system

Abstract

The system according to this embodiment aims to perform employee evaluations efficiently and objectively. [Solution] The system according to the embodiment comprises a measurement unit, a deliverable collection unit, a contribution evaluation unit, a questionnaire collection unit, a generation unit, and a feedback unit. The measurement unit collects measurement data from a PC. The deliverable collection unit collects deliverables. The contribution evaluation unit evaluates the degree of contribution. The questionnaire collection unit collects questionnaires from employees. The generation unit analyzes the data collected by the measurement unit, deliverable collection unit, contribution evaluation unit, and questionnaire collection unit and generates an evaluation for each employee. The feedback unit provides feedback to the employees on the evaluation results generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the evaluation of employees takes time and subjectivity has a great influence.

[0005] The system according to the embodiment aims to efficiently and objectively evaluate employees.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a measurement unit, a deliverable collection unit, a contribution evaluation unit, a questionnaire collection unit, a generation unit, and a feedback unit. The measurement unit collects measurement data from a PC. The deliverable collection unit collects deliverables. The contribution evaluation unit evaluates the degree of contribution. The questionnaire collection unit collects questionnaires from employees. The generation unit analyzes the data collected by the measurement unit, deliverable collection unit, contribution evaluation unit, and questionnaire collection unit and generates an evaluation for each employee. The feedback unit provides feedback to the employees based on the evaluation results generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can perform employee evaluations efficiently and objectively. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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 evaluation system according to an embodiment of the present invention is a system for objectively evaluating employees. This evaluation system collects PC measurement data, deliverables, contributions, and employee questionnaires, and generates an evaluation for each employee using a generating AI. For example, the evaluation system evaluates work efficiency from PC measurement data, quality from deliverables, impact on the team from contributions, and trustworthiness from colleagues from questionnaires. Because this system ensures that evaluations are based on objective data and not influenced by subjectivity, it realizes a world where hard work is fairly rewarded. For example, if an employee works long hours and submits high-quality deliverables, making a significant contribution to the team, that effort will be fairly evaluated. Furthermore, since positive evaluations from colleagues are also taken into consideration, it contributes to fostering a corporate culture that values ​​teamwork. This evaluation system not only improves employee motivation but also contributes to improving the productivity of the entire company. Because employees know that their efforts will be fairly evaluated, they will strive even harder. In addition, because evaluations are based on objective data, feelings of unfairness are eliminated, and trust among employees is strengthened. In this way, the evaluation system can objectively evaluate employees.

[0029] The evaluation system according to the embodiment comprises a measurement unit, a deliverable collection unit, a contribution evaluation unit, a questionnaire collection unit, a generation unit, and a feedback unit. The measurement unit collects measurement data from the PC. The measurement unit records, for example, the PC usage time and the frequency of application usage. The measurement unit can also collect PC operation logs and evaluate work efficiency. For example, the measurement unit records the PC usage time and evaluates work efficiency. The measurement unit can also record the frequency of application usage and analyze work patterns. Furthermore, the measurement unit can collect PC operation logs and evaluate work efficiency. The deliverable collection unit collects deliverables such as project progress reports and completed products. For example, the deliverable collection unit collects project progress reports and evaluates the progress status. Furthermore, the deliverable collection unit can collect completed products and evaluate their quality. For example, the deliverable collection unit collects project progress reports and evaluates the progress status. Furthermore, the deliverable collection unit can collect completed products and evaluate their quality. Furthermore, the deliverable collection unit can collect project progress reports and completed products and evaluate the progress status and quality. The Contribution Evaluation Department evaluates the degree of participation in projects and the degree of contribution to the team. For example, the Contribution Evaluation Department evaluates the degree of participation in projects and the degree of contribution. The Contribution Evaluation Department can also evaluate the degree of contribution to the team and the degree of contribution. For example, the Contribution Evaluation Department evaluates the degree of participation in projects and the degree of contribution. The Contribution Evaluation Department can also evaluate the degree of contribution to the team and the degree of contribution. Furthermore, the Contribution Evaluation Department can also evaluate the degree of participation in projects and the degree of contribution to the team and the degree of contribution. The Survey Collection Department collects evaluations from colleagues. For example, the Survey Collection Department collects evaluations from colleagues and evaluates their reliability. Furthermore, the Survey Collection Department can also collect anonymous surveys and evaluate their reliability. For example, the Survey Collection Department collects evaluations from colleagues and evaluates their reliability. Furthermore, the Survey Collection Department can also collect anonymous surveys and evaluate their reliability. Furthermore, the Survey Collection Department can also collect evaluations from colleagues and evaluate their reliability. The Generation Department analyzes the collected data and generates evaluations for each employee.The generation unit, for example, analyzes the collected data and evaluates the performance of each employee. The generation unit can also analyze the data and generate evaluations using a generation AI. The feedback unit provides feedback to employees on the evaluation results generated by the generation unit. The feedback unit, for example, notifies employees of the evaluation results via email. The feedback unit can also notify employees of the evaluation results through a dedicated evaluation system. This allows the evaluation system according to the embodiment to objectively evaluate employees.

[0030] The measurement unit collects measurement data from the PC. For example, the measurement unit records PC usage time and application usage frequency. Specifically, it records PC usage time in minutes, allowing for a detailed understanding of which applications were used and for how long. This makes it possible to analyze the user's work patterns and efficiency. The measurement unit can also collect PC operation logs and evaluate work efficiency. Operation logs include keyboard input frequency, mouse click count, and window switching count. By analyzing this data, it is possible to gain a detailed understanding of how the user operates the PC and identify bottlenecks and areas for improvement in their work. Furthermore, the measurement unit monitors the usage of specific applications, for example, recording the frequency of use of development tools or design software, which allows for evaluation of project progress and work concentration. In this way, the measurement unit collects PC usage data from multiple perspectives and can evaluate the user's work efficiency and performance in detail.

[0031] The Deliverable Collection Department collects deliverables such as project progress reports and completed products. Specifically, it regularly collects project progress reports and evaluates the progress. Progress reports include information on task completion status, incomplete tasks, and next steps. This allows for real-time monitoring of project progress and enables resource reallocation and schedule adjustments as needed. The Deliverable Collection Department can also collect completed products and evaluate their quality. For example, in a software development project, it collects completed code and released applications to check for bugs and verify functionality. Furthermore, the Deliverable Collection Department can collect project progress reports and completed products and evaluate their progress and quality. This allows for a comprehensive evaluation of the overall project progress and deliverable quality, enabling appropriate measures to be taken to ensure project success.

[0032] The Contribution Evaluation Department assesses the degree of participation in the project and the contribution to the team. Specifically, it evaluates participation and contribution. For example, it evaluates based on the number of tasks completed by each member, meeting attendance rate, and the number of proposals and ideas submitted for the project. The Contribution Evaluation Department can also evaluate and assess the contribution to the team. For example, it uses the frequency and quality of communication among team members and the degree of support and cooperation with other members as evaluation criteria. Furthermore, the Contribution Evaluation Department can evaluate participation and contribution to the project and assess contribution. This allows the Contribution Evaluation Department to evaluate each member's contribution to the project in detail and provide feedback to improve the overall team performance.

[0033] The survey collection department collects evaluations from colleagues. Specifically, it collects evaluations from colleagues and assesses their reliability. The surveys are conducted in a format that ensures anonymity and include items that evaluate each member's performance, cooperation, and communication skills. This allows for the collection of frank opinions and evaluations from colleagues, clearly identifying each member's strengths and areas for improvement. The survey collection department can also collect anonymous surveys and assess their reliability. For example, it can use online survey tools to collect anonymous responses and then aggregate and analyze the data. Furthermore, the survey collection department can collect evaluations from colleagues and assess their reliability. This allows the survey collection department to conduct a detailed evaluation of each member's performance and reliability within the team based on evaluations from colleagues.

[0034] The generation unit analyzes collected data and generates evaluations for each employee. Specifically, it analyzes collected data and evaluates each employee's performance. For example, it integrates PC usage data and operation logs collected from the measurement unit, progress reports and finished products from the deliverable collection unit, participation and contribution levels from the contribution evaluation unit, and peer evaluations from the survey collection unit to perform a comprehensive evaluation. The generation unit can also analyze data and generate evaluations using a generation AI. The generation AI receives various data as input and builds models for performance and contribution evaluations. For example, it uses machine learning algorithms to learn evaluation criteria from past data and generate evaluations based on new data. Furthermore, the generation unit can analyze collected data and evaluate each employee's performance. This allows the generation unit to provide objective and fair evaluations, supporting improved employee motivation and skill development.

[0035] The Feedback Department provides feedback to employees on the evaluation results generated by the Generation Department. Specifically, evaluation results are notified via email. The evaluation results include each employee's performance evaluation, contribution evaluation, and feedback from colleagues. The Feedback Department can also notify evaluation results through a dedicated evaluation system. For example, evaluation results can be viewed through an employee-only portal site or application. Furthermore, the Feedback Department can also provide feedback on the evaluation results to employees. This allows employees to understand their strengths and areas for improvement, and use this information to set future goals and improve their skills. By communicating evaluation results quickly and accurately, the Feedback Department can support employee growth and contribute to improving the overall performance of the organization.

[0036] The measurement unit can collect data to evaluate the work efficiency of employees. For example, the measurement unit can record the completion time of an employee's task and evaluate work efficiency. The measurement unit can also record the error rate of an employee and evaluate work efficiency. For example, the measurement unit can record the completion time of an employee's task and evaluate work efficiency. The measurement unit can also record the error rate of an employee and evaluate work efficiency. Furthermore, the measurement unit can collect data to evaluate the work efficiency of employees. This allows the measurement unit to collect data to evaluate the work efficiency of employees. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the completion time of an employee's task into the AI ​​and have the AI ​​perform the work efficiency evaluation.

[0037] The deliverable collection unit can collect deliverables such as project progress reports and completed products. For example, the deliverable collection unit can collect project progress reports and evaluate the progress status. The deliverable collection unit can also collect completed products and evaluate their quality. For example, the deliverable collection unit can collect project progress reports and evaluate the progress status. The deliverable collection unit can also collect completed products and evaluate their quality. Furthermore, the deliverable collection unit can collect project progress reports and completed products and evaluate the progress status and quality. This allows for the collection of deliverables such as project progress reports and completed products. Some or all of the above processing in the deliverable collection unit may be performed using AI, for example, or not using AI. For example, the deliverable collection unit can input project progress reports into AI and have AI perform the progress evaluation.

[0038] The contribution evaluation unit can evaluate the degree of participation in the project and the degree of contribution to the team. For example, the contribution evaluation unit can evaluate the degree of participation in the project and evaluate the degree of contribution. The contribution evaluation unit can also evaluate the degree of contribution to the team and evaluate the degree of contribution. For example, the contribution evaluation unit can evaluate the degree of participation in the project and evaluate the degree of contribution. The contribution evaluation unit can also evaluate the degree of contribution to the team and evaluate the degree of contribution. Furthermore, the contribution evaluation unit can evaluate the degree of participation in the project and the degree of contribution to the team and evaluate the degree of contribution. This makes it possible to evaluate the degree of participation in the project and the degree of contribution to the team. Some or all of the above processing in the contribution evaluation unit may be performed using AI, for example, or not using AI. For example, the contribution evaluation unit can input the degree of participation in the project into AI and have AI perform the evaluation of the degree of contribution.

