system

Abstract

The system according to this embodiment aims to objectively evaluate employees' performance and abilities and to achieve fair evaluation. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a feedback unit. The collection unit collects employee performance data and KPIs. The analysis unit analyzes the data collected by the collection unit. The evaluation unit performs a fair evaluation based on the analysis results obtained by the analysis unit. The feedback unit provides feedback on the evaluation results obtained by the evaluation 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 performed by at least one processor, the method including 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 are biases and prejudices in the evaluation of employees, and there is a risk that a fair evaluation is not conducted.

[0005] The system according to the embodiment aims to objectively evaluate the performance and ability of employees and achieve a fair evaluation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a feedback unit. The data collection unit collects employee performance data and KPIs. The analysis unit analyzes the data collected by the data collection unit. The evaluation unit performs a fair evaluation based on the analysis results obtained by the analysis unit. The feedback unit provides feedback on the evaluation results obtained by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can objectively evaluate employees' performance and abilities, and achieve fair evaluation. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 AI ​​Fair Evaluation Agent according to an embodiment of the present invention is a system that objectively evaluates employees' performance and abilities, achieving fair evaluations free from bias and prejudice. The AI ​​Fair Evaluation Agent collects and analyzes employee performance data and KPIs (Key Performance Indicators) to provide unbiased evaluations. For example, the AI ​​Fair Evaluation Agent collects data such as the sales revenue achieved and the number of projects completed by employees. Next, the AI ​​Fair Evaluation Agent analyzes the collected data. The AI ​​Fair Evaluation Agent uses a bias reduction algorithm to eliminate human bias and perform objective evaluations based on data. For example, the AI ​​Fair Evaluation Agent evaluates employees purely on performance, regardless of their gender or age. Furthermore, the AI ​​Fair Evaluation Agent provides real-time feedback. This allows employees to receive immediate feedback on their performance, promoting continuous improvement and growth. For example, an employee can receive feedback from the AI ​​Fair Evaluation Agent immediately after completing a project. This system improves employee satisfaction and increases the transparency of performance evaluations. Employees feel their evaluations are fair, leading to increased motivation and improved corporate productivity. Additionally, appropriate personnel allocation optimizes employee turnover. The AI ​​Fair Performance Assessment Agent utilizes cloud-based AI analytics tools, featuring evolving machine learning models and a user-friendly interface. This makes it easy for businesses to deploy and use. As a result, the AI ​​Fair Performance Assessment Agent can objectively evaluate employee performance and capabilities, achieving fair evaluations free from bias and prejudice.

[0029] The AI ​​fair evaluation agent according to this embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a feedback unit. The data collection unit collects employee performance data and KPIs. For example, the data collection unit collects data such as sales revenue achieved and the number of projects completed by employees. The data collection unit can also collect data such as work results, project progress, and the degree of collaboration within a team. For example, the data collection unit collects sales revenue and the number of completed projects in order to quantitatively evaluate employees' work results. The data collection unit can also collect milestone achievement status and task completion rates in order to understand project progress. The data collection unit can also collect communication frequency and collaborative work results in order to evaluate the degree of collaboration within a team. The analysis unit analyzes the data collected by the data collection unit. The analysis unit eliminates human bias using bias reduction algorithms. For example, the analysis unit uses a fairness algorithm to reduce bias in the data. The analysis unit can also correct data bias using rebalancing techniques. The analysis unit can perform data analysis using AI. For example, the analysis department uses AI models to extract data patterns and provide the information necessary for evaluation. The evaluation department conducts fair evaluations based on the analysis results obtained by the analysis department. The evaluation department evaluates employees purely on performance, regardless of gender or age. For example, the evaluation department calculates evaluation scores based on employee performance data. The evaluation department can use only performance data as the evaluation criterion, without considering employee attribute information. The evaluation department can conduct fair evaluations using AI. For example, the evaluation department uses AI models to evaluate employee performance data and provide fair evaluation results. The feedback department provides feedback on the evaluation results obtained by the evaluation department. The feedback department provides feedback in real time. For example, the feedback department provides feedback on evaluation results immediately after an employee completes a project. The feedback department ensures that employees receive evaluation results immediately and can identify areas for improvement. The feedback department can provide feedback using AI.For example, the feedback unit uses an AI model to analyze employee evaluation results and provide appropriate feedback. This allows the AI ​​fair evaluation agent, according to this embodiment, to objectively evaluate employee performance and abilities, achieving a fair evaluation free from bias and prejudice.

[0030] The data collection department collects employee performance data and KPIs. Specifically, it collects data such as sales figures achieved and the number of projects completed by employees. In addition, the data collection department can also collect data such as work outcomes, project progress, and the degree of collaboration within teams. For example, to quantitatively evaluate employee work outcomes, it collects sales figures and the number of completed projects. This data serves as an important indicator for objectively evaluating employee performance. Furthermore, the data collection department can also collect milestone achievement status and task completion rates to understand project progress. This allows for real-time monitoring of project progress and appropriate actions to be taken as needed. The data collection department can also collect data on the frequency of communication and the results of collaborative work to evaluate the degree of collaboration within teams. For example, it collects the frequency of email and chat exchanges between team members, and the number and content of documents created jointly. This allows for evaluation of the collaborative relationships and the quality of communication within teams. The data collection department centrally manages this data and can link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and evaluation departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, it uses bias reduction algorithms to eliminate human bias. For example, it uses fairness algorithms to reduce data bias, thereby ensuring fairness in employee evaluations. Furthermore, the analysis department can also correct data bias using rebalancing techniques, which evenly distributes data that is biased towards specific attributes, enabling fair evaluations. The analysis department can also use AI to analyze data. For example, it can use AI models to extract data patterns and provide information necessary for evaluation. AI can process large amounts of data quickly and find complex patterns, allowing the analysis department to provide foundational data for accurately evaluating employee performance. In addition, the analysis department can leverage historical data and statistics to analyze long-term trends and performance fluctuations. For example, based on past evaluation data, it can identify trends in employee performance improvement or decline and reflect them in future evaluations. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0032] The evaluation department conducts fair evaluations based on the analysis results obtained by the analysis department. Specifically, evaluations are based purely on performance, regardless of the employee's gender or age. For example, evaluation scores are calculated based on employee performance data. The evaluation department can use only performance data as the evaluation criterion, without considering employee attribute information. This ensures fairness in evaluations. The evaluation department can use AI to conduct fair evaluations. For example, an AI model can be used to evaluate employee performance data and provide fair evaluation results. AI can evaluate employee performance data quickly and accurately, eliminating human bias. Furthermore, the evaluation department can clearly show the evaluation process and evaluation criteria to ensure transparency in evaluation results. This allows employees to understand and accept how their evaluation was conducted. The evaluation department can also periodically review evaluation results and improve evaluation criteria and processes as needed. This improves the accuracy and reliability of evaluations. In addition, the evaluation department can plan employee career paths and skill development based on evaluation results. For example, based on evaluation results, they can identify employees' strengths and weaknesses and provide appropriate training and career advancement opportunities. This can improve employee motivation and enhance overall organizational performance.

[0033] The Feedback Department provides feedback on the evaluation results obtained by the Evaluation Department. Specifically, it provides feedback in real time. For example, it provides feedback on the evaluation results immediately after an employee completes a project. This allows employees to receive evaluation results immediately and understand areas for improvement. The Feedback Department can also provide feedback using AI. For example, it can use an AI model to analyze employee evaluation results and provide appropriate feedback. Based on the employee's evaluation results, the AI ​​can suggest specific areas for improvement and next steps. This allows employees to obtain a concrete action plan to improve their performance. Furthermore, the Feedback Department can customize the content of the feedback on an individual basis. For example, it can adjust the content and format of the feedback according to the employee's position and job responsibilities. This ensures that employees receive the most beneficial feedback for them. The Feedback Department can also continuously monitor the effectiveness of the feedback and improve the content and methods of feedback as needed. For example, it can track changes in employee performance and evaluate the effectiveness of the feedback. This allows the Feedback Department to support employee growth and improve the overall performance of the organization. In addition, the Feedback Department can collect employee reactions and opinions on the feedback and use them to improve the feedback process. This allows the Feedback Department to provide effective feedback tailored to employee needs and improve the overall performance of the organization.

[0034] The analysis unit can eliminate human bias using bias reduction algorithms. For example, the analysis unit can reduce data bias using a fairness algorithm. A fairness algorithm is an algorithm for detecting and correcting data bias. For example, a fairness algorithm evaluates and corrects the impact of bias based on data attribute information. The analysis unit can also correct data bias using a rebalancing technique. A rebalancing technique is a technique for equalizing the distribution of data. For example, a rebalancing technique adjusts data sampling to minimize the impact of bias. This allows the analysis unit to eliminate human bias and make data-driven, objective evaluations. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can execute bias reduction algorithms using an AI model that detects and corrects data bias.

