system

Abstract

The system according to this embodiment aims to provide optimal training and learning plans based on employees' career goals and skill sets, and to manage progress and provide feedback in real time. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a progress unit, a feedback unit, and a project unit. The reception unit receives the employee's career goals, current skill set, job content, and available time as input. The generation unit analyzes the information received by the reception unit and generates an optimal training or learning plan. The progress unit manages progress based on the learning plan generated by the generation unit. The feedback unit provides real-time feedback based on the progress managed by the progress unit. The project unit provides a project-based learning plan based on the feedback provided by the feedback unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of 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]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide an optimal training or learning plan based on the career goals and skill sets of employees, and there is a problem that progress management and feedback are not sufficiently provided.

[0005] The system according to the embodiment aims to provide an optimal training or learning plan based on the career goals and skill sets of employees, and to perform progress management and feedback in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a progress unit, a feedback unit, and a project unit. The reception unit receives employee career goals, current skill sets, job duties, and available time as input. The generation unit analyzes the information received by the reception unit and generates an optimal training or learning plan. The progress unit manages progress based on the learning plan generated by the generation unit. The feedback unit provides real-time feedback based on the progress managed by the progress unit. The project unit provides a project-based learning plan based on the feedback provided by the feedback unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal training and learning plans based on employees' career goals and skill sets, and can manage progress and provide feedback in real time. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The generative AI agent according to an embodiment of the present invention is a system that recommends optimal training and learning for employees, taking into account their career goals, current skill set, job duties, and available time. This generative AI agent generates individual learning plans and provides total support, from progress management and motivation maintenance to test preparation. It also provides real-time feedback according to the learning progress to ensure effective learning. Furthermore, it provides project-based learning plans so that employees can acquire the necessary skills while working on actual projects, and supports practical skill development by providing real-time feedback and advice. For example, the generative AI agent accepts the employee's career goals, current skill set, job duties, and available time as input. Next, the generative AI agent analyzes this information and generates an optimal training or learning plan. For example, if an employee is aiming for career advancement, the generative AI agent proposes a training or learning plan tailored to that goal. It also provides a learning plan to acquire the necessary skills based on the employee's current skill set and job duties. The generative AI agent provides real-time feedback according to the learning progress. For example, if there are areas where the employee's understanding is insufficient as they progress through their learning, the generative AI agent proposes an additional learning plan to strengthen those areas. Furthermore, to maintain employee motivation, progress is managed and feedback is provided at the appropriate time. In addition, the generating AI agent provides project-based learning plans so that employees can acquire the necessary skills while working on actual projects. For example, when an employee joins a new project, it provides a learning plan to acquire the skills related to that project. The generating AI agent provides real-time feedback and advice to help employees acquire practical skills. In this way, the generating AI agent considers the employee's career goals, current skill set, job responsibilities, and available time to recommend the most suitable training and learning and generate individualized learning plans.Furthermore, it provides comprehensive support, from progress management and motivation maintenance to test preparation, and offers real-time feedback according to the learning progress. In addition, it provides project-based learning plans to support practical skill development. This allows employees to efficiently acquire the necessary skills and achieve career advancement. The generating AI agent considers the employee's career goals, current skill set, job responsibilities, and available time to recommend the most suitable training and learning, generate individualized learning plans, and provide comprehensive support, from progress management and motivation maintenance to test preparation.

[0029] The generation AI agent according to this embodiment comprises a reception unit, a generation unit, a progress unit, a feedback unit, and a project unit. The reception unit receives the employee's career goals, current skill set, job duties, and available time as input. For example, if an employee is aiming for career advancement, the reception unit can receive information tailored to that goal. The reception unit can also receive necessary information based on the employee's current skill set and job duties. Furthermore, the reception unit can receive optimal information considering the employee's available time. The generation unit analyzes the information received by the reception unit and generates an optimal training or learning plan. For example, the generation unit can generate an optimal training plan based on the employee's career goals. The generation unit can also generate a learning plan to acquire the necessary skills based on the employee's current skill set and job duties. Furthermore, the generation unit can generate an optimal learning plan considering the employee's available time. The progress unit manages progress based on the learning plan generated by the generation unit. For example, the progress unit can manage the employee's learning progress in real time. Furthermore, the progress department can manage progress to maintain employee motivation. In addition, the progress department can provide timely feedback according to the employee's learning progress. The feedback department provides real-time feedback based on the progress managed by the progress department. For example, if an employee has insufficient understanding as they progress through their learning, the feedback department can provide feedback to strengthen those areas. The feedback department can also provide timely feedback to maintain employee motivation. Furthermore, the feedback department can provide real-time feedback according to the employee's learning progress. The project department provides project-based learning plans based on the feedback provided by the feedback department. For example, when an employee joins a new project, the project department can provide a learning plan to acquire skills related to that project.Furthermore, the project department can provide project-based learning plans so that employees can acquire the necessary skills while working on actual projects. In addition, the project department can provide real-time feedback and advice to support employees in acquiring practical skills. As a result, the generating AI agent in this embodiment can recommend the most suitable training and learning, considering the employee's career goals, current skill set, job responsibilities, and available time, generate individual learning plans, and provide comprehensive support from progress management and motivation maintenance to test preparation.

[0030] The reception department receives input from employees regarding their career goals, current skill sets, job responsibilities, and available time. Specifically, if an employee is aiming for career advancement, it can receive information tailored to those goals. For example, if an employee aspires to a management position in the future, it can receive information on leadership and project management. The reception department can also receive necessary information based on the employee's current skill set and job responsibilities. For example, if an employee with programming skills wants to learn a new programming language, it can receive appropriate learning resources based on that skill set. Furthermore, the reception department can receive optimal information considering the employee's available time. For example, since full-time employees and part-time employees have different available time, it can receive learning plans tailored to each. The reception department centrally manages this information and builds a foundation for providing it to the generation and progress departments. This allows the reception department to play a crucial role in responding to the diverse needs of employees and generating individualized learning plans.

[0031] The generation unit analyzes the information received by the reception unit and generates optimal training and learning plans. Specifically, it can generate optimal training plans based on employees' career goals. For example, if an employee aims to become a data scientist, it will generate training plans related to data analysis and machine learning. The generation unit can also generate learning plans to acquire necessary skills based on an employee's current skill set and job responsibilities. For example, if an employee with programming skills wants to learn a new programming language, it will provide appropriate learning resources based on that skill set. Furthermore, the generation unit can generate optimal learning plans considering the time available to each employee. For example, full-time employees and part-time employees have different available time, so it will provide learning plans tailored to each. The generation unit uses AI to analyze this information and generate optimal learning plans. The AI ​​can suggest optimal learning plans based on past learning data and success stories. This allows the generation unit to play a crucial role in responding to the diverse needs of employees and generating individualized learning plans.

[0032] The Progress Department manages progress based on the learning plans generated by the Generation Department. Specifically, it can manage employees' learning progress in real time. For example, it can grasp the progress of employees as they progress through their learning in real time and provide support as needed. The Progress Department can also manage progress to maintain employee motivation. For example, if learning progress is behind schedule, it provides feedback at the appropriate time to support maintaining motivation. Furthermore, the Progress Department can provide feedback at the appropriate time according to the employee's learning progress. For example, if learning progress is on track, it provides feedback to move on to the next step, and if learning progress is behind schedule, it provides feedback for improvement. The Progress Department builds a foundation for centrally managing this information and providing it to the Feedback Department and Project Department. This allows the Progress Department to play a crucial role in managing employees' learning progress in real time and providing appropriate support.

