System

Abstract

An object of a system according to an embodiment is to objectively perform work management and evaluation of employees.SOLUTION: A system includes a business management part, an evaluation part, and a prohibition part. The work management part manages the work of the employee. The evaluation part evaluates the performance and action of the employee. The prohibition unit prohibits direct intervention by a human supervisor.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the subjectivity of human supervisors was involved in managing and evaluating employees' work, making it difficult to make objective evaluations.

[0005] The system according to the embodiment aims to objectively manage and evaluate the work of employees. [Means for solving the problem]

[0006] The system according to the embodiment includes a work management unit, an evaluation unit, and a prohibition unit. The work management unit manages the work of employees. The evaluation unit evaluates the performance and behavior of employees. The prohibition unit prohibits direct intervention by human supervisors. [Effects of the Invention]

[0007] The system according to the embodiment can objectively manage and evaluate the work of employees. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​supervisor system according to an embodiment of the present invention is a system that automatically manages and evaluates employee work, prohibiting direct intervention by human supervisors. This enables the AI ​​supervisor system to achieve work efficiency and fair evaluation.

[0029] The AI ​​supervisor system according to the embodiment includes a work management unit, an evaluation unit, and a prohibition unit. The work management unit manages employee work. For example, the work management unit monitors employee work content and progress in real time and issues appropriate instructions. The work management unit can also grasp the progress of projects and automatically assign necessary tasks. The work management unit also optimizes employee schedules to reduce wasted time. The evaluation unit evaluates employee performance and behavior. For example, the evaluation unit collects data on employees' work results and work attitudes and evaluates them based on that data. The evaluation unit can also provide fair feedback based on data on employees' performance and behavior. Furthermore, because the evaluation unit evaluates based on data, there is no room for personal feelings or prejudices. The prohibition unit prohibits direct intervention by human supervisors. For example, when an employee performs work according to instructions from an AI supervisor, the prohibition unit prohibits the human supervisor from changing those instructions. The prohibition unit also ensures that evaluations and instructions given to employees are fair and consistent. As a result, the AI ​​boss system according to the embodiment can improve work efficiency and provide fair evaluations. For example, the AI ​​boss system can grasp the progress of a project in real time and provide appropriate instructions, thereby improving work efficiency. Furthermore, by evaluating employee performance and behavior based on data, fair evaluations can be achieved. This is expected to improve employee motivation and the performance of the entire organization.

[0030] The business management department can analyze employees' past work history and predict and assign the optimal tasks to each employee. For example, the business management department retrieves employees' past work history from a database and analyzes patterns using a machine learning algorithm. For example, it extracts the characteristics of tasks in which employees have performed well in the past and predicts and assigns similar tasks. The business management department can also assign optimal tasks taking into account employees' skill sets and the difficulty of the tasks. For example, it selects and assigns appropriate tasks based on the employee's skill set. This allows the optimal tasks to be assigned to employees, thereby improving work efficiency.

[0031] The work management department can monitor employees' stress levels and suggest breaks at appropriate times. For example, the work management department can monitor employees' biometric data (heart rate, electrodermal activity, etc.) and analyze stress levels in real time. For example, it can suggest breaks when stress levels exceed a certain threshold. The work management department can also analyze employees' work patterns and suggest breaks at appropriate times. For example, it can suggest breaks when employees have been working continuously for a long period of time. The work management department can also suggest relaxation methods to reduce employees' stress levels. For example, it can suggest short stretches or deep breathing. This reduces employee stress and provides a healthier work environment.

[0032] The business management department can analyze the communication patterns of the entire team and propose efficient communication methods. For example, the business management department can analyze email and chat history between team members to identify communication patterns. For example, it can identify time periods when communication frequently stops and schedule meetings during those times. The business management department can also analyze the frequency and content of communication between team members and propose efficient communication methods. For example, it can improve communication efficiency by using specific tools. The business management department can also visualize the communication patterns of the entire team and identify areas for improvement. For example, it can identify communication bottlenecks and propose improvement measures. This improves the team's communication efficiency.

[0033] The business management department can visualize the progress of a project so that all team members can check it in real time. For example, the business management department obtains data from a project management tool and visualizes the progress. For example, it displays the progress of each task using a Gantt chart or progress bar. The business management department can also update the project progress in real time so that all team members can check the progress. For example, it can display the progress on a dashboard so that everyone can access it. By visualizing the project progress, the business management department can also detect problems early and take measures. For example, it can identify tasks that are delayed and reallocate resources. This makes the project progress visible and allows the entire team to share the progress.

[0034] The evaluation unit can integrate an employee's self-assessment and evaluation by others to provide a comprehensive evaluation. For example, the evaluation unit collects and integrates an employee's self-assessment data and evaluation by others. For example, it calculates the average of the self-assessment and evaluation by others to provide a comprehensive evaluation. The evaluation unit can also analyze the difference between the self-assessment and evaluation by others to maintain the consistency of the evaluation. For example, if the self-assessment is high and the evaluation by others is low, it can provide feedback. The evaluation unit can also achieve a fair evaluation by integrating the self-assessment and evaluation by others. For example, it can perform a comprehensive evaluation based on feedback from multiple evaluators. In this way, a fair evaluation can be achieved by integrating the self-assessment and evaluation by others.