[0035] The feedback unit can provide feedback in real time. For example, the feedback unit can provide feedback on evaluation results immediately after an employee completes a project. The feedback unit enables employees to receive evaluation results immediately and understand areas for improvement. The feedback unit can provide feedback using AI. For example, the feedback unit can use an AI model to analyze an employee's evaluation results and provide appropriate feedback. This allows employees to receive immediate feedback on their performance, promoting continuous improvement and growth. Some or all of the processes described above in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can provide feedback in real time using an AI model that takes an employee's evaluation results as input and outputs feedback.

[0036] The data collection unit can collect data such as work results, project progress, and the degree of collaboration within teams. For example, the data collection unit can collect sales figures and the number of completed projects to quantitatively evaluate employee work results. The data collection unit can also collect milestone achievement and task completion rates to understand project progress. The data collection unit can also collect communication frequency and collaborative work results to evaluate the degree of collaboration within teams. This allows the data collection unit to collect employee performance data from multiple perspectives, enabling more accurate evaluations. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect work result data using an AI model that takes employee work result data as input and performs data collection.

[0037] The evaluation department can perform evaluations based purely on performance, regardless of the employee's gender or age. For example, the evaluation department can calculate an evaluation score based on the employee's performance data. The evaluation department can use only performance data as the evaluation criterion, without considering the employee's attribute information. The evaluation department can use AI to perform fair evaluations. For example, the evaluation department can use an AI model to evaluate the employee's performance data and provide fair evaluation results. This allows the evaluation department to perform fair evaluations regardless of the employee's gender or age. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI. For example, the evaluation department can perform performance-based evaluations using an AI model that takes employee performance data as input and outputs an evaluation score.

[0038] The feedback department can provide feedback to employees immediately after they complete a project. For example, the feedback department provides evaluation results immediately after an employee completes a project. The feedback department ensures that employees receive evaluation results immediately and can identify areas for improvement. The feedback department can provide feedback using AI. For example, the feedback department can use an AI model to analyze employee evaluation results and provide appropriate feedback. This allows employees to receive feedback immediately after completing a project, immediately identify areas for improvement, and apply them to future projects. Some or all of the above processes in the feedback department may be performed using AI, or not. For example, the feedback department can provide feedback immediately after project completion using an AI model that takes employee evaluation results as input and outputs feedback.

[0039] The cloud-based unit uses cloud-based AI analytics tools. For example, the cloud-based unit uses cloud-based AI analytics tools to collect, analyze, and evaluate data. By using cloud-based AI analytics tools, the cloud-based unit can be easily deployed and utilized by companies. Some or all of the processes described above in the cloud-based unit may be performed using AI, or not. For example, the cloud-based unit can run an AI model using cloud-based AI analytics tools to collect, analyze, and evaluate data.

[0040] The evolutionary unit uses evolving machine learning models. The evolutionary unit improves the system's accuracy by using, for example, self-learning models or continuous learning models. The system's accuracy improves by using evolving machine learning models. Some or all of the above processes in the evolutionary unit may be performed using, for example, AI, or not using AI. For example, the evolutionary unit can use a self-learning model to run an AI model that collects, analyzes, and evaluates data.

[0041] The interface unit provides a user-friendly interface. For example, the interface unit provides an interface with intuitive operability and a visually appealing design. By providing a user-friendly interface, the interface unit enables businesses to easily utilize the system. Some or all of the above-described processes in the interface unit may be performed using AI, or not. For example, the interface unit can analyze the user's operation history and run an AI model to provide an optimal interface in order to provide a user-friendly interface.

[0042] The data collection unit can analyze employees' past performance data and select the optimal collection method. For example, the data collection unit can identify the time of day when employees perform at their best from past data and collect data during that time. The data collection unit can analyze past data collection methods for employees and select the most efficient method. The data collection unit can optimize the collection frequency based on employees' past performance data. This enables efficient data collection by selecting the optimal collection method based on employees' past performance data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can select a data collection method using an AI model that takes employees' past performance data as input and selects the optimal collection method.

[0043] The data collection unit can filter performance data based on employees' current projects and areas of interest. For example, the data collection unit can collect only data related to ongoing projects. The data collection unit can prioritize the collection of data related to employees' areas of interest. The data collection unit can adjust the types of data collected according to the progress of projects. This allows the data collection unit to collect highly relevant data by filtering it based on employees' current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect data using an AI model that takes data on employees' current projects and areas of interest as input and performs filtering.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of employees when collecting performance data. For example, if an employee is working in a specific region, the data collection unit can prioritize the collection of data related to that region. The data collection unit can prioritize the collection of data related to geographically close projects. The data collection unit can prioritize the collection of data related to the employee's work location. This allows the data collection unit to perform more accurate evaluations by prioritizing the collection of highly relevant data by considering the geographical location of employees. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes the geographical location of employees as input and prioritizes the collection of highly relevant data.

[0045] The data collection unit can analyze employees' social media activities and collect relevant data when collecting performance data. For example, the data collection unit can analyze the content of employees' social media activities and collect work-related data. The data collection unit can identify employees' areas of interest on social media and collect data related to those areas. The data collection unit can analyze employees' social media networks and collect work-related data. This allows the data collection unit to perform a more multifaceted evaluation by analyzing employees' social media activities and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect data using an AI model that takes employee social media activity data as input and collects relevant data.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data, and a simplified analysis on less important data. The analysis unit can adjust the depth of the analysis according to the importance of the data. This allows the analysis unit to perform efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform data analysis using an AI model that takes data importance as input and adjusts the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a performance evaluation algorithm to performance data. For project progress data, it can apply a project management algorithm. For team collaboration data, it can apply a team dynamics algorithm. By applying different analysis algorithms depending on the data category, the analysis unit can obtain more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform data analysis using an AI model that takes data categories as input and applies different analysis algorithms.

[0048] The analysis department can determine the priority of analysis based on the data submission date. For example, the analysis department can prioritize the analysis of the most recent data. The analysis department can prioritize the analysis of data with approaching submission deadlines. The analysis department can adjust the order of analysis based on the submission date. This enables efficient data analysis by allowing the analysis department to prioritize analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can perform data analysis using an AI model that takes the data submission date as input and determines the priority of analysis.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can postpone the analysis of less relevant data. The analysis unit can optimize the order of analysis based on the relevance of the data. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform data analysis using an AI model that takes data relevance as input and adjusts the order of analysis.

[0050] The evaluation department can improve the accuracy of evaluations by considering employee relationships during the evaluation process. For example, the evaluation department can reflect collaborative relationships within a team in the evaluation. The evaluation department can incorporate feedback between employees into the evaluation. The evaluation department can balance evaluations by considering interpersonal relationships. As a result, the evaluation department can conduct more accurate evaluations by considering employee relationships. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI. For example, the evaluation department can use employee relationship data as input and perform evaluations using an AI model that improves the accuracy of the evaluation.

[0051] The evaluation department can perform evaluations while taking employee attribute information into consideration. For example, the evaluation department can adjust evaluation criteria according to the employee's position and job duties. The evaluation department can perform evaluations while taking employee years of experience into consideration. The evaluation department can perform evaluations based on the employee's skill set. As a result, the evaluation department can perform more accurate evaluations by taking employee attribute information into consideration. Some or all of the above processes in the evaluation department may be performed using AI, for example, or without using AI. For example, the evaluation department can perform evaluations using an AI model that takes employee attribute information as input and performs the evaluation.

[0052] The evaluation department can conduct evaluations while considering the geographical distribution of employees. For example, the evaluation department can fairly evaluate employees who are geographically separated. The evaluation department can conduct evaluations while considering regional performance. The evaluation department can adjust evaluation criteria to take geographical constraints into account. This allows the evaluation department to conduct more accurate evaluations by considering the geographical distribution of employees. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI. For example, the evaluation department can conduct evaluations using an AI model that takes employee geographical distribution data as input.

[0053] The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature related to the employee during the evaluation process. For example, the evaluation unit can perform evaluations by referring to literature related to the employee's performance. The evaluation unit can perform evaluations by referring to literature related to the employee's skills. The evaluation unit can perform evaluations by referring to literature related to the employee's projects. This allows the evaluation unit to perform more accurate evaluations by referring to relevant literature related to the employee. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can perform evaluations using an AI model that improves the accuracy of evaluations, with employee relevant literature data as input.

[0054] The feedback unit can optimize current feedback by referring to past feedback data during the feedback process. For example, the feedback unit can adjust the content of current feedback based on past feedback data. The feedback unit can evaluate employee growth by referring to past feedback data. The feedback unit can optimize the tone and content of feedback based on past feedback data. As a result, the feedback unit can provide more effective feedback by optimizing current feedback by referring to past feedback data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can provide feedback using an AI model that takes past feedback data as input and optimizes current feedback.

[0055] The feedback department can apply different feedback methods to different employee categories when providing feedback. For example, the feedback department can provide educational feedback to new employees. For mid-career employees, it can provide feedback that includes specific areas for improvement. For managers, it can provide feedback from a strategic perspective. In this way, the feedback department can provide more effective feedback by applying different feedback methods to different employee categories. Some or all of the above processes in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department can provide feedback using an AI model that takes employee category data as input and applies different feedback methods.