[0033] The Feedback Department provides real-time feedback based on progress managed by the Progress Department. Specifically, if an employee's understanding is insufficient as they progress through their learning, the Feedback Department can provide feedback to strengthen those areas. For example, if an employee's understanding of a particular skill or knowledge is insufficient, the Feedback Department can provide additional learning resources or advice to reinforce that understanding. The Feedback Department can also provide feedback at the appropriate time to maintain employee motivation. For example, if learning progress is on track, the Feedback Department can provide encouraging messages to move on to the next step, and if learning progress is behind schedule, it can provide specific advice for improvement. Furthermore, the Feedback Department can provide real-time feedback according to the employee's learning progress. For example, if learning progress is behind schedule, the Feedback Department can provide immediate feedback and support for improvement. The Feedback Department will establish a platform to centrally manage this information and provide it to the Project Department. This will enable the Feedback Department to play a crucial role in understanding employee learning progress in real time and providing appropriate feedback.

[0034] The Project Department provides project-based learning plans based on feedback provided by the Feedback Department. Specifically, when employees participate in a new project, they can be provided with learning plans to acquire project-related skills. For example, they can provide learning resources to acquire specific technologies and knowledge required for the new project. The Project Department can also provide project-based learning plans so that employees can acquire the necessary skills while working on actual projects. For example, they can provide learning plans to acquire the necessary skills and knowledge step by step as the project progresses. Furthermore, the Project Department can provide real-time feedback and advice to support employees in acquiring practical skills. For example, they can provide appropriate advice and support for challenges and problems that arise during the progress of the project, helping employees acquire practical skills. The Project Department centrally manages this information and builds a foundation to support employee learning and project progress. In this way, the Project Department can play a crucial role in helping employees acquire practical skills.

[0035] The generation unit can generate optimal training and learning plans by considering employees' career goals, current skill sets, job duties, and available time. For example, the generation unit can generate an optimal training plan based on an employee's career goals. The generation unit can also generate a learning plan to acquire necessary skills based on an employee's current skill set and job duties. The generation unit can also generate an optimal learning plan by considering an employee's available time. This allows the generation unit to generate optimal training and learning plans by considering employees' career goals, current skill sets, job duties, and available time. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can generate training and learning plans using a generation AI model that takes an employee's career goals, current skill set, job duties, and available time as input and outputs an optimal training or learning plan.

[0036] The progress unit can manage progress based on the generated learning plan. For example, the progress unit can manage the learning progress of employees in real time. For example, the progress unit can also manage progress to maintain employee motivation. For example, the progress unit can provide feedback at the appropriate time according to the learning progress of employees. This allows the progress unit to manage progress based on the generated learning plan. Some or all of the above processes in the progress unit may be performed using AI, for example, or not using AI. For example, the progress unit can input employee learning progress data into AI and have the AI ​​perform optimization of progress management.

[0037] The feedback unit can provide real-time feedback based on progress. For example, if an employee has insufficient understanding as they progress through their learning, the feedback unit can provide feedback to reinforce those areas. The feedback unit can also provide feedback at the appropriate time to maintain employee motivation. The feedback unit can also provide real-time feedback according to the employee's learning progress. This allows the feedback unit to provide real-time feedback based on progress. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input progress data into AI and have the AI ​​provide real-time feedback.

[0038] The project department can provide project-based learning plans and offer real-time feedback and advice. For example, when an employee joins a new project, the project department can provide a learning plan to help them acquire the skills relevant to that project. The project department can also provide project-based learning plans so that employees can acquire the necessary skills while working on actual projects. The project department can, for example, provide real-time feedback and advice to help employees acquire practical skills. This allows the project department to provide project-based learning plans and offer real-time feedback and advice. Some or all of the processes described above in the project department may be performed using AI, for example, or not. For example, the project department can input project progress data into AI and have the AI ​​provide real-time feedback and advice.

[0039] The reception desk can analyze the user's past career goals and skill set history and select the optimal input method. For example, the reception desk can automatically display relevant skill sets as input candidates, referencing the career goals the user has set in the past. For example, the reception desk can analyze the user's past skill set history and suggest the most efficient input method. For example, the reception desk can select a method that prioritizes inputting the necessary skill sets based on the user's career goals. In this way, the reception desk can select the optimal input method by analyzing the user's past career goals and skill set history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past career goals and skill set history data into AI and have the AI ​​select the optimal input method.

[0040] The reception unit can filter information based on the user's current projects and areas of interest when it is entered. For example, the reception unit prioritizes inputting information related to the project the user is currently working on. For example, the reception unit filters and inputs relevant information based on the user's areas of interest. For example, the reception unit appropriately filters and inputs necessary information according to the progress of the user's project. In this way, the reception unit can efficiently input highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's current project and area of ​​interest data into AI and have the AI ​​perform the information filtering.

[0041] The reception unit can prioritize inputting highly relevant information by considering the user's geographical location when inputting information. For example, if the user is in a specific region, the reception unit will prioritize inputting information related to that region. For example, the reception unit will filter and input relevant information based on the user's current location. For example, if the user is on the move, the reception unit will input the most appropriate information according to the user's current location. In this way, the reception unit can efficiently input information by prioritizing the input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into AI and have the AI ​​perform the priority input of highly relevant information.

[0042] The reception unit can analyze the user's social media activity and input relevant information when information is entered. For example, the reception unit can analyze the content of the user's social media posts and input relevant information. For example, the reception unit can input relevant information based on the user's social media followers and the accounts they follow. For example, the reception unit can analyze the user's social media activity history and input the most relevant information. In this way, the reception unit can efficiently input relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​perform the input of relevant information.

[0043] The generation unit can adjust the level of detail of a learning plan based on the importance of the career goals when generating the learning plan. For example, if the career goals are high, the generation unit generates a detailed learning plan. For example, if the career goals are of moderate importance, the generation unit generates a learning plan with appropriate level of detail. For example, if the career goals are low, the generation unit generates a concise learning plan. In this way, the generation unit can provide the user with the optimal learning plan by adjusting the level of detail of the plan based on the importance of the career goals. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input career goal importance data into the generation AI and have the generation AI perform the adjustment of the level of detail of the plan.

[0044] The generation unit can apply different generation algorithms depending on the skill set category when generating a learning plan. For example, in the case of technical skills, the generation unit applies a generation algorithm specialized in technical content. For example, in the case of soft skills, the generation unit applies a generation algorithm specialized in communication and leadership. For example, in the case of management skills, the generation unit applies a generation algorithm specialized in project management and team leadership. In this way, the generation unit can provide the user with the optimal learning plan by applying different generation algorithms depending on the skill set category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input skill set category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0045] The generation unit can determine the priority of learning plans based on the target achievement date of career goals when generating learning plans. For example, if the target achievement date of a career goal is approaching, the generation unit will prioritize generating learning plans. For example, if the target achievement date of a career goal is in the middle of the process, the generation unit will generate learning plans with a moderate priority. For example, if the target achievement date of a career goal is far off, the generation unit will postpone generating learning plans. In this way, the generation unit can provide the user with the optimal learning plan by determining the priority of plans based on the target achievement date of career goals. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input career goal achievement date data into a generation AI and have the generation AI perform the determination of plan priorities.

