system

Abstract

The system according to this embodiment aims to provide optimal learning programs and training tailored to the individual needs of employees. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a progress management unit, and an evaluation unit. The data collection unit collects data such as the PC operation status of employees. The analysis unit analyzes the data collected by the data collection unit and provides individually optimized learning programs and training. The progress management unit manages the progress of the learning programs and training provided by the analysis unit. The evaluation unit provides periodic feedback and evaluations based on the progress managed by the progress management unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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] [[ID=2�]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that learning programs and trainings for improving employees' skills are uniform and cannot meet individual needs.

[0005] The system according to the embodiment aims to provide an optimal learning program and training that meet the individual needs of employees.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a progress management unit, and an evaluation unit. The data collection unit collects data such as the employees' PC operation status. The analysis unit analyzes the data collected by the data collection unit and provides individually optimized learning programs and training. The progress management unit manages the progress of the learning programs and training provided by the analysis unit. The evaluation unit provides periodic feedback and evaluations based on the progress managed by the progress management unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal learning programs and training tailored to the individual needs of employees. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides a personalized learning and training system to support employee skill development. This system collects data such as employees' PC operation status, and an AI agent provides individually optimized learning programs and training. This allows employees to efficiently acquire necessary skills in a short period of time. The system also includes an AI agent that manages online course progress, customizes learning content, and provides regular feedback and evaluation. For example, it collects data such as employees' PC operation status. This includes detailed data such as what operations employees are performing and which applications they are using. For instance, it collects operation logs of software used by employees and data on work time. This allows for an understanding of employees' skill levels and learning needs. Next, the AI ​​agent analyzes the collected data and provides individually optimized learning programs and training. The AI ​​agent evaluates employees' skill levels and learning barriers and presents learning content and schedules tailored to each employee. For example, for employees lacking specific skills, it provides training programs to strengthen those skills. It also adjusts learning content according to progress and provides real-time feedback. Furthermore, the AI ​​agent manages the progress of online courses. It monitors how far employees are progressing in their learning and provides additional resources and support as needed. For example, employees who are falling behind in their learning can be supported by providing additional learning materials and training. The AI ​​agent also provides regular feedback and evaluations. It assesses employees' learning progress and provides feedback as needed. For instance, it evaluates learning progress and achievements and provides advice for the next step. This helps employees maintain motivation to continue learning. This system allows employees to receive individually optimized learning programs and training, enabling them to acquire necessary skills efficiently in a short period. Furthermore, progress management of online courses and regular feedback and evaluations maximize the effectiveness of learning.For example, IT companies and technology companies need continuous learning to keep up with rapidly changing technological trends, and by introducing this system, they can efficiently improve employee skills and develop talent. Thus, personalized learning and training systems can efficiently support employee skill improvement.

[0029] The personalized learning and training system according to the embodiment comprises a data collection unit, an analysis unit, a progress management unit, and an evaluation unit. The data collection unit collects data such as the employee's PC operation status. The data collection unit collects, for example, operation logs of software used by the employee and data on work time. The data collection unit can collect, for example, data such as keystrokes, mouse movements, and application usage history. The data collection unit can collect, for example, detailed data such as what operations the employee is performing and which applications are being used. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or not using AI. The analysis unit analyzes the data collected by the data collection unit and provides individually optimized learning programs and training. The analysis unit evaluates, for example, the employee's skill level and learning barriers, and presents learning content and schedules tailored to each employee. The analysis unit provides, for example, a training program to strengthen skills for employees who lack certain skills. The analysis unit can adjust the learning content according to progress and provide real-time feedback. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not. The progress management unit manages the progress of the learning programs and training provided by the analysis unit. The progress management unit monitors, for example, how far employees are progressing in their learning and provides additional resources and support as needed. The progress management unit can support the learning progress of employees who are falling behind by providing additional learning materials and training, for example. Some or all of the above-described processes in the progress management unit may be performed using AI, for example, or not. The evaluation unit provides periodic feedback and evaluations based on the progress managed by the progress management unit. The evaluation unit evaluates employees' learning status and provides feedback as needed. The evaluation unit can evaluate learning progress and achievement and provide advice for moving on to the next step, for example. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or not.As a result, the personalized learning and training system according to this embodiment can efficiently support the skill improvement of employees.

[0030] The data collection unit collects data such as employees' PC operation status. Specifically, it collects operation logs of the software employees use and data on work time. For example, it can collect data such as keystrokes, mouse movements, and application usage history. This allows the data collection unit to collect detailed data on what operations employees are performing and which applications they are using. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. Specifically, when using AI, natural language processing technology and machine learning algorithms can be utilized to automatically classify and organize the collected data and extract important information. For example, AI can analyze keystroke patterns to identify what tasks employees are spending time on. It can also analyze mouse movements and click frequency to evaluate employees' work efficiency and concentration levels. Furthermore, by analyzing application usage history, it is possible to understand which software employees use frequently and which functions they use most often. This allows the data collection unit to gain a detailed understanding of employees' work patterns and skill levels, and collect foundational data to provide individually optimized learning programs and training.

[0031] The analysis unit analyzes the data collected by the data collection unit and provides individually optimized learning programs and training. Specifically, it evaluates employees' skill levels and learning barriers and presents learning content and schedules tailored to each employee. For example, for employees lacking a particular skill, it provides a training program to strengthen that skill. The analysis unit can adjust the learning content as progress is made and provide real-time feedback. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. Specifically, when using AI, machine learning algorithms can be utilized to analyze employee learning data and generate optimal training plans that meet individual learning needs. For example, AI can suggest optimal learning content and training methods based on an employee's past learning history and current skill level. In addition, AI can analyze data collected in real time and evaluate the progress and effectiveness of learning. This allows the analysis unit to respond flexibly to employees' learning needs and support efficient skill improvement. Furthermore, the analysis unit can collect employee feedback and use it to improve the learning program. For example, it's possible to evaluate how employees responded to the provided training program, identify which parts were effective, and incorporate that feedback into future training sessions. This allows the analysis department to consistently provide optimal learning programs based on the latest information, efficiently supporting employee skill development.

