities and health status. For example, the feedback unit can use a generating AI to consider the employee's extra-work activities and provide appropriate feedback. The feedback unit can also use a generating AI to consider the employee's health status and provide appropriate feedback. Furthermore, the feedback unit can use a generating AI to analyze the employee's extra-work activities and health status and propose optimal feedback. This allows for more comprehensive feedback by considering the employee's extra-work activities and health status. Some or all of the above processing in the feedback unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the feedback unit can input data on the employee's extra-work activities and health status into a generating AI and have the generating AI adjust the feedback content.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The analytics department can take into account employees' personal interests and preferences when analyzing their past performance data and skill sets. For example, if an employee has a strong interest in a particular field, the analytics department can prioritize analyzing projects and tasks related to that field. Similarly, if an employee wants to acquire new skills, the analytics department can focus on analyzing data related to those skills. Furthermore, if an employee is pursuing a specific career path, the analytics department can analyze data aligned with that path to support their growth. This allows for more motivating goal setting by considering employees' personal interests and preferences.

[0113] The data collection department can consider employee self-assessments and feedback from colleagues when collecting employee skill data. For example, if an employee rates a particular skill highly in their self-assessment, the department can prioritize collecting data related to that skill. Similarly, if a particular skill is highly rated in feedback from colleagues, the department can focus on collecting data related to that skill. Furthermore, the department can collect data on skills that employees feel need improvement in their self-assessments and use this information to propose training programs. By considering employee self-assessments and feedback from colleagues, the department can collect more accurate skill data.

[0114] The evaluation department can estimate the employee's emotions when providing feedback on evaluation results and adjust the content of the feedback based on those emotions. For example, if an employee is stressed, the generating AI can provide feedback in gentle language. If the employee is relaxed, the generating AI can provide detailed feedback. Furthermore, if the employee is in a hurry, the generating AI can provide concise feedback. In this way, by adjusting the content of feedback based on the employee's emotions, feedback can be provided in a way that is easily accepted by the employee.

[0115] The analysis unit can consider project difficulty and team composition when analyzing employees' past performance data. For example, the generating AI can evaluate the difficulty of past projects and analyze employee performance. The generating AI can also consider team composition and analyze employee roles and contributions. Furthermore, the generating AI can analyze employees' skill sets based on project success rates. This allows for more accurate performance evaluation by considering project difficulty and team composition.

[0116] The goal-setting unit can estimate employees' emotions and adjust goal deadlines based on those emotions. For example, if an employee is stressed, the generating AI will extend the goal deadline. Conversely, if an employee is relaxed, the generating AI can shorten the deadline. Furthermore, if an employee is in a hurry, the generating AI can adjust the deadline. By adjusting goal deadlines based on employee emotions, this system can reduce employee stress and increase the likelihood of achieving goals.

[0117] The monitoring unit can consider the employee's work environment and working conditions when monitoring employee work progress and results in real time. For example, the generating AI can consider the employee's work environment and select an appropriate monitoring method. The generating AI can also consider the employee's working conditions and select an appropriate monitoring method. Furthermore, the generating AI can analyze the employee's work environment and working conditions and propose the optimal monitoring method. This allows for more appropriate monitoring by considering the employee's work environment and working conditions.

[0118] The analysis unit can estimate employees' emotions and prioritize analysis based on those emotions. For example, if an employee is stressed, the generating AI will prioritize analyzing tasks that help them relax and reduce stress. If an employee is highly motivated, the generating AI can prioritize analyzing challenging projects. Furthermore, if an employee is tired, the generating AI can prioritize analyzing simple tasks. By prioritizing analysis based on employees' emotions, it is possible to reduce employee stress and improve motivation.

[0119] The goal-setting unit can consider employees' career paths and future goals when setting goals. For example, the generating AI can consider an employee's career path and set appropriate goals. It can also consider an employee's future goals and set appropriate goals. Furthermore, the generating AI can consider an employee's skill set and set goals aligned with their career path. This allows employees to work towards their goals with a long-term perspective by considering their career paths and future goals.

[0120] The monitoring unit can estimate employees' emotions and adjust the monitoring frequency based on those estimates. For example, if an employee is stressed, the generating AI can reduce the monitoring frequency. Conversely, if an employee is relaxed, the generating AI can increase the monitoring frequency. Furthermore, if an employee is in a hurry, the generating AI can adjust the monitoring frequency. By adjusting the monitoring frequency based on employee emotions, it is possible to reduce employee stress and optimize performance.