[0035] The evaluation department can analyze an employee's skill set and propose a training program for skill improvement. For example, the evaluation department retrieves and analyzes an employee's skill set from a database. For example, the evaluation department evaluates the employee's current skill level and proposes a training program for skill improvement. The evaluation department can also identify an employee's skill gaps and propose an appropriate training program. For example, if a specific skill is lacking, the evaluation department can propose training to improve that skill. The evaluation department can also propose a training program for skill improvement based on the employee's career path. For example, the evaluation department can propose training that is aligned with the employee's future career goals. This helps employees improve their skills and improves work efficiency.

[0036] The evaluation department can compare employee performance across different projects and propose optimal project assignments. For example, the evaluation department collects employees' past project data and compares performance. For example, it evaluates the results of each project and proposes optimal project assignments. The evaluation department can also propose optimal project assignments taking into account the employee's skill set and the difficulty of the work. For example, it selects and assigns appropriate projects based on the employee's skill set. The evaluation department can also propose optimal project assignments taking into account the project progress and resource usage. For example, it assigns appropriate employees to projects with a shortage of resources. In this way, optimal project assignments are made based on the employee's aptitude.

[0037] The evaluation department can visualize employee evaluation results to enable employees to intuitively understand areas for self-improvement. For example, the evaluation department can visualize employee evaluation results and display them in graphs or charts. For example, the department can show the progress of evaluation scores in a line graph to clarify areas for self-improvement. The evaluation department can also update employee evaluation results in real time to enable intuitive understanding. For example, the evaluation results can be displayed on a dashboard so that everyone can access them. By visualizing the evaluation results, the evaluation department can also detect problems early and take measures. For example, the department can identify items with low ratings and propose improvement measures. In this way, visualizing the evaluation results makes it easier for employees to understand areas for self-improvement.

[0038] The Prohibition Department can provide guidelines to improve employees' self-management skills. For example, the Prohibition Department automatically generates guidelines to improve employees' self-management skills. For example, it can provide specific methods for time management and task management. The Prohibition Department can also evaluate employees' self-management skills and provide appropriate guidelines. For example, it can suggest specific improvement measures for employees with low self-management skills. The Prohibition Department can also periodically review the content of the guidelines and provide the latest information. For example, it can introduce new time management techniques and tools. This can improve employees' self-management skills and improve work efficiency.

[0039] The prohibition unit can hold regular meetings with a human supervisor and report on the progress of work. The prohibition unit, for example, builds a system that holds regular meetings with a human supervisor and reports on the progress of work. For example, it automatically generates a progress report at a weekly meeting and provides it to the supervisor. The prohibition unit can also record the content of meetings so that they can be referenced later. For example, it can automatically generate and share meeting minutes. The prohibition unit can also analyze the content of meetings and identify areas for improvement. For example, it can identify tasks that are behind schedule and take measures. In this way, the progress of work can be reported while maintaining cooperation with the human supervisor.

[0040] The prohibition unit can introduce a mechanism for collecting employee opinions and providing feedback to human supervisors. The prohibition unit, for example, builds a system that automatically collects employee opinions and provides feedback to human supervisors. For example, it conducts regular surveys and reports the results to supervisors. The prohibition unit can also analyze employee opinions and provide appropriate feedback. For example, it can emphasize positive opinions and improve negative opinions. The prohibition unit can also make the content of feedback transparent and share it with employees. For example, it can explain the criteria for feedback and the timing of providing it. In this way, employee opinions are collected and feedback is provided to human supervisors.

[0041] The work management department can analyze employees' work patterns and propose optimal work flows. For example, the work management department can analyze employees' work history and propose optimal work flows. For example, it can propose an efficient task order based on past data. The work management department can also propose optimal work flows taking into account employees' skill sets and the difficulty of the work. For example, it can design an appropriate work flow based on employees' skill sets. The work management department can also monitor the progress of work in real time and adjust the work flow. For example, it can identify tasks that are lagging behind and reallocate resources. In this way, the work management department can analyze employees' work patterns and propose optimal work flows.

[0042] The business management department can monitor employees' work environments and make suggestions to provide an efficient work environment. For example, the business management department can monitor employees' work environments and make suggestions to provide an efficient work environment. For example, it can suggest adjusting lighting and temperature. The business management department can also monitor employees' work environments in real time and identify problem areas. For example, it can suggest countermeasures if the noise level is high. The business management department can also suggest specific methods to improve employees' work environments. For example, it can suggest desk layouts based on ergonomics. In this way, the business management department can monitor employees' work environments and provide an efficient work environment.

[0043] The business management department can optimize resource allocation among different teams and improve overall business efficiency. For example, the business management department develops algorithms to optimize resource allocation among different teams. For example, it analyzes the resource usage of each team and proposes optimal allocation. The business management department can also monitor project progress and resource usage in real time and adjust resource allocation. For example, it can reallocate appropriate resources to teams that are short of resources. The business management department can also clarify resource allocation criteria so that everyone can understand them. For example, it can provide the resource allocation rules and criteria in a document. This optimizes resource allocation among different teams and improves overall business efficiency.

[0044] The business management department can visualize employees' work progress in real time, allowing everyone to share the progress status. For example, the business management department builds a system that visualizes employees' work progress in real time. For example, the progress status can be displayed in graphs and charts so that everyone can share it. The business management department can also update work progress in real time and make it accessible to everyone. For example, the progress status can be displayed on a dashboard so that everyone can check it. Furthermore, by visualizing work progress, the business management department can detect problems early and take measures. For example, they can identify tasks that are experiencing delays and reallocate resources. In this way, the business management department can visualize employees' work progress in real time, allowing everyone to share the progress status.

