System

Abstract

An object of a system according to an embodiment is to visualize work contents and achievements of employees in work-from-home and provide appropriate feedback.SOLUTION: A system includes a work content analysis part, a progress state display part, a result evaluation part, and a feedback provision part. The work content analysis unit analyzes the work content of the employee. The progress status display unit displays the work content analyzed by the work content analysis unit on the dashboard. The result evaluation part evaluates the result of the employee based on the work content displayed by the progress state display part. The feedback providing unit provides feedback based on the result evaluated by the result evaluating unit.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, it is difficult to grasp the work content and results of employees working from home, and there is room for improvement.

[0005] The system according to the embodiment aims to visualize the work content and results of employees working from home and provide appropriate feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a work content analysis unit, a progress status display unit, a performance evaluation unit, and a feedback provision unit. The work content analysis unit analyzes the work content of an employee. The progress status display unit displays the work content analyzed by the work content analysis unit on a dashboard. The performance evaluation unit evaluates the performance of the employee based on the work content displayed by the progress status display unit. The feedback provision unit provides feedback based on the performance evaluated by the performance evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can visualize the work content and results of employees working from home and provide appropriate feedback. [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 telecommuting support system according to an embodiment of the present invention is a system that grasps the activity status and results of subordinates in real time, and promotes early problem solving, output optimization, and the growth of the entire organization. As a result, the telecommuting support system grasps the activity status and results of subordinates in real time, and promotes early problem solving, output optimization, and the growth of the entire organization.

[0029] A telecommuting support system according to an embodiment includes a work content analysis unit, a progress display unit, an outcome evaluation unit, and a feedback provision unit. The work content analysis unit analyzes the work content of an employee. For example, the generation AI analyzes the tasks an employee is currently working on and their progress. The generation AI can also perform analysis based on information related to the employee's work content. The generation AI can also analyze the employee's work content in real time. The progress display unit displays the work content analyzed by the work content analysis unit on a dashboard. For example, the generation AI displays the employee's work content on the dashboard. The generation AI can also display the employee's progress in graphs or charts. The generation AI can also display the employee's work content in real time. The outcome evaluation unit evaluates the employee's performance based on the work content displayed by the progress display unit. For example, the generation AI analyzes reports and project progress submitted by the employee and evaluates their quality and degree of achievement. The generation AI can also perform evaluation based on information related to the employee's deliverables. The generation AI can also evaluate the employee's performance in real time. The feedback providing unit provides feedback based on the results evaluated by the result evaluation unit. For example, the generation AI provides appropriate feedback to employees. The generation AI can also suggest areas for improvement based on the employee's results. The generation AI can also provide feedback in real time based on the employee's results. As a result, the telecommuting support system according to the embodiment can improve work efficiency and results by understanding the work content and progress of employees in real time and providing appropriate feedback.

[0030] When analyzing the work content of employees, the work content analysis unit can evaluate the quality and efficiency of the work and suggest areas for improvement. For example, the work content analysis unit allows the generation AI to analyze the work content of employees and analyze the detailed content and progress of each task to evaluate the quality of the work. For example, the work content analysis unit can evaluate the efficiency of the work based on the task completion time and the resources used and suggest areas for improvement. The work content analysis unit can also evaluate the quality and efficiency of the work based on error rate and completion rate when the generation AI analyzes the work content of employees. The work content analysis unit can also evaluate the quality and efficiency of the work based on customer satisfaction when the generation AI analyzes the work content of employees. This allows the quality and efficiency of employees to be evaluated and areas for improvement to be suggested, thereby optimizing work.

[0031] When analyzing an employee's work content, the work content analysis unit can compare it with past work history to analyze progress trends and make predictions. For example, the generation AI in the work content analysis unit analyzes the employee's work content and compares it with past work history to analyze progress trends. For example, the generation AI predicts the current work progress based on past task completion times and progress statuses and warns of possible delays. The work content analysis unit can also evaluate based on work logs and project history, so that the generation AI analyzes the employee's work content and compares it with past work history to analyze progress trends. The work content analysis unit can also evaluate based on progress rates and delay patterns, so that the generation AI analyzes the employee's work content and compares it with past work history to analyze progress trends. This allows for early detection of work delays and problems by analyzing progress trends and making predictions by comparing it with past work history.

[0032] When analyzing an employee's work content, the work content analysis unit visualizes the cooperative relationships and dependencies with other employees, thereby optimizing the efficiency of the entire team. For example, the work content analysis unit uses a generation AI to analyze an employee's work content and visualize the cooperative relationships and dependencies with other employees. For example, the work content analysis unit displays task dependencies in a graph and makes suggestions for optimizing the efficiency of the entire team. The work content analysis unit can also evaluate employees based on the frequency of collaboration and the quality of communication in order to visualize the cooperative relationships and dependencies with other employees. The work content analysis unit can also evaluate employees based on task dependencies and resource dependencies in order to visualize the cooperative relationships and dependencies with other employees in order to visualize the cooperative relationships and dependencies with other employees. This visualizes the cooperative relationships and dependencies with other employees and optimizes the efficiency of the entire team, thereby increasing the success rate of projects.

[0033] The work content analysis unit can make suggestions for optimizing resource allocation between different projects when analyzing the work content of employees. For example, the work content analysis unit has the generation AI analyze the work content of employees and make suggestions for optimizing resource allocation between different projects. For example, the work content analysis unit can propose resource reallocation based on the progress of each project. The work content analysis unit can also evaluate the work content of employees based on the type and scale of projects when the generation AI analyzes the work content of employees and makes suggestions for optimizing resource allocation between different projects. The work content analysis unit can also evaluate the work content of employees based on the type of resource and allocation priority when the generation AI analyzes the work content of employees and makes suggestions for optimizing resource allocation between different projects. This makes it possible to achieve efficient resource utilization by optimizing resource allocation between different projects.

[0034] The performance evaluation department can compare employee performance with industry standards and best practices when evaluating employee performance. For example, when the generation AI evaluates employee performance, the performance evaluation department compares it with industry standards and best practices. For example, the employee performance is evaluated based on industry benchmark data. Furthermore, the performance evaluation department can also evaluate based on industry benchmarks and standard KPIs so that the generation AI can compare it with industry standards and best practices when evaluating employee performance. Furthermore, the performance evaluation department can also evaluate based on success stories and effective methods so that the generation AI can compare it with industry standards and best practices when evaluating employee performance. In this way, by comparing it with industry standards and best practices, employee performance can be objectively evaluated and areas for improvement can be identified.