[0056] The feedback unit can analyze changes in feedback based on the employee's submission timing. For example, the feedback unit can provide prompt feedback to employees who submit early, and detailed feedback to employees who submit late. The feedback unit can adjust the content of the feedback based on the submission timing. This allows the feedback unit to provide more effective feedback by analyzing changes in feedback based on the employee's submission timing. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can use an AI model that takes employee submission timing data as input and analyzes changes in feedback to provide feedback.

[0057] The feedback unit can analyze feedback by referring to relevant market data of employees when providing feedback. The feedback unit can adjust the content of the feedback based on market trends, for example. The feedback unit can optimize the content of the feedback by referring to competitor data. The feedback unit can evaluate employee performance based on market data. As a result, the feedback unit can provide more effective feedback by analyzing feedback by referring to relevant market data of employees. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can provide feedback using an AI model that takes relevant market data of employees as input and analyzes the feedback.

[0058] The cloud-based unit can adjust the timing of data processing according to the load status of the cloud environment. For example, if the load on the cloud environment is high, the cloud-based unit can delay data processing. If the load on the cloud environment is low, the cloud-based unit can speed up data processing. The cloud-based unit can optimize the timing of data processing according to the load status of the cloud environment. As a result, the cloud-based unit can perform efficient data processing by adjusting the timing of data processing according to the load status of the cloud environment. Some or all of the above processing in the cloud-based unit may be performed using AI, for example, or without using AI. For example, the cloud-based unit can take cloud environment load status data as input and perform data processing using an AI model that adjusts the timing of data processing.

[0059] The cloud-based unit can enhance cloud-based security protocols and ensure data security. For example, the cloud-based unit can enhance data encryption. The cloud-based unit can periodically update security protocols. The cloud-based unit can enhance security monitoring and ensure data security. Thus, the cloud-based unit can ensure data security by enhancing cloud-based security protocols. Some or all of the above-described processes in the cloud-based unit may be performed using AI, for example, or without AI. For example, the cloud-based unit can ensure data security by using an AI model that takes data encryption protocols as input and enhances security protocols.

[0060] The cloud-based unit can monitor the usage of cloud resources in real time and perform optimal resource allocation. For example, the cloud-based unit monitors the usage of cloud resources in real time. The cloud-based unit can adjust resource allocation according to resource usage. The cloud-based unit can perform optimal resource allocation based on real-time monitoring data. As a result, the cloud-based unit can efficiently manage resources by monitoring the usage of cloud resources in real time and performing optimal resource allocation. Some or all of the above processes in the cloud-based unit may be performed using AI, for example, or without AI. For example, the cloud-based unit can perform resource management using an AI model that takes cloud resource usage data as input and performs optimal resource allocation.

[0061] The cloud-based unit can expand its cloud-based data storage to store more data. For example, the cloud-based unit can expand the data storage capacity. As the data storage expands, the cloud-based unit can review its data management methods. By expanding the data storage, the cloud-based unit can increase its data storage capacity. For example, the cloud-based unit can expand the data storage capacity. As the data storage expands, the cloud-based unit can review its data management methods. By expanding the data storage, the cloud-based unit can increase its data storage capacity. This allows the cloud-based unit to store more data by expanding its cloud-based data storage. Some or all of the above processing in the cloud-based unit may be performed using AI, for example, or without AI. For example, the cloud-based unit can use an AI model that takes the data storage capacity as input and expands the data storage to store data.

[0062] The cloud-based unit can improve cloud-based data processing capabilities and enable rapid processing of large amounts of data. For example, the cloud-based unit can enhance its cloud infrastructure to improve data processing capabilities. As data processing capabilities improve, the cloud-based unit can optimize processing speed. The cloud-based unit can optimize cloud resources to process large amounts of data quickly. This allows the cloud-based unit to process large amounts of data quickly by improving its cloud-based data processing capabilities. Some or all of the above-described processing in the cloud-based unit may be performed using AI, for example, or without AI. For example, the cloud-based unit can process data using an AI model that improves data processing capabilities, with data from the cloud infrastructure as input.

[0063] The cloud-based unit can implement a cloud-based data backup system to ensure data redundancy. For example, the cloud-based unit can implement a data backup system. The cloud-based unit can adjust the backup frequency to ensure data redundancy. The cloud-based unit can periodically test the data backup system to ensure redundancy. Thus, the cloud-based unit can ensure data redundancy by implementing a cloud-based data backup system. Some or all of the above processes in the cloud-based unit may be performed using AI, for example, or without AI. For example, the cloud-based unit can take the data backup system as input and perform data backups using an AI model that ensures data redundancy.

[0064] The evolutionary unit can optimize the algorithm as the machine learning model evolves. For example, the evolutionary unit can incorporate a new dataset and optimize the algorithm. The evolutionary unit can adjust the algorithm parameters in accordance with the evolution of the machine learning model. By optimizing the algorithm, the evolutionary unit can improve the accuracy of the model. Thus, the evolutionary unit can improve the accuracy of the model by optimizing the algorithm as the machine learning model evolves. Some or all of the above processes in the evolutionary unit may be performed using AI, for example, or without AI. For example, the evolutionary unit can take evolutionary data of the machine learning model as input and use an AI model that optimizes the algorithm to perform algorithm optimization.

[0065] The evolutionary component can improve the accuracy of the model by incorporating new datasets. For example, the evolutionary component can periodically incorporate new datasets. The evolutionary component can evaluate the accuracy of the model based on the new datasets. The evolutionary component can improve the accuracy of the model by incorporating new datasets. Thus, the evolutionary component can improve the accuracy of the model by incorporating new datasets. Some or all of the above processes in the evolutionary component may be performed using AI, for example, or without AI. For example, the evolutionary component can take a new dataset as input and perform data analysis using an AI model that improves the accuracy of the model.

[0066] The evolutionary unit can improve the overall system performance in accordance with the evolution of the model. For example, the evolutionary unit optimizes the system performance as the model evolves. The evolutionary unit can leverage the evolution of the model to improve the overall system performance. The evolutionary unit can evaluate the system performance in accordance with the evolution of the model. In this way, the evolutionary unit can increase the efficiency of the system by improving the overall system performance in accordance with the evolution of the model. Some or all of the above-described processes in the evolutionary unit may be performed using AI, for example, or without AI. For example, the evolutionary unit can take the evolutionary data of the model as input and optimize the system performance using an AI model that improves the overall system performance.

[0067] The evolutionary unit can add new functions as the machine learning model evolves. For example, the evolutionary unit develops new functions in accordance with the model's evolution. The evolutionary unit can improve the system's usability by adding new functions. The evolutionary unit can implement new functions by leveraging the evolution of the machine learning model. In this way, the evolutionary unit can improve the system's usability by adding new functions as the machine learning model evolves. Some or all of the above-described processes in the evolutionary unit may be performed using AI, for example, or without AI. For example, the evolutionary unit can extend the system's functionality using an AI model that takes evolutionary data of the machine learning model as input and adds new functions.

[0068] The evolution unit can improve the user interface in accordance with the evolution of the model. For example, the evolution unit optimizes the user interface as the model evolves. The evolution unit can improve usability by improving the user interface. The evolution unit can improve the user interface by leveraging the evolution of the model. In this way, the evolution unit can improve usability by improving the user interface in accordance with the evolution of the model. Some or all of the above processing in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can take the evolution data of the model as input and use an AI model that improves the user interface to optimize the interface.

[0069] The evolutionary unit can improve the system's scalability as the model evolves. For example, the evolutionary unit optimizes the system's scalability in accordance with the model's evolution. By improving scalability, the evolutionary unit can enhance the system's extensibility. The evolutionary unit can improve the system's scalability by leveraging the model's evolution. Thus, the evolutionary unit can enhance the system's extensibility by improving the system's scalability as the model evolves. Some or all of the above-described processes in the evolutionary unit may be performed using AI, for example, or without AI. For example, the evolutionary unit can take model evolution data as input and optimize the system's extensibility using an AI model that improves the system's scalability.

[0070] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can suggest the optimal display method based on the user's past operation history. The interface unit can prioritize the display of frequently used functions based on the user's operation history. The interface unit can provide a display method tailored to the user's preferences by referring to past operation history. In this way, the interface unit can provide a user-friendly interface by selecting the optimal display method by referring to the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can provide an interface using an AI model that takes the user's past operation history data as input and selects the optimal display method.

[0071] The interface unit can improve the interface design and enhance usability. For example, the interface unit can simplify the interface design and improve operability. The interface unit can optimize the interface layout to improve usability. The interface unit can improve the interface design and enhance visibility. Thus, the interface unit can improve usability by improving the interface design. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can take interface design data as input and optimize the interface using an AI model that improves the design.

[0072] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method that matches the screen size. If the user is using a tablet, the interface unit can provide a display method optimized for a larger screen. If the user is using a desktop, the interface unit can provide a display method that utilizes multiple windows. In this way, the interface unit can provide a user-friendly interface by selecting the optimal display method considering the user's device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can provide an interface using an AI model that takes the user's device information as input and selects the optimal display method.