[0045] The evaluation department can make employee evaluation criteria transparent so that everyone can understand them. For example, the evaluation department can build a system to make employee evaluation criteria transparent. For example, it can provide a document that explains the evaluation criteria in detail and make it accessible to everyone. The evaluation department can also regularly review the evaluation criteria and provide the latest information. For example, it can introduce new evaluation criteria or methods and notify everyone. The evaluation department can also visualize the evaluation process to improve the transparency of the evaluation criteria. For example, it can clearly explain each step of the evaluation so that everyone can understand it. This makes the evaluation criteria transparent so that everyone can understand the evaluation criteria.

[0046] The evaluation department can regularly review employee evaluation results to maintain consistency in evaluations. For example, the evaluation department can regularly review employee evaluation results and build a system to maintain consistency in evaluations. For example, they can regularly review evaluation results and revise them as necessary. The evaluation department can also clarify the evaluation criteria and methods to maintain consistency in evaluation results. For example, they can provide the evaluation criteria in a document and make it accessible to everyone. By regularly reviewing the evaluation results, the evaluation department can also discover problems early and take measures. For example, they can identify bias in the evaluations and propose improvement measures. This allows the evaluation results to be regularly reviewed to maintain consistency in evaluations.

[0047] The evaluation department can compare evaluation methods in multiple industries with different evaluation criteria and introduce the most appropriate evaluation method. For example, the evaluation department can build a system to collect and compare evaluation criteria from different industries. For example, it can compare evaluation criteria from technical, design, and marketing fields and introduce the most appropriate evaluation method. The evaluation department can also analyze the differences in evaluation criteria and propose evaluation methods appropriate for each industry. For example, it can propose adjustment methods when applying technical evaluation criteria to design fields. The evaluation department can also visualize the evaluation process to improve the transparency of the evaluation method. For example, it can clearly explain each step of the evaluation so that everyone can understand it. This allows it to compare evaluation methods from different industries and introduce the most appropriate evaluation method.

[0048] The evaluation department can visualize the evaluation results of employees so that everyone can intuitively understand them. For example, the evaluation department can visualize the evaluation results of employees and display them in graphs or charts. For example, the evaluation department can show the progress of evaluation scores in a line graph to clarify points for self-improvement. The evaluation department can also update the evaluation results of employees in real time so that they can be intuitively understood. For example, the evaluation results can be displayed on a dashboard so that everyone can check them. Furthermore, by visualizing the evaluation results, the evaluation department can detect problems early and take measures. For example, it can identify items with low ratings and propose improvement measures. In this way, the evaluation results are visualized so that everyone can intuitively understand the evaluation results.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The business management department can monitor employees' health status, predict health risks, and propose countermeasures. For example, it can monitor employees' biometric data (heart rate, blood pressure, body temperature, etc.) and issue an alert if an abnormality is detected. The business management department can also propose appropriate health management programs based on employees' health status. For example, it can suggest regular health checkups and fitness programs. The business management department can also analyze employees' health data and identify areas for improvement in the work environment. For example, it can suggest adjustments to air conditioning and lighting. This helps maintain employee health and improve work efficiency.

[0051] The evaluation department can predict an employee's career path and propose a long-term career plan. For example, it can analyze an employee's past work history and skill set to predict their future career direction. The evaluation department can also suggest appropriate training programs and projects based on the employee's career goals. For example, it can suggest programs aimed at leadership training or improving specialized skills. The evaluation department can also visualize an employee's career path to make it easier to understand intuitively. For example, it can display career progress in graphs and charts. This allows it to predict an employee's career path and propose a long-term career plan.

[0052] The evaluation department can help employees set individual goals based on their performance data. For example, they can analyze an employee's past performance data and set realistic but challenging goals. The evaluation department can also suggest appropriate goals based on the employee's skill set and career goals. For example, they can set goals aimed at improving specific skills. The evaluation department can also monitor progress toward achieving goals in real time and provide feedback as needed. For example, they can provide advice and support for achieving goals. This helps employees set goals and improve their performance.

[0053] Based on the results of an employee's evaluation, the evaluation department can propose specific action plans to improve performance. For example, they can analyze the evaluation results and identify areas for improvement in specific skills or work processes. The evaluation department can also propose appropriate training programs or coaching sessions based on the results of an employee's evaluation. For example, they can suggest leadership training or time management coaching. The evaluation department can also propose action plans based on the evaluation results that are in line with the employee's career path. For example, they can suggest assigning projects that align with future career goals. This helps improve employee performance.

[0054] Based on the employee evaluation results, the evaluation department can propose strategies to improve the performance of the entire team. For example, they can analyze the evaluation results of each member and identify the strengths and weaknesses of the entire team. The evaluation department can also propose appropriate training programs and workshops to improve the performance of the entire team. For example, they can propose team building workshops and training to improve skills. The evaluation department can also use the evaluation results to suggest improvements to team goal setting and work processes. For example, they can clarify the team's goals and make work processes more efficient. This will improve the performance of the entire team.