[0046] The generation unit can adjust the order of learning plans based on the relevance of skill sets when generating learning plans. For example, the generation unit prioritizes generating learning plans when skill sets are highly relevant. For example, the generation unit generates learning plans in an appropriate order when skill sets are moderately relevant. For example, the generation unit postpones generating learning plans when skill sets are less relevant. In this way, the generation unit can provide the user with the optimal learning plan by adjusting the order of plans based on the relevance of skill sets. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input skill set relevance data into a generation AI and have the generation AI perform the adjustment of the order of plans.

[0047] The progress unit can optimize its management algorithm by referring to past progress data during progress management. For example, the progress unit can analyze past progress data and apply the optimal management algorithm. For example, the progress unit can predict delays in progress from past progress data and adjust the management algorithm. For example, the progress unit can optimize the frequency and method of progress management based on past progress data. In this way, the progress unit can efficiently manage progress by optimizing the management algorithm by referring to past progress data. Some or all of the above processes in the progress unit may be performed using AI, for example, or without AI. For example, the progress unit can input past progress data into AI and have AI perform the optimization of the management algorithm.

[0048] The progress unit can customize its management methods based on the user's work content during progress management. For example, the progress unit can select the optimal progress management method according to the user's work content. For example, the progress unit can adjust the frequency and method of progress management based on the user's work content. For example, the progress unit can customize the progress management algorithm according to the user's work content. In this way, the progress unit can efficiently manage progress by customizing its management methods based on the user's work content. Some or all of the above processes in the progress unit may be performed using AI, for example, or without AI. For example, the progress unit can input user work content data into AI and have AI perform the customization of the management method.

[0049] The progress unit can select the optimal management method when managing progress, taking into account the user's geographical location information. For example, if the user is in a specific region, the progress unit will select a progress management method relevant to that region. For example, the progress unit will select the optimal progress management method based on the user's current location. For example, if the user is on the move, the progress unit will select the optimal progress management method according to the user's current location. In this way, the progress unit can efficiently manage progress by selecting the optimal management method while taking into account the user's geographical location information. Some or all of the above processing in the progress unit may be performed using AI, for example, or without AI. For example, the progress unit can input the user's geographical location data into AI and have AI select the optimal management method.

[0050] The progress unit can analyze a user's social media activity and propose management methods during progress management. For example, the progress unit can analyze the content of a user's social media posts and propose the optimal progress management method. For example, the progress unit can propose the optimal progress management method based on a user's social media followers and followed accounts. For example, the progress unit can analyze a user's social media activity history and propose the optimal progress management method. In this way, the progress unit can efficiently manage progress by analyzing the user's social media activity and proposing management methods. Some or all of the above processes in the progress unit may be performed using AI, for example, or without AI. For example, the progress unit can input user social media activity data into AI and have the AI ​​execute the proposal of management methods.

[0051] The feedback unit can adjust the level of detail of the feedback based on the importance of the progress when providing feedback. For example, if the progress is important, the feedback unit will provide detailed feedback. For example, if the progress is moderate, the feedback unit will provide feedback with a moderate level of detail. For example, if the progress is low, the feedback unit will provide concise feedback. In this way, the feedback unit can provide the user with the best possible feedback by adjusting the level of detail of the feedback based on the importance of the progress. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input progress importance data into AI and have AI perform the adjustment of the level of detail of the feedback.

[0052] The feedback unit can apply different feedback algorithms depending on the category of progress when providing feedback. For example, the feedback unit can apply a feedback algorithm specialized in technical content to the progress of technical skills. For example, the feedback unit can apply a feedback algorithm specialized in communication and leadership to the progress of soft skills. For example, the feedback unit can apply a feedback algorithm specialized in project management and team leadership to the progress of management skills. In this way, the feedback unit can provide the user with the most appropriate feedback by applying different feedback algorithms depending on the category of progress. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input progress category data into AI and have AI perform the application of the feedback algorithm.

[0053] The feedback unit can determine the priority of feedback based on the completion date of the progress status when providing feedback. For example, the feedback unit will provide feedback preferentially if the completion date of the progress status is approaching. For example, the feedback unit will provide feedback with a moderate priority if the completion date of the progress status is in the middle. For example, the feedback unit will postpone providing feedback if the completion date of the progress status is far off. In this way, the feedback unit can provide the user with the best possible feedback by determining the priority of feedback based on the completion date of the progress status. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input progress status completion date data into AI and have the AI ​​perform the determination of feedback priority.

[0054] The feedback unit can adjust the order of feedback based on the relevance of the progress status when providing feedback. For example, the feedback unit will prioritize providing feedback when the relevance of the progress status is high. For example, the feedback unit will provide feedback in an appropriate order when the relevance of the progress status is moderate. For example, the feedback unit will postpone providing feedback when the relevance of the progress status is low. In this way, the feedback unit can provide the user with the best possible feedback by adjusting the order of feedback based on the relevance of the progress status. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input progress status relevance data into AI and have AI perform the adjustment of the feedback order.

[0055] The project department can adjust the level of detail of project-based learning plans based on the importance of the project. For example, if the project is highly important, the project department will provide a detailed project-based learning plan. If the project is of moderate importance, the project department will provide a project-based learning plan with appropriate detail. If the project is of low importance, the project department will provide a concise project-based learning plan. In this way, the project department can provide the optimal learning plan for the user by adjusting the level of detail of the plan based on the importance of the project. Some or all of the above processing in the project department may be performed using AI, for example, or not using AI. For example, the project department can input project importance data into AI and have the AI ​​perform the adjustment of the level of detail of the plan.

[0056] The Project Department can apply different plan generation algorithms depending on the project category when providing project-based learning plans. For example, in the case of a technical project, the Project Department can apply a plan generation algorithm specialized in technical content. For example, in the case of a management project, the Project Department can apply a plan generation algorithm specialized in project management and team leadership. For example, in the case of a creative project, the Project Department can apply a plan generation algorithm specialized in creative content. In this way, the Project Department can provide the optimal learning plan for the user by applying different plan generation algorithms depending on the project category. Some or all of the above processing in the Project Department may be performed using AI, for example, or not using AI. For example, the Project Department can input project category data into AI and have the AI ​​perform the application of the plan generation algorithm.

[0057] The project department can prioritize project-based learning plans based on the project's completion date when providing them. For example, if the project's completion date is approaching, the project department will prioritize providing project-based learning plans. If the project's completion date is in the middle, the project department will provide project-based learning plans with a moderate priority. If the project's completion date is far off, the project department will postpone providing project-based learning plans. In this way, the project department can provide the optimal learning plan for the user by prioritizing plans based on the project's completion date. Some or all of the above processing in the project department may be performed using AI, for example, or not. For example, the project department can input project completion date data into AI and have the AI ​​perform the determination of plan priorities.

[0058] The project department can adjust the order of project-based learning plans based on project relevance when providing them. For example, if a project is highly relevant, the project department will prioritize providing the project-based learning plan. If a project is moderately relevant, the project department will provide the project-based learning plan in a suitable order. If a project is less relevant, the project department will postpone providing the project-based learning plan. In this way, the project department can provide the optimal learning plan for the user by adjusting the order of plans based on project relevance. Some or all of the above processing in the project department may be performed using AI, for example, or not using AI. For example, the project department can input project relevance data into AI and have the AI ​​perform the adjustment of the plan order.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception desk can analyze a user's past learning history and propose the optimal learning plan. For example, based on a user's history of past training and learning plans, it can propose a learning plan that best suits their current career goals. The reception desk can also evaluate a user's past learning achievements and suggest effective learning methods. Furthermore, based on a user's past learning history, the reception desk can predict their learning progress and provide feedback at the appropriate time.