[0121] The evaluation unit can consider employees' self-assessments and feedback from colleagues during the evaluation process. For example, a generating AI can consider an employee's self-assessment when performing the evaluation. It can also consider feedback from colleagues when performing the evaluation. Furthermore, the generating AI can integrate the employee's self-assessment and feedback from colleagues to perform a comprehensive evaluation. This allows for a more comprehensive evaluation by considering both employee self-assessments and feedback from colleagues.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The analysis unit analyzes employees' past performance data and skill sets. For example, it collects data on employees' past project results, acquired qualifications, and skills, and analyzes it using generative AI. This allows the system to understand employees' strengths and weaknesses and evaluate their skill sets. Step 2: The goal-setting unit sets goals based on the analysis results obtained by the analysis unit. For example, it sets goals that leverage employees' strengths or goals to overcome their weaknesses. This allows for the setting of goals that enable employees to demonstrate leadership by leveraging their strengths or goals that enable them to acquire specific skills. Step 3: The monitoring unit monitors employee performance in real time based on the goals set by the goal-setting unit. For example, it monitors employees' work progress and results in real time and evaluates them using generated AI. This allows for real-time understanding of how well employees are achieving their set goals. Step 4: The evaluation unit evaluates the performance monitored by the monitoring unit. For example, it evaluates employees' work progress and results and provides feedback on the evaluation results using generated AI. This allows for appropriate evaluation and feedback on employees' work progress and results.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the analysis unit, target setting unit, monitoring unit, evaluation unit, and data acquisition unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The target setting unit is implemented by the specific processing unit 290 of the data processing device 12. The monitoring unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12. The data acquisition unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the analysis unit, target setting unit, monitoring unit, evaluation unit, and data acquisition unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The target setting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The data acquisition unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the analysis unit, target setting unit, monitoring unit, evaluation unit, and data acquisition unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The target setting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The monitoring unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The data acquisition unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the analysis unit, target setting unit, monitoring unit, evaluation unit, and data acquisition unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The target setting unit is implemented by the specific processing unit 290 of the data processing unit 12. The monitoring unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12. The data acquisition unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) The analysis department analyzes employees' past performance data and skill sets, A target setting unit sets targets based on the analysis results obtained by the aforementioned analysis unit, A monitoring unit that monitors employee performance in real time based on the goals set by the aforementioned goal-setting unit, The system includes an evaluation unit that evaluates the performance monitored by the monitoring unit. A system characterized by the following features. (Note 2) It includes a data collection unit that collects employee skill data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, It includes a feedback unit that provides feedback on the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyze data on employees' past project achievements, acquired qualifications, and skills. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned target setting unit, Set goals that leverage employees' strengths and goals that help overcome their weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 6) The monitoring unit, Monitor employee work progress and results in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, The system estimates employee sentiment and determines analysis priorities based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing employees' past performance data, consider the project's difficulty and team composition. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing an employee's skill set, evaluate the speed at which they acquire skills and their learning methods. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates employee emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing past employee performance data, a relative evaluation is performed by comparing it with other employees. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing employee skill sets, industry standards and market trends should be taken into consideration during the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned target setting unit, The system estimates employee emotions and adjusts the difficulty level of goals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned target setting unit, When setting goals, consider the employee's career path and future aspirations. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned target setting unit, When setting goals, consider the roles and responsibilities of employees within the team. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned target setting unit, Estimate employee sentiment and adjust goal completion deadlines based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned target setting unit, When setting goals, consider the employee's past goal achievement rate. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned target setting unit, When setting goals, take into account the personal interests and concerns of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 19) The monitoring unit, Estimate employee sentiment and adjust monitoring frequency based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The monitoring unit, When monitoring, consider the employees' work environment and working conditions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The monitoring unit, When monitoring, consider not only the progress of individual employees but also the progress of the entire team. The system described in Appendix 1, characterized by the features described herein. (Note 22) The monitoring unit, The system estimates employee sentiment and adjusts how monitoring results are displayed based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The monitoring unit, During monitoring, the evaluation should take into account employees' non-work activities and health status. The system described in Appendix 1, characterized by the features described herein. (Note 24) The monitoring unit, During monitoring, evaluate employees' work progress by comparing it with other projects and teams. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, Estimate employee sentiment and adjust evaluation criteria based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit, When evaluating performance, we take into account employee self-assessments and feedback from colleagues. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit, When evaluating employees, we take into account their long-term growth and career paths. The system described in Appendix 1, characterized by the features described herein. (Note 28) The evaluation unit, We estimate employee emotions and adjust the feedback method for performance evaluations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The evaluation unit, When evaluating employees, their past performance and areas for improvement should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The evaluation unit, During performance evaluations, employees' activities and contributions outside of their regular work duties should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned data acquisition unit, We estimate employee sentiment and adjust the timing of data collection based on the estimated employee sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned data acquisition unit, When collecting data, select a data collection method that takes employee privacy into consideration. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned data acquisition unit, We estimate employee sentiment and adjust the types of data we collect based on the estimated employee sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned data acquisition unit, During data collection, we also collect data on employees' non-work-related activities and health status. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned feedback unit is The system estimates employee emotions and adjusts the content of feedback based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned feedback unit is When providing feedback, refer to the employee's past feedback history to provide the most appropriate feedback. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned feedback unit is Estimate employees' emotions and adjust the timing of feedback based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned feedback unit is When providing feedback, take into account the employee's extracurricular activities and health condition. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes employees' past performance data and skill sets, A target setting unit sets targets based on the analysis results obtained by the aforementioned analysis unit, A monitoring unit that monitors employee performance in real time based on the goals set by the aforementioned goal-setting unit, The system includes an evaluation unit that evaluates the performance monitored by the monitoring unit. A system characterized by the following features.

2. It includes a data collection unit that collects employee skill data. The system according to feature 1.

3. The evaluation unit described above, It includes a feedback unit that provides feedback on the evaluation results. The system according to feature 1.

4. The aforementioned analysis unit, Analyze data on employees' past project achievements, acquired qualifications, and skills. The system according to feature 1.

5. The aforementioned target setting unit, Set goals that leverage employees' strengths and goals that help overcome their weaknesses. The system according to feature 1.

6. The monitoring unit, Monitor employee work progress and results in real time. The system according to feature 1.

7. The aforementioned analysis unit, The system estimates employee sentiment and determines analysis priorities based on the estimated employee sentiment. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing employees' past performance data, consider the project's difficulty and team composition. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing an employee's skill set, evaluate the speed at which they acquire skills and their learning methods. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates employee emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

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