[0035] When evaluating an employee's performance, the performance evaluation unit can analyze long-term performance trends and make career growth suggestions. For example, when the generation AI evaluates an employee's performance, the performance evaluation unit can analyze long-term performance trends and make career growth suggestions. For example, the performance evaluation unit can suggest specific actions for career growth based on past performance data. Furthermore, when the generation AI evaluates an employee's performance, the performance evaluation unit can also evaluate based on past achievements and growth rates in order to analyze long-term performance trends and make career growth suggestions. Furthermore, when the generation AI evaluates an employee's performance, the performance evaluation unit can also evaluate based on opportunities for skill development and promotion possibilities in order to analyze long-term performance trends and make career growth suggestions. In this way, by analyzing long-term performance trends and making career growth suggestions, it is possible to support employee growth.

[0036] The performance evaluation department can compare performance across different departments or projects and share best practices. For example, when the generative AI evaluates employee performance, the performance evaluation department can compare performance across different departments or projects and share best practices. For example, best practices can be identified and shared based on success stories from each department. The performance evaluation department can also evaluate based on success stories and effective methods when the generative AI evaluates employee performance, in order to compare performance across different departments or projects and share best practices. The performance evaluation department can also evaluate based on industry benchmarks or standard KPIs when the generative AI evaluates employee performance, in order to compare performance across different departments or projects and share best practices. This makes it possible to improve the performance of the entire organization by comparing performance across different departments or projects and sharing best practices.

[0037] The performance evaluation unit can visualize the content of the feedback to make it easier to understand visually. For example, when the generation AI evaluates an employee's performance, the performance evaluation unit visualizes the content of the feedback to make it easier to understand visually. For example, the performance evaluation unit can display the main points of the feedback in a graph or chart. Furthermore, when the generation AI evaluates an employee's performance, the performance evaluation unit can visualize the content of the feedback to make it easier to understand visually, and can also evaluate based on infographics. Furthermore, when the generation AI evaluates an employee's performance, the performance evaluation unit can visualize the content of the feedback to make it easier to understand visually, and can also evaluate based on color usage and layout. In this way, by visualizing the content of the feedback to make it easier to understand visually, employees can make effective use of the feedback.

[0038] The communication analysis unit can analyze communication between employees, evaluate the frequency and quality of communication, and suggest areas for improvement. For example, the communication analysis unit uses a generation AI to analyze communication between employees and evaluate the frequency and quality of communication. For example, it analyzes chat logs and email content and suggests areas for improvement in communication. The communication analysis unit can also use a generation AI to analyze communication between employees and evaluate the frequency and quality of communication, making an evaluation based on the content of emails and chats. The communication analysis unit can also use a generation AI to analyze communication between employees and evaluate the frequency and quality of communication, making an evaluation based on the content of meetings. This makes it possible to evaluate the frequency and quality of communication between employees and suggest areas for improvement, thereby facilitating smooth communication.

[0039] The communication analysis unit can analyze communication between employees and identify effective communication patterns based on past communication history. For example, the communication analysis unit allows the generation AI to analyze communication between employees and identify effective communication patterns based on past communication history. For example, the communication patterns of successful projects can be analyzed and applied to other projects. The communication analysis unit can also evaluate communication between employees based on email logs and chat history in order to identify effective communication patterns based on past communication history. The communication analysis unit can also evaluate communication between employees based on frequency and content in order to identify effective communication patterns based on past communication history. This makes it possible to improve the quality of communication by identifying effective communication patterns based on past communication history.

[0040] The communication analysis unit can make suggestions for optimizing communication between different departments or projects. For example, the communication analysis unit analyzes communication between employees using the generation AI and makes suggestions for optimizing communication between different departments or projects. For example, it analyzes the frequency of communication between departments and suggests the optimal communication channel. The communication analysis unit can also evaluate communication between employees based on the selection of communication tools and a review of communication flow so that the generation AI can analyze communication between employees and make suggestions for optimizing communication between different departments or projects. The communication analysis unit can also evaluate communication between employees based on the frequency and content of communication so that the generation AI can analyze communication between employees and make suggestions for optimizing communication between different departments or projects. This makes it possible to improve cooperation and efficiency throughout the organization by optimizing communication between different departments and projects.

[0041] The communication analysis unit can automatically summarize the content of the communication and extract important information. For example, the communication analysis unit has the generation AI analyze communication between employees, automatically summarize the content of the communication, and extract important information. For example, it summarizes the content of chat logs and emails and extracts important points. The communication analysis unit can also evaluate the content of the communication based on natural language processing technology and summarization algorithms so that the generation AI can analyze communication between employees, automatically summarize the content of the communication, and extract important information. The communication analysis unit can also evaluate the content of the communication based on decisions and action items so that the generation AI can analyze communication between employees, automatically summarize the content of the communication, and extract important information. This automatically summarizes the content of the communication and extracts important information, thereby improving the efficiency of information transmission.

[0042] The problem detection unit can analyze employees' work status, compare it with past data to detect abnormalities, and issue early warnings. For example, the problem detection unit uses a generation AI to analyze employees' work status, compare it with past data to detect abnormalities, and issue early warnings. For example, it can issue an alert if task progress is delayed. The problem detection unit can also evaluate employees' work status based on delays and frequent errors, so that the generation AI can analyze employees' work status, compare it with past data to detect abnormalities, and issue early warnings. The problem detection unit can also evaluate employees' work status based on alert notifications or email notifications, so that the generation AI can analyze employees' work status, compare it with past data to detect abnormalities, and issue early warnings. This enables early detection of problems and countermeasures by analyzing employees' work status, comparing it with past data to detect abnormalities, and issuing early warnings.

[0043] The problem discovery unit can analyze employees' work situations, identify the root cause of the problem, and propose specific solutions. For example, the problem discovery unit uses a generation AI to analyze employees' work situations, identify the root cause of the problem, and propose specific solutions. For example, the problem discovery unit can identify the cause of a task delay and propose resource reallocation. The problem discovery unit can also perform evaluations based on cause analysis and problem traceback so that the generation AI can analyze employees' work situations, identify the root cause of the problem, and propose specific solutions. The problem discovery unit can also perform evaluations based on process reviews and tool introduction so that the generation AI can analyze employees' work situations, identify the root cause of the problem, and propose specific solutions. This makes it possible to improve problem-solving efficiency by analyzing employees' work situations, identifying the root cause of the problem, and proposing specific solutions.