[0032] The Progress Management Department manages the progress of learning programs and training provided by the Analysis Department. Specifically, it monitors how far employees are progressing in their learning and provides additional resources and support as needed. For example, it can support the learning progress of employees who are falling behind by providing additional learning materials and training. Some or all of the above processes in the Progress Management Department may be performed using AI, for example, or not. Specifically, when using AI, learning data can be analyzed in real time and employees' learning progress can be automatically evaluated. For example, the AI ​​can detect learning delays or stagnation based on the employee's learning history and current progress, and provide appropriate support. The AI ​​can also analyze employees' learning patterns and behaviors and suggest the optimal learning schedule and resources. This allows the Progress Management Department to efficiently manage employees' learning progress and provide necessary support quickly. Furthermore, the Progress Management Department can collect employee feedback and use it to improve learning programs. For example, it can evaluate how employees responded to the support provided, which parts were effective, and reflect this in future support. This allows the progress management department to provide optimal support based on the latest information at all times, and to efficiently manage employees' learning progress.

[0033] The evaluation department provides regular feedback and evaluations based on the progress managed by the progress management department. Specifically, it evaluates employees' learning status and provides feedback as needed. For example, it can evaluate learning progress and achievement and provide advice for moving to the next step. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. Specifically, when using AI, learning data can be analyzed and the effectiveness of employees' learning can be automatically evaluated. For example, the AI ​​can evaluate the effectiveness and achievement of learning based on the employee's learning history and current progress and provide appropriate feedback. The AI ​​can also analyze the employee's learning patterns and behavior and suggest the best advice for moving to the next step. This allows the evaluation department to efficiently evaluate employees' learning status and provide necessary feedback quickly. Furthermore, the evaluation department can collect employee feedback and use it to improve the learning program. For example, it can evaluate how employees reacted to the feedback provided, which parts were effective, and reflect this in the next feedback. This allows the evaluation department to always provide optimal feedback based on the latest information and efficiently evaluate the effectiveness of employees' learning.

[0034] The data collection unit can analyze an employee's past operation history and select the optimal data collection method. For example, the data collection unit can prioritize collecting operation logs of applications that employees frequently use. For example, the data collection unit can analyze patterns of operations performed by employees in the past and propose an efficient data collection method. For example, the data collection unit can concentrate data collection from employees' operation history during specific time periods. This enables efficient data collection based on employees' operation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employees' past operation history data into a generating AI and have the generating AI select the optimal data collection method.

[0035] The data collection unit can filter data based on an employee's current projects and work content during data collection. For example, the data collection unit can collect only operational data related to currently ongoing projects. For example, the data collection unit can prioritize the collection of data related to specific work content. For example, the data collection unit can select and collect necessary data according to the progress of a project. This allows for the priority collection of data related to the current project and work content. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee's current project and work content data into a generating AI and have the generating AI perform the filtering.

[0036] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of employees during data collection. For example, if an employee is in the office, the data collection unit can prioritize the collection of operational data from the office. For example, if an employee is on a business trip, the data collection unit can prioritize the collection of operational data from the business trip destination. For example, if an employee is working remotely, the data collection unit can prioritize the collection of operational data from home. This allows for the collection of highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee geographical location information data into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0037] The data collection unit can analyze employees' social media activities and collect relevant data during data collection. For example, the data collection unit can collect relevant operational data based on information shared by employees on social media. For example, the data collection unit can extract and collect work-related data from employees' social media activities. For example, the data collection unit can prioritize the collection of data related to projects mentioned by employees on social media. This allows for the collection of relevant data based on social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0038] The analysis unit can adjust the level of detail of the learning program based on the employee's skill level during analysis. For example, the analysis unit can provide a learning program with basic content to a beginner employee, an advanced learning program to an intermediate employee, and a specialized learning program to an advanced employee. This allows for the provision of learning programs tailored to each employee's skill level. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee skill level data into a generating AI and have the generating AI adjust the level of detail of the learning program.

[0039] The analysis unit can apply different analysis algorithms to employees depending on their job category during analysis. For example, the analysis unit can apply an analysis algorithm to enhance programming skills to employees in the IT department. For example, the analysis unit can apply an analysis algorithm to enhance communication skills to employees in the sales department. For example, the analysis unit can apply an analysis algorithm to enhance leadership skills to employees in the administrative department. This enables analysis tailored to job category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee job category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0040] The analysis unit can determine the priority of learning programs based on the employee's work history during analysis. For example, the analysis unit can provide a program that prioritizes learning skills related to tasks the employee has performed in the past. For example, the analysis unit can predict the skills that will be needed in the future from the employee's work history and provide a program that prioritizes learning those skills. For example, the analysis unit can analyze the employee's work history and provide the most effective learning program. This allows the priority of learning programs to be determined based on work history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee work history data into a generating AI and have the generating AI perform the determination of learning program priorities.

[0041] The analysis unit can adjust the order of learning programs based on the employee's relevant tasks during analysis. For example, the analysis unit can provide a program that first teaches skills related to the tasks the employee is currently performing. For example, the analysis unit can optimize the order of learning programs according to the employee's work content. For example, the analysis unit can adjust the order of learning programs based on the priority of the employee's tasks. This allows the order of learning programs to be adjusted based on relevant tasks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee relevant work data into a generating AI and have the generating AI perform the adjustment of the learning program order.

[0042] The progress management department can improve the accuracy of progress management by considering the relationships between employees. For example, the progress management department can manage progress by considering the communication status between team members. For example, the progress management department can improve the accuracy of progress management by analyzing the cooperative relationships between employees. For example, the progress management department can propose the optimal progress management method based on the relationships between employees. This allows for improved accuracy of progress management based on the relationships between employees. Some or all of the above processes in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input employee relationship data into a generating AI and have the generating AI perform the improvement of progress management accuracy.