[0061] The generation unit can analyze the user's learning style and generate an optimal learning plan. For example, if the user prefers visual learning, it can generate a learning plan that includes a lot of visual content. Similarly, if the user prefers auditory learning, it can generate a learning plan that includes a lot of audio content. Furthermore, if the user prefers hands-on learning, it can generate a learning plan that includes a lot of practical exercises.

[0062] The progress tracking system can visualize users' learning progress and provide tools to maintain motivation. For example, it can display users' learning progress in graphs and charts, allowing them to visually understand their progress. It can also display users' progress toward learning goals in real time and provide feedback to maintain motivation. Furthermore, it can provide rewards and incentives at appropriate times based on users' learning progress.

[0063] The feedback unit can evaluate the user's learning outcomes and provide feedback that specifically indicates areas for improvement. For example, the feedback unit can evaluate the user's learning outcomes through tests and quizzes and identify areas where understanding is insufficient. Furthermore, the feedback unit can provide feedback that specifically indicates areas for improvement based on the user's learning outcomes. In addition, the feedback unit can compare the user's learning outcomes with those of other users and provide a relative evaluation.

[0064] The project team can monitor the user's project progress in real time and provide support as needed. For example, the project team can monitor the user's project progress in real time and issue alerts if progress is behind schedule. The project team can also provide necessary resources and support based on the user's project progress. Furthermore, the project team can share the user's project progress with other team members and provide support for collaborative problem-solving.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives input from employees regarding their career goals, current skill sets, job responsibilities, and available time. For example, if an employee is aiming for career advancement, the system can receive information tailored to that goal. It can also receive necessary information based on the employee's current skill set and job responsibilities. Furthermore, it can receive the most relevant information considering the employee's available time. Step 2: The generation unit analyzes the information received by the reception unit and generates the optimal training or learning plan. For example, it can generate an optimal training plan based on the employee's career goals. It can also generate a learning plan to acquire the necessary skills based on the employee's current skill set and job responsibilities. Furthermore, it can generate an optimal learning plan that takes into account the employee's available time. Step 3: The progress unit manages progress based on the learning plan generated by the generation unit. For example, it can manage employees' learning progress in real time. It can also manage progress to maintain employee motivation. Furthermore, it can provide feedback at the appropriate time according to the employee's learning progress. Step 4: The Feedback Department provides real-time feedback based on the progress managed by the Progress Department. For example, if an employee has insufficient understanding as they progress through their learning, the Feedback Department can provide feedback to strengthen that area. They can also provide feedback at the appropriate time to maintain employee motivation. Furthermore, they can provide real-time feedback according to the employee's learning progress. Step 5: The Project Department provides project-based learning plans based on the feedback provided by the Feedback Department. For example, when an employee joins a new project, the Project Department can provide a learning plan to help them acquire the skills relevant to that project. Alternatively, they can provide project-based learning plans to help employees acquire the necessary skills while working on actual projects. Furthermore, they can provide real-time feedback and advice to support employees in developing practical skills.

[0067] (Example of form 2) The generative AI agent according to an embodiment of the present invention is a system that recommends optimal training and learning for employees, taking into account their career goals, current skill set, job duties, and available time. This generative AI agent generates individual learning plans and provides total support, from progress management and motivation maintenance to test preparation. It also provides real-time feedback according to the learning progress to ensure effective learning. Furthermore, it provides project-based learning plans so that employees can acquire the necessary skills while working on actual projects, and supports practical skill development by providing real-time feedback and advice. For example, the generative AI agent accepts the employee's career goals, current skill set, job duties, and available time as input. Next, the generative AI agent analyzes this information and generates an optimal training or learning plan. For example, if an employee is aiming for career advancement, the generative AI agent proposes a training or learning plan tailored to that goal. It also provides a learning plan to acquire the necessary skills based on the employee's current skill set and job duties. The generative AI agent provides real-time feedback according to the learning progress. For example, if there are areas where the employee's understanding is insufficient as they progress through their learning, the generative AI agent proposes an additional learning plan to strengthen those areas. Furthermore, to maintain employee motivation, progress is managed and feedback is provided at the appropriate time. In addition, the generating AI agent provides project-based learning plans so that employees can acquire the necessary skills while working on actual projects. For example, when an employee joins a new project, it provides a learning plan to acquire the skills related to that project. The generating AI agent provides real-time feedback and advice to help employees acquire practical skills. In this way, the generating AI agent considers the employee's career goals, current skill set, job responsibilities, and available time to recommend the most suitable training and learning and generate individualized learning plans.Furthermore, it provides comprehensive support, from progress management and motivation maintenance to test preparation, and offers real-time feedback according to the learning progress. In addition, it provides project-based learning plans to support practical skill development. This allows employees to efficiently acquire the necessary skills and achieve career advancement. The generating AI agent considers the employee's career goals, current skill set, job responsibilities, and available time to recommend the most suitable training and learning, generate individualized learning plans, and provide comprehensive support, from progress management and motivation maintenance to test preparation.

[0068] The generation AI agent according to this embodiment comprises a reception unit, a generation unit, a progress unit, a feedback unit, and a project unit. The reception unit receives the employee's career goals, current skill set, job duties, and available time as input. For example, if an employee is aiming for career advancement, the reception unit can receive information tailored to that goal. The reception unit can also receive necessary information based on the employee's current skill set and job duties. Furthermore, the reception unit can receive optimal information considering the employee's available time. The generation unit analyzes the information received by the reception unit and generates an optimal training or learning plan. For example, the generation unit can generate an optimal training plan based on the employee's career goals. The generation unit can also generate a learning plan to acquire the necessary skills based on the employee's current skill set and job duties. Furthermore, the generation unit can generate an optimal learning plan considering the employee's available time. The progress unit manages progress based on the learning plan generated by the generation unit. For example, the progress unit can manage the employee's learning progress in real time. Furthermore, the progress department can manage progress to maintain employee motivation. In addition, the progress department can provide timely feedback according to the employee's learning progress. The feedback department provides real-time feedback based on the progress managed by the progress department. For example, if an employee has insufficient understanding as they progress through their learning, the feedback department can provide feedback to strengthen those areas. The feedback department can also provide timely feedback to maintain employee motivation. Furthermore, the feedback department can provide real-time feedback according to the employee's learning progress. The project department provides project-based learning plans based on the feedback provided by the feedback department. For example, when an employee joins a new project, the project department can provide a learning plan to acquire skills related to that project.Furthermore, the project department can provide project-based learning plans so that employees can acquire the necessary skills while working on actual projects. In addition, the project department can provide real-time feedback and advice to support employees in acquiring practical skills. As a result, the generating AI agent in this embodiment can recommend the most suitable training and learning, considering the employee's career goals, current skill set, job responsibilities, and available time, generate individual learning plans, and provide comprehensive support from progress management and motivation maintenance to test preparation.