[0039] The survey collection unit can collect evaluations from colleagues. The survey collection unit can, for example, collect evaluations from colleagues and evaluate their reliability. The survey collection unit can also collect anonymous surveys and evaluate their reliability. For example, the survey collection unit can collect evaluations from colleagues and evaluate their reliability. The survey collection unit can also collect anonymous surveys and evaluate their reliability. Furthermore, the survey collection unit can also collect evaluations from colleagues and evaluate their reliability. This allows for the collection of evaluations from colleagues. Some or all of the above processing in the survey collection unit may be performed using AI, for example, or without AI. For example, the survey collection unit can input evaluations from colleagues into AI and have AI perform the reliability evaluation.

[0040] The generation unit can analyze the collected data and objectively evaluate the performance of each employee. For example, the generation unit can analyze the collected data and evaluate the performance of each employee. The generation unit can also analyze the data and generate evaluations using a generation AI. For example, the generation unit can analyze the collected data and evaluate the performance of each employee. The generation unit can also analyze the data and generate evaluations using a generation AI. Furthermore, the generation unit can analyze the collected data and evaluate the performance of each employee. This allows for an objective evaluation of each employee's performance. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the collected data into a generation AI and have the generation AI perform the performance evaluation.

[0041] The feedback unit can provide feedback to employees on the evaluation results generated by the generation unit. The feedback unit can, for example, notify employees of the evaluation results via email. The feedback unit can also notify employees of the evaluation results through a dedicated evaluation system. For example, the feedback unit can notify employees of the evaluation results via email. The feedback unit can also notify employees of the evaluation results through a dedicated evaluation system. Furthermore, the feedback unit can provide feedback to employees on the evaluation results. This allows the evaluation results to be provided to employees. Some or all of the above-described processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the evaluation results into AI and have AI generate the feedback.

[0042] The feedback unit may provide methods for notifying evaluation results via email or through a dedicated evaluation system. For example, the feedback unit may notify evaluation results via email. Alternatively, the feedback unit may also notify evaluation results through a dedicated evaluation system. Furthermore, the feedback unit may provide feedback on evaluation results to employees. This allows for notification via email or through a dedicated evaluation system. Some or all of the above-described processes in the feedback unit may be performed using AI, or without AI. For example, the feedback unit may input evaluation results into AI and have the AI ​​select the notification method.

[0043] The measurement unit can record in detail the frequency of use of specific applications and tools when collecting measurement data. For example, the measurement unit can record the start and end times of applications used by employees and analyze their usage frequency. The measurement unit can also collect operation logs when employees use specific tools and evaluate work efficiency. For example, the measurement unit can record the start and end times of applications used by employees and analyze their usage frequency. The measurement unit can also collect operation logs when employees use specific tools and evaluate work efficiency. Furthermore, the measurement unit can record the types of applications used by employees and their usage time and analyze work patterns. This allows for detailed recording of the frequency of use of specific applications and tools. Some or all of the above processing in the measurement unit may be performed using AI, for example, or not. For example, the measurement unit can input application usage data into AI and have AI perform the usage frequency analysis.

[0044] The measurement unit can take into account employee break times and interruptions during work when evaluating work efficiency. For example, the measurement unit can record employee break times and reflect them in the evaluation of work efficiency. The measurement unit can also record the time employees were interrupted during work and reflect it in the evaluation of work efficiency. For example, the measurement unit can record employee break times and reflect them in the evaluation of work efficiency. The measurement unit can also record the time employees were interrupted during work and reflect it in the evaluation of work efficiency. Furthermore, the measurement unit can comprehensively analyze employee break times and interruptions and adjust the evaluation criteria for work efficiency. This allows for consideration of employee break times and interruptions during work. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without using AI. For example, the measurement unit can input break time and interruption time data into AI and have the AI ​​perform the evaluation of work efficiency.

[0045] The measurement unit can analyze employees' device usage patterns when collecting measurement data and propose efficient work methods. For example, the measurement unit can analyze employees' device usage patterns and propose an optimal work schedule. Furthermore, the measurement unit can also propose efficient tool usage methods based on employees' device usage patterns. For example, the measurement unit can analyze employees' device usage patterns and propose an optimal work schedule. Furthermore, the measurement unit can also analyze employees' device usage patterns and provide advice to improve work efficiency. This allows the measurement unit to analyze employees' device usage patterns and propose efficient work methods. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input device usage data into AI and have the AI ​​perform usage pattern analysis and work method proposals.

[0046] The measurement unit can correct the data when collecting measurement data, taking into account the employees' work environment (e.g., office temperature and lighting). For example, the measurement unit can record the office temperature and reflect it in the evaluation of work efficiency. The measurement unit can also record the office lighting conditions and reflect them in the evaluation of work efficiency. For example, the measurement unit can record the office temperature and reflect it in the evaluation of work efficiency. The measurement unit can also record the office lighting conditions and reflect them in the evaluation of work efficiency. Furthermore, the measurement unit can comprehensively analyze the work environment data and adjust the evaluation criteria for work efficiency. This allows the data to be corrected taking into account the employees' work environment. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input work environment data into AI and have AI perform data correction.

[0047] The deliverable collection unit can be equipped with a function to track project progress in real time when collecting deliverables. For example, the deliverable collection unit can track project progress in real time and reflect it in the collection of deliverables. The deliverable collection unit can also adjust the timing of deliverable collection based on project progress. For example, the deliverable collection unit can track project progress in real time and reflect it in the collection of deliverables. The deliverable collection unit can also adjust the timing of deliverable collection based on project progress. Furthermore, the deliverable collection unit can monitor project progress in real time and evaluate the quality of deliverables. This allows for real-time tracking of project progress. Some or all of the above processes in the deliverable collection unit may be performed using AI, for example, or not. For example, the deliverable collection unit can input project progress data into AI and have the AI ​​perform progress tracking and adjustment of collection timing.

[0048] The deliverable collection unit can introduce detailed metrics for quality evaluation when collecting deliverables. For example, the deliverable collection unit can introduce detailed metrics for evaluating the quality of deliverables and clarify the evaluation criteria. The deliverable collection unit can also provide feedback on the evaluation results based on the metrics for evaluating the quality of deliverables. For example, the deliverable collection unit can introduce detailed metrics for evaluating the quality of deliverables and clarify the evaluation criteria. The deliverable collection unit can also provide feedback on the evaluation results based on the metrics for evaluating the quality of deliverables. Furthermore, the deliverable collection unit can introduce metrics for evaluating the quality of deliverables and ensure transparency in the evaluation. This allows for the introduction of detailed metrics for quality evaluation. Some or all of the above processes in the deliverable collection unit may be performed using AI, for example, or not. For example, the deliverable collection unit can input quality evaluation metrics into AI and have the AI ​​set evaluation criteria and provide feedback on evaluation results.

[0049] The deliverable collection unit can adjust its deliverable collection method according to the scale and importance of the project. For example, in the case of a large-scale project, the deliverable collection unit can collect detailed deliverables and set strict evaluation criteria. Conversely, in the case of a small-scale project, the deliverable collection unit can collect simplified deliverables and relax evaluation criteria. Furthermore, in the case of a high-priority project, the deliverable collection unit can set stricter deliverable collection methods and clarify evaluation criteria. This allows the collection method to be adjusted according to the scale and importance of the project. Some or all of the above processes in the deliverable collection unit may be performed using AI, for example, or not. For example, the deliverable collection unit can input project scale and importance data into the AI ​​and have the AI ​​adjust the collection method.

[0050] The deliverable collection unit can perform evaluations by referring to external evaluation criteria and industry standards when collecting deliverables. For example, the deliverable collection unit can set evaluation criteria for deliverables by referring to external evaluation criteria. The deliverable collection unit can also set evaluation criteria for deliverables based on industry standards. For example, the deliverable collection unit can set evaluation criteria for deliverables by referring to external evaluation criteria. The deliverable collection unit can also set evaluation criteria for deliverables based on industry standards. Furthermore, the deliverable collection unit can also provide feedback on the evaluation results of deliverables by referring to external evaluation criteria and industry standards. This allows evaluations to be performed by referring to external evaluation criteria and industry standards. Some or all of the above processes in the deliverable collection unit may be performed using AI, for example, or not using AI. For example, the deliverable collection unit can input data on external evaluation criteria and industry standards into AI and have the AI ​​perform the setting of evaluation criteria and the feedback of evaluation results.

[0051] The contribution evaluation unit can consider project success and team performance when evaluating contributions. For example, the contribution evaluation unit can evaluate contributions based on project success. It can also evaluate contributions based on team performance. For example, the contribution evaluation unit can evaluate contributions based on project success. It can also evaluate contributions based on team performance. Furthermore, the contribution evaluation unit can comprehensively consider project success and team performance to evaluate contributions. This allows for the evaluation of contributions while considering project success and team performance. Some or all of the above processing in the contribution evaluation unit may be performed using AI, for example, or without AI. For example, the contribution evaluation unit can input project success and team performance data into AI and have the AI ​​perform the contribution evaluation.

[0052] The contribution evaluation department can assess an employee's leadership and problem-solving abilities when evaluating their contribution. For example, the contribution evaluation department can evaluate an employee's leadership and reflect it in their contribution. The contribution evaluation department can also evaluate an employee's problem-solving abilities and reflect it in their contribution. For example, the contribution evaluation department can evaluate an employee's leadership and reflect it in their contribution. The contribution evaluation department can also evaluate an employee's problem-solving abilities and reflect it in their contribution. Furthermore, the contribution evaluation department can comprehensively evaluate an employee's leadership and problem-solving abilities and reflect it in their contribution. This allows for the evaluation of an employee's leadership and problem-solving abilities to assess their contribution. Some or all of the above processes in the contribution evaluation department may be performed using AI, for example, or not. For example, the contribution evaluation department can input data on an employee's leadership and problem-solving abilities into an AI and have the AI ​​perform the contribution evaluation.

[0053] The contribution evaluation unit can evaluate an employee's contribution by referring to their past project history. For example, the contribution evaluation unit can refer to an employee's past project history to evaluate their contribution. The contribution evaluation unit can also set evaluation criteria based on an employee's past project history. For example, the contribution evaluation unit can refer to an employee's past project history to evaluate their contribution. The contribution evaluation unit can also set evaluation criteria based on an employee's past project history. Furthermore, the contribution evaluation unit can comprehensively analyze an employee's past project history to evaluate their contribution. This allows the contribution evaluation unit to refer to an employee's past project history to evaluate their contribution. Some or all of the above processes in the contribution evaluation unit may be performed using AI, for example, or not. For example, the contribution evaluation unit can input data on an employee's past project history into an AI and have the AI ​​perform the contribution evaluation.