[0073] The interface unit can add customization features to the interface, allowing users to adjust it to their preferences. For example, the interface unit can provide a function that allows users to customize the interface's colors and layout. The interface unit can provide a function that allows users to set frequently used functions as shortcuts. The interface unit can provide a function that allows users to adjust the interface's font size and icon size. In this way, by adding customization features to the interface unit, users can adjust the interface to their preferences. Some or all of the above processing in the interface unit may be performed using AI, for example, or not using AI. For example, the interface unit can provide a customization function using an AI model that takes user customization data as input and adjusts the interface.

[0074] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0075] The data collection unit can collect employee health data in addition to employee performance data and KPIs. For example, the data collection unit can collect health data such as employee heart rate and sleep duration and analyze it in combination with performance data. This allows for the evaluation of the impact of employee health on performance and helps improve health management. The data collection unit can also be linked with wearable devices and health management apps to collect employee health data.

[0076] The analytics department can consider employees' career goals and self-assessment data when analyzing employee performance data. For example, by evaluating the progress of employees toward their set career goals and comparing it with self-assessment data, the analytics department can identify the gap between employees' self-perception and actual performance. This can support employees' career development and promote their personal growth. The analytics department can also conduct regular surveys and interviews to collect employees' career goals and self-assessment data.

[0077] The performance evaluation department can add indicators to assess employee creativity and innovation when evaluating employee performance. For example, the department can evaluate the number of new ideas and projects proposed by employees and calculate a creativity score. The department can also evaluate the results of innovative initiatives and improvement suggestions implemented by employees. This allows for the reflection of employee creativity and innovation in evaluations and promotes innovation. The performance evaluation department can also implement employee idea submission systems or innovation contests to assess creativity and innovation.

[0078] The feedback department can choose a feedback method that suits each employee's learning style and preferences when providing feedback. For example, the feedback department can provide feedback using graphs and charts to employees who prefer visual feedback. They can also provide feedback in the form of an interview to employees who prefer verbal feedback. This allows for feedback tailored to each employee's learning style and preferences, enabling effective communication. The feedback department can also conduct surveys or interviews in advance to understand each employee's learning style and preferences.

[0079] The data collection department can collect data that assesses employee job satisfaction and stress levels when gathering employee performance data. For example, the department can conduct regular surveys to assess employee job satisfaction and use stress check tools to assess stress levels. This allows them to understand employee job satisfaction and stress levels and use this information to improve the workplace environment. The data collection department can also combine and analyze employee feedback and health data to assess job satisfaction and stress levels.

[0080] The following briefly describes the processing flow for example form 1.

[0081] Step 1: The data collection department collects employee performance data and KPIs. For example, it collects data such as sales revenue achieved, number of projects completed, work outcomes, project progress, and level of collaboration within teams. The data collection department can collect sales revenue, number of completed projects, milestone achievement, task completion rates, communication frequency, and collaborative work outcomes. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses bias reduction algorithms, fairness algorithms, and rebalancing techniques to reduce data bias and correct imbalances. Furthermore, it uses AI to extract data patterns and provide the information necessary for evaluation. Step 3: The evaluation department conducts fair evaluations based on the analysis results obtained by the analysis department. The evaluation department calculates evaluation scores based solely on performance data, regardless of the employee's gender or age. AI is used to conduct fair evaluations and provide fair evaluation results. Step 4: The Feedback Department provides feedback on the evaluation results obtained by the Evaluation Department. The Feedback Department provides feedback in real time, giving evaluation results to employees immediately after they complete a project. AI is used to analyze the evaluation results and provide appropriate feedback.

[0082] (Example of form 2) The AI ​​Fair Evaluation Agent according to an embodiment of the present invention is a system that objectively evaluates employees' performance and abilities, achieving fair evaluations free from bias and prejudice. The AI ​​Fair Evaluation Agent collects and analyzes employee performance data and KPIs (Key Performance Indicators) to provide unbiased evaluations. For example, the AI ​​Fair Evaluation Agent collects data such as the sales revenue achieved and the number of projects completed by employees. Next, the AI ​​Fair Evaluation Agent analyzes the collected data. The AI ​​Fair Evaluation Agent uses a bias reduction algorithm to eliminate human bias and perform objective evaluations based on data. For example, the AI ​​Fair Evaluation Agent evaluates employees purely on performance, regardless of their gender or age. Furthermore, the AI ​​Fair Evaluation Agent provides real-time feedback. This allows employees to receive immediate feedback on their performance, promoting continuous improvement and growth. For example, an employee can receive feedback from the AI ​​Fair Evaluation Agent immediately after completing a project. This system improves employee satisfaction and increases the transparency of performance evaluations. Employees feel their evaluations are fair, leading to increased motivation and improved corporate productivity. Additionally, appropriate personnel allocation optimizes employee turnover. The AI ​​Fair Performance Assessment Agent utilizes cloud-based AI analytics tools, featuring evolving machine learning models and a user-friendly interface. This makes it easy for businesses to deploy and use. As a result, the AI ​​Fair Performance Assessment Agent can objectively evaluate employee performance and capabilities, achieving fair evaluations free from bias and prejudice.

[0083] The AI ​​fair evaluation agent according to this embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a feedback unit. The data collection unit collects employee performance data and KPIs. For example, the data collection unit collects data such as sales revenue achieved and the number of projects completed by employees. The data collection unit can also collect data such as work results, project progress, and the degree of collaboration within a team. For example, the data collection unit collects sales revenue and the number of completed projects in order to quantitatively evaluate employees' work results. The data collection unit can also collect milestone achievement status and task completion rates in order to understand project progress. The data collection unit can also collect communication frequency and collaborative work results in order to evaluate the degree of collaboration within a team. The analysis unit analyzes the data collected by the data collection unit. The analysis unit eliminates human bias using bias reduction algorithms. For example, the analysis unit uses a fairness algorithm to reduce bias in the data. The analysis unit can also correct data bias using rebalancing techniques. The analysis unit can perform data analysis using AI. For example, the analysis department uses AI models to extract data patterns and provide the information necessary for evaluation. The evaluation department conducts fair evaluations based on the analysis results obtained by the analysis department. The evaluation department evaluates employees purely on performance, regardless of gender or age. For example, the evaluation department calculates evaluation scores based on employee performance data. The evaluation department can use only performance data as the evaluation criterion, without considering employee attribute information. The evaluation department can conduct fair evaluations using AI. For example, the evaluation department uses AI models to evaluate employee performance data and provide fair evaluation results. The feedback department provides feedback on the evaluation results obtained by the evaluation department. The feedback department provides feedback in real time. For example, the feedback department provides feedback on evaluation results immediately after an employee completes a project. The feedback department ensures that employees receive evaluation results immediately and can identify areas for improvement. The feedback department can provide feedback using AI.For example, the feedback unit uses an AI model to analyze employee evaluation results and provide appropriate feedback. This allows the AI ​​fair evaluation agent, according to this embodiment, to objectively evaluate employee performance and abilities, achieving a fair evaluation free from bias and prejudice.

[0084] The data collection department collects employee performance data and KPIs. Specifically, it collects data such as sales figures achieved and the number of projects completed by employees. In addition, the data collection department can also collect data such as work outcomes, project progress, and the degree of collaboration within teams. For example, to quantitatively evaluate employee work outcomes, it collects sales figures and the number of completed projects. This data serves as an important indicator for objectively evaluating employee performance. Furthermore, the data collection department can also collect milestone achievement status and task completion rates to understand project progress. This allows for real-time monitoring of project progress and appropriate actions to be taken as needed. The data collection department can also collect data on the frequency of communication and the results of collaborative work to evaluate the degree of collaboration within teams. For example, it collects the frequency of email and chat exchanges between team members, and the number and content of documents created jointly. This allows for evaluation of the collaborative relationships and the quality of communication within teams. The data collection department centrally manages this data and can link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and evaluation departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0085] The analysis department analyzes the data collected by the data collection department. Specifically, it uses bias reduction algorithms to eliminate human bias. For example, it uses fairness algorithms to reduce data bias, thereby ensuring fairness in employee evaluations. Furthermore, the analysis department can also correct data bias using rebalancing techniques, which evenly distributes data that is biased towards specific attributes, enabling fair evaluations. The analysis department can also use AI to analyze data. For example, it can use AI models to extract data patterns and provide information necessary for evaluation. AI can process large amounts of data quickly and find complex patterns, allowing the analysis department to provide foundational data for accurately evaluating employee performance. In addition, the analysis department can leverage historical data and statistics to analyze long-term trends and performance fluctuations. For example, based on past evaluation data, it can identify trends in employee performance improvement or decline and reflect them in future evaluations. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0086] The evaluation department conducts fair evaluations based on the analysis results obtained by the analysis department. Specifically, evaluations are based purely on performance, regardless of the employee's gender or age. For example, evaluation scores are calculated based on employee performance data. The evaluation department can use only performance data as the evaluation criterion, without considering employee attribute information. This ensures fairness in evaluations. The evaluation department can use AI to conduct fair evaluations. For example, an AI model can be used to evaluate employee performance data and provide fair evaluation results. AI can evaluate employee performance data quickly and accurately, eliminating human bias. Furthermore, the evaluation department can clearly show the evaluation process and evaluation criteria to ensure transparency in evaluation results. This allows employees to understand and accept how their evaluation was conducted. The evaluation department can also periodically review evaluation results and improve evaluation criteria and processes as needed. This improves the accuracy and reliability of evaluations. In addition, the evaluation department can plan employee career paths and skill development based on evaluation results. For example, based on evaluation results, they can identify employees' strengths and weaknesses and provide appropriate training and career advancement opportunities. This can improve employee motivation and enhance overall organizational performance.