[0043] The progress management department can perform progress management while taking into account employee attribute information. For example, the progress management department can perform progress management while taking into account the employee's age and years of experience. For example, the progress management department can perform progress management while taking into account the employee's job duties and position. For example, the progress management department can perform progress management while taking into account the employee's skill level. This allows progress management to be performed based on employee attribute information. Some or all of the above processes in the progress management department may be performed using AI, for example, or without using AI. For example, the progress management department can input employee attribute information data into a generating AI and have the generating AI perform progress management.

[0044] The progress management department can manage progress while considering the geographical distribution of employees. For example, if employees are in different regions, the progress management department can manage progress while considering the progress status of each region. For example, if employees are working remotely, the progress management department can manage progress while considering the progress of remote work. For example, if employees are on a business trip, the progress management department can manage progress while considering the progress status at the business trip destination. This allows progress management to be performed based on geographical distribution. Some or all of the above processes in the progress management department may be performed using AI, for example, or without using AI. For example, the progress management department can input geographical distribution data of employees into a generating AI and have the generating AI perform progress management.

[0045] The progress management department can improve the accuracy of progress management by referring to relevant literature used by employees during progress management. For example, the progress management department can improve the accuracy of progress management based on literature referenced by employees. For example, the progress management department can improve the accuracy of progress management by analyzing literature related to employees' work. For example, the progress management department can improve the accuracy of progress management based on literature referenced by employees in the past. This allows for improvement in the accuracy of progress management based on relevant literature. Some or all of the above processes in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input employee relevant literature data into a generating AI and have the generating AI perform the improvement of progress management accuracy.

[0046] The evaluation unit can predict the current evaluation by referring to past evaluation data during the evaluation process. For example, the evaluation unit predicts the current evaluation based on an employee's past evaluation data. For example, the evaluation unit can predict an employee's growth rate from past evaluation data and reflect this in the current evaluation. For example, the evaluation unit can analyze past evaluation data and optimize the current evaluation. This allows the evaluation unit to predict the current evaluation based on past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input an employee's past evaluation data into a generating AI and have the generating AI perform a prediction of the current evaluation.

[0047] The evaluation department can apply different evaluation and analysis methods to each employee's job category during the evaluation process. For example, the evaluation department can apply an evaluation and analysis method that emphasizes technical skills to employees in the IT department. For example, the evaluation department can apply an evaluation and analysis method that emphasizes sales performance to employees in the sales department. For example, the evaluation department can apply an evaluation and analysis method that emphasizes leadership skills to employees in the administrative department. This enables evaluation and analysis tailored to each job category. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can input employee job category data into a generating AI and have the generating AI execute the application of different evaluation and analysis methods.

[0048] The evaluation unit can analyze changes in an employee's evaluation based on their work history during the evaluation process. For example, the evaluation unit can analyze changes in an employee's evaluation based on their past work history. For example, the evaluation unit can reflect an employee's growth in their evaluation based on their work history. For example, the evaluation unit can analyze work history and predict changes in evaluation. This allows for the analysis of changes in evaluation based on work history. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input employee work history data into a generating AI and have the generating AI perform an analysis of changes in evaluation.

[0049] The evaluation unit can analyze employee evaluations by referring to relevant market data during the evaluation process. For example, the evaluation unit can analyze evaluations based on market data related to the employee's work. For example, the evaluation unit can reflect employee performance in evaluations based on market data. For example, the evaluation unit can analyze market data to improve the accuracy of evaluations. This allows for evaluation analysis based on relevant market data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant market data of employees into a generating AI and have the generating AI perform the evaluation analysis.

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

[0051] The data collection unit can monitor employees' health status and adjust the timing of data collection based on that status. For example, if an employee is feeling unwell, the frequency of data collection can be reduced and resumed when the employee has recovered. The data collection unit can also collect detailed operational data when an employee is in good health, based on the results of a health checkup. For example, if an employee is feeling fatigued, the data collection unit can temporarily stop data collection and resume it after they have rested. This allows data to be collected at an appropriate time according to the employee's health status. Health status monitoring can be performed, for example, using wearable devices or health management apps. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input health data acquired from a wearable device into a generating AI and have the generating AI estimate the employee's health status.

[0052] The progress management department can manage progress while taking into account employees' working hours. For example, if an employee is working long hours, the progress management criteria can be relaxed. For example, if an employee is working short hours, the progress management department can tighten the progress management criteria. For example, if an employee is using a flexible working hours system, the progress management department can adjust the progress management criteria according to their working hours. This allows progress management to be performed based on working hours. Some or all of the above processes in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input employee working hour data into a generating AI and have the generating AI adjust the progress management criteria.

[0053] The data collection unit can monitor employees' device usage and adjust the data collection method based on the device being used. For example, if an employee is using a smartphone, the unit can prioritize collecting smartphone operation data. If an employee is using a tablet, the unit can prioritize collecting tablet operation data. If an employee is using a desktop PC, the unit can prioritize collecting desktop PC operation data. This enables efficient data collection based on the device being used. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee device usage data into a generating AI and have the generating AI select the optimal data collection method.

[0054] The progress management department can manage project progress while considering the priority of each employee's projects. For example, it can set stricter progress management criteria for high-priority projects. For example, it can relax progress management criteria for low-priority projects. For example, if multiple projects are running simultaneously, the progress management department can adjust the progress management criteria according to the priority of each project. This allows for progress management based on project priority. Some or all of the above processes in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input project priority data into a generating AI and have the generating AI adjust the progress management criteria.