[0054] The contribution evaluation department can evaluate employees' skill sets and expertise when assessing their contributions. For example, the contribution evaluation department can evaluate employees' skill sets and reflect them in their contributions. It can also evaluate employees' expertise and reflect it in their contributions. Furthermore, the contribution evaluation department can comprehensively evaluate employees' skill sets and expertise and reflect it in their contributions. This allows the contribution evaluation department to consider employees' skill sets and expertise when assessing their contributions. Some or all of the above processes in the contribution evaluation department may be performed using AI, for example, or not. For example, the contribution evaluation department can input employee skill set and expertise data into an AI and have the AI ​​perform the contribution evaluation.

[0055] The survey collection unit can enhance its functions to ensure anonymity when collecting surveys. For example, the survey collection unit can anonymize the personal information of survey respondents to ensure the reliability of their responses. The survey collection unit can also ensure anonymity by not recording the IP addresses of survey respondents. For example, the survey collection unit can anonymize the personal information of survey respondents to ensure the reliability of their responses. The survey collection unit can also ensure anonymity by not recording the IP addresses of survey respondents. Furthermore, the survey collection unit can ensure anonymity by encrypting the content of survey respondents' responses. This ensures the anonymity of the survey. Some or all of the above processing in the survey collection unit may be performed using AI, for example, or without AI. For example, the survey collection unit can input the respondent's personal information into AI and have AI perform the anonymization process.

[0056] The survey collection unit can introduce metrics to evaluate the reliability of responses when collecting surveys. For example, the survey collection unit can introduce metrics to evaluate the consistency of survey responses. The survey collection unit can also provide feedback on the response results based on the metrics for evaluating the reliability of survey responses. For example, the survey collection unit can introduce metrics to evaluate the consistency of survey responses. The survey collection unit can also provide feedback on the response results based on the metrics for evaluating the reliability of survey responses. Furthermore, the survey collection unit can introduce metrics for evaluating the reliability of survey responses and ensure transparency in the evaluation. This allows for the evaluation of the reliability of survey responses. Some or all of the above processing in the survey collection unit may be performed using AI, for example, or not using AI. For example, the survey collection unit can input metrics for evaluating the reliability of responses into AI and have the AI ​​perform the evaluation and feedback.

[0057] The survey collection unit can customize the content of survey questions according to the employee's job duties and position when collecting survey responses. For example, the survey collection unit can provide relevant questions according to the employee's job duties. The survey collection unit can also provide appropriate questions according to the employee's position. For example, the survey collection unit can provide relevant questions according to the employee's job duties. The survey collection unit can also provide appropriate questions according to the employee's position. Furthermore, the survey collection unit can customize the content of survey questions by comprehensively considering the employee's job duties and position. This allows for the customization of survey questions according to the employee's job duties and position. Some or all of the above processing in the survey collection unit may be performed using AI, for example, or not using AI. For example, the survey collection unit can input employee job duties and position data into AI and have the AI ​​customize the content of the survey questions.

[0058] The survey collection unit can add a reminder function to facilitate regular feedback when collecting surveys. For example, the survey collection unit can send reminders periodically to encourage respondents to answer the survey. The survey collection unit can also send reminders when the deadline for answering the survey is approaching. For example, the survey collection unit can send reminders periodically to encourage respondents to answer the survey. The survey collection unit can also send reminders when the deadline for answering the survey is approaching. Furthermore, the survey collection unit can monitor the survey response status and send reminders to those who have not yet responded. This adds a reminder function to facilitate regular feedback. Some or all of the above processes in the survey collection unit may be performed using AI, for example, or not using AI. For example, the survey collection unit can have AI perform the sending of reminders.

[0059] The generation unit can improve the accuracy of the evaluation by considering the interrelationships of the collected data during generation. For example, the generation unit can analyze the interrelationships of the collected data and reflect them in the evaluation results. The generation unit can also set evaluation criteria based on the interrelationships of the data. For example, the generation unit can analyze the interrelationships of the collected data and reflect them in the evaluation results. The generation unit can also set evaluation criteria based on the interrelationships of the data. Furthermore, the generation unit can comprehensively consider the interrelationships of the data and improve the accuracy of the evaluation results. This makes it possible to improve the accuracy of the evaluation by considering the interrelationships of the collected data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the collected data into a generation AI and have the generation AI perform the analysis of the interrelationships of the data and the setting of evaluation criteria.

[0060] The generation unit can optimize the evaluation algorithm by referring to past evaluation results during generation. For example, the generation unit can optimize the evaluation algorithm by referring to past evaluation results. The generation unit can also set evaluation criteria based on past evaluation results. For example, the generation unit can optimize the evaluation algorithm by referring to past evaluation results. The generation unit can also set evaluation criteria based on past evaluation results. Furthermore, the generation unit can optimize the evaluation algorithm by comprehensively analyzing past evaluation results. This allows the evaluation algorithm to be optimized by referring to past evaluation results. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input past evaluation results into a generation AI and have the generation AI perform the optimization of the evaluation algorithm.

[0061] The generation unit can suggest routes while considering the user's current weather information when generating videos. For example, in rainy weather, the generation unit will prioritize suggesting routes with roofs or underpasses. In addition, in sunny weather, the generation unit can suggest routes with good scenery. Furthermore, on snowy days, the generation unit can suggest routes that are less slippery. This allows the system to suggest the optimal route while considering the user's current weather information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input weather information into a generation AI and have the generation AI suggest the optimal route.

[0062] The generation unit can suggest the optimal walking route while considering the user's health condition when generating a video. For example, if the user is tired, the generation unit will suggest the shortest route. Also, if the user is seeking healthy exercise, the generation unit can suggest a slightly longer route. Furthermore, if the user is feeling unwell, the generation unit can suggest a route that includes rest points. This allows the generation unit to suggest the optimal walking route while considering the user's health condition. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on the user's health condition into the generation AI and have the generation AI suggest the optimal walking route.

[0063] The feedback department can, when providing feedback, refer to the employee's past evaluation results to suggest specific areas for improvement. For example, the feedback department can refer to past evaluation results to suggest specific areas for improvement. The feedback department can also propose improvement measures based on past evaluation results. For example, the feedback department can refer to past evaluation results to suggest specific areas for improvement. The feedback department can also comprehensively analyze past evaluation results to suggest specific areas for improvement. This allows the feedback department to refer to the employee's past evaluation results to suggest specific areas for improvement. Some or all of the above processes in the feedback department may be performed using AI, for example, or not. For example, the feedback department can input past evaluation results into AI and have the AI ​​perform the task of suggesting specific areas for improvement.

[0064] The feedback department can provide advice based on the employee's career path and goals during the feedback process. For example, the feedback department can consider the employee's career path and provide appropriate advice. Furthermore, the feedback department can provide specific advice based on the employee's goals. For example, the feedback department can consider the employee's career path and provide appropriate advice. Furthermore, the feedback department can provide specific advice based on the employee's goals. In addition, the feedback department can provide advice by comprehensively considering the employee's career path and goals. This allows the feedback department to provide advice based on the employee's career path and goals. Some or all of the above processes in the feedback department may be performed using AI, for example, or not. For example, the feedback department can input data on the employee's career path and goals into an AI and have the AI ​​provide advice.

[0065] The feedback unit can select the optimal notification method when providing feedback, taking into account the employee's device information. For example, if the employee is using a smartphone, the feedback unit can provide feedback using push notifications. Alternatively, if the employee is using a tablet, the feedback unit can provide feedback using email notifications. Furthermore, if the employee is using a desktop PC, the feedback unit can provide feedback through a dedicated evaluation system. This allows the system to select the optimal notification method, taking into account the employee's device information. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input the employee's device information into an AI and have the AI ​​select the optimal notification method.

[0066] The feedback unit can propose training programs based on evaluation results during the feedback process. For example, the feedback unit can propose training programs to improve employees' skills based on evaluation results. Furthermore, the feedback unit can also propose training programs aligned with employees' career paths based on evaluation results. This allows for the proposal of training programs based on evaluation results. Some or all of the above-described processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input evaluation results into AI and have the AI ​​propose training programs.

[0067] The feedback department can provide optimal advice when giving feedback, taking into account the employee's health condition. For example, if an employee is tired, the feedback department can provide advice encouraging them to rest. Furthermore, if an employee is stressed, the feedback department can suggest stress reduction measures. In addition, the feedback department can provide appropriate advice by comprehensively considering the employee's health condition. This allows for the provision of optimal advice while taking the employee's health condition into account. Some or all of the above processes in the feedback department may be performed using AI, for example, or not. For example, the feedback department can input employee health data into AI and have the AI ​​provide optimal advice.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The evaluation system can monitor employees' health status and adjust evaluation criteria based on that status. For example, if an employee is unwell, the evaluation criteria can be relaxed to prioritize their recovery. If an employee is healthy, the normal evaluation criteria can be applied to accurately assess their performance. Furthermore, if an employee is overworked, evaluation criteria that encourage rest can be applied to promote long-term health maintenance. This allows evaluation criteria to be adjusted based on the employee's health status. Health status monitoring is performed using wearable devices or health management apps. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input health data into the AI ​​and have the AI ​​adjust the evaluation criteria.

[0070] The evaluation system can analyze employees' device usage patterns and suggest efficient work methods. For example, it can analyze employees' device usage patterns and suggest an optimal work schedule. It can also suggest efficient tool usage methods based on employees' device usage patterns. Furthermore, it can analyze employees' device usage patterns and provide advice to improve work efficiency. This allows for the analysis of employees' device usage patterns and the suggestion of efficient work methods. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input device usage data into the AI ​​and have the AI ​​perform usage pattern analysis and suggest work methods.

[0071] The evaluation system can optimize its evaluation algorithm by referring to employees' past evaluation results. For example, it can optimize the evaluation algorithm by referring to past evaluation results. It can also set evaluation criteria based on past evaluation results. Furthermore, it can optimize the evaluation algorithm by comprehensively analyzing past evaluation results. This allows the evaluation algorithm to be optimized by referring to past evaluation results. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input past evaluation results into a generation AI and have the generation AI perform the optimization of the evaluation algorithm.

[0072] The evaluation system can customize evaluation criteria according to an employee's job duties and position. For example, relevant evaluation criteria can be set according to an employee's job duties. Appropriate evaluation criteria can also be set according to an employee's position. Furthermore, evaluation criteria can be customized by comprehensively considering an employee's job duties and position. This allows for the customization of evaluation criteria according to an employee's job duties and position. Some or all of the above processes in the evaluation department may be performed using AI, or not. For example, the evaluation department can input employee job duties and position data into the AI ​​and have the AI ​​perform the customization of evaluation criteria.