[0055] The evaluation department can provide feedback to improve performance based on the employee evaluation results. For example, they can analyze the evaluation results and identify specific areas for improvement. The evaluation department can also provide appropriate feedback based on the employee evaluation results. For example, they can point out areas for improvement in specific skills or work processes and provide specific advice. The evaluation department can also provide feedback that is aligned with the employee's career path based on the evaluation results. For example, they can provide feedback that is aligned with the employee's future career goals. This helps improve the employee's performance.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The work management department manages employee work. For example, the work management department monitors employees' work content and progress in real time and issues appropriate instructions. The work management department can also grasp the progress of projects and automatically assign necessary tasks. Furthermore, the work management department optimizes employee schedules and reduces wasted time. Step 2: The evaluation department evaluates the employee's performance and behavior. For example, the evaluation department collects data on the employee's work performance and work attitude and bases the evaluation on that data. The evaluation department can also provide fair feedback based on the data on the employee's performance and behavior. Furthermore, because the evaluation department bases the evaluation on data, there is no room for personal feelings or prejudices. Step 3: The Prohibition Unit prohibits direct intervention by human supervisors. For example, if an employee follows instructions from an AI supervisor, the Prohibition Unit prohibits the human supervisor from changing those instructions. The Prohibition Unit also ensures that evaluations and instructions given to employees are fair and consistent.

[0058] (Example 2) The AI ​​supervisor system according to an embodiment of the present invention is a system that automatically manages and evaluates employee work, prohibiting direct intervention by human supervisors. This enables the AI ​​supervisor system to achieve work efficiency and fair evaluation.

[0059] The AI ​​supervisor system according to the embodiment includes a work management unit, an evaluation unit, and a prohibition unit. The work management unit manages employee work. For example, the work management unit monitors employee work content and progress in real time and issues appropriate instructions. The work management unit can also grasp the progress of projects and automatically assign necessary tasks. The work management unit also optimizes employee schedules to reduce wasted time. The evaluation unit evaluates employee performance and behavior. For example, the evaluation unit collects data on employees' work results and work attitudes and evaluates them based on that data. The evaluation unit can also provide fair feedback based on data on employees' performance and behavior. Furthermore, because the evaluation unit evaluates based on data, there is no room for personal feelings or prejudices. The prohibition unit prohibits direct intervention by human supervisors. For example, when an employee performs work according to instructions from an AI supervisor, the prohibition unit prohibits the human supervisor from changing those instructions. The prohibition unit also ensures that evaluations and instructions given to employees are fair and consistent. As a result, the AI ​​boss system according to the embodiment can improve work efficiency and provide fair evaluations. For example, the AI ​​boss system can grasp the progress of a project in real time and provide appropriate instructions, thereby improving work efficiency. Furthermore, by evaluating employee performance and behavior based on data, fair evaluations can be achieved. This is expected to improve employee motivation and the performance of the entire organization.

[0060] The business management department can analyze employees' past work history and predict and assign the optimal tasks to each employee. For example, the business management department retrieves employees' past work history from a database and analyzes patterns using a machine learning algorithm. For example, it extracts the characteristics of tasks in which employees have performed well in the past and predicts and assigns similar tasks. The business management department can also assign optimal tasks taking into account employees' skill sets and the difficulty of the tasks. For example, it selects and assigns appropriate tasks based on the employee's skill set. This allows the optimal tasks to be assigned to employees, thereby improving work efficiency.

[0061] The work management department can monitor employees' stress levels and suggest breaks at appropriate times. For example, the work management department can monitor employees' biometric data (heart rate, electrodermal activity, etc.) and analyze stress levels in real time. For example, it can suggest breaks when stress levels exceed a certain threshold. The work management department can also analyze employees' work patterns and suggest breaks at appropriate times. For example, it can suggest breaks when employees have been working continuously for a long period of time. The work management department can also suggest relaxation methods to reduce employees' stress levels. For example, it can suggest short stretches or deep breathing. This reduces employee stress and provides a healthier work environment.

[0062] The business management department can use the emotion estimation function to grasp the emotional state of employees and send encouraging messages if their motivation is low. For example, the business management department analyzes employees' facial expressions and voice tones to estimate their emotional state in real time. For example, if it determines that their motivation is low using an emotion estimation algorithm, it can send encouraging messages. The business management department can also monitor employees' biometric data (heart rate, electrodermal activity, etc.) to estimate their emotional state. For example, it can estimate their emotional state based on heart rate fluctuations and send encouraging messages. The business management department can also grasp employees' emotional state and provide appropriate feedback. For example, it can increase their motivation by emphasizing positive feedback. This maintains employee motivation and improves work efficiency.

[0063] The business management department can analyze the communication patterns of the entire team and propose efficient communication methods. For example, the business management department can analyze email and chat history between team members to identify communication patterns. For example, it can identify time periods when communication frequently stops and schedule meetings during those times. The business management department can also analyze the frequency and content of communication between team members and propose efficient communication methods. For example, it can improve communication efficiency by using specific tools. The business management department can also visualize the communication patterns of the entire team and identify areas for improvement. For example, it can identify communication bottlenecks and propose improvement measures. This improves the team's communication efficiency.

[0064] The business management department can visualize the progress of a project so that all team members can check it in real time. For example, the business management department obtains data from a project management tool and visualizes the progress. For example, it displays the progress of each task using a Gantt chart or progress bar. The business management department can also update the project progress in real time so that all team members can check the progress. For example, it can display the progress on a dashboard so that everyone can access it. By visualizing the project progress, the business management department can also detect problems early and take measures. For example, it can identify tasks that are delayed and reallocate resources. This makes the project progress visible and allows the entire team to share the progress.

[0065] The business management department can use the emotion estimation function to analyze the emotional state of the entire team and propose team-building activities. For example, the business management department analyzes the emotional states of team members in real time and evaluates the emotional state of the entire team. For example, the business management department can use the emotion estimation algorithm to propose team-building activities if the team's motivation is low. The business management department can also grasp the emotional state of team members and propose appropriate activities. For example, it can propose workshops or team events. The business management department can also visualize the emotional state of the entire team and identify areas for improvement. For example, it can display fluctuations in emotional state in a graph and take measures to improve motivation. This allows the business management department to understand the emotional state of the team and promote team building.