[0055] The data collection unit can adjust its data collection methods to take employee data privacy into consideration. For example, if an employee is handling privacy-sensitive data, the collection unit may refrain from collecting that data. The collection unit may reduce the frequency of data collection if an employee has expressed privacy concerns. The collection unit may prioritize the collection of data if an employee has explicitly given privacy consent. This enables data collection that respects data privacy. Some or all of the above processes in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit may input employee privacy data into a generating AI and have the generating AI select the optimal data collection method.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects data such as employees' PC usage status. For example, it can collect detailed data such as operation logs of software used by employees, work time data, keystrokes, mouse movements, and application usage history. The processing in the data collection unit may be performed using AI or not. Step 2: The analysis unit analyzes the data collected by the collection unit and provides individually optimized learning programs and training. For example, it evaluates employees' skill levels and learning barriers and presents learning content and schedules tailored to each employee. For employees lacking specific skills, it can provide training programs to strengthen those skills, adjust the learning content according to progress, and provide real-time feedback. The processing in the analysis unit may or may not be performed using AI. Step 3: The progress management department manages the progress of the learning programs and training provided by the analysis department. For example, it monitors how far employees are progressing in their learning and provides additional resources and support as needed. Employees who are falling behind can be supported in their learning progress by being provided with additional learning materials and training. The processing in the progress management department may or may not be performed using AI. Step 4: The evaluation department provides periodic feedback and evaluations based on the progress managed by the progress management department. For example, it evaluates employees' learning progress and provides feedback as needed. It can evaluate learning progress and achievements and provide advice for moving on to the next step. The processing in the evaluation department may or may not be performed using AI.

[0058] (Example of form 2) An embodiment of the present invention provides a personalized learning and training system to support employee skill development. This system collects data such as employees' PC operation status, and an AI agent provides individually optimized learning programs and training. This allows employees to efficiently acquire necessary skills in a short period of time. The system also includes an AI agent that manages online course progress, customizes learning content, and provides regular feedback and evaluation. For example, it collects data such as employees' PC operation status. This includes detailed data such as what operations employees are performing and which applications they are using. For instance, it collects operation logs of software used by employees and data on work time. This allows for an understanding of employees' skill levels and learning needs. Next, the AI ​​agent analyzes the collected data and provides individually optimized learning programs and training. The AI ​​agent evaluates employees' skill levels and learning barriers and presents learning content and schedules tailored to each employee. For example, for employees lacking specific skills, it provides training programs to strengthen those skills. It also adjusts learning content according to progress and provides real-time feedback. Furthermore, the AI ​​agent manages the progress of online courses. It monitors how far employees are progressing in their learning and provides additional resources and support as needed. For example, employees who are falling behind in their learning can be supported by providing additional learning materials and training. The AI ​​agent also provides regular feedback and evaluations. It assesses employees' learning progress and provides feedback as needed. For instance, it evaluates learning progress and achievements and provides advice for the next step. This helps employees maintain motivation to continue learning. This system allows employees to receive individually optimized learning programs and training, enabling them to acquire necessary skills efficiently in a short period. Furthermore, progress management of online courses and regular feedback and evaluations maximize the effectiveness of learning.For example, IT companies and technology companies need continuous learning to keep up with rapidly changing technological trends, and by introducing this system, they can efficiently improve employee skills and develop talent. Thus, personalized learning and training systems can efficiently support employee skill improvement.

[0059] The personalized learning and training system according to the embodiment comprises a data collection unit, an analysis unit, a progress management unit, and an evaluation unit. The data collection unit collects data such as the employee's PC operation status. The data collection unit collects, for example, operation logs of software used by the employee and data on work time. The data collection unit can collect, for example, data such as keystrokes, mouse movements, and application usage history. The data collection unit can collect, for example, detailed data such as what operations the employee is performing and which applications are being used. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or not using AI. The analysis unit analyzes the data collected by the data collection unit and provides individually optimized learning programs and training. The analysis unit evaluates, for example, the employee's skill level and learning barriers, and presents learning content and schedules tailored to each employee. The analysis unit provides, for example, a training program to strengthen skills for employees who lack certain skills. The analysis unit can adjust the learning content according to progress and provide real-time feedback. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not. The progress management unit manages the progress of the learning programs and training provided by the analysis unit. The progress management unit monitors, for example, how far employees are progressing in their learning and provides additional resources and support as needed. The progress management unit can support the learning progress of employees who are falling behind by providing additional learning materials and training, for example. Some or all of the above-described processes in the progress management unit may be performed using AI, for example, or not. The evaluation unit provides periodic feedback and evaluations based on the progress managed by the progress management unit. The evaluation unit evaluates employees' learning status and provides feedback as needed. The evaluation unit can evaluate learning progress and achievement and provide advice for moving on to the next step, for example. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or not.As a result, the personalized learning and training system according to this embodiment can efficiently support the skill improvement of employees.

[0060] The data collection unit collects data such as employees' PC operation status. Specifically, it collects operation logs of the software employees use and data on work time. For example, it can collect data such as keystrokes, mouse movements, and application usage history. This allows the data collection unit to collect detailed data on what operations employees are performing and which applications they are using. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. Specifically, when using AI, natural language processing technology and machine learning algorithms can be utilized to automatically classify and organize the collected data and extract important information. For example, AI can analyze keystroke patterns to identify what tasks employees are spending time on. It can also analyze mouse movements and click frequency to evaluate employees' work efficiency and concentration levels. Furthermore, by analyzing application usage history, it is possible to understand which software employees use frequently and which functions they use most often. This allows the data collection unit to gain a detailed understanding of employees' work patterns and skill levels, and collect foundational data to provide individually optimized learning programs and training.

[0061] The analysis unit analyzes the data collected by the data collection unit and provides individually optimized learning programs and training. Specifically, it evaluates employees' skill levels and learning barriers and presents learning content and schedules tailored to each employee. For example, for employees lacking a particular skill, it provides a training program to strengthen that skill. The analysis unit can adjust the learning content as progress is made and provide real-time feedback. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. Specifically, when using AI, machine learning algorithms can be utilized to analyze employee learning data and generate optimal training plans that meet individual learning needs. For example, AI can suggest optimal learning content and training methods based on an employee's past learning history and current skill level. In addition, AI can analyze data collected in real time and evaluate the progress and effectiveness of learning. This allows the analysis unit to respond flexibly to employees' learning needs and support efficient skill improvement. Furthermore, the analysis unit can collect employee feedback and use it to improve the learning program. For example, it's possible to evaluate how employees responded to the provided training program, identify which parts were effective, and incorporate that feedback into future training sessions. This allows the analysis department to consistently provide optimal learning programs based on the latest information, efficiently supporting employee skill development.