[0044] The problem discovery unit can compare issues between different projects and propose common solutions. For example, the generation AI in the problem discovery unit analyzes employee work status, compares issues between different projects, and proposes common solutions. For example, the generation AI can identify the causes of delays occurring in multiple projects and propose common solutions. The problem discovery unit can also share best practices and evaluate based on standardized methods in order to analyze employee work status, compare issues between different projects, and propose common solutions. The problem discovery unit can also evaluate based on the type and scale of the project in order to analyze employee work status, compare issues between different projects, and propose common solutions. This makes it possible to compare issues between different projects and propose common solutions, thereby achieving efficient problem solving.

[0045] The problem discovery unit can automatically allocate the resources necessary to solve a problem. For example, the generation AI in the problem discovery unit analyzes the work status of employees and automatically allocates the resources necessary to solve a problem. For example, it automatically allocates the necessary personnel and time for a specific task. The problem discovery unit can also evaluate based on the type of resource and allocation method so that the generation AI can analyze the work status of employees and automatically allocate the resources necessary to solve a problem. The problem discovery unit can also evaluate based on the priority and allocation of resources so that the generation AI can analyze the work status of employees and automatically allocate the resources necessary to solve a problem. This makes it possible to achieve efficient resource management by automatically allocating the resources necessary to solve a problem.

[0046] The Growth Promotion Department can analyze employee skill and performance data and propose the optimal training program for each employee. For example, the Growth Promotion Department uses generative AI to analyze employee skill and performance data and propose the optimal training program for each employee. For example, it can identify skill gaps and propose necessary training. The Growth Promotion Department can also use generative AI to analyze employee skill and performance data and evaluate employees based on technical skills and soft skills in order to propose the optimal training program for each employee. The Growth Promotion Department can also use generative AI to analyze employee skill and performance data and evaluate employees based on years of experience and performance evaluation in order to propose the optimal training program for each employee. In this way, by analyzing employee skill and performance data and proposing the optimal training program for each employee, it is possible to support employee skill improvement and career growth.

[0047] The Growth Promotion Department can analyze employee skill and performance data, identify areas for improvement across the organization, and propose strategic measures. For example, the Growth Promotion Department uses generative AI to analyze employee skill and performance data, identify areas for improvement across the organization, and propose strategic measures. For example, it can identify skill gaps and propose training programs for the entire organization. The Growth Promotion Department can also conduct evaluations based on skill gap analysis and performance reviews using generative AI to analyze employee skill and performance data, identify areas for improvement across the organization, and propose strategic measures. The Growth Promotion Department can also conduct evaluations based on the introduction of training programs and revisions to organizational structure using generative AI to analyze employee skill and performance data, identify areas for improvement across the organization, and propose strategic measures. In this way, it is possible to promote the growth of the entire organization by analyzing employee skill and performance data, identifying areas for improvement across the organization, and proposing strategic measures.

[0048] The Growth Promotion Department can analyze employee skill and performance data and share best practices across different departments and projects. For example, the Growth Promotion Department uses generative AI to analyze employee skill and performance data and share best practices across different departments and projects. For example, best practices are identified and shared based on success stories from each department. The Growth Promotion Department can also use generative AI to analyze employee skill and performance data and evaluate best practices based on success stories and effective methods in order to share best practices across different departments and projects. The Growth Promotion Department can also use generative AI to analyze employee skill and performance data and evaluate best practices across different departments and projects based on industry benchmarks and standard KPIs. In this way, analyzing employee skill and performance data and sharing best practices across different departments and projects can improve the performance of the entire organization.

[0049] The Growth Acceleration Department can analyze employee skill and performance data and propose new roles and positions to promote growth across the organization. For example, the Growth Acceleration Department uses generative AI to analyze employee skill and performance data and propose new roles and positions to promote growth across the organization. For example, it can identify skill gaps and propose new roles. The Growth Acceleration Department can also use generative AI to analyze employee skill and performance data and conduct evaluations based on new job descriptions and required skill sets to propose new roles and positions to promote growth across the organization. The Growth Acceleration Department can also use generative AI to analyze employee skill and performance data and conduct evaluations based on skill gap analysis and performance reviews to propose new roles and positions to promote growth across the organization. This supports organizational flexibility and growth by analyzing employee skill and performance data and proposing new roles and positions to promote growth across the organization.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The telecommuting support system can also be equipped with a health management unit. The health management unit can monitor employees' health status and detect health risks early. For example, it can analyze employees' heart rates and sleep patterns and issue alerts if abnormalities are detected. The health management unit can also suggest healthy lifestyle habits based on employees' diet and exercise records. Furthermore, the health management unit can send reminders for regular health checks to support employees in maintaining their health. This allows for comprehensive management of employees' health status and reduces health risks.

[0052] The telecommuting support system can further include a learning support department. The learning support department can provide appropriate learning resources to help employees improve their skills. For example, it can identify employees' skill gaps and suggest necessary training courses. The learning support department can also monitor employees' learning progress and evaluate the effectiveness of their learning. Furthermore, the learning support department can introduce an incentive system to increase employees' motivation when they engage in learning. This can promote employee skill improvement and improve the performance of the entire organization.

[0053] The telecommuting support system can further include a communication promotion department. The communication promotion department can propose measures to facilitate communication between employees. For example, it can propose a schedule for regular online meetings to encourage information sharing among team members. The communication promotion department can also suggest the introduction of a virtual office so that employees can communicate more easily. Furthermore, the communication promotion department can provide training programs to improve employees' communication skills. This can facilitate communication between employees and strengthen teamwork.

[0054] The telecommuting support system can further include a project management department. The project management department can comprehensively manage the progress of projects and propose optimal resource allocation. For example, it can monitor the progress of each project in real time and propose resource reallocation. The project management department can also evaluate project risks and propose risk avoidance measures. Furthermore, the project management department can share best practices to increase the success rate of projects. This makes it possible to comprehensively manage the progress of projects and achieve efficient resource allocation.

[0055] The telecommuting support system can further include a data analysis unit. The data analysis unit can analyze employee work data and make suggestions to improve work efficiency. For example, it can identify bottlenecks in work processes based on the work data and propose improvements. The data analysis unit can also analyze employee work patterns and propose efficient work methods. Furthermore, the data analysis unit can propose the introduction of tools and technologies to improve work efficiency. This makes it possible to analyze employee work data and make specific suggestions to improve work efficiency.