[0069] The reception department receives input from employees regarding their career goals, current skill sets, job responsibilities, and available time. Specifically, if an employee is aiming for career advancement, it can receive information tailored to those goals. For example, if an employee aspires to a management position in the future, it can receive information on leadership and project management. The reception department can also receive necessary information based on the employee's current skill set and job responsibilities. For example, if an employee with programming skills wants to learn a new programming language, it can receive appropriate learning resources based on that skill set. Furthermore, the reception department can receive optimal information considering the employee's available time. For example, since full-time employees and part-time employees have different available time, it can receive learning plans tailored to each. The reception department centrally manages this information and builds a foundation for providing it to the generation and progress departments. This allows the reception department to play a crucial role in responding to the diverse needs of employees and generating individualized learning plans.

[0070] The generation unit analyzes the information received by the reception unit and generates optimal training and learning plans. Specifically, it can generate optimal training plans based on employees' career goals. For example, if an employee aims to become a data scientist, it will generate training plans related to data analysis and machine learning. The generation unit can also generate learning plans to acquire necessary skills based on an employee's current skill set and job responsibilities. For example, if an employee with programming skills wants to learn a new programming language, it will provide appropriate learning resources based on that skill set. Furthermore, the generation unit can generate optimal learning plans considering the time available to each employee. For example, full-time employees and part-time employees have different available time, so it will provide learning plans tailored to each. The generation unit uses AI to analyze this information and generate optimal learning plans. The AI ​​can suggest optimal learning plans based on past learning data and success stories. This allows the generation unit to play a crucial role in responding to the diverse needs of employees and generating individualized learning plans.

[0071] The Progress Department manages progress based on the learning plans generated by the Generation Department. Specifically, it can manage employees' learning progress in real time. For example, it can grasp the progress of employees as they progress through their learning in real time and provide support as needed. The Progress Department can also manage progress to maintain employee motivation. For example, if learning progress is behind schedule, it provides feedback at the appropriate time to support maintaining motivation. Furthermore, the Progress Department can provide feedback at the appropriate time according to the employee's learning progress. For example, if learning progress is on track, it provides feedback to move on to the next step, and if learning progress is behind schedule, it provides feedback for improvement. The Progress Department builds a foundation for centrally managing this information and providing it to the Feedback Department and Project Department. This allows the Progress Department to play a crucial role in managing employees' learning progress in real time and providing appropriate support.

[0072] The Feedback Department provides real-time feedback based on progress managed by the Progress Department. Specifically, if an employee's understanding is insufficient as they progress through their learning, the Feedback Department can provide feedback to strengthen those areas. For example, if an employee's understanding of a particular skill or knowledge is insufficient, the Feedback Department can provide additional learning resources or advice to reinforce that understanding. The Feedback Department can also provide feedback at the appropriate time to maintain employee motivation. For example, if learning progress is on track, the Feedback Department can provide encouraging messages to move on to the next step, and if learning progress is behind schedule, it can provide specific advice for improvement. Furthermore, the Feedback Department can provide real-time feedback according to the employee's learning progress. For example, if learning progress is behind schedule, the Feedback Department can provide immediate feedback and support for improvement. The Feedback Department will establish a platform to centrally manage this information and provide it to the Project Department. This will enable the Feedback Department to play a crucial role in understanding employee learning progress in real time and providing appropriate feedback.

[0073] The Project Department provides project-based learning plans based on feedback provided by the Feedback Department. Specifically, when employees participate in a new project, they can be provided with learning plans to acquire project-related skills. For example, they can provide learning resources to acquire specific technologies and knowledge required for the new project. The Project Department can also provide project-based learning plans so that employees can acquire the necessary skills while working on actual projects. For example, they can provide learning plans to acquire the necessary skills and knowledge step by step as the project progresses. Furthermore, the Project Department can provide real-time feedback and advice to support employees in acquiring practical skills. For example, they can provide appropriate advice and support for challenges and problems that arise during the progress of the project, helping employees acquire practical skills. The Project Department centrally manages this information and builds a foundation to support employee learning and project progress. In this way, the Project Department can play a crucial role in helping employees acquire practical skills.

[0074] The generation unit can generate optimal training and learning plans by considering employees' career goals, current skill sets, job duties, and available time. For example, the generation unit can generate an optimal training plan based on an employee's career goals. The generation unit can also generate a learning plan to acquire necessary skills based on an employee's current skill set and job duties. The generation unit can also generate an optimal learning plan by considering an employee's available time. This allows the generation unit to generate optimal training and learning plans by considering employees' career goals, current skill sets, job duties, and available time. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can generate training and learning plans using a generation AI model that takes an employee's career goals, current skill set, job duties, and available time as input and outputs an optimal training or learning plan.

[0075] The progress unit can manage progress based on the generated learning plan. For example, the progress unit can manage the learning progress of employees in real time. For example, the progress unit can also manage progress to maintain employee motivation. For example, the progress unit can provide feedback at the appropriate time according to the learning progress of employees. This allows the progress unit to manage progress based on the generated learning plan. Some or all of the above processes in the progress unit may be performed using AI, for example, or not using AI. For example, the progress unit can input employee learning progress data into AI and have the AI ​​perform optimization of progress management.

[0076] The feedback unit can provide real-time feedback based on progress. For example, if an employee has insufficient understanding as they progress through their learning, the feedback unit can provide feedback to reinforce those areas. The feedback unit can also provide feedback at the appropriate time to maintain employee motivation. The feedback unit can also provide real-time feedback according to the employee's learning progress. This allows the feedback unit to provide real-time feedback based on progress. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input progress data into AI and have the AI ​​provide real-time feedback.

[0077] The project department can provide project-based learning plans and offer real-time feedback and advice. For example, when an employee joins a new project, the project department can provide a learning plan to help them acquire the skills relevant to that project. The project department can also provide project-based learning plans so that employees can acquire the necessary skills while working on actual projects. The project department can, for example, provide real-time feedback and advice to help employees acquire practical skills. This allows the project department to provide project-based learning plans and offer real-time feedback and advice. Some or all of the processes described above in the project department may be performed using AI, for example, or not. For example, the project department can input project progress data into AI and have the AI ​​provide real-time feedback and advice.

[0078] The reception desk can estimate the user's emotions and adjust the timing of information input based on the estimated emotions. For example, if the user is stressed, the reception desk can delay the input timing to provide time for relaxation. For example, if the user is concentrating, the reception desk can speed up the input timing to allow for efficient information input. For example, if the user is tired, the reception desk can adjust the input timing to allow for breaks. In this way, the reception desk can reduce user stress and enable efficient information input by adjusting the timing of information input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user emotion data into AI and have the AI ​​adjust the timing of information input.

[0079] The reception desk can analyze the user's past career goals and skill set history and select the optimal input method. For example, the reception desk can automatically display relevant skill sets as input candidates, referencing the career goals the user has set in the past. For example, the reception desk can analyze the user's past skill set history and suggest the most efficient input method. For example, the reception desk can select a method that prioritizes inputting the necessary skill sets based on the user's career goals. In this way, the reception desk can select the optimal input method by analyzing the user's past career goals and skill set history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past career goals and skill set history data into AI and have the AI ​​select the optimal input method.

[0080] The reception unit can filter information based on the user's current projects and areas of interest when it is entered. For example, the reception unit prioritizes inputting information related to the project the user is currently working on. For example, the reception unit filters and inputs relevant information based on the user's areas of interest. For example, the reception unit appropriately filters and inputs necessary information according to the progress of the user's project. In this way, the reception unit can efficiently input highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's current project and area of ​​interest data into AI and have the AI ​​perform the information filtering.