[0087] The Feedback Department provides feedback on the evaluation results obtained by the Evaluation Department. Specifically, it provides feedback in real time. For example, it provides feedback on the evaluation results immediately after an employee completes a project. This allows employees to receive evaluation results immediately and understand areas for improvement. The Feedback Department can also provide feedback using AI. For example, it can use an AI model to analyze employee evaluation results and provide appropriate feedback. Based on the employee's evaluation results, the AI ​​can suggest specific areas for improvement and next steps. This allows employees to obtain a concrete action plan to improve their performance. Furthermore, the Feedback Department can customize the content of the feedback on an individual basis. For example, it can adjust the content and format of the feedback according to the employee's position and job responsibilities. This ensures that employees receive the most beneficial feedback for them. The Feedback Department can also continuously monitor the effectiveness of the feedback and improve the content and methods of feedback as needed. For example, it can track changes in employee performance and evaluate the effectiveness of the feedback. This allows the Feedback Department to support employee growth and improve the overall performance of the organization. In addition, the Feedback Department can collect employee reactions and opinions on the feedback and use them to improve the feedback process. This allows the Feedback Department to provide effective feedback tailored to employee needs and improve the overall performance of the organization.

[0088] The analysis unit can eliminate human bias using bias reduction algorithms. For example, the analysis unit can reduce data bias using a fairness algorithm. A fairness algorithm is an algorithm for detecting and correcting data bias. For example, a fairness algorithm evaluates and corrects the impact of bias based on data attribute information. The analysis unit can also correct data bias using a rebalancing technique. A rebalancing technique is a technique for equalizing the distribution of data. For example, a rebalancing technique adjusts data sampling to minimize the impact of bias. This allows the analysis unit to eliminate human bias and make data-driven, objective evaluations. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can execute bias reduction algorithms using an AI model that detects and corrects data bias.

[0089] The feedback unit can provide feedback in real time. For example, the feedback unit can provide feedback on evaluation results immediately after an employee completes a project. The feedback unit enables employees to receive evaluation results immediately and understand areas for improvement. The feedback unit can provide feedback using AI. For example, the feedback unit can use an AI model to analyze an employee's evaluation results and provide appropriate feedback. This allows employees to receive immediate feedback on their performance, promoting continuous improvement and growth. Some or all of the processes described above in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can provide feedback in real time using an AI model that takes an employee's evaluation results as input and outputs feedback.

[0090] The data collection unit can collect data such as work results, project progress, and the degree of collaboration within teams. For example, the data collection unit can collect sales figures and the number of completed projects to quantitatively evaluate employee work results. The data collection unit can also collect milestone achievement and task completion rates to understand project progress. The data collection unit can also collect communication frequency and collaborative work results to evaluate the degree of collaboration within teams. This allows the data collection unit to collect employee performance data from multiple perspectives, enabling more accurate evaluations. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect work result data using an AI model that takes employee work result data as input and performs data collection.

[0091] The evaluation department can perform evaluations based purely on performance, regardless of the employee's gender or age. For example, the evaluation department can calculate an evaluation score based on the employee's performance data. The evaluation department can use only performance data as the evaluation criterion, without considering the employee's attribute information. The evaluation department can use AI to perform fair evaluations. For example, the evaluation department can use an AI model to evaluate the employee's performance data and provide fair evaluation results. This allows the evaluation department to perform fair evaluations regardless of the employee's gender or age. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI. For example, the evaluation department can perform performance-based evaluations using an AI model that takes employee performance data as input and outputs an evaluation score.

[0092] The feedback department can provide feedback to employees immediately after they complete a project. For example, the feedback department provides evaluation results immediately after an employee completes a project. The feedback department ensures that employees receive evaluation results immediately and can identify areas for improvement. The feedback department can provide feedback using AI. For example, the feedback department can use an AI model to analyze employee evaluation results and provide appropriate feedback. This allows employees to receive feedback immediately after completing a project, immediately identify areas for improvement, and apply them to future projects. Some or all of the above processes in the feedback department may be performed using AI, or not. For example, the feedback department can provide feedback immediately after project completion using an AI model that takes employee evaluation results as input and outputs feedback.

[0093] The cloud-based unit uses cloud-based AI analytics tools. For example, the cloud-based unit uses cloud-based AI analytics tools to collect, analyze, and evaluate data. By using cloud-based AI analytics tools, the cloud-based unit can be easily deployed and utilized by companies. Some or all of the processes described above in the cloud-based unit may be performed using AI, or not. For example, the cloud-based unit can run an AI model using cloud-based AI analytics tools to collect, analyze, and evaluate data.

[0094] The evolutionary unit uses evolving machine learning models. The evolutionary unit improves the system's accuracy by using, for example, self-learning models or continuous learning models. The system's accuracy improves by using evolving machine learning models. Some or all of the above processes in the evolutionary unit may be performed using, for example, AI, or not using AI. For example, the evolutionary unit can use a self-learning model to run an AI model that collects, analyzes, and evaluates data.

[0095] The interface unit provides a user-friendly interface. For example, the interface unit provides an interface with intuitive operability and a visually appealing design. By providing a user-friendly interface, the interface unit enables businesses to easily utilize the system. Some or all of the above-described processes in the interface unit may be performed using AI, or not. For example, the interface unit can analyze the user's operation history and run an AI model to provide an optimal interface in order to provide a user-friendly interface.

[0096] The data collection unit can estimate the user's emotions and adjust the timing of performance data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. If the user is relaxed, the data collection unit can advance the collection timing to maintain data freshness. If the user is in a hurry, the data collection unit can adjust the collection timing to collect data quickly. In this way, the data collection unit can reduce the user's burden and maintain data freshness by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can adjust the timing of performance data collection using an AI model that takes user emotion data as input and adjusts the collection timing.

[0097] The data collection unit can analyze employees' past performance data and select the optimal collection method. For example, the data collection unit can identify the time of day when employees perform at their best from past data and collect data during that time. The data collection unit can analyze past data collection methods for employees and select the most efficient method. The data collection unit can optimize the collection frequency based on employees' past performance data. This enables efficient data collection by selecting the optimal collection method based on employees' past performance data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can select a data collection method using an AI model that takes employees' past performance data as input and selects the optimal collection method.

[0098] The data collection unit can filter performance data based on employees' current projects and areas of interest. For example, the data collection unit can collect only data related to ongoing projects. The data collection unit can prioritize the collection of data related to employees' areas of interest. The data collection unit can adjust the types of data collected according to the progress of projects. This allows the data collection unit to collect highly relevant data by filtering it based on employees' current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect data using an AI model that takes data on employees' current projects and areas of interest as input and performs filtering.

[0099] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. If the user is relaxed, the data collection unit can prioritize the collection of highly important data. If the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can collect data using an AI model that takes user emotion data as input and determines the priority of data to collect.

[0100] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of employees when collecting performance data. For example, if an employee is working in a specific region, the data collection unit can prioritize the collection of data related to that region. The data collection unit can prioritize the collection of data related to geographically close projects. The data collection unit can prioritize the collection of data related to the employee's work location. This allows the data collection unit to perform more accurate evaluations by prioritizing the collection of highly relevant data by considering the geographical location of employees. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes the geographical location of employees as input and prioritizes the collection of highly relevant data.

[0101] The data collection unit can analyze employees' social media activities and collect relevant data when collecting performance data. For example, the data collection unit can analyze the content of employees' social media activities and collect work-related data. The data collection unit can identify employees' areas of interest on social media and collect data related to those areas. The data collection unit can analyze employees' social media networks and collect work-related data. This allows the data collection unit to perform a more multifaceted evaluation by analyzing employees' social media activities and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect data using an AI model that takes employee social media activity data as input and collects relevant data.

[0102] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can take user emotion data as input and provide analysis results using an AI model that adjusts the presentation of the analysis.

[0103] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data, and a simplified analysis on less important data. The analysis unit can adjust the depth of the analysis according to the importance of the data. This allows the analysis unit to perform efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform data analysis using an AI model that takes data importance as input and adjusts the level of detail of the analysis.

[0104] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a performance evaluation algorithm to performance data. For project progress data, it can apply a project management algorithm. For team collaboration data, it can apply a team dynamics algorithm. By applying different analysis algorithms depending on the data category, the analysis unit can obtain more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform data analysis using an AI model that takes data categories as input and applies different analysis algorithms.