[0056] The telecommuting support system can further include a security management department. The security management department can propose measures to strengthen the security of employees' work environments. For example, it can propose data encryption and strengthened access control. The security management department can also provide training programs to raise employees' security awareness. Furthermore, the security management department can also propose countermeasures in the event of a security incident. This strengthens the security of employees' work environments and prevents information leaks and unauthorized access.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The work content analysis unit analyzes the work content of employees. For example, the generation AI analyzes the tasks that employees are currently working on and their progress. The generation AI can also perform analysis based on information about the work content of employees. Furthermore, the generation AI can analyze the work content of employees in real time. Step 2: The progress display unit displays the work content analyzed by the work content analysis unit on a dashboard. For example, the generation AI displays the employee's work content on a dashboard. The generation AI can also display the employee's progress in graphs and charts. Furthermore, the generation AI can display the employee's work content in real time. Step 3: The performance evaluation unit evaluates the employee's performance based on the work content displayed by the progress display unit. For example, the generation AI analyzes reports submitted by employees and project progress to evaluate their quality and degree of achievement. The generation AI can also make evaluations based on information about the employee's deliverables. Furthermore, the generation AI can evaluate the employee's performance in real time. Step 4: The feedback providing unit provides feedback based on the performance evaluated by the performance evaluation unit. For example, the generation AI provides appropriate feedback to the employee. The generation AI can also suggest areas for improvement based on the employee's performance. Furthermore, the generation AI can provide feedback in real time based on the employee's performance.

[0059] (Example 2) The telecommuting support system according to an embodiment of the present invention is a system that grasps the activity status and results of subordinates in real time, and promotes early problem solving, output optimization, and the growth of the entire organization. As a result, the telecommuting support system grasps the activity status and results of subordinates in real time, and promotes early problem solving, output optimization, and the growth of the entire organization.

[0060] A telecommuting support system according to an embodiment includes a work content analysis unit, a progress display unit, an outcome evaluation unit, and a feedback provision unit. The work content analysis unit analyzes the work content of an employee. For example, the generation AI analyzes the tasks an employee is currently working on and their progress. The generation AI can also perform analysis based on information related to the employee's work content. The generation AI can also analyze the employee's work content in real time. The progress display unit displays the work content analyzed by the work content analysis unit on a dashboard. For example, the generation AI displays the employee's work content on the dashboard. The generation AI can also display the employee's progress in graphs or charts. The generation AI can also display the employee's work content in real time. The outcome evaluation unit evaluates the employee's performance based on the work content displayed by the progress display unit. For example, the generation AI analyzes reports and project progress submitted by the employee and evaluates their quality and degree of achievement. The generation AI can also perform evaluation based on information related to the employee's deliverables. The generation AI can also evaluate the employee's performance in real time. The feedback providing unit provides feedback based on the results evaluated by the result evaluation unit. For example, the generation AI provides appropriate feedback to employees. The generation AI can also suggest areas for improvement based on the employee's results. The generation AI can also provide feedback in real time based on the employee's results. As a result, the telecommuting support system according to the embodiment can improve work efficiency and results by understanding the work content and progress of employees in real time and providing appropriate feedback.

[0061] When analyzing the work content of employees, the work content analysis unit can evaluate the quality and efficiency of the work and suggest areas for improvement. For example, the work content analysis unit allows the generation AI to analyze the work content of employees and analyze the detailed content and progress of each task to evaluate the quality of the work. For example, the work content analysis unit can evaluate the efficiency of the work based on the task completion time and the resources used and suggest areas for improvement. The work content analysis unit can also evaluate the quality and efficiency of the work based on error rate and completion rate when the generation AI analyzes the work content of employees. The work content analysis unit can also evaluate the quality and efficiency of the work based on customer satisfaction when the generation AI analyzes the work content of employees. This allows the quality and efficiency of employees to be evaluated and areas for improvement to be suggested, thereby optimizing work.

[0062] When analyzing an employee's work content, the work content analysis unit can compare it with past work history to analyze progress trends and make predictions. For example, the generation AI in the work content analysis unit analyzes the employee's work content and compares it with past work history to analyze progress trends. For example, the generation AI predicts the current work progress based on past task completion times and progress statuses and warns of possible delays. The work content analysis unit can also evaluate based on work logs and project history, so that the generation AI analyzes the employee's work content and compares it with past work history to analyze progress trends. The work content analysis unit can also evaluate based on progress rates and delay patterns, so that the generation AI analyzes the employee's work content and compares it with past work history to analyze progress trends. This allows for early detection of work delays and problems by analyzing progress trends and making predictions by comparing it with past work history.

[0063] The work content analysis unit can use the emotion estimation function to analyze the emotional state of employees and visualize fluctuations in stress and motivation. The work content analysis unit can, for example, use the emotion estimation function to analyze the emotional state of employees and visualize fluctuations in stress and motivation. For example, the work content analysis unit can analyze the facial expressions and voice data of employees, calculate an emotion score, and display it on a dashboard. The work content analysis unit can also use the emotion estimation function to analyze the emotional state of employees and evaluate them based on facial expression recognition and voice analysis in order to visualize fluctuations in stress and motivation. The work content analysis unit can also use the emotion estimation function to analyze the emotional state of employees and evaluate them based on questionnaire results in order to visualize fluctuations in stress and motivation. In this way, the psychological health of employees can be managed by analyzing the emotional state of employees and visualizing fluctuations in stress and motivation.

[0064] When analyzing an employee's work content, the work content analysis unit visualizes the cooperative relationships and dependencies with other employees, thereby optimizing the efficiency of the entire team. For example, the work content analysis unit uses a generation AI to analyze an employee's work content and visualize the cooperative relationships and dependencies with other employees. For example, the work content analysis unit displays task dependencies in a graph and makes suggestions for optimizing the efficiency of the entire team. The work content analysis unit can also evaluate employees based on the frequency of collaboration and the quality of communication in order to visualize the cooperative relationships and dependencies with other employees. The work content analysis unit can also evaluate employees based on task dependencies and resource dependencies in order to visualize the cooperative relationships and dependencies with other employees in order to visualize the cooperative relationships and dependencies with other employees. This visualizes the cooperative relationships and dependencies with other employees and optimizes the efficiency of the entire team, thereby increasing the success rate of projects.