[0062] The Progress Management Department manages the progress of learning programs and training provided by the Analysis Department. Specifically, it monitors how far employees are progressing in their learning and provides additional resources and support as needed. For example, it can support the learning progress of employees who are falling behind by providing additional learning materials and training. Some or all of the above processes in the Progress Management Department may be performed using AI, for example, or not. Specifically, when using AI, learning data can be analyzed in real time and employees' learning progress can be automatically evaluated. For example, the AI ​​can detect learning delays or stagnation based on the employee's learning history and current progress, and provide appropriate support. The AI ​​can also analyze employees' learning patterns and behaviors and suggest the optimal learning schedule and resources. This allows the Progress Management Department to efficiently manage employees' learning progress and provide necessary support quickly. Furthermore, the Progress Management Department can collect employee feedback and use it to improve learning programs. For example, it can evaluate how employees responded to the support provided, which parts were effective, and reflect this in future support. This allows the progress management department to provide optimal support based on the latest information at all times, and to efficiently manage employees' learning progress.

[0063] The evaluation department provides regular feedback and evaluations based on the progress managed by the progress management department. Specifically, it evaluates employees' learning status and provides feedback as needed. For example, it can evaluate learning progress and achievement and provide advice for moving to the next step. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. Specifically, when using AI, learning data can be analyzed and the effectiveness of employees' learning can be automatically evaluated. For example, the AI ​​can evaluate the effectiveness and achievement of learning based on the employee's learning history and current progress and provide appropriate feedback. The AI ​​can also analyze the employee's learning patterns and behavior and suggest the best advice for moving to the next step. This allows the evaluation department to efficiently evaluate employees' learning status and provide necessary feedback quickly. Furthermore, the evaluation department can collect employee feedback and use it to improve the learning program. For example, it can evaluate how employees reacted to the feedback provided, which parts were effective, and reflect this in the next feedback. This allows the evaluation department to always provide optimal feedback based on the latest information and efficiently evaluate the effectiveness of employees' learning.

[0064] The data collection unit can estimate employees' emotions and adjust the timing of data collection based on the estimated emotions. For example, if an employee is stressed, the data collection unit can reduce the frequency of data collection and collect data when the employee is relaxed. For example, if an employee is focused, the data collection unit can collect detailed operational data at that time. For example, if an employee is tired, the data collection unit can temporarily stop data collection and resume it after a break. This allows data to be collected at the appropriate time according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input image data of employees captured by a camera into a generative AI and have the generative AI perform the estimation of the employees' emotions.

[0065] The data collection unit can analyze an employee's past operation history and select the optimal data collection method. For example, the data collection unit can prioritize collecting operation logs of applications that employees frequently use. For example, the data collection unit can analyze patterns of operations performed by employees in the past and propose an efficient data collection method. For example, the data collection unit can concentrate data collection from employees' operation history during specific time periods. This enables efficient data collection based on employees' operation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employees' past operation history data into a generating AI and have the generating AI select the optimal data collection method.

[0066] The data collection unit can filter data based on an employee's current projects and work content during data collection. For example, the data collection unit can collect only operational data related to currently ongoing projects. For example, the data collection unit can prioritize the collection of data related to specific work content. For example, the data collection unit can select and collect necessary data according to the progress of a project. This allows for the priority collection of data related to the current project and work content. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee's current project and work content data into a generating AI and have the generating AI perform the filtering.

[0067] The data collection unit can estimate employees' emotions and prioritize the data to be collected based on the estimated emotions. For example, if an employee is stressed, the data collection unit will postpone the collection of less important data. For example, if an employee is relaxed, the data collection unit can prioritize the collection of detailed data. For example, if an employee is focused, the data collection unit can prioritize the collection of important operational data. This allows for the adjustment of data priorities according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input image data of employees captured by a camera into a generative AI and have the generative AI perform the estimation of the employees' emotions.

[0068] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of employees during data collection. For example, if an employee is in the office, the data collection unit can prioritize the collection of operational data from the office. For example, if an employee is on a business trip, the data collection unit can prioritize the collection of operational data from the business trip destination. For example, if an employee is working remotely, the data collection unit can prioritize the collection of operational data from home. This allows for the collection of highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee geographical location information data into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0069] The data collection unit can analyze employees' social media activities and collect relevant data during data collection. For example, the data collection unit can collect relevant operational data based on information shared by employees on social media. For example, the data collection unit can extract and collect work-related data from employees' social media activities. For example, the data collection unit can prioritize the collection of data related to projects mentioned by employees on social media. This allows for the collection of relevant data based on social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0070] The analysis unit can estimate an employee's emotions and adjust the content of the learning program based on the estimated emotions. For example, if an employee is stressed, the analysis unit can provide a learning program with relaxing content. For example, if an employee is concentrating, the analysis unit can provide a learning program with challenging content. For example, if an employee is tired, the analysis unit can provide a learning program with easy content. This allows for the provision of learning programs tailored to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input image data of employees captured by a camera into the generative AI and have the generative AI perform the estimation of the employees' emotions.

[0071] The analysis unit can adjust the level of detail of the learning program based on the employee's skill level during analysis. For example, the analysis unit can provide a learning program with basic content to a beginner employee, an advanced learning program to an intermediate employee, and a specialized learning program to an advanced employee. This allows for the provision of learning programs tailored to each employee's skill level. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee skill level data into a generating AI and have the generating AI adjust the level of detail of the learning program.