[0105] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. If the user is excited, the analysis unit can provide an analysis with visually stimulating effects. In this way, the analysis unit can provide the optimal analysis result for the user by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can take user emotion data as input and provide analysis results using an AI model that adjusts the length of the analysis.

[0106] The analysis department can determine the priority of analysis based on the data submission date. For example, the analysis department can prioritize the analysis of the most recent data. The analysis department can prioritize the analysis of data with approaching submission deadlines. The analysis department can adjust the order of analysis based on the submission date. This enables efficient data analysis by allowing the analysis department to prioritize analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can perform data analysis using an AI model that takes the data submission date as input and determines the priority of analysis.

[0107] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can postpone the analysis of less relevant data. The analysis unit can optimize the order of analysis based on the relevance of the data. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform data analysis using an AI model that takes data relevance as input and adjusts the order of analysis.

[0108] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is tense, the evaluation unit can relax the evaluation criteria to reduce the user's burden. If the user is relaxed, the evaluation unit can apply strict evaluation criteria. If the user is in a hurry, the evaluation unit can adjust the criteria to perform the evaluation quickly. In this way, the evaluation unit can provide the user with the best possible evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can take user emotion data as input and perform the evaluation using an AI model that adjusts the evaluation criteria.

[0109] The evaluation department can improve the accuracy of evaluations by considering employee relationships during the evaluation process. For example, the evaluation department can reflect collaborative relationships within a team in the evaluation. The evaluation department can incorporate feedback between employees into the evaluation. The evaluation department can balance evaluations by considering interpersonal relationships. As a result, the evaluation department can conduct more accurate evaluations by considering employee relationships. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI. For example, the evaluation department can use employee relationship data as input and perform evaluations using an AI model that improves the accuracy of the evaluation.

[0110] The evaluation department can perform evaluations while taking employee attribute information into consideration. For example, the evaluation department can adjust evaluation criteria according to the employee's position and job duties. The evaluation department can perform evaluations while taking employee years of experience into consideration. The evaluation department can perform evaluations based on the employee's skill set. As a result, the evaluation department can perform more accurate evaluations by taking employee attribute information into consideration. Some or all of the above processes in the evaluation department may be performed using AI, for example, or without using AI. For example, the evaluation department can perform evaluations using an AI model that takes employee attribute information as input and performs the evaluation.

[0111] The evaluation unit can estimate the user's emotions and adjust the order in which the evaluation results are displayed based on the estimated emotions. For example, if the user is nervous, the evaluation unit can display positive results first. If the user is relaxed, the evaluation unit can display negative results first. If the user is in a hurry, the evaluation unit can display important results first. In this way, the evaluation unit can provide the user with the best possible evaluation result by adjusting the order in which the evaluation results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can provide evaluation results using an AI model that takes user emotion data as input and adjusts the order in which the evaluation results are displayed.

[0112] The evaluation department can conduct evaluations while considering the geographical distribution of employees. For example, the evaluation department can fairly evaluate employees who are geographically separated. The evaluation department can conduct evaluations while considering regional performance. The evaluation department can adjust evaluation criteria to take geographical constraints into account. This allows the evaluation department to conduct more accurate evaluations by considering the geographical distribution of employees. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI. For example, the evaluation department can conduct evaluations using an AI model that takes employee geographical distribution data as input.

[0113] The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature related to the employee during the evaluation process. For example, the evaluation unit can perform evaluations by referring to literature related to the employee's performance. The evaluation unit can perform evaluations by referring to literature related to the employee's skills. The evaluation unit can perform evaluations by referring to literature related to the employee's projects. This allows the evaluation unit to perform more accurate evaluations by referring to relevant literature related to the employee. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can perform evaluations using an AI model that improves the accuracy of evaluations, with employee relevant literature data as input.

[0114] The feedback unit can estimate the user's emotions and adjust how the feedback is displayed based on the estimated emotions. For example, if the user is nervous, the feedback unit can highlight positive feedback. If the user is relaxed, the feedback unit can display detailed feedback. If the user is in a hurry, the feedback unit can display concise, to-the-point feedback. In this way, the feedback unit can provide the user with the most appropriate feedback by adjusting how the feedback is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can provide feedback using an AI model that takes user emotion data as input and adjusts how the feedback is displayed.

[0115] The feedback unit can optimize current feedback by referring to past feedback data during the feedback process. For example, the feedback unit can adjust the content of current feedback based on past feedback data. The feedback unit can evaluate employee growth by referring to past feedback data. The feedback unit can optimize the tone and content of feedback based on past feedback data. As a result, the feedback unit can provide more effective feedback by optimizing current feedback by referring to past feedback data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can provide feedback using an AI model that takes past feedback data as input and optimizes current feedback.

[0116] The feedback department can apply different feedback methods to different employee categories when providing feedback. For example, the feedback department can provide educational feedback to new employees. For mid-career employees, it can provide feedback that includes specific areas for improvement. For managers, it can provide feedback from a strategic perspective. In this way, the feedback department can provide more effective feedback by applying different feedback methods to different employee categories. Some or all of the above processes in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department can provide feedback using an AI model that takes employee category data as input and applies different feedback methods.

[0117] The feedback unit can estimate the user's emotions and adjust the importance of the feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit may postpone less important feedback. If the user is relaxed, the feedback unit may prioritize providing more important feedback. If the user is in a hurry, the feedback unit may prioritize feedback that can be provided quickly. In this way, the feedback unit can provide the user with optimal feedback by adjusting the importance of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit may take user emotion data as input and provide feedback using an AI model that adjusts the importance of the feedback.

[0118] The feedback unit can analyze changes in feedback based on the employee's submission timing. For example, the feedback unit can provide prompt feedback to employees who submit early, and detailed feedback to employees who submit late. The feedback unit can adjust the content of the feedback based on the submission timing. This allows the feedback unit to provide more effective feedback by analyzing changes in feedback based on the employee's submission timing. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can use an AI model that takes employee submission timing data as input and analyzes changes in feedback to provide feedback.

[0119] The feedback unit can analyze feedback by referring to relevant market data of employees when providing feedback. The feedback unit can adjust the content of the feedback based on market trends, for example. The feedback unit can optimize the content of the feedback by referring to competitor data. The feedback unit can evaluate employee performance based on market data. As a result, the feedback unit can provide more effective feedback by analyzing feedback by referring to relevant market data of employees. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can provide feedback using an AI model that takes relevant market data of employees as input and analyzes the feedback.

[0120] The cloud-based unit can adjust the timing of data processing according to the load status of the cloud environment. For example, if the load on the cloud environment is high, the cloud-based unit can delay data processing. If the load on the cloud environment is low, the cloud-based unit can speed up data processing. The cloud-based unit can optimize the timing of data processing according to the load status of the cloud environment. As a result, the cloud-based unit can perform efficient data processing by adjusting the timing of data processing according to the load status of the cloud environment. Some or all of the above processing in the cloud-based unit may be performed using AI, for example, or without using AI. For example, the cloud-based unit can take cloud environment load status data as input and perform data processing using an AI model that adjusts the timing of data processing.

[0121] The cloud-based unit can enhance cloud-based security protocols and ensure data security. For example, the cloud-based unit can enhance data encryption. The cloud-based unit can periodically update security protocols. The cloud-based unit can enhance security monitoring and ensure data security. Thus, the cloud-based unit can ensure data security by enhancing cloud-based security protocols. Some or all of the above-described processes in the cloud-based unit may be performed using AI, for example, or without AI. For example, the cloud-based unit can ensure data security by using an AI model that takes data encryption protocols as input and enhances security protocols.

[0122] The cloud-based unit can monitor the usage of cloud resources in real time and perform optimal resource allocation. For example, the cloud-based unit monitors the usage of cloud resources in real time. The cloud-based unit can adjust resource allocation according to resource usage. The cloud-based unit can perform optimal resource allocation based on real-time monitoring data. As a result, the cloud-based unit can efficiently manage resources by monitoring the usage of cloud resources in real time and performing optimal resource allocation. Some or all of the above processes in the cloud-based unit may be performed using AI, for example, or without AI. For example, the cloud-based unit can perform resource management using an AI model that takes cloud resource usage data as input and performs optimal resource allocation.

[0123] The cloud-based unit can expand its cloud-based data storage to store more data. For example, the cloud-based unit can expand the data storage capacity. As the data storage expands, the cloud-based unit can review its data management methods. By expanding the data storage, the cloud-based unit can increase its data storage capacity. For example, the cloud-based unit can expand the data storage capacity. As the data storage expands, the cloud-based unit can review its data management methods. By expanding the data storage, the cloud-based unit can increase its data storage capacity. This allows the cloud-based unit to store more data by expanding its cloud-based data storage. Some or all of the above processing in the cloud-based unit may be performed using AI, for example, or without AI. For example, the cloud-based unit can use an AI model that takes the data storage capacity as input and expands the data storage to store data.