[0066] The evaluation unit can integrate an employee's self-assessment and evaluation by others to provide a comprehensive evaluation. For example, the evaluation unit collects and integrates an employee's self-assessment data and evaluation by others. For example, it calculates the average of the self-assessment and evaluation by others to provide a comprehensive evaluation. The evaluation unit can also analyze the difference between the self-assessment and evaluation by others to maintain the consistency of the evaluation. For example, if the self-assessment is high and the evaluation by others is low, it can provide feedback. The evaluation unit can also achieve a fair evaluation by integrating the self-assessment and evaluation by others. For example, it can perform a comprehensive evaluation based on feedback from multiple evaluators. In this way, a fair evaluation can be achieved by integrating the self-assessment and evaluation by others.

[0067] The evaluation department can analyze an employee's skill set and propose a training program for skill improvement. For example, the evaluation department retrieves and analyzes an employee's skill set from a database. For example, the evaluation department evaluates the employee's current skill level and proposes a training program for skill improvement. The evaluation department can also identify an employee's skill gaps and propose an appropriate training program. For example, if a specific skill is lacking, the evaluation department can propose training to improve that skill. The evaluation department can also propose a training program for skill improvement based on the employee's career path. For example, the evaluation department can propose training that is aligned with the employee's future career goals. This helps employees improve their skills and improves work efficiency.

[0068] The evaluation unit can use an emotion estimation function to analyze employees' emotional responses to feedback and emphasize positive feedback. The evaluation unit, for example, analyzes employees' emotional responses to feedback in real time and emphasizes positive feedback. For example, it uses an emotion estimation algorithm to prioritize displaying feedback with a high number of positive responses. The evaluation unit can also analyze employees' emotional responses and adjust the content of the feedback. For example, if there are a lot of negative responses, it can improve the expression of the feedback. The evaluation unit can also improve employee motivation by emphasizing positive feedback. For example, emphasizing positive feedback can increase employees' self-esteem. In this way, emphasizing positive feedback improves employee motivation.

[0069] The evaluation department can compare employee performance across different projects and propose optimal project assignments. For example, the evaluation department collects employees' past project data and compares performance. For example, it evaluates the results of each project and proposes optimal project assignments. The evaluation department can also propose optimal project assignments taking into account the employee's skill set and the difficulty of the work. For example, it selects and assigns appropriate projects based on the employee's skill set. The evaluation department can also propose optimal project assignments taking into account the project progress and resource usage. For example, it assigns appropriate employees to projects with a shortage of resources. In this way, optimal project assignments are made based on the employee's aptitude.

[0070] The evaluation department can visualize employee evaluation results to enable employees to intuitively understand areas for self-improvement. For example, the evaluation department can visualize employee evaluation results and display them in graphs or charts. For example, the department can show the progress of evaluation scores in a line graph to clarify areas for self-improvement. The evaluation department can also update employee evaluation results in real time to enable intuitive understanding. For example, the evaluation results can be displayed on a dashboard so that everyone can access them. By visualizing the evaluation results, the evaluation department can also detect problems early and take measures. For example, the department can identify items with low ratings and propose improvement measures. In this way, visualizing the evaluation results makes it easier for employees to understand areas for self-improvement.

[0071] The evaluation department can use the emotion estimation function to analyze employees' emotional reactions to the evaluation results and identify areas for improvement in the evaluation process. For example, the evaluation department can analyze employees' emotional reactions to the evaluation results in real time and identify areas for improvement in the evaluation process. For example, the emotion estimation algorithm can be used to improve evaluation items that receive a lot of negative reactions. The evaluation department can also analyze employees' emotional reactions to improve the transparency of the evaluation process. For example, the evaluation department can clearly explain the evaluation criteria and evaluation methods. The evaluation department can also adjust the content of feedback based on the emotional reactions. For example, the evaluation department can emphasize positive feedback and improve negative feedback. This identifies areas for improvement in the evaluation process and achieves fair evaluations.

[0072] The prohibition unit can introduce a mechanism to collect feedback from human superiors and indirectly communicate it to employees. For example, the prohibition unit builds a system that automatically collects feedback from human superiors and indirectly communicates it to employees. For example, the supervisor's feedback is collected in text format, and the AI ​​supervisor notifies the employee at an appropriate time. The prohibition unit can also analyze the content of the feedback and provide appropriate feedback. For example, it can emphasize positive feedback and improve negative feedback. The prohibition unit can also clarify the content of the feedback and how it is provided to improve feedback transparency. For example, it can explain the criteria for feedback and the timing of providing it. In this way, fair evaluations can be maintained by indirectly communicating the feedback from the human superior.

[0073] The Prohibition Department can provide guidelines to improve employees' self-management skills. For example, the Prohibition Department automatically generates guidelines to improve employees' self-management skills. For example, it can provide specific methods for time management and task management. The Prohibition Department can also evaluate employees' self-management skills and provide appropriate guidelines. For example, it can suggest specific improvement measures for employees with low self-management skills. The Prohibition Department can also periodically review the content of the guidelines and provide the latest information. For example, it can introduce new time management techniques and tools. This can improve employees' self-management skills and improve work efficiency.