[0072] The analysis unit can apply different analysis algorithms to employees depending on their job category during analysis. For example, the analysis unit can apply an analysis algorithm to enhance programming skills to employees in the IT department. For example, the analysis unit can apply an analysis algorithm to enhance communication skills to employees in the sales department. For example, the analysis unit can apply an analysis algorithm to enhance leadership skills to employees in the administrative department. This enables analysis tailored to job category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee job category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0073] The analysis unit can estimate an employee's emotions and adjust the length of the learning program based on the estimated emotions. For example, if an employee is stressed, the analysis unit can provide a learning program that can be completed in a short time. For example, if an employee is relaxed, the analysis unit can provide a longer learning program. For example, if an employee is focused, the analysis unit can provide a learning program of appropriate length. This allows for providing learning program lengths that are appropriate to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of an employee taken by a camera into the generative AI and have the generative AI perform the estimation of the employee's emotions.

[0074] The analysis unit can determine the priority of learning programs based on the employee's work history during analysis. For example, the analysis unit can provide a program that prioritizes learning skills related to tasks the employee has performed in the past. For example, the analysis unit can predict the skills that will be needed in the future from the employee's work history and provide a program that prioritizes learning those skills. For example, the analysis unit can analyze the employee's work history and provide the most effective learning program. This allows the priority of learning programs to be determined based on work history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee work history data into a generating AI and have the generating AI perform the determination of learning program priorities.

[0075] The analysis unit can adjust the order of learning programs based on the employee's relevant tasks during analysis. For example, the analysis unit can provide a program that first teaches skills related to the tasks the employee is currently performing. For example, the analysis unit can optimize the order of learning programs according to the employee's work content. For example, the analysis unit can adjust the order of learning programs based on the priority of the employee's tasks. This allows the order of learning programs to be adjusted based on relevant tasks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee relevant work data into a generating AI and have the generating AI perform the adjustment of the learning program order.

[0076] The progress management department can estimate employees' emotions and adjust progress management standards based on the estimated emotions. For example, if an employee is stressed, the progress management department can relax the progress management standards. For example, if an employee is relaxed, the progress management department can tighten the progress management standards. For example, if an employee is focused, the progress management department can adjust the progress management standards appropriately. This allows for the provision of progress management standards that are appropriate to the emotions of employees. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress management department may be performed using AI, for example, or not using AI. For example, the progress management department can input image data of employees taken by a camera into a generative AI and have the generative AI perform the estimation of the employees' emotions.

[0077] The progress management department can improve the accuracy of progress management by considering the relationships between employees. For example, the progress management department can manage progress by considering the communication status between team members. For example, the progress management department can improve the accuracy of progress management by analyzing the cooperative relationships between employees. For example, the progress management department can propose the optimal progress management method based on the relationships between employees. This allows for improved accuracy of progress management based on the relationships between employees. Some or all of the above processes in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input employee relationship data into a generating AI and have the generating AI perform the improvement of progress management accuracy.

[0078] The progress management department can perform progress management while taking into account employee attribute information. For example, the progress management department can perform progress management while taking into account the employee's age and years of experience. For example, the progress management department can perform progress management while taking into account the employee's job duties and position. For example, the progress management department can perform progress management while taking into account the employee's skill level. This allows progress management to be performed based on employee attribute information. Some or all of the above processes in the progress management department may be performed using AI, for example, or without using AI. For example, the progress management department can input employee attribute information data into a generating AI and have the generating AI perform progress management.

[0079] The progress management unit can estimate employees' emotions and adjust the order in which progress management results are displayed based on the estimated emotions. For example, if an employee is stressed, the progress management unit can display less important results first. If an employee is relaxed, the progress management unit can display more important results first. If an employee is focused, the progress management unit can display results in an appropriate order. This allows progress management results to be displayed in an order that corresponds to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress management unit may be performed using AI or not. For example, the progress management unit can input image data of employees taken by a camera into a generative AI and have the generative AI perform the estimation of the employees' emotions.

[0080] The progress management department can manage progress while considering the geographical distribution of employees. For example, if employees are in different regions, the progress management department can manage progress while considering the progress status of each region. For example, if employees are working remotely, the progress management department can manage progress while considering the progress of remote work. For example, if employees are on a business trip, the progress management department can manage progress while considering the progress status at the business trip destination. This allows progress management to be performed based on geographical distribution. Some or all of the above processes in the progress management department may be performed using AI, for example, or without using AI. For example, the progress management department can input geographical distribution data of employees into a generating AI and have the generating AI perform progress management.

[0081] The progress management department can improve the accuracy of progress management by referring to relevant literature used by employees during progress management. For example, the progress management department can improve the accuracy of progress management based on literature referenced by employees. For example, the progress management department can improve the accuracy of progress management by analyzing literature related to employees' work. For example, the progress management department can improve the accuracy of progress management based on literature referenced by employees in the past. This allows for improvement in the accuracy of progress management based on relevant literature. Some or all of the above processes in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input employee relevant literature data into a generating AI and have the generating AI perform the improvement of progress management accuracy.

[0082] The evaluation unit can estimate an employee's emotions and adjust the display method of the evaluation based on the estimated emotions. For example, if an employee is stressed, the evaluation unit can provide a simple and highly visible display method. For example, if an employee is relaxed, the evaluation unit can provide a display method that includes detailed information. For example, if an employee is focused, the evaluation unit can provide a display method that gets straight to the point. This allows the evaluation to be provided in an appropriate display method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input image data of employees taken by a camera into a generative AI and have the generative AI perform the estimation of the employees' emotions.

[0083] The evaluation unit can predict the current evaluation by referring to past evaluation data during the evaluation process. For example, the evaluation unit predicts the current evaluation based on an employee's past evaluation data. For example, the evaluation unit can predict an employee's growth rate from past evaluation data and reflect this in the current evaluation. For example, the evaluation unit can analyze past evaluation data and optimize the current evaluation. This allows the evaluation unit to predict the current evaluation based on past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input an employee's past evaluation data into a generating AI and have the generating AI perform a prediction of the current evaluation.