[0124] The cloud-based unit can improve cloud-based data processing capabilities and enable rapid processing of large amounts of data. For example, the cloud-based unit can enhance its cloud infrastructure to improve data processing capabilities. As data processing capabilities improve, the cloud-based unit can optimize processing speed. The cloud-based unit can optimize cloud resources to process large amounts of data quickly. This allows the cloud-based unit to process large amounts of data quickly by improving its cloud-based data processing capabilities. Some or all of the above-described processing in the cloud-based unit may be performed using AI, for example, or without AI. For example, the cloud-based unit can process data using an AI model that improves data processing capabilities, with data from the cloud infrastructure as input.

[0125] The cloud-based unit can implement a cloud-based data backup system to ensure data redundancy. For example, the cloud-based unit can implement a data backup system. The cloud-based unit can adjust the backup frequency to ensure data redundancy. The cloud-based unit can periodically test the data backup system to ensure redundancy. Thus, the cloud-based unit can ensure data redundancy by implementing a cloud-based data backup system. Some or all of the above processes in the cloud-based unit may be performed using AI, for example, or without AI. For example, the cloud-based unit can take the data backup system as input and perform data backups using an AI model that ensures data redundancy.

[0126] The evolutionary unit can optimize the algorithm as the machine learning model evolves. For example, the evolutionary unit can incorporate a new dataset and optimize the algorithm. The evolutionary unit can adjust the algorithm parameters in accordance with the evolution of the machine learning model. By optimizing the algorithm, the evolutionary unit can improve the accuracy of the model. Thus, the evolutionary unit can improve the accuracy of the model by optimizing the algorithm as the machine learning model evolves. Some or all of the above processes in the evolutionary unit may be performed using AI, for example, or without AI. For example, the evolutionary unit can take evolutionary data of the machine learning model as input and use an AI model that optimizes the algorithm to perform algorithm optimization.

[0127] The evolutionary component can improve the accuracy of the model by incorporating new datasets. For example, the evolutionary component can periodically incorporate new datasets. The evolutionary component can evaluate the accuracy of the model based on the new datasets. The evolutionary component can improve the accuracy of the model by incorporating new datasets. Thus, the evolutionary component can improve the accuracy of the model by incorporating new datasets. Some or all of the above processes in the evolutionary component may be performed using AI, for example, or without AI. For example, the evolutionary component can take a new dataset as input and perform data analysis using an AI model that improves the accuracy of the model.

[0128] The evolutionary unit can improve the overall system performance in accordance with the evolution of the model. For example, the evolutionary unit optimizes the system performance as the model evolves. The evolutionary unit can leverage the evolution of the model to improve the overall system performance. The evolutionary unit can evaluate the system performance in accordance with the evolution of the model. In this way, the evolutionary unit can increase the efficiency of the system by improving the overall system performance in accordance with the evolution of the model. Some or all of the above-described processes in the evolutionary unit may be performed using AI, for example, or without AI. For example, the evolutionary unit can take the evolutionary data of the model as input and optimize the system performance using an AI model that improves the overall system performance.

[0129] The evolutionary unit can add new functions as the machine learning model evolves. For example, the evolutionary unit develops new functions in accordance with the model's evolution. The evolutionary unit can improve the system's usability by adding new functions. The evolutionary unit can implement new functions by leveraging the evolution of the machine learning model. In this way, the evolutionary unit can improve the system's usability by adding new functions as the machine learning model evolves. Some or all of the above-described processes in the evolutionary unit may be performed using AI, for example, or without AI. For example, the evolutionary unit can extend the system's functionality using an AI model that takes evolutionary data of the machine learning model as input and adds new functions.

[0130] The evolution unit can improve the user interface in accordance with the evolution of the model. For example, the evolution unit optimizes the user interface as the model evolves. The evolution unit can improve usability by improving the user interface. The evolution unit can improve the user interface by leveraging the evolution of the model. In this way, the evolution unit can improve usability by improving the user interface in accordance with the evolution of the model. Some or all of the above processing in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can take the evolution data of the model as input and use an AI model that improves the user interface to optimize the interface.

[0131] The evolutionary unit can improve the system's scalability as the model evolves. For example, the evolutionary unit optimizes the system's scalability in accordance with the model's evolution. By improving scalability, the evolutionary unit can enhance the system's extensibility. The evolutionary unit can improve the system's scalability by leveraging the model's evolution. Thus, the evolutionary unit can enhance the system's extensibility by improving the system's scalability as the model evolves. Some or all of the above-described processes in the evolutionary unit may be performed using AI, for example, or without AI. For example, the evolutionary unit can take model evolution data as input and optimize the system's extensibility using an AI model that improves the system's scalability.

[0132] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is tense, the interface unit can provide an interface with calm colors. If the user is enjoying themselves, the interface unit can provide an interface with bright colors. If the user is tired, the interface unit can provide a simple and highly visible interface. In this way, the interface unit can provide the optimal interface for the user by adjusting the interface display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can provide an interface using an AI model that takes user emotion data as input and adjusts the interface display method.

[0133] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can suggest the optimal display method based on the user's past operation history. The interface unit can prioritize the display of frequently used functions based on the user's operation history. The interface unit can provide a display method tailored to the user's preferences by referring to past operation history. In this way, the interface unit can provide a user-friendly interface by selecting the optimal display method by referring to the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can provide an interface using an AI model that takes the user's past operation history data as input and selects the optimal display method.

[0134] The interface unit can improve the interface design and enhance usability. For example, the interface unit can simplify the interface design and improve operability. The interface unit can optimize the interface layout to improve usability. The interface unit can improve the interface design and enhance visibility. Thus, the interface unit can improve usability by improving the interface design. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can take interface design data as input and optimize the interface using an AI model that improves the design.

[0135] The interface unit can estimate the user's emotions and adjust the interface's operating procedures based on the estimated user emotions. For example, if the user is tense, the interface unit can simplify the operating procedures. If the user is relaxed, the interface unit can provide detailed operating procedures. If the user is in a hurry, the interface unit can provide procedures that allow for quick operation. In this way, the interface unit can provide user-friendly operating procedures by adjusting the interface's operating procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can provide an interface using an AI model that takes user emotion data as input and adjusts the operating procedures.

[0136] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method that matches the screen size. If the user is using a tablet, the interface unit can provide a display method optimized for a larger screen. If the user is using a desktop, the interface unit can provide a display method that utilizes multiple windows. In this way, the interface unit can provide a user-friendly interface by selecting the optimal display method considering the user's device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can provide an interface using an AI model that takes the user's device information as input and selects the optimal display method.

[0137] The interface unit can add customization features to the interface, allowing users to adjust it to their preferences. For example, the interface unit can provide a function that allows users to customize the interface's colors and layout. The interface unit can provide a function that allows users to set frequently used functions as shortcuts. The interface unit can provide a function that allows users to adjust the interface's font size and icon size. In this way, by adding customization features to the interface unit, users can adjust the interface to their preferences. Some or all of the above processing in the interface unit may be performed using AI, for example, or not using AI. For example, the interface unit can provide a customization function using an AI model that takes user customization data as input and adjusts the interface.

[0138] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0139] The data collection unit can collect employee health data in addition to employee performance data and KPIs. For example, the data collection unit can collect health data such as employee heart rate and sleep duration and analyze it in combination with performance data. This allows for the evaluation of the impact of employee health on performance and helps improve health management. The data collection unit can also be linked with wearable devices and health management apps to collect employee health data.

[0140] The analytics department can consider employees' career goals and self-assessment data when analyzing employee performance data. For example, by evaluating the progress of employees toward their set career goals and comparing it with self-assessment data, the analytics department can identify the gap between employees' self-perception and actual performance. This can support employees' career development and promote their personal growth. The analytics department can also conduct regular surveys and interviews to collect employees' career goals and self-assessment data.

[0141] The performance evaluation department can add indicators to assess employee creativity and innovation when evaluating employee performance. For example, the department can evaluate the number of new ideas and projects proposed by employees and calculate a creativity score. The department can also evaluate the results of innovative initiatives and improvement suggestions implemented by employees. This allows for the reflection of employee creativity and innovation in evaluations and promotes innovation. The performance evaluation department can also implement employee idea submission systems or innovation contests to assess creativity and innovation.

[0142] The feedback department can choose a feedback method that suits each employee's learning style and preferences when providing feedback. For example, the feedback department can provide feedback using graphs and charts to employees who prefer visual feedback. They can also provide feedback in the form of an interview to employees who prefer verbal feedback. This allows for feedback tailored to each employee's learning style and preferences, enabling effective communication. The feedback department can also conduct surveys or interviews in advance to understand each employee's learning style and preferences.

[0143] The data collection department can collect data that assesses employee job satisfaction and stress levels when gathering employee performance data. For example, the department can conduct regular surveys to assess employee job satisfaction and use stress check tools to assess stress levels. This allows them to understand employee job satisfaction and stress levels and use this information to improve the workplace environment. The data collection department can also combine and analyze employee feedback and health data to assess job satisfaction and stress levels.

[0144] The evaluation unit can estimate the user's emotions and adjust the evaluation feedback based on those emotions. For example, if the user is feeling down, the evaluation unit can emphasize and provide positive feedback. If the user is confident, the evaluation unit can also provide feedback that specifically points out areas for improvement. In this way, the evaluation unit can maintain the user's motivation and promote their growth by providing feedback that is tailored to the user's emotions. Emotion estimation can be achieved, for example, using an emotion engine or generative AI.