[0074] The prohibition unit can use the emotion estimation function to suggest measures to reduce the stress that employees feel in response to instructions from their AI supervisor. For example, the prohibition unit can analyze the employee's emotional state in real time and evaluate the stress level in response to instructions. For example, if stress is high, the prohibition unit can adjust the content and timing of instructions. The prohibition unit can also suggest relaxation methods to reduce employee stress. For example, it can suggest short stretches or deep breathing. The prohibition unit can also grasp the employee's emotional state and provide appropriate feedback. For example, it can reduce stress by emphasizing positive feedback. This reduces employee stress and provides a healthier work environment.

[0075] The prohibition unit can hold regular meetings with a human supervisor and report on the progress of work. The prohibition unit, for example, builds a system that holds regular meetings with a human supervisor and reports on the progress of work. For example, it automatically generates a progress report at a weekly meeting and provides it to the supervisor. The prohibition unit can also record the content of meetings so that they can be referenced later. For example, it can automatically generate and share meeting minutes. The prohibition unit can also analyze the content of meetings and identify areas for improvement. For example, it can identify tasks that are behind schedule and take measures. In this way, the progress of work can be reported while maintaining cooperation with the human supervisor.

[0076] The prohibition unit can introduce a mechanism for collecting employee opinions and providing feedback to human supervisors. The prohibition unit, for example, builds a system that automatically collects employee opinions and provides feedback to human supervisors. For example, it conducts regular surveys and reports the results to supervisors. The prohibition unit can also analyze employee opinions and provide appropriate feedback. For example, it can emphasize positive opinions and improve negative opinions. The prohibition unit can also make the content of feedback transparent and share it with employees. For example, it can explain the criteria for feedback and the timing of providing it. In this way, employee opinions are collected and feedback is provided to human supervisors.

[0077] The prohibition unit uses the emotion estimation function to analyze the emotions employees feel in response to instructions from an AI supervisor and can improve how instructions are given. For example, the prohibition unit analyzes the emotional state of employees in real time and evaluates their emotional reactions to instructions. For example, if there are a lot of negative emotions, it adjusts the content and timing of instructions. The prohibition unit can also analyze the emotional reactions of employees and improve the expression of instructions. For example, using positive expressions can make instructions more easily accepted. The prohibition unit can also adjust the content of feedback based on the emotional reactions. For example, it emphasizes positive feedback and improves negative feedback. This analyzes employees' emotions and improves how instructions are given.

[0078] The work management department can analyze employees' work patterns and propose optimal work flows. For example, the work management department can analyze employees' work history and propose optimal work flows. For example, it can propose an efficient task order based on past data. The work management department can also propose optimal work flows taking into account employees' skill sets and the difficulty of the work. For example, it can design an appropriate work flow based on employees' skill sets. The work management department can also monitor the progress of work in real time and adjust the work flow. For example, it can identify tasks that are lagging behind and reallocate resources. In this way, the work management department can analyze employees' work patterns and propose optimal work flows.

[0079] The business management department can monitor employees' work environments and make suggestions to provide an efficient work environment. For example, the business management department can monitor employees' work environments and make suggestions to provide an efficient work environment. For example, it can suggest adjusting lighting and temperature. The business management department can also monitor employees' work environments in real time and identify problem areas. For example, it can suggest countermeasures if the noise level is high. The business management department can also suggest specific methods to improve employees' work environments. For example, it can suggest desk layouts based on ergonomics. In this way, the business management department can monitor employees' work environments and provide an efficient work environment.

[0080] The work management department can use the emotion estimation function to assign tasks to increase employee motivation. The work management department, for example, analyzes the emotional state of employees in real time and assigns tasks to increase motivation. For example, it prioritizes assignment of tasks that evoke strong positive emotions. The work management department can also grasp the emotional state of employees and assign appropriate tasks. For example, it can assign easy tasks to employees whose motivation is low. The work management department can also visualize the emotional state of employees and clarify the criteria for task assignment. For example, it can display fluctuations in emotional state in a graph and explain the criteria for task assignment. In this way, it can assign tasks to increase employee motivation.

[0081] The business management department can optimize resource allocation among different teams and improve overall business efficiency. For example, the business management department develops algorithms to optimize resource allocation among different teams. For example, it analyzes the resource usage of each team and proposes optimal allocation. The business management department can also monitor project progress and resource usage in real time and adjust resource allocation. For example, it can reallocate appropriate resources to teams that are short of resources. The business management department can also clarify resource allocation criteria so that everyone can understand them. For example, it can provide the resource allocation rules and criteria in a document. This optimizes resource allocation among different teams and improves overall business efficiency.

[0082] The business management department can visualize employees' work progress in real time, allowing everyone to share the progress status. For example, the business management department builds a system that visualizes employees' work progress in real time. For example, the progress status can be displayed in graphs and charts so that everyone can share it. The business management department can also update work progress in real time and make it accessible to everyone. For example, the progress status can be displayed on a dashboard so that everyone can check it. Furthermore, by visualizing work progress, the business management department can detect problems early and take measures. For example, they can identify tasks that are experiencing delays and reallocate resources. In this way, the business management department can visualize employees' work progress in real time, allowing everyone to share the progress status.

[0083] The work management department can use the emotion estimation function to analyze the emotional state of employees and allocate tasks efficiently. For example, the work management department can analyze the emotional state of employees in real time and allocate tasks efficiently. For example, important tasks can be assigned to employees with strong positive emotions. The work management department can also grasp the emotional state of employees and allocate tasks appropriately. For example, easy tasks can be assigned to employees with low motivation. The work management department can also visualize the emotional state of employees and clarify the criteria for work allocation. For example, fluctuations in emotional state can be displayed in a graph and the criteria for work allocation explained. In this way, the emotional state of employees can be analyzed and efficient work allocation can be performed.