[0084] The evaluation department can apply different evaluation and analysis methods to each employee's job category during the evaluation process. For example, the evaluation department can apply an evaluation and analysis method that emphasizes technical skills to employees in the IT department. For example, the evaluation department can apply an evaluation and analysis method that emphasizes sales performance to employees in the sales department. For example, the evaluation department can apply an evaluation and analysis method that emphasizes leadership skills to employees in the administrative department. This enables evaluation and analysis tailored to each job category. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can input employee job category data into a generating AI and have the generating AI execute the application of different evaluation and analysis methods.

[0085] The evaluation unit can estimate an employee's emotions and adjust the importance of the evaluation based on the estimated emotions. For example, if an employee is stressed, the evaluation unit can set the importance of the evaluation low. For example, if an employee is relaxed, the evaluation unit can set the importance of the evaluation high. For example, if an employee is focused, the evaluation unit can perform the evaluation with an appropriate level of importance. This allows for providing evaluation importance that is appropriate to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input image data of employees taken by a camera into a generative AI and have the generative AI perform the estimation of the employees' emotions.

[0086] The evaluation unit can analyze changes in an employee's evaluation based on their work history during the evaluation process. For example, the evaluation unit can analyze changes in an employee's evaluation based on their past work history. For example, the evaluation unit can reflect an employee's growth in their evaluation based on their work history. For example, the evaluation unit can analyze work history and predict changes in evaluation. This allows for the analysis of changes in evaluation based on work history. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input employee work history data into a generating AI and have the generating AI perform an analysis of changes in evaluation.

[0087] The evaluation unit can analyze employee evaluations by referring to relevant market data during the evaluation process. For example, the evaluation unit can analyze evaluations based on market data related to the employee's work. For example, the evaluation unit can reflect employee performance in evaluations based on market data. For example, the evaluation unit can analyze market data to improve the accuracy of evaluations. This allows for evaluation analysis based on relevant market data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant market data of employees into a generating AI and have the generating AI perform the evaluation analysis.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The data collection unit can monitor employees' health status and adjust the timing of data collection based on that status. For example, if an employee is feeling unwell, the frequency of data collection can be reduced and resumed when the employee has recovered. The data collection unit can also collect detailed operational data when an employee is in good health, based on the results of a health checkup. For example, if an employee is feeling fatigued, the data collection unit can temporarily stop data collection and resume it after they have rested. This allows data to be collected at an appropriate time according to the employee's health status. Health status monitoring can be performed, for example, using wearable devices or health management apps. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input health data acquired from a wearable device into a generating AI and have the generating AI estimate the employee's health status.

[0090] The analysis unit can estimate employees' hobbies and interests and adjust the content of learning programs based on those estimates. For example, if an employee is interested in sports, it can provide a learning program related to sports. If an employee is interested in music, the analysis unit can provide a learning program related to music. If an employee is interested in travel, the analysis unit can provide a learning program related to travel. This allows for the provision of learning programs tailored to employees' hobbies and interests. Hobbies and interests are estimated using, for example, social media posts or survey results. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input social media post data into a generating AI and have the generating AI perform the estimation of employees' hobbies and interests.

[0091] The progress management department can manage progress while taking into account employees' working hours. For example, if an employee is working long hours, the progress management criteria can be relaxed. For example, if an employee is working short hours, the progress management department can tighten the progress management criteria. For example, if an employee is using a flexible working hours system, the progress management department can adjust the progress management criteria according to their working hours. This allows progress management to be performed based on working hours. Some or all of the above processes in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input employee working hour data into a generating AI and have the generating AI adjust the progress management criteria.

[0092] The evaluation unit can estimate an employee's emotions and adjust the evaluation feedback method based on the estimated emotions. For example, if an employee is stressed, positive feedback may be prioritized. If an employee is relaxed, the evaluation unit may provide detailed feedback. If an employee is focused, the evaluation unit may provide feedback that includes specific areas for improvement. This allows evaluations to be provided using appropriate feedback methods tailored to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit may input image data of employees captured by a camera into a generative AI and have the generative AI perform the estimation of the employees' emotions.

[0093] The data collection unit can monitor employees' device usage and adjust the data collection method based on the device being used. For example, if an employee is using a smartphone, the unit can prioritize collecting smartphone operation data. If an employee is using a tablet, the unit can prioritize collecting tablet operation data. If an employee is using a desktop PC, the unit can prioritize collecting desktop PC operation data. This enables efficient data collection based on the device being used. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee device usage data into a generating AI and have the generating AI select the optimal data collection method.

[0094] The analysis unit can estimate an employee's learning style and adjust the format of the learning program based on the estimated learning style. For example, if an employee has a visual learning style, the analysis unit can provide a learning program that includes a lot of visual content. If an employee has an auditory learning style, the analysis unit can provide a learning program that includes a lot of audio content. If an employee has an experiential learning style, the analysis unit can provide a learning program that includes a lot of practical exercises. This allows for the provision of learning programs tailored to each employee's learning style. The estimation of learning styles is performed, for example, using questionnaires or past learning history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input questionnaire data into a generating AI and have the generating AI perform the estimation of employees' learning styles.

[0095] The progress management department can manage project progress while considering the priority of each employee's projects. For example, it can set stricter progress management criteria for high-priority projects. For example, it can relax progress management criteria for low-priority projects. For example, if multiple projects are running simultaneously, the progress management department can adjust the progress management criteria according to the priority of each project. This allows for progress management based on project priority. Some or all of the above processes in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input project priority data into a generating AI and have the generating AI adjust the progress management criteria.

[0096] The evaluation unit can estimate an employee's emotions and adjust the timing of the evaluation based on the estimated emotions. For example, if an employee is stressed, the evaluation timing can be delayed. If an employee is relaxed, the evaluation timing can be advanced. If an employee is focused, the evaluation can be conducted at an appropriate time. This allows for evaluations to be provided at the appropriate time according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input image data of employees captured by a camera into a generative AI and have the generative AI perform the estimation of the employees' emotions.