[0065] The work content analysis unit can make suggestions for optimizing resource allocation between different projects when analyzing the work content of employees. For example, the work content analysis unit has the generation AI analyze the work content of employees and make suggestions for optimizing resource allocation between different projects. For example, the work content analysis unit can propose resource reallocation based on the progress of each project. The work content analysis unit can also evaluate the work content of employees based on the type and scale of projects when the generation AI analyzes the work content of employees and makes suggestions for optimizing resource allocation between different projects. The work content analysis unit can also evaluate the work content of employees based on the type of resource and allocation priority when the generation AI analyzes the work content of employees and makes suggestions for optimizing resource allocation between different projects. This makes it possible to achieve efficient resource utilization by optimizing resource allocation between different projects.

[0066] The work content analysis unit can use the emotion estimation function to suggest breaks and refreshment at appropriate times based on the employee's emotional state. The work content analysis unit, for example, uses the emotion estimation function to analyze the employee's emotional state and suggest breaks and refreshment at appropriate times. For example, the work content analysis unit can suggest a break when stress is high. The work content analysis unit can also use the emotion estimation function to analyze the employee's emotional state and evaluate the employee based on the progress of work and the employee's emotional state in order to suggest breaks and refreshment at appropriate times. The work content analysis unit can also use the emotion estimation function to analyze the employee's emotional state and evaluate the employee based on the length and frequency of breaks in order to suggest breaks and refreshment at appropriate times. In this way, by suggesting breaks and refreshment at appropriate times based on the employee's emotional state, it is possible to reduce employee stress and improve work efficiency.

[0067] The performance evaluation department can compare employee performance with industry standards and best practices when evaluating employee performance. For example, when the generation AI evaluates employee performance, the performance evaluation department compares it with industry standards and best practices. For example, the employee performance is evaluated based on industry benchmark data. Furthermore, the performance evaluation department can also evaluate based on industry benchmarks and standard KPIs so that the generation AI can compare it with industry standards and best practices when evaluating employee performance. Furthermore, the performance evaluation department can also evaluate based on success stories and effective methods so that the generation AI can compare it with industry standards and best practices when evaluating employee performance. In this way, by comparing it with industry standards and best practices, employee performance can be objectively evaluated and areas for improvement can be identified.

[0068] When evaluating an employee's performance, the performance evaluation unit can analyze long-term performance trends and make career growth suggestions. For example, when the generation AI evaluates an employee's performance, the performance evaluation unit can analyze long-term performance trends and make career growth suggestions. For example, the performance evaluation unit can suggest specific actions for career growth based on past performance data. Furthermore, when the generation AI evaluates an employee's performance, the performance evaluation unit can also evaluate based on past achievements and growth rates in order to analyze long-term performance trends and make career growth suggestions. Furthermore, when the generation AI evaluates an employee's performance, the performance evaluation unit can also evaluate based on opportunities for skill development and promotion possibilities in order to analyze long-term performance trends and make career growth suggestions. In this way, by analyzing long-term performance trends and making career growth suggestions, it is possible to support employee growth.

[0069] The performance evaluation unit can use the emotion estimation function to evaluate the emotional impact of the feedback content on the employee and reinforce positive feedback. The performance evaluation unit, for example, uses the emotion estimation function to evaluate the emotional impact of the feedback content on the employee and reinforce positive feedback. For example, the performance evaluation unit analyzes the content of the feedback and emphasizes positive elements. The performance evaluation unit can also use the emotion estimation function to evaluate the emotional impact of the feedback content on the employee and perform evaluation based on facial expression recognition or voice analysis to reinforce positive feedback. The performance evaluation unit can also use the emotion estimation function to evaluate the emotional impact of the feedback content on the employee and perform evaluation based on text analysis to reinforce positive feedback. In this way, employee motivation can be improved by evaluating the emotional impact of the feedback content on the employee and reinforcing positive feedback.

[0070] The performance evaluation department can compare performance across different departments or projects and share best practices. For example, when the generative AI evaluates employee performance, the performance evaluation department can compare performance across different departments or projects and share best practices. For example, best practices can be identified and shared based on success stories from each department. The performance evaluation department can also evaluate based on success stories and effective methods when the generative AI evaluates employee performance, in order to compare performance across different departments or projects and share best practices. The performance evaluation department can also evaluate based on industry benchmarks or standard KPIs when the generative AI evaluates employee performance, in order to compare performance across different departments or projects and share best practices. This makes it possible to improve the performance of the entire organization by comparing performance across different departments or projects and sharing best practices.

[0071] The performance evaluation unit can visualize the content of the feedback to make it easier to understand visually. For example, when the generation AI evaluates an employee's performance, the performance evaluation unit visualizes the content of the feedback to make it easier to understand visually. For example, the performance evaluation unit can display the main points of the feedback in a graph or chart. Furthermore, when the generation AI evaluates an employee's performance, the performance evaluation unit can visualize the content of the feedback to make it easier to understand visually, and can also evaluate based on infographics. Furthermore, when the generation AI evaluates an employee's performance, the performance evaluation unit can visualize the content of the feedback to make it easier to understand visually, and can also evaluate based on color usage and layout. In this way, by visualizing the content of the feedback to make it easier to understand visually, employees can make effective use of the feedback.

[0072] The performance evaluation unit can use the emotion estimation function to analyze the impact of the feedback content on employee motivation and propose an optimal feedback method. The performance evaluation unit, for example, uses the emotion estimation function to analyze the impact of the feedback content on employee motivation and propose an optimal feedback method. For example, the performance evaluation unit analyzes the content of the feedback and provides specific advice to increase motivation. The performance evaluation unit can also use the emotion estimation function to analyze the impact of the feedback content on employee motivation and perform evaluation based on facial expression recognition or voice analysis to propose an optimal feedback method. The performance evaluation unit can also use the emotion estimation function to analyze the impact of the feedback content on employee motivation and perform evaluation based on text analysis to propose an optimal feedback method. In this way, the impact of the feedback content on employee motivation can be analyzed and an optimal feedback method proposed, thereby improving employee motivation.

[0073] The communication analysis unit can analyze communication between employees, evaluate the frequency and quality of communication, and suggest areas for improvement. For example, the communication analysis unit uses a generation AI to analyze communication between employees and evaluate the frequency and quality of communication. For example, it analyzes chat logs and email content and suggests areas for improvement in communication. The communication analysis unit can also use a generation AI to analyze communication between employees and evaluate the frequency and quality of communication, making an evaluation based on the content of emails and chats. The communication analysis unit can also use a generation AI to analyze communication between employees and evaluate the frequency and quality of communication, making an evaluation based on the content of meetings. This makes it possible to evaluate the frequency and quality of communication between employees and suggest areas for improvement, thereby facilitating smooth communication.