[0073] The evaluation system can set evaluation criteria based on employees' career paths and goals. For example, it can set appropriate evaluation criteria considering an employee's career path. It can also set specific evaluation criteria based on an employee's goals. Furthermore, it can set evaluation criteria by comprehensively considering both the employee's career path and goals. This allows evaluation criteria to be set based on an employee's career path and goals. Some or all of the above processes in the evaluation department may be performed using AI or not. For example, the evaluation department can input data on employees' career paths and goals into the AI ​​and have the AI ​​set the evaluation criteria.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The measurement unit collects measurement data from the PC. For example, it records PC usage time and application usage frequency, and collects PC operation logs to evaluate work efficiency. Step 2: The deliverable collection department collects deliverables such as project progress reports and completed products. For example, they collect progress reports to evaluate progress and collect completed products to evaluate quality. Step 3: The contribution evaluation department evaluates the degree of participation in the project and the degree of contribution to the team. For example, they evaluate the degree of participation in the project and the degree of contribution to the team, and then evaluate the degree of contribution. Step 4: The survey collection team collects feedback from colleagues. For example, they collect feedback from colleagues and then collect anonymous surveys to assess the reliability of the feedback. Step 5: The generation unit analyzes the collected data and generates an evaluation for each employee. For example, it analyzes the collected data and uses a generation AI to evaluate each employee's performance. Step 6: The feedback unit provides feedback to employees on the evaluation results generated by the generation unit. For example, the evaluation results are notified via email or through a dedicated evaluation system.

[0076] (Example of form 2) The evaluation system according to an embodiment of the present invention is a system for objectively evaluating employees. This evaluation system collects PC measurement data, deliverables, contributions, and employee questionnaires, and generates an evaluation for each employee using a generating AI. For example, the evaluation system evaluates work efficiency from PC measurement data, quality from deliverables, impact on the team from contributions, and trustworthiness from colleagues from questionnaires. Because this system ensures that evaluations are based on objective data and not influenced by subjectivity, it realizes a world where hard work is fairly rewarded. For example, if an employee works long hours and submits high-quality deliverables, making a significant contribution to the team, that effort will be fairly evaluated. Furthermore, since positive evaluations from colleagues are also taken into consideration, it contributes to fostering a corporate culture that values ​​teamwork. This evaluation system not only improves employee motivation but also contributes to improving the productivity of the entire company. Because employees know that their efforts will be fairly evaluated, they will strive even harder. In addition, because evaluations are based on objective data, feelings of unfairness are eliminated, and trust among employees is strengthened. In this way, the evaluation system can objectively evaluate employees.

[0077] The evaluation system according to the embodiment comprises a measurement unit, a deliverable collection unit, a contribution evaluation unit, a questionnaire collection unit, a generation unit, and a feedback unit. The measurement unit collects measurement data from the PC. The measurement unit records, for example, the PC usage time and the frequency of application usage. The measurement unit can also collect PC operation logs and evaluate work efficiency. For example, the measurement unit records the PC usage time and evaluates work efficiency. The measurement unit can also record the frequency of application usage and analyze work patterns. Furthermore, the measurement unit can collect PC operation logs and evaluate work efficiency. The deliverable collection unit collects deliverables such as project progress reports and completed products. For example, the deliverable collection unit collects project progress reports and evaluates the progress status. Furthermore, the deliverable collection unit can collect completed products and evaluate their quality. For example, the deliverable collection unit collects project progress reports and evaluates the progress status. Furthermore, the deliverable collection unit can collect completed products and evaluate their quality. Furthermore, the deliverable collection unit can collect project progress reports and completed products and evaluate the progress status and quality. The Contribution Evaluation Department evaluates the degree of participation in projects and the degree of contribution to the team. For example, the Contribution Evaluation Department evaluates the degree of participation in projects and the degree of contribution. The Contribution Evaluation Department can also evaluate the degree of contribution to the team and the degree of contribution. For example, the Contribution Evaluation Department evaluates the degree of participation in projects and the degree of contribution. The Contribution Evaluation Department can also evaluate the degree of contribution to the team and the degree of contribution. Furthermore, the Contribution Evaluation Department can also evaluate the degree of participation in projects and the degree of contribution to the team and the degree of contribution. The Survey Collection Department collects evaluations from colleagues. For example, the Survey Collection Department collects evaluations from colleagues and evaluates their reliability. Furthermore, the Survey Collection Department can also collect anonymous surveys and evaluate their reliability. For example, the Survey Collection Department collects evaluations from colleagues and evaluates their reliability. Furthermore, the Survey Collection Department can also collect anonymous surveys and evaluate their reliability. Furthermore, the Survey Collection Department can also collect evaluations from colleagues and evaluate their reliability. The Generation Department analyzes the collected data and generates evaluations for each employee.The generation unit, for example, analyzes the collected data and evaluates the performance of each employee. The generation unit can also analyze the data and generate evaluations using a generation AI. The feedback unit provides feedback to employees on the evaluation results generated by the generation unit. The feedback unit, for example, notifies employees of the evaluation results via email. The feedback unit can also notify employees of the evaluation results through a dedicated evaluation system. This allows the evaluation system according to the embodiment to objectively evaluate employees.

[0078] The measurement unit collects measurement data from the PC. For example, the measurement unit records PC usage time and application usage frequency. Specifically, it records PC usage time in minutes, allowing for a detailed understanding of which applications were used and for how long. This makes it possible to analyze the user's work patterns and efficiency. The measurement unit can also collect PC operation logs and evaluate work efficiency. Operation logs include keyboard input frequency, mouse click count, and window switching count. By analyzing this data, it is possible to gain a detailed understanding of how the user operates the PC and identify bottlenecks and areas for improvement in their work. Furthermore, the measurement unit monitors the usage of specific applications, for example, recording the frequency of use of development tools or design software, which allows for evaluation of project progress and work concentration. In this way, the measurement unit collects PC usage data from multiple perspectives and can evaluate the user's work efficiency and performance in detail.

[0079] The Deliverable Collection Department collects deliverables such as project progress reports and completed products. Specifically, it regularly collects project progress reports and evaluates the progress. Progress reports include information on task completion status, incomplete tasks, and next steps. This allows for real-time monitoring of project progress and enables resource reallocation and schedule adjustments as needed. The Deliverable Collection Department can also collect completed products and evaluate their quality. For example, in a software development project, it collects completed code and released applications to check for bugs and verify functionality. Furthermore, the Deliverable Collection Department can collect project progress reports and completed products and evaluate their progress and quality. This allows for a comprehensive evaluation of the overall project progress and deliverable quality, enabling appropriate measures to be taken to ensure project success.

[0080] The Contribution Evaluation Department assesses the degree of participation in the project and the contribution to the team. Specifically, it evaluates participation and contribution. For example, it evaluates based on the number of tasks completed by each member, meeting attendance rate, and the number of proposals and ideas submitted for the project. The Contribution Evaluation Department can also evaluate and assess the contribution to the team. For example, it uses the frequency and quality of communication among team members and the degree of support and cooperation with other members as evaluation criteria. Furthermore, the Contribution Evaluation Department can evaluate participation and contribution to the project and assess contribution. This allows the Contribution Evaluation Department to evaluate each member's contribution to the project in detail and provide feedback to improve the overall team performance.

[0081] The survey collection department collects evaluations from colleagues. Specifically, it collects evaluations from colleagues and assesses their reliability. The surveys are conducted in a format that ensures anonymity and include items that evaluate each member's performance, cooperation, and communication skills. This allows for the collection of frank opinions and evaluations from colleagues, clearly identifying each member's strengths and areas for improvement. The survey collection department can also collect anonymous surveys and assess their reliability. For example, it can use online survey tools to collect anonymous responses and then aggregate and analyze the data. Furthermore, the survey collection department can collect evaluations from colleagues and assess their reliability. This allows the survey collection department to conduct a detailed evaluation of each member's performance and reliability within the team based on evaluations from colleagues.

[0082] The generation unit analyzes collected data and generates evaluations for each employee. Specifically, it analyzes collected data and evaluates each employee's performance. For example, it integrates PC usage data and operation logs collected from the measurement unit, progress reports and finished products from the deliverable collection unit, participation and contribution levels from the contribution evaluation unit, and peer evaluations from the survey collection unit to perform a comprehensive evaluation. The generation unit can also analyze data and generate evaluations using a generation AI. The generation AI receives various data as input and builds models for performance and contribution evaluations. For example, it uses machine learning algorithms to learn evaluation criteria from past data and generate evaluations based on new data. Furthermore, the generation unit can analyze collected data and evaluate each employee's performance. This allows the generation unit to provide objective and fair evaluations, supporting improved employee motivation and skill development.

[0083] The Feedback Department provides feedback to employees on the evaluation results generated by the Generation Department. Specifically, evaluation results are notified via email. The evaluation results include each employee's performance evaluation, contribution evaluation, and feedback from colleagues. The Feedback Department can also notify evaluation results through a dedicated evaluation system. For example, evaluation results can be viewed through an employee-only portal site or application. Furthermore, the Feedback Department can also provide feedback on the evaluation results to employees. This allows employees to understand their strengths and areas for improvement, and use this information to set future goals and improve their skills. By communicating evaluation results quickly and accurately, the Feedback Department can support employee growth and contribute to improving the overall performance of the organization.

[0084] The measurement unit can collect data to evaluate the work efficiency of employees. For example, the measurement unit can record the completion time of an employee's task and evaluate work efficiency. The measurement unit can also record the error rate of an employee and evaluate work efficiency. For example, the measurement unit can record the completion time of an employee's task and evaluate work efficiency. The measurement unit can also record the error rate of an employee and evaluate work efficiency. Furthermore, the measurement unit can collect data to evaluate the work efficiency of employees. This allows the measurement unit to collect data to evaluate the work efficiency of employees. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the completion time of an employee's task into the AI ​​and have the AI ​​perform the work efficiency evaluation.

[0085] The deliverable collection unit can collect deliverables such as project progress reports and completed products. For example, the deliverable collection unit can collect project progress reports and evaluate the progress status. The deliverable collection unit can also collect completed products and evaluate their quality. For example, the deliverable collection unit can collect project progress reports and evaluate the progress status. The deliverable collection unit can also collect completed products and evaluate their quality. Furthermore, the deliverable collection unit can collect project progress reports and completed products and evaluate the progress status and quality. This allows for the collection of deliverables such as project progress reports and completed products. Some or all of the above processing in the deliverable collection unit may be performed using AI, for example, or not using AI. For example, the deliverable collection unit can input project progress reports into AI and have AI perform the progress evaluation.

[0086] The contribution evaluation unit can evaluate the degree of participation in the project and the degree of contribution to the team. For example, the contribution evaluation unit can evaluate the degree of participation in the project and evaluate the degree of contribution. The contribution evaluation unit can also evaluate the degree of contribution to the team and evaluate the degree of contribution. For example, the contribution evaluation unit can evaluate the degree of participation in the project and evaluate the degree of contribution. The contribution evaluation unit can also evaluate the degree of contribution to the team and evaluate the degree of contribution. Furthermore, the contribution evaluation unit can evaluate the degree of participation in the project and the degree of contribution to the team and evaluate the degree of contribution. This makes it possible to evaluate the degree of participation in the project and the degree of contribution to the team. Some or all of the above processing in the contribution evaluation unit may be performed using AI, for example, or not using AI. For example, the contribution evaluation unit can input the degree of participation in the project into AI and have AI perform the evaluation of the degree of contribution.