[0081] The reception desk can estimate the user's emotions and determine the priority of the information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk will adjust the priority by delaying less important information. For example, if the user is relaxed, the reception desk will prioritize the input of highly important information. For example, if the user is in a hurry, the reception desk will prioritize the input of the most important information. In this way, the reception desk can efficiently input important information by determining the priority of the information to be entered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user emotion data into an AI and have the AI ​​perform the determination of information priority.

[0082] The reception unit can prioritize inputting highly relevant information by considering the user's geographical location when inputting information. For example, if the user is in a specific region, the reception unit will prioritize inputting information related to that region. For example, the reception unit will filter and input relevant information based on the user's current location. For example, if the user is on the move, the reception unit will input the most appropriate information according to the user's current location. In this way, the reception unit can efficiently input information by prioritizing the input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into AI and have the AI ​​perform the priority input of highly relevant information.

[0083] The reception unit can analyze the user's social media activity and input relevant information when information is entered. For example, the reception unit can analyze the content of the user's social media posts and input relevant information. For example, the reception unit can input relevant information based on the user's social media followers and the accounts they follow. For example, the reception unit can analyze the user's social media activity history and input the most relevant information. In this way, the reception unit can efficiently input relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​perform the input of relevant information.

[0084] The generation unit can estimate the user's emotions and adjust the presentation of the learning plan based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a learning plan that includes detailed explanations. If the user is in a hurry, the generation unit generates a concise learning plan that gets straight to the point. If the user is excited, the generation unit generates a learning plan with visually stimulating effects. In this way, the generation unit can provide the user with the optimal learning plan by adjusting the presentation of the learning plan based on the user'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 generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the learning plan.

[0085] The generation unit can adjust the level of detail of a learning plan based on the importance of the career goals when generating the learning plan. For example, if the career goals are high, the generation unit generates a detailed learning plan. For example, if the career goals are of moderate importance, the generation unit generates a learning plan with appropriate level of detail. For example, if the career goals are low, the generation unit generates a concise learning plan. In this way, the generation unit can provide the user with the optimal learning plan by adjusting the level of detail of the plan based on the importance of the career goals. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input career goal importance data into the generation AI and have the generation AI perform the adjustment of the level of detail of the plan.

[0086] The generation unit can apply different generation algorithms depending on the skill set category when generating a learning plan. For example, in the case of technical skills, the generation unit applies a generation algorithm specialized in technical content. For example, in the case of soft skills, the generation unit applies a generation algorithm specialized in communication and leadership. For example, in the case of management skills, the generation unit applies a generation algorithm specialized in project management and team leadership. In this way, the generation unit can provide the user with the optimal learning plan by applying different generation algorithms depending on the skill set category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input skill set category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0087] The generation unit can estimate the user's emotions and adjust the length of the learning plan based on the estimated emotions. For example, if the user is in a hurry, the generation unit generates a short, concise learning plan. If the user is relaxed, the generation unit generates a longer learning plan with detailed explanations. If the user is excited, the generation unit generates a learning plan with visually stimulating effects. In this way, the generation unit can provide the user with the optimal learning plan by adjusting the length of the learning plan based on the user'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 generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the length of the learning plan.

[0088] The generation unit can determine the priority of learning plans based on the target achievement date of career goals when generating learning plans. For example, if the target achievement date of a career goal is approaching, the generation unit will prioritize generating learning plans. For example, if the target achievement date of a career goal is in the middle of the process, the generation unit will generate learning plans with a moderate priority. For example, if the target achievement date of a career goal is far off, the generation unit will postpone generating learning plans. In this way, the generation unit can provide the user with the optimal learning plan by determining the priority of plans based on the target achievement date of career goals. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input career goal achievement date data into a generation AI and have the generation AI perform the determination of plan priorities.

[0089] The generation unit can adjust the order of learning plans based on the relevance of skill sets when generating learning plans. For example, the generation unit prioritizes generating learning plans when skill sets are highly relevant. For example, the generation unit generates learning plans in an appropriate order when skill sets are moderately relevant. For example, the generation unit postpones generating learning plans when skill sets are less relevant. In this way, the generation unit can provide the user with the optimal learning plan by adjusting the order of plans based on the relevance of skill sets. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input skill set relevance data into a generation AI and have the generation AI perform the adjustment of the order of plans.

[0090] The progress unit can estimate the user's emotions and adjust the progress management method based on the estimated user emotions. For example, if the user is stressed, the progress unit can reduce the frequency of progress management to provide time for relaxation. For example, if the user is focused, the progress unit can increase the frequency of progress management to manage progress efficiently. For example, if the user is tired, the progress unit can adjust the frequency of progress management to include breaks. In this way, the progress unit can reduce user stress and manage progress efficiently by adjusting the progress management method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress unit may be performed using AI, for example, or not using AI. For example, the progress unit can input user emotion data into AI and have the AI ​​perform the adjustment of the progress management method.

[0091] The progress unit can optimize its management algorithm by referring to past progress data during progress management. For example, the progress unit can analyze past progress data and apply the optimal management algorithm. For example, the progress unit can predict delays in progress from past progress data and adjust the management algorithm. For example, the progress unit can optimize the frequency and method of progress management based on past progress data. In this way, the progress unit can efficiently manage progress by optimizing the management algorithm by referring to past progress data. Some or all of the above processes in the progress unit may be performed using AI, for example, or without AI. For example, the progress unit can input past progress data into AI and have AI perform the optimization of the management algorithm.

[0092] The progress unit can customize its management methods based on the user's work content during progress management. For example, the progress unit can select the optimal progress management method according to the user's work content. For example, the progress unit can adjust the frequency and method of progress management based on the user's work content. For example, the progress unit can customize the progress management algorithm according to the user's work content. In this way, the progress unit can efficiently manage progress by customizing its management methods based on the user's work content. Some or all of the above processes in the progress unit may be performed using AI, for example, or without AI. For example, the progress unit can input user work content data into AI and have AI perform the customization of the management method.

[0093] The progress unit can estimate the user's emotions and determine the priority of progress management based on the estimated emotions. For example, if the user is stressed, the progress unit will adjust the priority by postponing less important progress management tasks. For example, if the user is relaxed, the progress unit will prioritize high-priority progress management tasks. For example, if the user is in a hurry, the progress unit will prioritize the most important progress management tasks. In this way, the progress unit can efficiently perform important progress management tasks by determining the priority of progress management tasks based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress unit may be performed using AI, for example, or not using AI. For example, the progress unit can input user emotion data into an AI and have the AI ​​perform the determination of progress management priorities.

[0094] The progress unit can select the optimal management method when managing progress, taking into account the user's geographical location information. For example, if the user is in a specific region, the progress unit will select a progress management method relevant to that region. For example, the progress unit will select the optimal progress management method based on the user's current location. For example, if the user is on the move, the progress unit will select the optimal progress management method according to the user's current location. In this way, the progress unit can efficiently manage progress by selecting the optimal management method while taking into account the user's geographical location information. Some or all of the above processing in the progress unit may be performed using AI, for example, or without AI. For example, the progress unit can input the user's geographical location data into AI and have AI select the optimal management method.

[0095] The progress unit can analyze a user's social media activity and propose management methods during progress management. For example, the progress unit can analyze the content of a user's social media posts and propose the optimal progress management method. For example, the progress unit can propose the optimal progress management method based on a user's social media followers and followed accounts. For example, the progress unit can analyze a user's social media activity history and propose the optimal progress management method. In this way, the progress unit can efficiently manage progress by analyzing the user's social media activity and proposing management methods. Some or all of the above processes in the progress unit may be performed using AI, for example, or without AI. For example, the progress unit can input user social media activity data into AI and have the AI ​​execute the proposal of management methods.