[0084] The evaluation department can make employee evaluation criteria transparent so that everyone can understand them. For example, the evaluation department can build a system to make employee evaluation criteria transparent. For example, it can provide a document that explains the evaluation criteria in detail and make it accessible to everyone. The evaluation department can also regularly review the evaluation criteria and provide the latest information. For example, it can introduce new evaluation criteria or methods and notify everyone. The evaluation department can also visualize the evaluation process to improve the transparency of the evaluation criteria. For example, it can clearly explain each step of the evaluation so that everyone can understand it. This makes the evaluation criteria transparent so that everyone can understand the evaluation criteria.

[0085] The evaluation department can regularly review employee evaluation results to maintain consistency in evaluations. For example, the evaluation department can regularly review employee evaluation results and build a system to maintain consistency in evaluations. For example, they can regularly review evaluation results and revise them as necessary. The evaluation department can also clarify the evaluation criteria and methods to maintain consistency in evaluation results. For example, they can provide the evaluation criteria in a document and make it accessible to everyone. By regularly reviewing the evaluation results, the evaluation department can also discover problems early and take measures. For example, they can identify bias in the evaluations and propose improvement measures. This allows the evaluation results to be regularly reviewed to maintain consistency in evaluations.

[0086] The evaluation department can use the emotion estimation function to analyze employees' emotional reactions to their evaluations and identify areas for improvement to ensure fair evaluations. The evaluation department, for example, analyzes employees' emotional reactions to their evaluations in real time and identifies areas for improvement to ensure fair evaluations. For example, it uses an emotion estimation algorithm to improve evaluation items that receive a lot of negative reactions. The evaluation department can also analyze employees' emotional reactions to improve the transparency of the evaluation process. For example, it can clearly explain the evaluation criteria and evaluation methods. The evaluation department can also adjust the content of the feedback based on the emotional reactions. For example, it can emphasize positive feedback and improve negative feedback. In this way, employees' emotional reactions to their evaluations are analyzed and areas for improvement to ensure fair evaluations are identified.

[0087] The evaluation department can compare evaluation methods in multiple industries with different evaluation criteria and introduce the most appropriate evaluation method. For example, the evaluation department can build a system to collect and compare evaluation criteria from different industries. For example, it can compare evaluation criteria from technical, design, and marketing fields and introduce the most appropriate evaluation method. The evaluation department can also analyze the differences in evaluation criteria and propose evaluation methods appropriate for each industry. For example, it can propose adjustment methods when applying technical evaluation criteria to design fields. The evaluation department can also visualize the evaluation process to improve the transparency of the evaluation method. For example, it can clearly explain each step of the evaluation so that everyone can understand it. This allows it to compare evaluation methods from different industries and introduce the most appropriate evaluation method.

[0088] The evaluation department can visualize the evaluation results of employees so that everyone can intuitively understand them. For example, the evaluation department can visualize the evaluation results of employees and display them in graphs or charts. For example, the evaluation department can show the progress of evaluation scores in a line graph to clarify points for self-improvement. The evaluation department can also update the evaluation results of employees in real time so that they can be intuitively understood. For example, the evaluation results can be displayed on a dashboard so that everyone can check them. Furthermore, by visualizing the evaluation results, the evaluation department can detect problems early and take measures. For example, it can identify items with low ratings and propose improvement measures. In this way, the evaluation results are visualized so that everyone can intuitively understand the evaluation results.

[0089] The evaluation department can use the emotion estimation function to monitor employees' emotional reactions to evaluations in real time and continuously improve the evaluation process. The evaluation department, for example, builds a system that monitors employees' emotional reactions to evaluations in real time and continuously improves the evaluation process. For example, it uses an emotion estimation algorithm to improve evaluation items that receive a lot of negative reactions. The evaluation department can also analyze employees' emotional reactions and improve the transparency of the evaluation process. For example, it can clearly explain the evaluation criteria and evaluation methods. The evaluation department can also adjust the content of feedback based on the emotional reactions. For example, it can emphasize positive feedback and improve negative feedback. In this way, the evaluation department can monitor employees' emotional reactions to evaluations in real time and continuously improve the evaluation process.

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

[0091] The business management department can monitor employees' health status, predict health risks, and propose countermeasures. For example, it can monitor employees' biometric data (heart rate, blood pressure, body temperature, etc.) and issue an alert if an abnormality is detected. The business management department can also propose appropriate health management programs based on employees' health status. For example, it can suggest regular health checkups and fitness programs. The business management department can also analyze employees' health data and identify areas for improvement in the work environment. For example, it can suggest adjustments to air conditioning and lighting. This helps maintain employee health and improve work efficiency.

[0092] The evaluation department can predict an employee's career path and propose a long-term career plan. For example, it can analyze an employee's past work history and skill set to predict their future career direction. The evaluation department can also suggest appropriate training programs and projects based on the employee's career goals. For example, it can suggest programs aimed at leadership training or improving specialized skills. The evaluation department can also visualize an employee's career path to make it easier to understand intuitively. For example, it can display career progress in graphs and charts. This allows it to predict an employee's career path and propose a long-term career plan.