[0074] The communication analysis unit can analyze communication between employees and identify effective communication patterns based on past communication history. For example, the communication analysis unit allows the generation AI to analyze communication between employees and identify effective communication patterns based on past communication history. For example, the communication patterns of successful projects can be analyzed and applied to other projects. The communication analysis unit can also evaluate communication between employees based on email logs and chat history in order to identify effective communication patterns based on past communication history. The communication analysis unit can also evaluate communication between employees based on frequency and content in order to identify effective communication patterns based on past communication history. This makes it possible to improve the quality of communication by identifying effective communication patterns based on past communication history.

[0075] The communication analysis unit can use the emotion estimation function to evaluate the emotional impact of the content of communication on employees and promote positive communication. The communication analysis unit, for example, uses the emotion estimation function to evaluate the emotional impact of the content of communication on employees and promote positive communication. For example, the communication analysis unit analyzes the content of chats and emails and makes suggestions to increase positive expressions. The communication analysis unit can also use the emotion estimation function to evaluate the emotional impact of the content of communication on employees and make evaluations based on facial expression recognition and voice analysis to promote positive communication. The communication analysis unit can also use the emotion estimation function to evaluate the emotional impact of the content of communication on employees and make evaluations based on text analysis to promote positive communication. In this way, by evaluating the emotional impact of the content of communication on employees and promoting positive communication, it is possible to improve employee motivation and teamwork.

[0076] The communication analysis unit can make suggestions for optimizing communication between different departments or projects. For example, the communication analysis unit analyzes communication between employees using the generation AI and makes suggestions for optimizing communication between different departments or projects. For example, it analyzes the frequency of communication between departments and suggests the optimal communication channel. The communication analysis unit can also evaluate communication between employees based on the selection of communication tools and a review of communication flow so that the generation AI can analyze communication between employees and make suggestions for optimizing communication between different departments or projects. The communication analysis unit can also evaluate communication between employees based on the frequency and content of communication so that the generation AI can analyze communication between employees and make suggestions for optimizing communication between different departments or projects. This makes it possible to improve cooperation and efficiency throughout the organization by optimizing communication between different departments and projects.

[0077] The communication analysis unit can automatically summarize the content of the communication and extract important information. For example, the communication analysis unit has the generation AI analyze communication between employees, automatically summarize the content of the communication, and extract important information. For example, it summarizes the content of chat logs and emails and extracts important points. The communication analysis unit can also evaluate the content of the communication based on natural language processing technology and summarization algorithms so that the generation AI can analyze communication between employees, automatically summarize the content of the communication, and extract important information. The communication analysis unit can also evaluate the content of the communication based on decisions and action items so that the generation AI can analyze communication between employees, automatically summarize the content of the communication, and extract important information. This automatically summarizes the content of the communication and extracts important information, thereby improving the efficiency of information transmission.

[0078] The communication analysis unit can use the emotion estimation function to analyze the impact of the content of communication on the emotional state of an employee and propose an appropriate communication method. The communication analysis unit, for example, uses the emotion estimation function to analyze the impact of the content of communication on the emotional state of an employee and propose an appropriate communication method. For example, the communication analysis unit analyzes the content of chats or emails, evaluates the emotional impact, and proposes an appropriate communication method. The communication analysis unit can also use the emotion estimation function to analyze the impact of the content of communication on the emotional state of an employee and make an evaluation based on facial expression recognition or voice analysis to propose an appropriate communication method. The communication analysis unit can also use the emotion estimation function to analyze the impact of the content of communication on the emotional state of an employee and make an evaluation based on text analysis to propose an appropriate communication method. In this way, by analyzing the impact of the content of communication on the emotional state of an employee and proposing an appropriate communication method, it is possible to maintain the emotional state of an employee in a good state.

[0079] The problem detection unit can analyze employees' work status, compare it with past data to detect abnormalities, and issue early warnings. For example, the problem detection unit uses a generation AI to analyze employees' work status, compare it with past data to detect abnormalities, and issue early warnings. For example, it can issue an alert if task progress is delayed. The problem detection unit can also evaluate employees' work status based on delays and frequent errors, so that the generation AI can analyze employees' work status, compare it with past data to detect abnormalities, and issue early warnings. The problem detection unit can also evaluate employees' work status based on alert notifications or email notifications, so that the generation AI can analyze employees' work status, compare it with past data to detect abnormalities, and issue early warnings. This enables early detection of problems and countermeasures by analyzing employees' work status, comparing it with past data to detect abnormalities, and issuing early warnings.

[0080] The problem discovery unit can analyze employees' work situations, identify the root cause of the problem, and propose specific solutions. For example, the problem discovery unit uses a generation AI to analyze employees' work situations, identify the root cause of the problem, and propose specific solutions. For example, the problem discovery unit can identify the cause of a task delay and propose resource reallocation. The problem discovery unit can also perform evaluations based on cause analysis and problem traceback so that the generation AI can analyze employees' work situations, identify the root cause of the problem, and propose specific solutions. The problem discovery unit can also perform evaluations based on process reviews and tool introduction so that the generation AI can analyze employees' work situations, identify the root cause of the problem, and propose specific solutions. This makes it possible to improve problem-solving efficiency by analyzing employees' work situations, identifying the root cause of the problem, and proposing specific solutions.

[0081] The problem detection unit can use the emotion estimation function to analyze the emotional state of employees and take measures before stress and dissatisfaction increase. The problem detection unit, for example, uses the emotion estimation function to analyze the emotional state of employees and take measures before stress and dissatisfaction increase. For example, based on the emotion score, the problem detection unit can suggest a break to an employee whose stress is increasing. The problem detection unit can also use the emotion estimation function to analyze the emotional state of employees and make an evaluation based on measuring stress levels and identifying stress factors in order to take measures before stress and dissatisfaction increase. The problem detection unit can also use the emotion estimation function to analyze the emotional state of employees and make an evaluation based on the cause and frequency of dissatisfaction in order to take measures before stress and dissatisfaction increase. In this way, by analyzing the emotional state of employees and taking measures before stress and dissatisfaction increase, it is possible to maintain the psychological health of employees.

[0082] The problem discovery unit can compare issues between different projects and propose common solutions. For example, the generation AI in the problem discovery unit analyzes employee work status, compares issues between different projects, and proposes common solutions. For example, the generation AI can identify the causes of delays occurring in multiple projects and propose common solutions. The problem discovery unit can also share best practices and evaluate based on standardized methods in order to analyze employee work status, compare issues between different projects, and propose common solutions. The problem discovery unit can also evaluate based on the type and scale of the project in order to analyze employee work status, compare issues between different projects, and propose common solutions. This makes it possible to compare issues between different projects and propose common solutions, thereby achieving efficient problem solving.