[0097] The data collection unit can adjust its data collection methods to take employee data privacy into consideration. For example, if an employee is handling privacy-sensitive data, the collection unit may refrain from collecting that data. The collection unit may reduce the frequency of data collection if an employee has expressed privacy concerns. The collection unit may prioritize the collection of data if an employee has explicitly given privacy consent. This enables data collection that respects data privacy. Some or all of the above processes in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit may input employee privacy data into a generating AI and have the generating AI select the optimal data collection method.

[0098] The analysis unit can estimate employees' career goals and adjust the content of learning programs based on those estimated goals. For example, if an employee aims for a management position, it can provide a learning program to strengthen leadership skills. If an employee aims for a professional position, the analysis unit can provide a learning program to deepen their expertise. If an employee is considering changing jobs, the analysis unit can provide a learning program to acquire skills useful for job hunting. This allows for the provision of learning programs tailored to employees' career goals. Career goals are estimated using, for example, career counseling or survey results. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input career counseling data into a generating AI and have the generating AI perform the estimation of employees' career goals.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The data collection unit collects data such as employees' PC usage status. For example, it can collect detailed data such as operation logs of software used by employees, work time data, keystrokes, mouse movements, and application usage history. The processing in the data collection unit may be performed using AI or not. Step 2: The analysis unit analyzes the data collected by the collection unit and provides individually optimized learning programs and training. For example, it evaluates employees' skill levels and learning barriers and presents learning content and schedules tailored to each employee. For employees lacking specific skills, it can provide training programs to strengthen those skills, adjust the learning content according to progress, and provide real-time feedback. The processing in the analysis unit may or may not be performed using AI. Step 3: The progress management department manages the progress of the learning programs and training provided by the analysis department. For example, it monitors how far employees are progressing in their learning and provides additional resources and support as needed. Employees who are falling behind can be supported in their learning progress by being provided with additional learning materials and training. The processing in the progress management department may or may not be performed using AI. Step 4: The evaluation department provides periodic feedback and evaluations based on the progress managed by the progress management department. For example, it evaluates employees' learning progress and provides feedback as needed. It can evaluate learning progress and achievements and provide advice for moving on to the next step. The processing in the evaluation department may or may not be performed using AI.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements described above, including the data collection unit, analysis unit, progress management unit, and evaluation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect employee operation status and emotions, and the control unit 46A collects the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and provide individually optimized learning programs and training. The progress management unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to manage the progress of the learning programs and training. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to provide periodic feedback and evaluation based on progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the data collection unit, analysis unit, progress management unit, and evaluation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect employee operation status and emotions, and the control unit 46A collects the data. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and provides individually optimized learning programs and training. The progress management unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which manages the progress of the learning programs and training. The evaluation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which provides periodic feedback and evaluation based on progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

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

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

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

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, progress management unit, and evaluation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect employee operation status and emotions, and the control unit 46A collects the data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and provides individually optimized learning programs and training. The progress management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which manages the progress of the learning programs and training. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which provides periodic feedback and evaluation based on progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the data collection unit, analysis unit, progress management unit, and evaluation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect employee operation status and emotions, and the control unit 46A collects the data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and provides individually optimized learning programs and training. The progress management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which manages the progress of the learning programs and training. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which provides periodic feedback and evaluation based on progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) The data collection department collects data such as the PC usage status of employees, An analysis unit analyzes the data collected by the aforementioned collection unit and provides individually optimized learning programs and training, A progress management unit manages the learning program and training progress provided by the aforementioned analysis unit, The system includes an evaluation unit that provides periodic feedback and evaluations based on the progress managed by the aforementioned progress management unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate employees' emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze employees' past operation history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting data, filtering is performed based on the employee's current projects and work responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We estimate employees' emotions and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, analyze employees' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates employees' emotions and adjusts the content of the learning program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail in the learning program based on the employee's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the employee's job category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates employees' emotions and adjusts the length of the learning program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of learning programs is determined based on the employee's work history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of the learning program is adjusted based on the employee's relevant work. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned progress management unit, Estimate employees' emotions and adjust progress management criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned progress management unit, When managing progress, improve the accuracy of progress management by considering the relationships between employees. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned progress management unit, When managing progress, employee attribute information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned progress management unit, The system estimates employees' emotions and adjusts the order in which progress management results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned progress management unit, When managing progress, take into account the geographical distribution of employees. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply and sanitation management unit, When managing progress, we improve the accuracy of progress management by referring to relevant literature used by employees. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, The system estimates employees' emotions and adjusts how evaluations are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During the evaluation process, past evaluation data is referenced to predict the current evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, During the evaluation process, different evaluation and analysis methods are applied to each employee's job category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, The system estimates employees' emotions and adjusts the importance of evaluations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During the evaluation process, we analyze changes in evaluations based on the employee's work history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, During the evaluation process, we analyze the evaluation by referring to relevant market data of the employees. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects data such as the PC usage status of employees, An analysis unit analyzes the data collected by the aforementioned collection unit and provides individually optimized learning programs and training, A progress management unit manages the learning program and training progress provided by the aforementioned analysis unit, The system includes an evaluation unit that provides periodic feedback and evaluations based on the progress managed by the aforementioned progress management unit. A system characterized by the following features.

2. The aforementioned collection unit is We estimate employees' emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze employees' past operation history and select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting data, filtering is performed based on the employee's current projects and work responsibilities. The system according to feature 1.

5. The aforementioned collection unit is We estimate employees' emotions and prioritize the data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of employees. The system according to feature 1.

7. The aforementioned collection unit is During data collection, analyze employees' social media activity and collect relevant data. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates employees' emotions and adjusts the content of the learning program based on those estimated emotions. The system according to feature 1.

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