[0087] The survey collection unit can collect evaluations from colleagues. The survey collection unit can, for example, collect evaluations from colleagues and evaluate their reliability. The survey collection unit can also collect anonymous surveys and evaluate their reliability. For example, the survey collection unit can collect evaluations from colleagues and evaluate their reliability. The survey collection unit can also collect anonymous surveys and evaluate their reliability. Furthermore, the survey collection unit can also collect evaluations from colleagues and evaluate their reliability. This allows for the collection of evaluations from colleagues. Some or all of the above processing in the survey collection unit may be performed using AI, for example, or without AI. For example, the survey collection unit can input evaluations from colleagues into AI and have AI perform the reliability evaluation.

[0088] The generation unit can analyze the collected data and objectively evaluate the performance of each employee. For example, the generation unit can analyze the collected data and evaluate the performance of each employee. The generation unit can also analyze the data and generate evaluations using a generation AI. For example, the generation unit can analyze the collected data and evaluate the performance of each employee. The generation unit can also analyze the data and generate evaluations using a generation AI. Furthermore, the generation unit can analyze the collected data and evaluate the performance of each employee. This allows for an objective evaluation of each employee's performance. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the collected data into a generation AI and have the generation AI perform the performance evaluation.

[0089] The feedback unit can provide feedback to employees on the evaluation results generated by the generation unit. The feedback unit can, for example, notify employees of the evaluation results via email. The feedback unit can also notify employees of the evaluation results through a dedicated evaluation system. For example, the feedback unit can notify employees of the evaluation results via email. The feedback unit can also notify employees of the evaluation results through a dedicated evaluation system. Furthermore, the feedback unit can provide feedback to employees on the evaluation results. This allows the evaluation results to be provided to employees. Some or all of the above-described processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the evaluation results into AI and have AI generate the feedback.

[0090] The feedback unit may provide methods for notifying evaluation results via email or through a dedicated evaluation system. For example, the feedback unit may notify evaluation results via email. Alternatively, the feedback unit may also notify evaluation results through a dedicated evaluation system. Furthermore, the feedback unit may provide feedback on evaluation results to employees. This allows for notification via email or through a dedicated evaluation system. Some or all of the above-described processes in the feedback unit may be performed using AI, or without AI. For example, the feedback unit may input evaluation results into AI and have the AI ​​select the notification method.

[0091] The measurement unit can estimate employees' emotions and adjust the work efficiency evaluation criteria based on the estimated emotions. For example, if an employee is stressed, the measurement unit can relax the work efficiency evaluation criteria and suggest stress reduction measures. Furthermore, if an employee is relaxed, the measurement unit can apply the normal evaluation criteria to accurately assess work efficiency. For example, if an employee is stressed, the measurement unit can relax the work efficiency evaluation criteria and suggest stress reduction measures. Furthermore, if an employee is relaxed, the measurement unit can apply evaluation criteria that take break time into account to assess work efficiency. This allows for adjustment of work efficiency evaluation criteria based on employees' emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input employee emotional data into a generating AI, which can then use that AI to adjust the evaluation criteria for work efficiency.

[0092] The measurement unit can record in detail the frequency of use of specific applications and tools when collecting measurement data. For example, the measurement unit can record the start and end times of applications used by employees and analyze their usage frequency. The measurement unit can also collect operation logs when employees use specific tools and evaluate work efficiency. For example, the measurement unit can record the start and end times of applications used by employees and analyze their usage frequency. The measurement unit can also collect operation logs when employees use specific tools and evaluate work efficiency. Furthermore, the measurement unit can record the types of applications used by employees and their usage time and analyze work patterns. This allows for detailed recording of the frequency of use of specific applications and tools. Some or all of the above processing in the measurement unit may be performed using AI, for example, or not. For example, the measurement unit can input application usage data into AI and have AI perform the usage frequency analysis.

[0093] The measurement unit can take into account employee break times and interruptions during work when evaluating work efficiency. For example, the measurement unit can record employee break times and reflect them in the evaluation of work efficiency. The measurement unit can also record the time employees were interrupted during work and reflect it in the evaluation of work efficiency. For example, the measurement unit can record employee break times and reflect them in the evaluation of work efficiency. The measurement unit can also record the time employees were interrupted during work and reflect it in the evaluation of work efficiency. Furthermore, the measurement unit can comprehensively analyze employee break times and interruptions and adjust the evaluation criteria for work efficiency. This allows for consideration of employee break times and interruptions during work. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without using AI. For example, the measurement unit can input break time and interruption time data into AI and have the AI ​​perform the evaluation of work efficiency.

[0094] The measurement unit can estimate an employee's emotions and determine the priority of data to collect based on the estimated emotions. For example, if an employee is stressed, the measurement unit will prioritize collecting data related to stress reduction. Similarly, if an employee is relaxed, the measurement unit can prioritize collecting normal work data. Furthermore, if an employee is tired, the measurement unit can prioritize collecting data related to breaks and interruptions. This allows the system to determine the priority of data to collect based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the measurement unit may be performed using AI, or not. For example, the measurement unit can input employee emotion data into a generative AI and have the generative AI determine the data prioritization.

[0095] The measurement unit can analyze employees' device usage patterns when collecting measurement data and propose efficient work methods. For example, the measurement unit can analyze employees' device usage patterns and propose an optimal work schedule. Furthermore, the measurement unit can also propose efficient tool usage methods based on employees' device usage patterns. For example, the measurement unit can analyze employees' device usage patterns and propose an optimal work schedule. Furthermore, the measurement unit can also analyze employees' device usage patterns and provide advice to improve work efficiency. This allows the measurement unit to analyze employees' device usage patterns and propose efficient work methods. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input device usage data into AI and have the AI ​​perform usage pattern analysis and work method proposals.

[0096] The measurement unit can correct the data when collecting measurement data, taking into account the employees' work environment (e.g., office temperature and lighting). For example, the measurement unit can record the office temperature and reflect it in the evaluation of work efficiency. The measurement unit can also record the office lighting conditions and reflect them in the evaluation of work efficiency. For example, the measurement unit can record the office temperature and reflect it in the evaluation of work efficiency. The measurement unit can also record the office lighting conditions and reflect them in the evaluation of work efficiency. Furthermore, the measurement unit can comprehensively analyze the work environment data and adjust the evaluation criteria for work efficiency. This allows the data to be corrected taking into account the employees' work environment. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input work environment data into AI and have AI perform data correction.

[0097] The deliverable collection unit can estimate employees' emotions and adjust the evaluation criteria for deliverables based on those estimated emotions. For example, if an employee is stressed, the deliverable collection unit can relax the evaluation criteria for deliverables and suggest stress reduction measures. Furthermore, if an employee is relaxed, the deliverable collection unit can apply the normal evaluation criteria to accurately assess the deliverables. In addition, if an employee is tired, the deliverable collection unit can apply evaluation criteria that take break time into account when evaluating the deliverables. This allows for adjustment of deliverable evaluation criteria based on employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the deliverable collection unit may be performed using AI, for example, or without AI. For example, the deliverable collection unit can input employee emotional data into a generating AI and have the generating AI adjust the evaluation criteria.

[0098] The deliverable collection unit can be equipped with a function to track project progress in real time when collecting deliverables. For example, the deliverable collection unit can track project progress in real time and reflect it in the collection of deliverables. The deliverable collection unit can also adjust the timing of deliverable collection based on project progress. For example, the deliverable collection unit can track project progress in real time and reflect it in the collection of deliverables. The deliverable collection unit can also adjust the timing of deliverable collection based on project progress. Furthermore, the deliverable collection unit can monitor project progress in real time and evaluate the quality of deliverables. This allows for real-time tracking of project progress. Some or all of the above processes in the deliverable collection unit may be performed using AI, for example, or not. For example, the deliverable collection unit can input project progress data into AI and have the AI ​​perform progress tracking and adjustment of collection timing.

[0099] The deliverable collection unit can introduce detailed metrics for quality evaluation when collecting deliverables. For example, the deliverable collection unit can introduce detailed metrics for evaluating the quality of deliverables and clarify the evaluation criteria. The deliverable collection unit can also provide feedback on the evaluation results based on the metrics for evaluating the quality of deliverables. For example, the deliverable collection unit can introduce detailed metrics for evaluating the quality of deliverables and clarify the evaluation criteria. The deliverable collection unit can also provide feedback on the evaluation results based on the metrics for evaluating the quality of deliverables. Furthermore, the deliverable collection unit can introduce metrics for evaluating the quality of deliverables and ensure transparency in the evaluation. This allows for the introduction of detailed metrics for quality evaluation. Some or all of the above processes in the deliverable collection unit may be performed using AI, for example, or not. For example, the deliverable collection unit can input quality evaluation metrics into AI and have the AI ​​set evaluation criteria and provide feedback on evaluation results.

[0100] The deliverable collection unit can estimate employees' emotions and determine the priority of deliverables to collect based on the estimated emotions. For example, if an employee is stressed, the deliverable collection unit will prioritize collecting deliverables related to stress reduction. Conversely, if an employee is relaxed, the deliverable collection unit can also prioritize collecting normal deliverables. Furthermore, if an employee is tired, the deliverable collection unit can prioritize collecting deliverables related to breaks or interruptions. This allows for the prioritization of deliverables to be determined based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the deliverable collection unit may be performed using AI, for example, or without AI. For example, the deliverable collection department can input employee emotional data into a generating AI and have the AI ​​determine the priority of deliverables.

[0101] The deliverable collection unit can adjust its deliverable collection method according to the scale and importance of the project. For example, in the case of a large-scale project, the deliverable collection unit can collect detailed deliverables and set strict evaluation criteria. Conversely, in the case of a small-scale project, the deliverable collection unit can collect simplified deliverables and relax evaluation criteria. Furthermore, in the case of a high-priority project, the deliverable collection unit can set stricter deliverable collection methods and clarify evaluation criteria. This allows the collection method to be adjusted according to the scale and importance of the project. Some or all of the above processes in the deliverable collection unit may be performed using AI, for example, or not. For example, the deliverable collection unit can input project scale and importance data into the AI ​​and have the AI ​​adjust the collection method.

[0102] The deliverable collection unit can perform evaluations by referring to external evaluation criteria and industry standards when collecting deliverables. For example, the deliverable collection unit can set evaluation criteria for deliverables by referring to external evaluation criteria. The deliverable collection unit can also set evaluation criteria for deliverables based on industry standards. For example, the deliverable collection unit can set evaluation criteria for deliverables by referring to external evaluation criteria. The deliverable collection unit can also set evaluation criteria for deliverables based on industry standards. Furthermore, the deliverable collection unit can also provide feedback on the evaluation results of deliverables by referring to external evaluation criteria and industry standards. This allows evaluations to be performed by referring to external evaluation criteria and industry standards. Some or all of the above processes in the deliverable collection unit may be performed using AI, for example, or not using AI. For example, the deliverable collection unit can input data on external evaluation criteria and industry standards into AI and have the AI ​​perform the setting of evaluation criteria and the feedback of evaluation results.