[0096] The feedback unit can estimate the user's emotions and adjust the way it presents the feedback based on those emotions. For example, if the user is relaxed, the feedback unit provides detailed feedback. If the user is in a hurry, the feedback unit provides concise, to-the-point feedback. If the user is excited, the feedback unit provides feedback with visually stimulating effects. In this way, the feedback unit can provide the optimal feedback for the user by adjusting the way it presents the feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into an AI and have the AI ​​adjust the way it presents the feedback.

[0097] The feedback unit can adjust the level of detail of the feedback based on the importance of the progress when providing feedback. For example, if the progress is important, the feedback unit will provide detailed feedback. For example, if the progress is moderate, the feedback unit will provide feedback with a moderate level of detail. For example, if the progress is low, the feedback unit will provide concise feedback. In this way, the feedback unit can provide the user with the best possible feedback by adjusting the level of detail of the feedback based on the importance of the progress. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input progress importance data into AI and have AI perform the adjustment of the level of detail of the feedback.

[0098] The feedback unit can apply different feedback algorithms depending on the category of progress when providing feedback. For example, the feedback unit can apply a feedback algorithm specialized in technical content to the progress of technical skills. For example, the feedback unit can apply a feedback algorithm specialized in communication and leadership to the progress of soft skills. For example, the feedback unit can apply a feedback algorithm specialized in project management and team leadership to the progress of management skills. In this way, the feedback unit can provide the user with the most appropriate feedback by applying different feedback algorithms depending on the category of progress. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input progress category data into AI and have AI perform the application of the feedback algorithm.

[0099] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is in a hurry, the feedback unit will provide short, concise feedback. If the user is relaxed, the feedback unit will provide longer feedback with detailed explanations. If the user is excited, the feedback unit will provide feedback with visually stimulating effects. In this way, the feedback unit can provide the optimal feedback for the user by adjusting the length of the feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into an AI and have the AI ​​adjust the length of the feedback.

[0100] The feedback unit can determine the priority of feedback based on the completion date of the progress status when providing feedback. For example, the feedback unit will provide feedback preferentially if the completion date of the progress status is approaching. For example, the feedback unit will provide feedback with a moderate priority if the completion date of the progress status is in the middle. For example, the feedback unit will postpone providing feedback if the completion date of the progress status is far off. In this way, the feedback unit can provide the user with the best possible feedback by determining the priority of feedback based on the completion date of the progress status. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input progress status completion date data into AI and have the AI ​​perform the determination of feedback priority.

[0101] The feedback unit can adjust the order of feedback based on the relevance of the progress status when providing feedback. For example, the feedback unit will prioritize providing feedback when the relevance of the progress status is high. For example, the feedback unit will provide feedback in an appropriate order when the relevance of the progress status is moderate. For example, the feedback unit will postpone providing feedback when the relevance of the progress status is low. In this way, the feedback unit can provide the user with the best possible feedback by adjusting the order of feedback based on the relevance of the progress status. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input progress status relevance data into AI and have AI perform the adjustment of the feedback order.

[0102] The project department can estimate the user's emotions and adjust the presentation of the project-based learning plan based on the estimated emotions. For example, if the user is relaxed, the project department provides a project-based learning plan with detailed explanations. If the user is in a hurry, the project department provides a concise project-based learning plan that gets straight to the point. If the user is excited, the project department provides a project-based learning plan with visually stimulating effects. In this way, the project department can provide the optimal learning plan for the user by adjusting the presentation of the project-based learning plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the project department may be performed using AI or not using AI. For example, the project department can input user emotion data into AI and have the AI ​​adjust the presentation of the project-based learning plan.

[0103] The project department can adjust the level of detail of project-based learning plans based on the importance of the project. For example, if the project is highly important, the project department will provide a detailed project-based learning plan. If the project is of moderate importance, the project department will provide a project-based learning plan with appropriate detail. If the project is of low importance, the project department will provide a concise project-based learning plan. In this way, the project department can provide the optimal learning plan for the user by adjusting the level of detail of the plan based on the importance of the project. Some or all of the above processing in the project department may be performed using AI, for example, or not using AI. For example, the project department can input project importance data into AI and have the AI ​​perform the adjustment of the level of detail of the plan.

[0104] The Project Department can apply different plan generation algorithms depending on the project category when providing project-based learning plans. For example, in the case of a technical project, the Project Department can apply a plan generation algorithm specialized in technical content. For example, in the case of a management project, the Project Department can apply a plan generation algorithm specialized in project management and team leadership. For example, in the case of a creative project, the Project Department can apply a plan generation algorithm specialized in creative content. In this way, the Project Department can provide the optimal learning plan for the user by applying different plan generation algorithms depending on the project category. Some or all of the above processing in the Project Department may be performed using AI, for example, or not using AI. For example, the Project Department can input project category data into AI and have the AI ​​perform the application of the plan generation algorithm.

[0105] The project unit can estimate the user's emotions and adjust the length of the project-based learning plan based on those emotions. For example, if the user is in a hurry, the project unit can provide a short, concise project-based learning plan. If the user is relaxed, the project unit can provide a longer project-based learning plan with detailed explanations. If the user is excited, the project unit can provide a project-based learning plan with visually stimulating effects. In this way, the project unit can provide the user with the optimal learning plan by adjusting the length of the project-based learning plan based on their 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 project unit may be performed using AI or not using AI. For example, the project unit can input user emotion data into an AI and have the AI ​​adjust the length of the project-based learning plan.

[0106] The project department can prioritize project-based learning plans based on the project's completion date when providing them. For example, if the project's completion date is approaching, the project department will prioritize providing project-based learning plans. If the project's completion date is in the middle, the project department will provide project-based learning plans with a moderate priority. If the project's completion date is far off, the project department will postpone providing project-based learning plans. In this way, the project department can provide the optimal learning plan for the user by prioritizing plans based on the project's completion date. Some or all of the above processing in the project department may be performed using AI, for example, or not. For example, the project department can input project completion date data into AI and have the AI ​​perform the determination of plan priorities.

[0107] The project department can adjust the order of project-based learning plans based on project relevance when providing them. For example, if a project is highly relevant, the project department will prioritize providing the project-based learning plan. If a project is moderately relevant, the project department will provide the project-based learning plan in a suitable order. If a project is less relevant, the project department will postpone providing the project-based learning plan. In this way, the project department can provide the optimal learning plan for the user by adjusting the order of plans based on project relevance. Some or all of the above processing in the project department may be performed using AI, for example, or not using AI. For example, the project department can input project relevance data into AI and have the AI ​​perform the adjustment of the plan order.

[0108] The project unit can estimate the user's emotions and adjust the length of the project-based learning plan based on those emotions. For example, if the user is in a hurry, the project unit can provide a short, concise project-based learning plan. If the user is relaxed, the project unit can provide a longer project-based learning plan with detailed explanations. If the user is excited, the project unit can provide a project-based learning plan with visually stimulating effects. In this way, the project unit can provide the user with the optimal learning plan by adjusting the length of the project-based learning plan based on their 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 project unit may be performed using AI or not using AI. For example, the project unit can input user emotion data into an AI and have the AI ​​adjust the length of the project-based learning plan.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The reception desk can analyze a user's past learning history and propose the optimal learning plan. For example, based on a user's history of past training and learning plans, it can propose a learning plan that best suits their current career goals. The reception desk can also evaluate a user's past learning achievements and suggest effective learning methods. Furthermore, based on a user's past learning history, the reception desk can predict their learning progress and provide feedback at the appropriate time.