[0145] The analysis unit can estimate the user's emotions and adjust how the analysis results are presented based on those estimated emotions. For example, if the user is stressed, the analysis unit can provide simple, to-the-point analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. In this way, the analysis unit can provide information that is easy for the user to understand by providing analysis results that are tailored to the user's emotions. Emotion estimation can be achieved, for example, using an emotion engine or generative AI.

[0146] The feedback unit can estimate the user's emotions and adjust the timing of feedback based on those emotions. For example, if the user is tired, the feedback unit can delay the timing of feedback. If the user is relaxed, the feedback unit can also speed up the timing of feedback. This allows the feedback unit to provide feedback at a time that matches the user's emotions, thereby increasing user receptivity. Emotion estimation can be achieved, for example, using an emotion engine or generative AI.

[0147] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can refrain from collecting detailed data. If the user is relaxed, the data collection unit can collect detailed data. This allows the data collection unit to reduce the user's burden and improve data quality by collecting data according to the user's emotions. Emotion estimation can be achieved, for example, using an emotion engine or generative AI.

[0148] The evaluation unit can estimate the user's emotions and adjust the evaluation feedback method based on the estimated user emotions. For example, if the evaluation unit is tense, it can emphasize and provide positive feedback. If the user is relaxed, it can also provide detailed feedback. In this way, the evaluation unit can maintain the user's motivation and promote growth by providing feedback that is tailored to the user's emotions. Emotion estimation can be achieved, for example, using an emotion engine or generative AI.

[0149] The following briefly describes the processing flow for example form 2.

[0150] Step 1: The data collection department collects employee performance data and KPIs. For example, it collects data such as sales revenue achieved, number of projects completed, work outcomes, project progress, and level of collaboration within teams. The data collection department can collect sales revenue, number of completed projects, milestone achievement, task completion rates, communication frequency, and collaborative work outcomes. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses bias reduction algorithms, fairness algorithms, and rebalancing techniques to reduce data bias and correct imbalances. Furthermore, it uses AI to extract data patterns and provide the information necessary for evaluation. Step 3: The evaluation department conducts fair evaluations based on the analysis results obtained by the analysis department. The evaluation department calculates evaluation scores based solely on performance data, regardless of the employee's gender or age. AI is used to conduct fair evaluations and provide fair evaluation results. Step 4: The Feedback Department provides feedback on the evaluation results obtained by the Evaluation Department. The Feedback Department provides feedback in real time, giving evaluation results to employees immediately after they complete a project. AI is used to analyze the evaluation results and provide appropriate feedback.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, feedback unit, cloud-based unit, evolution unit, and interface unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects employee performance data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using a bias reduction algorithm. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a fair evaluation based on performance data. The feedback unit is implemented by the control unit 46A of the smart device 14 and provides feedback in real time. The cloud-based unit collects, analyzes, and evaluates data using cloud-based AI analysis tools. The evolution unit improves the accuracy of the system using an evolving machine learning model. The interface unit provides a user-friendly interface by 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.

[0155] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, feedback unit, cloud-based unit, evolution unit, and interface unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects employee performance data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data using a bias reduction algorithm. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs an unbiased evaluation based on performance data. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides real-time feedback. The cloud-based unit collects, analyzes, and evaluates data using cloud-based AI analysis tools. The evolution unit improves the accuracy of the system using an evolving machine learning model. The interface unit provides a user-friendly interface by the control unit 46A of the smart glasses 214. 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.

[0171] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, feedback unit, cloud-based unit, evolution unit, and interface unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects employee performance data using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using a bias reduction algorithm. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a fair evaluation based on performance data. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides real-time feedback. The cloud-based unit collects, analyzes, and evaluates data using cloud-based AI analysis tools. The evolution unit improves the accuracy of the system using an evolving machine learning model. The interface unit provides a user-friendly interface by the control unit 46A of the headset 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.

[0187] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.).

[0200] 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.

[0201] 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.

[0202] 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.

[0203] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, feedback unit, cloud-based unit, evolution unit, and interface unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects employee performance data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using a bias reduction algorithm. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs an unbiased evaluation based on performance data. The feedback unit is implemented by the control unit 46A of the robot 414 and provides real-time feedback. The cloud-based unit collects, analyzes, and evaluates data using cloud-based AI analysis tools. The evolution unit improves the accuracy of the system using an evolving machine learning model. The interface unit provides a user-friendly interface by the control unit 46A of the robot 414. 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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."

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] (Note 1) The data collection department collects employee performance data and KPIs, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that performs a fair evaluation based on the analysis results obtained by the aforementioned analysis unit, The system includes a feedback unit that provides feedback on the evaluation results obtained by the evaluation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Use bias reduction algorithms to eliminate human bias. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned feedback unit is Provide real-time feedback The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect data such as work results, project progress, and the level of cooperation within the team. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit described above, Employees are evaluated based purely on their performance, regardless of gender or age. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Provide feedback to employees immediately after they complete a project. The system described in Appendix 1, characterized by the features described herein. (Note 7) It features a cloud-based section that uses cloud-based AI analysis tools. The system described in Appendix 1, characterized by the features described herein. (Note 8) Features an evolutionary section that uses evolving machine learning models. The system described in Appendix 1, characterized by the features described herein. (Note 9) It features an interface section that provides a user-friendly interface. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and adjusts the timing of performance data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Analyze employees' past performance data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting performance data, filter it based on the employee's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting performance data, the system prioritizes collecting highly relevant data by considering the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When collecting performance data, analyze employees' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit described above, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit described above, When evaluating employees, consider their interpersonal relationships to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit described above, When evaluating employees, their attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit described above, It estimates the user's emotions and adjusts the order in which evaluation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit described above, When evaluating employees, the geographical distribution of those employees should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit described above, During evaluations, we refer to relevant literature related to employees to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, we optimize the current feedback by referring to past feedback data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, apply different feedback methods to each employee category. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is It estimates the user's emotions and adjusts the importance of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, analyze how feedback changes based on when employees submit it. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is When providing feedback, we analyze the feedback by referring to relevant market data of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned cloud-based section is Adjust the timing of data processing according to the load on the cloud environment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned cloud-based section is Enhance cloud-based security protocols to ensure data safety. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned cloud-based section is Monitor cloud resource usage in real time and allocate resources optimally. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned cloud-based section is Expand cloud-based data storage to enable the storage of more data. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned cloud-based section is Improve cloud-based data processing capabilities and enable rapid processing of large amounts of data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned cloud-based section is We will implement a cloud-based data backup system to ensure data redundancy. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned evolutionary section is As machine learning models evolve, we optimize the algorithms. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned evolutionary section is Incorporate new datasets to improve model accuracy. The system according to Appendix 1, characterized in that... (Appendix 42) The evolution unit Improves the performance of the entire system according to the evolution of the model The system according to Appendix 1, characterized in that... (Appendix 43) The evolution unit Adds new functions as the machine learning model evolves The system according to Appendix 1, characterized in that... (Appendix 44) The evolution unit Improves the user interface according to the evolution of the model The system according to Appendix 1, characterized in that... (Appendix 45) The evolution unit Improves the scalability of the system as the model evolves The system according to Appendix 1, characterized in that... (Appendix 46) The interface unit Estimates the user's emotion and adjusts the display method of the interface based on the estimated user emotion The system according to Appendix 1, characterized in that... (Appendix 47) The interface unit Selects the optimal display method by referring to the user's past operation history when displaying the interface The system according to Appendix 1, characterized in that... (Appendix 48) The interface unit Improves the design of the interface and enhances the usability The system according to Appendix 1, characterized in that... (Appendix 49) The interface unit Estimates the user's emotion and adjusts the operation procedure of the interface based on the estimated user emotion The system according to Appendix 1, characterized in that... (Note 50) The interface unit is When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 51) The interface unit is We've added an interface customization feature, allowing users to adjust it to their preferences. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0223] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects employee performance data and KPIs, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that performs a fair evaluation based on the analysis results obtained by the aforementioned analysis unit, The system includes a feedback unit that provides feedback on the evaluation results obtained by the evaluation unit. A system characterized by the following features.

2. The aforementioned analysis unit is Use bias reduction algorithms to eliminate human bias. The system according to feature 1.

3. The aforementioned feedback unit is Provide real-time feedback The system according to feature 1.

4. The aforementioned collection unit is Collect data such as work results, project progress, and the level of cooperation within the team. The system according to feature 1.

5. The evaluation unit, Employees are evaluated based purely on their performance, regardless of gender or age. The system according to feature 1.

6. The aforementioned feedback unit is Provide feedback to employees immediately after they complete a project. The system according to feature 1.

7. It includes a cloud-based section that uses cloud-based AI analysis tools. The system according to feature 1.

8. Features an evolutionary section that uses evolving machine learning models. The system according to feature 1.

Images