[0083] The problem discovery unit can automatically allocate the resources necessary to solve a problem. For example, the generation AI in the problem discovery unit analyzes the work status of employees and automatically allocates the resources necessary to solve a problem. For example, it automatically allocates the necessary personnel and time for a specific task. The problem discovery unit can also evaluate based on the type of resource and allocation method so that the generation AI can analyze the work status of employees and automatically allocate the resources necessary to solve a problem. The problem discovery unit can also evaluate based on the priority and allocation of resources so that the generation AI can analyze the work status of employees and automatically allocate the resources necessary to solve a problem. This makes it possible to achieve efficient resource management by automatically allocating the resources necessary to solve a problem.

[0084] The problem discovery unit can use the emotion estimation function to propose an optimal approach for problem solving based on the emotional state of the employee. The problem discovery unit, for example, uses the emotion estimation function to analyze the emotional state of the employee and propose an optimal approach for problem solving. For example, the problem discovery unit can propose a relaxation method for an employee who is experiencing high stress. The problem discovery unit can also use the emotion estimation function to analyze the emotional state of the employee and evaluate the employee based on a review of the problem-solving methods and processes in order to propose an optimal approach for problem solving. The problem discovery unit can also use the emotion estimation function to analyze the emotional state of the employee and evaluate the employee based on an emotion score or questionnaire results in order to propose an optimal approach for problem solving. In this way, by proposing an optimal approach for problem solving based on the emotional state of the employee, efficient problem solving can be achieved while maintaining the psychological health of the employee.

[0085] The Growth Promotion Department can analyze employee skill and performance data and propose the optimal training program for each employee. For example, the Growth Promotion Department uses generative AI to analyze employee skill and performance data and propose the optimal training program for each employee. For example, it can identify skill gaps and propose necessary training. The Growth Promotion Department can also use generative AI to analyze employee skill and performance data and evaluate employees based on technical skills and soft skills in order to propose the optimal training program for each employee. The Growth Promotion Department can also use generative AI to analyze employee skill and performance data and evaluate employees based on years of experience and performance evaluation in order to propose the optimal training program for each employee. In this way, by analyzing employee skill and performance data and proposing the optimal training program for each employee, it is possible to support employee skill improvement and career growth.

[0086] The Growth Promotion Department can analyze employee skill and performance data, identify areas for improvement across the organization, and propose strategic measures. For example, the Growth Promotion Department uses generative AI to analyze employee skill and performance data, identify areas for improvement across the organization, and propose strategic measures. For example, it can identify skill gaps and propose training programs for the entire organization. The Growth Promotion Department can also conduct evaluations based on skill gap analysis and performance reviews using generative AI to analyze employee skill and performance data, identify areas for improvement across the organization, and propose strategic measures. The Growth Promotion Department can also conduct evaluations based on the introduction of training programs and revisions to organizational structure using generative AI to analyze employee skill and performance data, identify areas for improvement across the organization, and propose strategic measures. In this way, it is possible to promote the growth of the entire organization by analyzing employee skill and performance data, identifying areas for improvement across the organization, and proposing strategic measures.

[0087] The growth promotion department can use the emotion estimation function to analyze the emotional state of employees and propose measures to increase their motivation. For example, the growth promotion department can use the emotion estimation function to analyze the emotional state of employees and propose measures to increase their motivation. For example, the growth promotion department can propose specific actions to increase motivation based on the emotion score. The growth promotion department can also use the emotion estimation function to analyze the emotional state of employees and evaluate them based on clarification of incentive systems and career paths in order to propose measures to increase their motivation. The growth promotion department can also use the emotion estimation function to analyze the emotional state of employees and evaluate them based on the emotion score and survey results in order to propose measures to increase their motivation. In this way, by analyzing the emotional state of employees and proposing measures to increase their motivation, it is possible to motivate employees and improve their performance.

[0088] The Growth Promotion Department can analyze employee skill and performance data and share best practices across different departments and projects. For example, the Growth Promotion Department uses generative AI to analyze employee skill and performance data and share best practices across different departments and projects. For example, best practices are identified and shared based on success stories from each department. The Growth Promotion Department can also use generative AI to analyze employee skill and performance data and evaluate best practices based on success stories and effective methods in order to share best practices across different departments and projects. The Growth Promotion Department can also use generative AI to analyze employee skill and performance data and evaluate best practices across different departments and projects based on industry benchmarks and standard KPIs. In this way, analyzing employee skill and performance data and sharing best practices across different departments and projects can improve the performance of the entire organization.

[0089] The Growth Acceleration Department can analyze employee skill and performance data and propose new roles and positions to promote growth across the organization. For example, the Growth Acceleration Department uses generative AI to analyze employee skill and performance data and propose new roles and positions to promote growth across the organization. For example, it can identify skill gaps and propose new roles. The Growth Acceleration Department can also use generative AI to analyze employee skill and performance data and conduct evaluations based on new job descriptions and required skill sets to propose new roles and positions to promote growth across the organization. The Growth Acceleration Department can also use generative AI to analyze employee skill and performance data and conduct evaluations based on skill gap analysis and performance reviews to propose new roles and positions to promote growth across the organization. This supports organizational flexibility and growth by analyzing employee skill and performance data and proposing new roles and positions to promote growth across the organization.

[0090] The growth promotion department can use the emotion estimation function to propose measures to increase engagement throughout the organization based on the emotional state of employees. For example, the growth promotion department can use the emotion estimation function to analyze the emotional state of employees and propose measures to increase engagement throughout the organization. For example, the growth promotion department can propose specific actions to increase engagement based on the emotion score. The growth promotion department can also use the emotion estimation function to analyze the emotional state of employees and conduct evaluations based on employee satisfaction surveys and engagement scores in order to propose measures to increase engagement throughout the organization. The growth promotion department can also use the emotion estimation function to analyze the emotional state of employees and conduct evaluations based on survey results and emotion scores in order to propose measures to increase engagement throughout the organization. In this way, by proposing measures to increase engagement throughout the organization based on the emotional state of employees, it is possible to improve employee satisfaction and organizational performance.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The telecommuting support system can also be equipped with a health management unit. The health management unit can monitor employees' health status and detect health risks early. For example, it can analyze employees' heart rates and sleep patterns and issue alerts if abnormalities are detected. The health management unit can also suggest healthy lifestyle habits based on employees' diet and exercise records. Furthermore, the health management unit can send reminders for regular health checks to support employees in maintaining their health. This allows for comprehensive management of employees' health status and reduces health risks.