[0103] The contribution evaluation department can estimate an employee's emotions and adjust the evaluation criteria for contribution based on the estimated emotions. For example, if an employee is stressed, the contribution evaluation department can relax the evaluation criteria for contribution and suggest stress reduction measures. Alternatively, if an employee is relaxed, the contribution evaluation department can apply the normal evaluation criteria to accurately assess their contribution. For example, if an employee is stressed, the contribution evaluation department can relax the evaluation criteria for contribution and suggest stress reduction measures. Alternatively, if an employee is relaxed, the contribution evaluation department can apply the normal evaluation criteria to accurately assess their contribution. Furthermore, if an employee is tired, the contribution evaluation department can apply evaluation criteria that take break time into account to assess their contribution. This allows for adjustment of the evaluation criteria for contribution based on an employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the contribution evaluation department may be performed using AI, for example, or without AI. For example, the contribution evaluation department can input employee emotional data into a generating AI and have the AI ​​adjust the evaluation criteria.

[0104] The contribution evaluation unit can consider project success and team performance when evaluating contributions. For example, the contribution evaluation unit can evaluate contributions based on project success. It can also evaluate contributions based on team performance. For example, the contribution evaluation unit can evaluate contributions based on project success. It can also evaluate contributions based on team performance. Furthermore, the contribution evaluation unit can comprehensively consider project success and team performance to evaluate contributions. This allows for the evaluation of contributions while considering project success and team performance. Some or all of the above processing in the contribution evaluation unit may be performed using AI, for example, or without AI. For example, the contribution evaluation unit can input project success and team performance data into AI and have the AI ​​perform the contribution evaluation.

[0105] The contribution evaluation department can assess an employee's leadership and problem-solving abilities when evaluating their contribution. For example, the contribution evaluation department can evaluate an employee's leadership and reflect it in their contribution. The contribution evaluation department can also evaluate an employee's problem-solving abilities and reflect it in their contribution. For example, the contribution evaluation department can evaluate an employee's leadership and reflect it in their contribution. The contribution evaluation department can also evaluate an employee's problem-solving abilities and reflect it in their contribution. Furthermore, the contribution evaluation department can comprehensively evaluate an employee's leadership and problem-solving abilities and reflect it in their contribution. This allows for the evaluation of an employee's leadership and problem-solving abilities to assess their contribution. Some or all of the above processes in the contribution evaluation department may be performed using AI, for example, or not. For example, the contribution evaluation department can input data on an employee's leadership and problem-solving abilities into an AI and have the AI ​​perform the contribution evaluation.

[0106] The contribution evaluation unit can estimate an employee's emotions and adjust the display method of the evaluation results based on the estimated emotions. For example, if an employee is stressed, the contribution evaluation unit can provide a simple and highly visible display method. Furthermore, if an employee is relaxed, the contribution evaluation unit can provide a display method that includes detailed information. In addition, if an employee is tired, the contribution evaluation unit can provide a concise display method. This allows the display method of evaluation results to be adjusted based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the contribution evaluation unit may be performed using AI, for example, or without AI. For example, the contribution evaluation department can input employee emotional data into a generating AI and have the AI ​​adjust how the data is displayed.

[0107] The contribution evaluation unit can evaluate an employee's contribution by referring to their past project history. For example, the contribution evaluation unit can refer to an employee's past project history to evaluate their contribution. The contribution evaluation unit can also set evaluation criteria based on an employee's past project history. For example, the contribution evaluation unit can refer to an employee's past project history to evaluate their contribution. The contribution evaluation unit can also set evaluation criteria based on an employee's past project history. Furthermore, the contribution evaluation unit can comprehensively analyze an employee's past project history to evaluate their contribution. This allows the contribution evaluation unit to refer to an employee's past project history to evaluate their contribution. Some or all of the above processes in the contribution evaluation unit may be performed using AI, for example, or not. For example, the contribution evaluation unit can input data on an employee's past project history into an AI and have the AI ​​perform the contribution evaluation.

[0108] The contribution evaluation department can evaluate employees' skill sets and expertise when assessing their contributions. For example, the contribution evaluation department can evaluate employees' skill sets and reflect them in their contributions. It can also evaluate employees' expertise and reflect it in their contributions. Furthermore, the contribution evaluation department can comprehensively evaluate employees' skill sets and expertise and reflect it in their contributions. This allows the contribution evaluation department to consider employees' skill sets and expertise when assessing their contributions. Some or all of the above processes in the contribution evaluation department may be performed using AI, for example, or not. For example, the contribution evaluation department can input employee skill set and expertise data into an AI and have the AI ​​perform the contribution evaluation.

[0109] The survey collection unit can estimate employees' emotions and adjust the survey questions based on those estimates. For example, if an employee is stressed, the survey collection unit can add questions related to stress reduction. Alternatively, if an employee is relaxed, the survey collection unit can provide standard questions. Furthermore, if an employee is tired, the survey collection unit can add questions related to breaks or interruptions. This allows the survey questions to be adjusted based on employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the survey collection unit may be performed using AI, or not. For example, the survey collection unit can input employee emotion data into a generative AI and have the generative AI adjust the questions.

[0110] The survey collection unit can enhance its functions to ensure anonymity when collecting surveys. For example, the survey collection unit can anonymize the personal information of survey respondents to ensure the reliability of their responses. The survey collection unit can also ensure anonymity by not recording the IP addresses of survey respondents. For example, the survey collection unit can anonymize the personal information of survey respondents to ensure the reliability of their responses. The survey collection unit can also ensure anonymity by not recording the IP addresses of survey respondents. Furthermore, the survey collection unit can ensure anonymity by encrypting the content of survey respondents' responses. This ensures the anonymity of the survey. Some or all of the above processing in the survey collection unit may be performed using AI, for example, or without AI. For example, the survey collection unit can input the respondent's personal information into AI and have AI perform the anonymization process.

[0111] The survey collection unit can introduce metrics to evaluate the reliability of responses when collecting surveys. For example, the survey collection unit can introduce metrics to evaluate the consistency of survey responses. The survey collection unit can also provide feedback on the response results based on the metrics for evaluating the reliability of survey responses. For example, the survey collection unit can introduce metrics to evaluate the consistency of survey responses. The survey collection unit can also provide feedback on the response results based on the metrics for evaluating the reliability of survey responses. Furthermore, the survey collection unit can introduce metrics for evaluating the reliability of survey responses and ensure transparency in the evaluation. This allows for the evaluation of the reliability of survey responses. Some or all of the above processing in the survey collection unit may be performed using AI, for example, or not using AI. For example, the survey collection unit can input metrics for evaluating the reliability of responses into AI and have the AI ​​perform the evaluation and feedback.

[0112] The survey collection unit can estimate employees' emotions and adjust the survey response method based on the estimated emotions. For example, if an employee is stressed, the survey collection unit can provide a simple multiple-choice response method. Furthermore, if an employee is relaxed, the survey collection unit can provide a detailed, descriptive response method. In addition, if an employee is tired, the survey collection unit can provide a response method that can be completed in a short time. This allows the survey response method to be adjusted based on employees' emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the survey collection unit may be performed using AI, for example, or without AI. For example, the survey collection department can input employee sentiment data into a generating AI and have the AI ​​adjust the response methods.

[0113] The survey collection unit can customize the content of survey questions according to the employee's job duties and position when collecting survey responses. For example, the survey collection unit can provide relevant questions according to the employee's job duties. The survey collection unit can also provide appropriate questions according to the employee's position. For example, the survey collection unit can provide relevant questions according to the employee's job duties. The survey collection unit can also provide appropriate questions according to the employee's position. Furthermore, the survey collection unit can customize the content of survey questions by comprehensively considering the employee's job duties and position. This allows for the customization of survey questions according to the employee's job duties and position. Some or all of the above processing in the survey collection unit may be performed using AI, for example, or not using AI. For example, the survey collection unit can input employee job duties and position data into AI and have the AI ​​customize the content of the survey questions.

[0114] The survey collection unit can add a reminder function to facilitate regular feedback when collecting surveys. For example, the survey collection unit can send reminders periodically to encourage respondents to answer the survey. The survey collection unit can also send reminders when the deadline for answering the survey is approaching. For example, the survey collection unit can send reminders periodically to encourage respondents to answer the survey. The survey collection unit can also send reminders when the deadline for answering the survey is approaching. Furthermore, the survey collection unit can monitor the survey response status and send reminders to those who have not yet responded. This adds a reminder function to facilitate regular feedback. Some or all of the above processes in the survey collection unit may be performed using AI, for example, or not using AI. For example, the survey collection unit can have AI perform the sending of reminders.

[0115] The generation unit can estimate an employee's emotions and adjust the method of generating evaluation results based on the estimated emotions. For example, if an employee is stressed, the generation unit can generate evaluation results that include stress reduction measures. Furthermore, if an employee is relaxed, the generation unit can generate normal evaluation results. In addition, if an employee is tired, the generation unit can generate evaluation results that take break times into account. This allows the method of generating evaluation results to be adjusted based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input employee emotion data into the generation AI and have the generation AI adjust the method of generating evaluation results.

[0116] The generation unit can improve the accuracy of the evaluation by considering the interrelationships of the collected data during generation. For example, the generation unit can analyze the interrelationships of the collected data and reflect them in the evaluation results. The generation unit can also set evaluation criteria based on the interrelationships of the data. For example, the generation unit can analyze the interrelationships of the collected data and reflect them in the evaluation results. The generation unit can also set evaluation criteria based on the interrelationships of the data. Furthermore, the generation unit can comprehensively consider the interrelationships of the data and improve the accuracy of the evaluation results. This makes it possible to improve the accuracy of the evaluation by considering the interrelationships of the collected data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the collected data into a generation AI and have the generation AI perform the analysis of the interrelationships of the data and the setting of evaluation criteria.

[0117] The generation unit can optimize the evaluation algorithm by referring to past evaluation results during generation. For example, the generation unit can optimize the evaluation algorithm by referring to past evaluation results. The generation unit can also set evaluation criteria based on past evaluation results. For example, the generation unit can optimize the evaluation algorithm by referring to past evaluation results. The generation unit can also set evaluation criteria based on past evaluation results. Furthermore, the generation unit can optimize the evaluation algorithm by comprehensively analyzing past evaluation results. This allows the evaluation algorithm to be optimized by referring to past evaluation results. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input past evaluation results into a generation AI and have the generation AI perform the optimization of the evaluation algorithm.

[0118] The generation unit can estimate the employee's emotions, and the generating AI can adjust the length and level of detail of the video based on the estimated emotions. For example, if an employee is in a hurry, the generating AI can generate a short, concise video. Conversely, if an employee is relaxed, the generating AI can generate a longer video with more detailed explanations. Furthermore, if an employee is excited, the generating AI can generate a video with visually stimulating effects. This allows the length and level of detail of the video to be adjusted 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input employee emotion data into the generation AI and have the generation AI adjust the length and detail level of the video.