[0093] The business management department can use the emotion estimation function to understand employees' emotional states and suggest actions to improve interpersonal relationships within the team. For example, it can analyze employees' emotional states in real time and suggest activities to relieve stress and tension within the team. The business management department can also use the emotion estimation function to provide feedback to improve the quality of communication between team members. For example, it can suggest workshops to promote positive communication. The business management department can also suggest team building events based on the emotional states. For example, it can suggest relaxation sessions or team lunches. This improves interpersonal relationships within the team and increases work efficiency.

[0094] The evaluation department can help employees set individual goals based on their performance data. For example, they can analyze an employee's past performance data and set realistic but challenging goals. The evaluation department can also suggest appropriate goals based on the employee's skill set and career goals. For example, they can set goals aimed at improving specific skills. The evaluation department can also monitor progress toward achieving goals in real time and provide feedback as needed. For example, they can provide advice and support for achieving goals. This helps employees set goals and improve their performance.

[0095] The business management department can use the emotion estimation function to analyze the emotional state of employees and make suggestions to provide an appropriate work environment. For example, it can analyze an employee's emotional state in real time and suggest the use of a relaxation space if stress levels are high. The business management department can also use the emotion estimation function to adjust the work environment according to the employee's emotional state. For example, it can suggest adjusting lighting or temperature. The business management department can also suggest specific methods to improve employees' work efficiency based on their emotional state. For example, it can suggest desk layouts based on ergonomics. In this way, the business management department can analyze the employee's emotional state and provide an appropriate work environment.

[0096] Based on the results of an employee's evaluation, the evaluation department can propose specific action plans to improve performance. For example, they can analyze the evaluation results and identify areas for improvement in specific skills or work processes. The evaluation department can also propose appropriate training programs or coaching sessions based on the results of an employee's evaluation. For example, they can suggest leadership training or time management coaching. The evaluation department can also propose action plans based on the evaluation results that are in line with the employee's career path. For example, they can suggest assigning projects that align with future career goals. This helps improve employee performance.

[0097] The business management department can use the emotion estimation function to understand the emotional state of employees and propose stress management programs. For example, the business management department can analyze the emotional state of employees in real time and propose a relaxation program if stress levels are high. The business management department can also use the emotion estimation function to propose stress management methods according to the emotional state of employees. For example, the business management department can propose a short meditation or yoga session. The business management department can also propose specific methods to reduce stress for employees based on their emotional state. For example, the business management department can propose a workshop for stress reduction. In this way, the business management department can understand the emotional state of employees and provide a stress management program.

[0098] Based on the employee evaluation results, the evaluation department can propose strategies to improve the performance of the entire team. For example, they can analyze the evaluation results of each member and identify the strengths and weaknesses of the entire team. The evaluation department can also propose appropriate training programs and workshops to improve the performance of the entire team. For example, they can propose team building workshops and training to improve skills. The evaluation department can also use the evaluation results to suggest improvements to team goal setting and work processes. For example, they can clarify the team's goals and make work processes more efficient. This will improve the performance of the entire team.

[0099] The business management department can use the emotion estimation function to analyze employees' emotional states and propose incentive programs to improve their motivation. For example, the business management department can analyze employees' emotional states in real time and provide incentives if their motivation is low. The business management department can also use the emotion estimation function to propose incentive programs according to employees' emotional states. For example, it can provide bonuses when specific goals are achieved. The business management department can also propose specific methods to improve employees' motivation based on their emotional states. For example, it can introduce an award system or a bonus system. In this way, the business management department can analyze employees' emotional states and propose incentive programs to improve their motivation.

[0100] The evaluation department can provide feedback to improve performance based on the employee evaluation results. For example, they can analyze the evaluation results and identify specific areas for improvement. The evaluation department can also provide appropriate feedback based on the employee evaluation results. For example, they can point out areas for improvement in specific skills or work processes and provide specific advice. The evaluation department can also provide feedback that is aligned with the employee's career path based on the evaluation results. For example, they can provide feedback that is aligned with the employee's future career goals. This helps improve the employee's performance.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The work management department manages employee work. For example, the work management department monitors employees' work content and progress in real time and issues appropriate instructions. The work management department can also grasp the progress of projects and automatically assign necessary tasks. Furthermore, the work management department optimizes employee schedules and reduces wasted time. Step 2: The evaluation department evaluates the employee's performance and behavior. For example, the evaluation department collects data on the employee's work performance and work attitude and bases the evaluation on that data. The evaluation department can also provide fair feedback based on the data on the employee's performance and behavior. Furthermore, because the evaluation department bases the evaluation on data, there is no room for personal feelings or prejudices. Step 3: The Prohibition Unit prohibits direct intervention by human supervisors. For example, if an employee follows instructions from an AI supervisor, the Prohibition Unit prohibits the human supervisor from changing those instructions. The Prohibition Unit also ensures that evaluations and instructions given to employees are fair and consistent.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a 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.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Business Management Department manages the work of employees; An evaluation department that evaluates employees' performance and behavior; Equipped with a prohibition section that prohibits direct intervention by human superiors A system characterized by:

2. The business management department Monitor the employee's stress level and suggest appropriate breaks 2. The system of claim 1.

3. The business management department Visualize project progress so all team members can see progress in real time 2. The system of claim 1.

4. The evaluation unit The employee's self-evaluation and the evaluations of others are combined to make a comprehensive evaluation.

2. The system of claim 1.

5. The prohibition section is Using emotion estimation functionality, we propose measures to reduce the stress that employees feel when receiving instructions from their AI superiors.

2. The system of claim 1.

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