[0093] The telecommuting support system can further include a learning support department. The learning support department can provide appropriate learning resources to help employees improve their skills. For example, it can identify employees' skill gaps and suggest necessary training courses. The learning support department can also monitor employees' learning progress and evaluate the effectiveness of their learning. Furthermore, the learning support department can introduce an incentive system to increase employees' motivation when they engage in learning. This can promote employee skill improvement and improve the performance of the entire organization.

[0094] The telecommuting support system can further include a communication promotion department. The communication promotion department can propose measures to facilitate communication between employees. For example, it can propose a schedule for regular online meetings to encourage information sharing among team members. The communication promotion department can also suggest the introduction of a virtual office so that employees can communicate more easily. Furthermore, the communication promotion department can provide training programs to improve employees' communication skills. This can facilitate communication between employees and strengthen teamwork.

[0095] The telecommuting support system can also use an emotion estimation function to provide appropriate feedback based on an employee's emotional state. For example, if an employee is feeling stressed, the system can suggest ways to relax. If an employee is highly motivated, the system can provide feedback praising their efforts. Furthermore, the emotion estimation function can also be used to adjust the content of feedback based on the employee's emotional state, so that it has a positive impact. This allows the system to provide feedback that takes into account the employee's emotional state and maintain their motivation.

[0096] The telecommuting support system can also use its emotion estimation function to suggest breaks at appropriate times based on the employee's emotional state. For example, if an employee feels tired, it can suggest a short break. Also, if an employee is lacking concentration, it can suggest a refreshing activity. Furthermore, the emotion estimation function can also be used to adjust the frequency and length of breaks based on the employee's emotional state, suggesting an optimal break schedule. This allows for breaks to be suggested taking into account the employee's emotional state, improving work efficiency.

[0097] The telecommuting support system can also use its emotion estimation function to suggest appropriate communication methods based on the employee's emotional state. For example, if an employee is feeling stressed, it can suggest communication in a relaxed atmosphere. Also, if an employee is highly motivated, it can provide proactive feedback. Furthermore, the emotion estimation function can also be used to adjust the content and timing of communication based on the employee's emotional state and suggest the optimal communication method. This enables communication that takes the employee's emotional state into consideration and strengthens teamwork.

[0098] The telecommuting support system can also use an emotion estimation function to assign appropriate tasks based on the emotional state of employees. For example, if an employee is feeling stressed, it can assign less burdensome tasks. On the other hand, if an employee is highly motivated, it can assign more challenging tasks. Furthermore, the emotion estimation function can also be used to adjust task priorities and schedules based on the employee's emotional state, resulting in optimal task assignment. This allows for task assignment that takes the employee's emotional state into consideration, improving work efficiency.

[0099] The telecommuting support system can further include a project management department. The project management department can comprehensively manage the progress of projects and propose optimal resource allocation. For example, it can monitor the progress of each project in real time and propose resource reallocation. The project management department can also evaluate project risks and propose risk avoidance measures. Furthermore, the project management department can share best practices to increase the success rate of projects. This makes it possible to comprehensively manage the progress of projects and achieve efficient resource allocation.

[0100] The telecommuting support system can further include a data analysis unit. The data analysis unit can analyze employee work data and make suggestions to improve work efficiency. For example, it can identify bottlenecks in work processes based on the work data and propose improvements. The data analysis unit can also analyze employee work patterns and propose efficient work methods. Furthermore, the data analysis unit can propose the introduction of tools and technologies to improve work efficiency. This makes it possible to analyze employee work data and make specific suggestions to improve work efficiency.

[0101] The telecommuting support system can further include a security management department. The security management department can propose measures to strengthen the security of employees' work environments. For example, it can propose data encryption and strengthened access control. The security management department can also provide training programs to raise employees' security awareness. Furthermore, the security management department can also propose countermeasures in the event of a security incident. This strengthens the security of employees' work environments and prevents information leaks and unauthorized access.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The work content analysis unit analyzes the work content of employees. For example, the generation AI analyzes the tasks that employees are currently working on and their progress. The generation AI can also perform analysis based on information about the work content of employees. Furthermore, the generation AI can analyze the work content of employees in real time. Step 2: The progress display unit displays the work content analyzed by the work content analysis unit on a dashboard. For example, the generation AI displays the employee's work content on a dashboard. The generation AI can also display the employee's progress in graphs and charts. Furthermore, the generation AI can display the employee's work content in real time. Step 3: The performance evaluation unit evaluates the employee's performance based on the work content displayed by the progress display unit. For example, the generation AI analyzes reports submitted by employees and project progress to evaluate their quality and degree of achievement. The generation AI can also make evaluations based on information about the employee's deliverables. Furthermore, the generation AI can evaluate the employee's performance in real time. Step 4: The feedback providing unit provides feedback based on the performance evaluated by the performance evaluation unit. For example, the generation AI provides appropriate feedback to the employee. The generation AI can also suggest areas for improvement based on the employee's performance. Furthermore, the generation AI can provide feedback in real time based on the employee's performance.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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, to avoid confusion and 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.

[0170] 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]

[0171] 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. a work content analysis unit that analyzes the work content of employees; a progress status display unit that displays the work content analyzed by the work content analysis unit on a dashboard; a performance evaluation unit that evaluates the performance of employees based on the work content displayed by the progress status display unit; a feedback providing unit that provides feedback based on the results evaluated by the result evaluating unit. A system characterized by:

2. The work content analysis unit When analyzing the work content of the employee, compare it with past work history to analyze progress trends and make predictions.

2. The system of claim 1.

3. The work content analysis unit When analyzing the work content of the employees, make recommendations to optimize resource allocation among the different projects.

2. The system of claim 1.

4. The outcome evaluation unit When evaluating the employee's performance, compare it against industry standards and best practices 2. The system of claim 1.

5. The communication analysis section Evaluate the emotional impact of communication on employees and promote positive communication.

2. The system of claim 1.

6. The problem discovery department Analyze the employee's emotional state and take action before stress or dissatisfaction increases 2. The system of claim 1.

7. The Growth Promotion Department Analyze the emotional state of the employees and propose measures to increase their motivation 2. The system of claim 1.

8. The work content analysis unit Analyze the emotional state of the employee and visualize fluctuations in stress and motivation 2. The system of claim 1.

Images