[0119] The generation unit can suggest routes while considering the user's current weather information when generating videos. For example, in rainy weather, the generation unit will prioritize suggesting routes with roofs or underpasses. In addition, in sunny weather, the generation unit can suggest routes with good scenery. Furthermore, on snowy days, the generation unit can suggest routes that are less slippery. This allows the system to suggest the optimal route while considering the user's current weather information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input weather information into a generation AI and have the generation AI suggest the optimal route.

[0120] The generation unit can suggest the optimal walking route while considering the user's health condition when generating a video. For example, if the user is tired, the generation unit will suggest the shortest route. Also, if the user is seeking healthy exercise, the generation unit can suggest a slightly longer route. Furthermore, if the user is feeling unwell, the generation unit can suggest a route that includes rest points. This allows the generation unit to suggest the optimal walking route while considering the user's health condition. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on the user's health condition into the generation AI and have the generation AI suggest the optimal walking route.

[0121] The feedback unit can estimate an employee's emotions and adjust the content of the feedback based on the estimated emotions. For example, if an employee is stressed, the feedback unit can provide feedback that includes stress reduction measures. It can also provide normal feedback if the employee is relaxed. Furthermore, if an employee is tired, the feedback unit can provide feedback that takes break time into consideration. This allows the content of the feedback to be adjusted based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input employee emotion data into a generative AI and have the generative AI adjust the content of the feedback.

[0122] The feedback department can, when providing feedback, refer to the employee's past evaluation results to suggest specific areas for improvement. For example, the feedback department can refer to past evaluation results to suggest specific areas for improvement. The feedback department can also propose improvement measures based on past evaluation results. For example, the feedback department can refer to past evaluation results to suggest specific areas for improvement. The feedback department can also comprehensively analyze past evaluation results to suggest specific areas for improvement. This allows the feedback department to refer to the employee's past evaluation results to suggest specific areas for improvement. Some or all of the above processes in the feedback department may be performed using AI, for example, or not. For example, the feedback department can input past evaluation results into AI and have the AI ​​perform the task of suggesting specific areas for improvement.

[0123] The feedback department can provide advice based on the employee's career path and goals during the feedback process. For example, the feedback department can consider the employee's career path and provide appropriate advice. Furthermore, the feedback department can provide specific advice based on the employee's goals. For example, the feedback department can consider the employee's career path and provide appropriate advice. Furthermore, the feedback department can provide specific advice based on the employee's goals. In addition, the feedback department can provide advice by comprehensively considering the employee's career path and goals. This allows the feedback department to provide advice based on the employee's career path and goals. Some or all of the above processes in the feedback department may be performed using AI, for example, or not. For example, the feedback department can input data on the employee's career path and goals into an AI and have the AI ​​provide advice.

[0124] The feedback unit can estimate an employee's emotions and adjust the timing of feedback based on the estimated emotions. For example, if an employee is stressed, the feedback unit can adjust the timing of providing feedback that includes stress reduction measures. It can also adjust the timing of providing normal feedback if an employee is relaxed. Furthermore, if an employee is tired, the feedback unit can adjust the timing of providing feedback that takes break time into consideration. This allows for adjusting the timing of feedback based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the feedback unit may be performed using AI, or not. For example, the feedback unit can input employee emotion data into a generative AI and have the generative AI adjust the timing of feedback.

[0125] The feedback unit can select the optimal notification method when providing feedback, taking into account the employee's device information. For example, if the employee is using a smartphone, the feedback unit can provide feedback using push notifications. Alternatively, if the employee is using a tablet, the feedback unit can provide feedback using email notifications. Furthermore, if the employee is using a desktop PC, the feedback unit can provide feedback through a dedicated evaluation system. This allows the system to select the optimal notification method, taking into account the employee's device information. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input the employee's device information into an AI and have the AI ​​select the optimal notification method.

[0126] The feedback unit can propose training programs based on evaluation results during the feedback process. For example, the feedback unit can propose training programs to improve employees' skills based on evaluation results. Furthermore, the feedback unit can also propose training programs aligned with employees' career paths based on evaluation results. This allows for the proposal of training programs based on evaluation results. Some or all of the above-described processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input evaluation results into AI and have the AI ​​propose training programs.

[0127] The feedback department can provide optimal advice when giving feedback, taking into account the employee's health condition. For example, if an employee is tired, the feedback department can provide advice encouraging them to rest. Furthermore, if an employee is stressed, the feedback department can suggest stress reduction measures. In addition, the feedback department can provide appropriate advice by comprehensively considering the employee's health condition. This allows for the provision of optimal advice while taking the employee's health condition into account. Some or all of the above processes in the feedback department may be performed using AI, for example, or not. For example, the feedback department can input employee health data into AI and have the AI ​​provide optimal advice.

[0128] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0129] The evaluation system can estimate an employee's emotions and adjust the evaluation feedback based on those emotions. For example, if an employee is stressed, the feedback can be concise and stress reduction measures can be suggested. If an employee is relaxed, detailed feedback can be provided, including specific advice for further growth. Furthermore, if an employee is tired, feedback can be provided to encourage rest and restore energy for the next step. This allows the feedback to be adjusted based on the employee's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input employee emotion data into a generative AI and have the generative AI adjust the feedback content.

[0130] The evaluation system can monitor employees' health status and adjust evaluation criteria based on that status. For example, if an employee is unwell, the evaluation criteria can be relaxed to prioritize their recovery. If an employee is healthy, the normal evaluation criteria can be applied to accurately assess their performance. Furthermore, if an employee is overworked, evaluation criteria that encourage rest can be applied to promote long-term health maintenance. This allows evaluation criteria to be adjusted based on the employee's health status. Health status monitoring is performed using wearable devices or health management apps. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input health data into the AI ​​and have the AI ​​adjust the evaluation criteria.

[0131] The evaluation system can estimate an employee's emotions and adjust the timing of the evaluation based on those emotions. For example, if an employee is stressed, the evaluation timing can be delayed, and stress reduction measures can be provided first. If an employee is relaxed, the evaluation can be conducted at the usual time, and feedback can be provided. Furthermore, if an employee is tired, the evaluation can be conducted after rest, and more effective feedback can be provided. This allows the timing of evaluations to be adjusted based on the employee's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input employee emotion data into a generative AI and have the generative AI adjust the timing of the evaluation.

[0132] The evaluation system can analyze employees' device usage patterns and suggest efficient work methods. For example, it can analyze employees' device usage patterns and suggest an optimal work schedule. It can also suggest efficient tool usage methods based on employees' device usage patterns. Furthermore, it can analyze employees' device usage patterns and provide advice to improve work efficiency. This allows for the analysis of employees' device usage patterns and the suggestion of efficient work methods. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input device usage data into the AI ​​and have the AI ​​perform usage pattern analysis and suggest work methods.

[0133] The evaluation system can estimate an employee's emotions and adjust how the evaluation results are displayed based on those emotions. For example, if an employee is stressed, a simple and highly visible display method can be provided. If an employee is relaxed, a display method including detailed information can be provided. Furthermore, if an employee is tired, a display method that focuses on the key points can be provided. This allows the display method of evaluation results to be adjusted based on the employee's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input employee emotion data into a generative AI and have the generative AI adjust the display method.

[0134] The evaluation system can optimize its evaluation algorithm by referring to employees' past evaluation results. For example, it can optimize the evaluation algorithm by referring to past evaluation results. It can also set evaluation criteria based on past evaluation results. Furthermore, it can optimize the evaluation algorithm by comprehensively analyzing past evaluation results. This allows the evaluation algorithm to be optimized by referring to past evaluation results. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input past evaluation results into a generation AI and have the generation AI perform the optimization of the evaluation algorithm.

[0135] The evaluation system can estimate an employee's emotions and adjust the method of generating evaluation results based on those estimated emotions. For example, if an employee is stressed, it can generate evaluation results that include stress reduction measures. If an employee is relaxed, it can generate a normal evaluation result. Furthermore, if an employee is tired, it can generate an evaluation result that takes break time into account. This allows the method of generating evaluation results to be adjusted based on the employee's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input employee emotion data into the generative AI and have the generative AI adjust the method of generating evaluation results.

[0136] The evaluation system can customize evaluation criteria according to an employee's job duties and position. For example, relevant evaluation criteria can be set according to an employee's job duties. Appropriate evaluation criteria can also be set according to an employee's position. Furthermore, evaluation criteria can be customized by comprehensively considering an employee's job duties and position. This allows for the customization of evaluation criteria according to an employee's job duties and position. Some or all of the above processes in the evaluation department may be performed using AI, or not. For example, the evaluation department can input employee job duties and position data into the AI ​​and have the AI ​​perform the customization of evaluation criteria.

[0137] The evaluation system can estimate an employee's emotions and adjust the timing of evaluation feedback based on those emotions. For example, if an employee is stressed, the timing of providing feedback that includes stress reduction measures can be adjusted. Similarly, if an employee is relaxed, the timing of providing standard feedback can be adjusted. Furthermore, if an employee is tired, the timing of providing feedback that takes break time into consideration can be adjusted. This allows for adjustment of feedback timing based on an employee's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input employee emotion data into a generative AI and have the generative AI adjust the timing of feedback.

[0138] The evaluation system can set evaluation criteria based on employees' career paths and goals. For example, it can set appropriate evaluation criteria considering an employee's career path. It can also set specific evaluation criteria based on an employee's goals. Furthermore, it can set evaluation criteria by comprehensively considering both the employee's career path and goals. This allows evaluation criteria to be set based on an employee's career path and goals. Some or all of the above processes in the evaluation department may be performed using AI or not. For example, the evaluation department can input data on employees' career paths and goals into the AI ​​and have the AI ​​set the evaluation criteria.

[0139] The following briefly describes the processing flow for example form 2.

[0140] Step 1: The measurement unit collects measurement data from the PC. For example, it records PC usage time and application usage frequency, and collects PC operation logs to evaluate work efficiency. Step 2: The deliverable collection department collects deliverables such as project progress reports and completed products. For example, they collect progress reports to evaluate progress and collect completed products to evaluate quality. Step 3: The contribution evaluation department evaluates the degree of participation in the project and the degree of contribution to the team. For example, they evaluate the degree of participation in the project and the degree of contribution to the team, and then evaluate the degree of contribution. Step 4: The survey collection team collects feedback from colleagues. For example, they collect feedback from colleagues and then collect anonymous surveys to assess the reliability of the feedback. Step 5: The generation unit analyzes the collected data and generates an evaluation for each employee. For example, it analyzes the collected data and uses a generation AI to evaluate each employee's performance. Step 6: The feedback unit provides feedback to employees on the evaluation results generated by the generation unit. For example, the evaluation results are notified via email or through a dedicated evaluation system.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the measurement unit, deliverable collection unit, contribution evaluation unit, questionnaire collection unit, generation unit, and feedback unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the measurement unit collects measurement data from a PC using the control unit 46A of the smart device 14 and evaluates work efficiency using the specific processing unit 290 of the data processing unit 12. The deliverable collection unit collects project progress reports and completed products using the control unit 46A of the smart device 14 and evaluates quality using the specific processing unit 290 of the data processing unit 12. The contribution evaluation unit evaluates the degree of participation in the project and the degree of contribution to the team using the specific processing unit 290 of the data processing unit 12. The questionnaire collection unit collects evaluations from colleagues using the control unit 46A of the smart device 14 and evaluates reliability using the specific processing unit 290 of the data processing unit 12. The generation unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and generates evaluations for each employee. The feedback unit, for example, provides feedback on the evaluation results to employees via the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the measurement unit, deliverable collection unit, contribution evaluation unit, questionnaire collection unit, generation unit, and feedback unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the measurement unit collects measurement data from a PC using the control unit 46A of the smart glasses 214 and evaluates work efficiency using the specific processing unit 290 of the data processing unit 12. The deliverable collection unit collects project progress reports and completed products using the control unit 46A of the smart glasses 214 and evaluates quality using the specific processing unit 290 of the data processing unit 12. The contribution evaluation unit evaluates the degree of participation in the project and the degree of contribution to the team using the specific processing unit 290 of the data processing unit 12. The questionnaire collection unit collects evaluations from colleagues using the control unit 46A of the smart glasses 214 and evaluates reliability using the specific processing unit 290 of the data processing unit 12. The generation unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and generates evaluations for each employee. The feedback unit, for example, uses the control unit 46A of the smart glasses 214 to provide feedback on the evaluation results to the employee. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[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 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.

[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 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.

[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 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.

[0176] Each of the multiple elements described above, including the measurement unit, deliverable collection unit, contribution evaluation unit, questionnaire collection unit, generation unit, and feedback unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the measurement unit collects measurement data from a PC using the control unit 46A of the headset terminal 314 and evaluates work efficiency using the specific processing unit 290 of the data processing unit 12. The deliverable collection unit collects project progress reports and completed products using, for example, the control unit 46A of the headset terminal 314 and evaluates quality using the specific processing unit 290 of the data processing unit 12. The contribution evaluation unit evaluates the degree of participation in the project and the degree of contribution to the team using, for example, the specific processing unit 290 of the data processing unit 12. The questionnaire collection unit collects evaluations from colleagues using, for example, the control unit 46A of the headset terminal 314 and evaluates reliability using the specific processing unit 290 of the data processing unit 12. The generation unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 and generates evaluations for each employee. The feedback unit, for example, provides feedback on the evaluation results to employees via the control unit 46A of the headset-type terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0177] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] Each of the multiple elements described above, including the measurement unit, deliverable collection unit, contribution evaluation unit, questionnaire collection unit, generation unit, and feedback unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the measurement unit collects measurement data from a PC using the control unit 46A of the robot 414 and evaluates work efficiency using the specific processing unit 290 of the data processing unit 12. The deliverable collection unit collects project progress reports and completed products using, for example, the control unit 46A of the robot 414 and evaluates quality using the specific processing unit 290 of the data processing unit 12. The contribution evaluation unit evaluates the degree of participation in the project and the degree of contribution to the team using, for example, the specific processing unit 290 of the data processing unit 12. The questionnaire collection unit collects evaluations from colleagues using, for example, the control unit 46A of the robot 414 and evaluates reliability using the specific processing unit 290 of the data processing unit 12. The generation unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 and generates evaluations for each employee. The feedback unit, for example, uses the control unit 46A of the robot 414 to provide feedback on the evaluation results to the employee. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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."

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] (Note 1) A measurement unit that collects measurement data from a PC, The deliverables collection department collects deliverables, The contribution evaluation department evaluates the degree of contribution, The Survey Collection Department collects surveys from employees, A generation unit analyzes the data collected by the measurement unit, the output collection unit, the contribution evaluation unit, and the questionnaire collection unit, and generates an evaluation for each employee. The system includes a feedback unit that provides feedback to employees on the evaluation results generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned measuring unit is Collect data to evaluate employee work efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned deliverable collection unit is: Collect project progress reports and deliverables such as completed products. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned contribution evaluation unit, Evaluate participation in the project and contribution to the team. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned questionnaire collection department, Collect feedback from colleagues. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The collected data is analyzed to objectively evaluate the performance of each employee. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned feedback unit is Provide feedback on the evaluation results to employees. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned feedback unit is The system includes methods for notifying users of evaluation results via email or through a dedicated evaluation system. The system described in Appendix 7, characterized by the features described herein. (Note 9) The aforementioned measuring unit is The system estimates employees' emotions and adjusts the evaluation criteria for work efficiency based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 10) The aforementioned measuring unit is When collecting measurement data, the frequency of use of specific applications and tools is recorded in detail. The system described in Appendix 2, characterized by the features described herein. (Note 11) The aforementioned measuring unit is When evaluating work efficiency, employee break times and interruptions during work should be taken into consideration. The system described in Appendix 2, characterized by the features described herein. (Note 12) The aforementioned measuring unit is We estimate employees' emotions and prioritize the data to collect based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 13) The aforementioned measuring unit is When collecting measurement data, we analyze employees' device usage patterns and propose more efficient work methods. The system described in Appendix 2, characterized by the features described herein. (Note 14) The aforementioned measuring unit is When collecting measurement data, the data is corrected to take into account the employees' work environment. The system described in Appendix 2, characterized by the features described herein. (Note 15) The aforementioned deliverable collection unit is: We estimate employees' emotions and adjust the evaluation criteria for deliverables based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 16) The aforementioned deliverable collection unit is: Add a feature to track project progress in real time when collecting deliverables. The system described in Appendix 3, characterized by the features described herein. (Note 17) The aforementioned deliverable collection unit is: When collecting deliverables, implement detailed metrics for quality evaluation. The system described in Appendix 3, characterized by the features described herein. (Note 18) The aforementioned deliverable collection unit is: Estimate employees' emotions and prioritize the deliverables to be collected based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 19) The aforementioned deliverable collection unit is: When collecting deliverables, adjust the collection method according to the scale and importance of the project. The system described in Appendix 3, characterized by the features described herein. (Note 20) The aforementioned deliverable collection unit is: When collecting deliverables, evaluation will be conducted by referring to external evaluation criteria and industry standards. The system described in Appendix 3, characterized by the features described herein. (Note 21) The aforementioned contribution evaluation unit, We estimate employees' emotions and adjust the performance evaluation criteria based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 22) The aforementioned contribution evaluation unit, When evaluating contributions, we will consider the project's success and the team's performance. The system described in Appendix 4, characterized by the features described herein. (Note 23) The aforementioned contribution evaluation unit, When evaluating contributions, we assess employees' leadership and problem-solving abilities. The system described in Appendix 4, characterized by the features described herein. (Note 24) The aforementioned contribution evaluation unit, The system estimates employees' emotions and adjusts how evaluation results are displayed based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 25) The aforementioned contribution evaluation unit, When evaluating contributions, the evaluation will refer to the employee's past project history. The system described in Appendix 4, characterized by the features described herein. (Note 26) The aforementioned contribution evaluation unit, When evaluating contributions, the employee's skill set and expertise should be taken into consideration. The system described in Appendix 4, characterized by the features described herein. (Note 27) The aforementioned questionnaire collection department, We estimate employees' emotions and adjust the survey questions based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 28) The aforementioned questionnaire collection department, We will enhance the features to ensure anonymity when collecting survey data. The system described in Appendix 5, characterized by the features described herein. (Note 29) The aforementioned questionnaire collection department, When collecting survey responses, introduce metrics to evaluate the reliability of the answers. The system described in Appendix 5, characterized by the features described herein. (Note 30) The aforementioned questionnaire collection department, We estimate employees' emotions and adjust the survey response method based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 31) The aforementioned questionnaire collection department, When collecting survey responses, customize the questions according to the employee's job duties and position. The system described in Appendix 5, characterized by the features described herein. (Note 32) The aforementioned questionnaire collection department, When collecting survey data, add a reminder function to encourage regular feedback. The system described in Appendix 5, characterized by the features described herein. (Note 33) The generating unit is We estimate employees' emotions and adjust the method of generating evaluation results based on the estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 34) The generating unit is During generation, the accuracy of the evaluation is improved by considering the interrelationships of the collected data. The system described in Appendix 6, characterized by the features described herein. (Note 35) The generating unit is During generation, the evaluation algorithm is optimized by referring to past evaluation results. The system described in Appendix 6, characterized by the features described herein. (Note 36) The generating unit is The AI ​​estimates employees' emotions and then adjusts the length and level of detail of the generated video based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 37) The generating unit is When generating a video, the system suggests a route that takes into account the user's current weather information. The system described in Appendix 6, characterized by the features described herein. (Note 38) The generating unit is When generating a video, the system suggests the optimal walking route, taking into account the user's health condition. The system described in Appendix 6, characterized by the features described herein. (Note 39) The aforementioned feedback unit is The system estimates employees' emotions and adjusts the content of feedback based on those estimated emotions. The system described in Appendix 7, characterized by the features described herein. (Note 40) The aforementioned feedback unit is When providing feedback, refer to the employee's past performance reviews to suggest specific areas for improvement. The system described in Appendix 7, characterized by the features described herein. (Note 41) The aforementioned feedback unit is When providing feedback, offer advice based on the employee's career path and goals. The system described in Appendix 7, characterized by the features described herein. (Note 42) The aforementioned feedback unit is The system estimates employees' emotions and adjusts the timing of feedback based on those estimated emotions. The system described in Appendix 7, characterized by the features described herein. (Note 43) The aforementioned feedback unit is When providing feedback, the optimal notification method is selected considering the employee's device information. The system described in Appendix 7, characterized by the features described herein. (Note 44) The aforementioned feedback unit is During the feedback session, we will propose a training program based on the evaluation results. The system described in Appendix 7, characterized by the features described herein. (Note 45) The aforementioned feedback unit is When providing feedback, we take the employee's health condition into consideration and offer the most appropriate advice. The system described in Appendix 7, characterized by the features described herein. [Explanation of Symbols]

[0213] 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. A measurement unit that collects measurement data from a PC, The deliverables collection department collects deliverables, The contribution evaluation department evaluates the degree of contribution, The Survey Collection Department collects surveys from employees, A generation unit analyzes the data collected by the measurement unit, the output collection unit, the contribution evaluation unit, and the questionnaire collection unit, and generates an evaluation for each employee. The system includes a feedback unit that provides feedback to employees on the evaluation results generated by the generation unit. A system characterized by the following features.

2. The aforementioned measuring unit is Collect data to evaluate employee work efficiency. The system according to feature 1.

3. The aforementioned deliverable collection unit is: Collect project progress reports and deliverables such as completed products. The system according to feature 1.

4. The aforementioned contribution evaluation unit, Evaluate participation in the project and contribution to the team. The system according to feature 1.

5. The aforementioned questionnaire collection department, Collect feedback from colleagues. The system according to feature 1.

6. The generating unit is The collected data is analyzed to objectively evaluate the performance of each employee. The system according to feature 1.

7. The aforementioned feedback unit is Provide feedback on the evaluation results to employees. The system according to feature 1.

8. The aforementioned feedback unit is The system includes methods for notifying users of evaluation results via email or through a dedicated evaluation system. The system according to feature 7.

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