[0111] The generation unit can analyze the user's learning style and generate an optimal learning plan. For example, if the user prefers visual learning, it can generate a learning plan that includes a lot of visual content. Similarly, if the user prefers auditory learning, it can generate a learning plan that includes a lot of audio content. Furthermore, if the user prefers hands-on learning, it can generate a learning plan that includes a lot of practical exercises.

[0112] The progress tracking system can visualize users' learning progress and provide tools to maintain motivation. For example, it can display users' learning progress in graphs and charts, allowing them to visually understand their progress. It can also display users' progress toward learning goals in real time and provide feedback to maintain motivation. Furthermore, it can provide rewards and incentives at appropriate times based on users' learning progress.

[0113] The feedback unit can evaluate the user's learning outcomes and provide feedback that specifically indicates areas for improvement. For example, the feedback unit can evaluate the user's learning outcomes through tests and quizzes and identify areas where understanding is insufficient. Furthermore, the feedback unit can provide feedback that specifically indicates areas for improvement based on the user's learning outcomes. In addition, the feedback unit can compare the user's learning outcomes with those of other users and provide a relative evaluation.

[0114] The project team can monitor the user's project progress in real time and provide support as needed. For example, the project team can monitor the user's project progress in real time and issue alerts if progress is behind schedule. The project team can also provide necessary resources and support based on the user's project progress. Furthermore, the project team can share the user's project progress with other team members and provide support for collaborative problem-solving.

[0115] The reception desk can estimate the user's emotions and adjust the difficulty level of the learning plan based on those estimates. For example, if the user is stressed, an easier learning plan can be provided. If the user is relaxed, a more challenging learning plan can be provided. Furthermore, if the user is excited, a more challenging learning plan can be provided.

[0116] The generation unit can estimate the user's emotions and adjust the content of the learning plan based on those emotions. For example, if the user is relaxed, it can provide a learning plan with detailed explanations. If the user is in a hurry, it can provide a concise learning plan that gets straight to the point. Furthermore, if the user is excited, it can provide a learning plan with visually stimulating effects.

[0117] The progress tracking unit can estimate the user's emotions and adjust the progress management method based on those emotions. For example, if the user is stressed, the frequency of progress tracking can be reduced to provide time for relaxation. If the user is focused, the frequency of progress tracking can be increased to manage progress efficiently. Furthermore, if the user is tired, the frequency of progress tracking can be adjusted to include breaks.

[0118] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is relaxed, it can provide detailed feedback. If the user is in a hurry, it can provide concise feedback that gets straight to the point. Furthermore, if the user is excited, it can provide feedback with visually stimulating effects.

[0119] The project team can estimate the user's emotions and adjust the content of the project-based learning plan based on those emotions. For example, if the user is relaxed, they can provide a project-based learning plan with detailed explanations. If the user is in a hurry, they can provide a concise project-based learning plan that gets straight to the point. Furthermore, if the user is excited, they can provide a project-based learning plan with visually stimulating effects.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception desk receives input from employees regarding their career goals, current skill sets, job responsibilities, and available time. For example, if an employee is aiming for career advancement, the system can receive information tailored to that goal. It can also receive necessary information based on the employee's current skill set and job responsibilities. Furthermore, it can receive the most relevant information considering the employee's available time. Step 2: The generation unit analyzes the information received by the reception unit and generates the optimal training or learning plan. For example, it can generate an optimal training plan based on the employee's career goals. It can also generate a learning plan to acquire the necessary skills based on the employee's current skill set and job responsibilities. Furthermore, it can generate an optimal learning plan that takes into account the employee's available time. Step 3: The progress unit manages progress based on the learning plan generated by the generation unit. For example, it can manage employees' learning progress in real time. It can also manage progress to maintain employee motivation. Furthermore, it can provide feedback at the appropriate time according to the employee's learning progress. Step 4: The Feedback Department provides real-time feedback based on the progress managed by the Progress Department. For example, if an employee has insufficient understanding as they progress through their learning, the Feedback Department can provide feedback to strengthen that area. They can also provide feedback at the appropriate time to maintain employee motivation. Furthermore, they can provide real-time feedback according to the employee's learning progress. Step 5: The Project Department provides project-based learning plans based on the feedback provided by the Feedback Department. For example, when an employee joins a new project, the Project Department can provide a learning plan to help them acquire the skills relevant to that project. Alternatively, they can provide project-based learning plans to help employees acquire the necessary skills while working on actual projects. Furthermore, they can provide real-time feedback and advice to support employees in developing practical skills.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the reception unit, generation unit, progress unit, feedback unit, and project unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the employee's career goals, current skill set, job content, and available time as input. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit to generate an optimal training or learning plan. The progress unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages progress based on the generated learning plan. The feedback unit is implemented by, for example, the control unit 46A of the smart device 14 and provides real-time feedback based on the progress status. The project unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides a project-based learning plan and provides real-time feedback and advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the reception unit, generation unit, progress unit, feedback unit, and project unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the employee's career goals, current skill set, job content, and available time as input. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit to generate an optimal training or learning plan. The progress unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages progress based on the generated learning plan. The feedback unit is implemented by, for example, the control unit 46A of the smart glasses 214 and provides real-time feedback based on the progress status. The project unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides a project-based learning plan and provides real-time feedback and advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the reception unit, generation unit, progress unit, feedback unit, and project unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the employee's career goals, current skill set, job content, and available time as input. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit to generate an optimal training or learning plan. The progress unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages progress based on the generated learning plan. The feedback unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides real-time feedback based on the progress status. The project unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides project-based learning plans and real-time feedback and advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the reception unit, generation unit, progress unit, feedback unit, and project unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the employee's career goals, current skill set, job content, and available time as input. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit to generate an optimal training or learning plan. The progress unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages progress based on the generated learning plan. The feedback unit is implemented by, for example, the control unit 46A of the robot 414 and provides real-time feedback based on the progress status. The project unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides a project-based learning plan and provides real-time feedback and advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) The reception desk accepts employee career goals, current skill sets, job responsibilities, and available time as input. A generation unit analyzes the information received by the reception unit and generates an optimal training or learning plan, A progress unit that manages progress based on the learning plan generated by the generation unit, A feedback unit provides real-time feedback based on the progress status managed by the aforementioned progress unit, The project unit provides a project-based learning plan based on the feedback provided by the aforementioned feedback unit. A system characterized by the following features. (Note 2) The generating unit is We generate optimal training and learning plans by considering employees' career goals, current skill sets, job responsibilities, and available time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned progress unit is, Manage progress based on the generated learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is Provide real-time feedback based on progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned project department, We provide project-based learning plans and offer real-time feedback and advice. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is We generate learning plans so that employees can acquire the necessary skills while working on actual projects. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past career goals and skill set history to select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts how the learning plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a learning plan, adjust the level of detail in the plan based on the importance of your career goals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a learning plan, different generation algorithms are applied depending on the skill set category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the learning plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a learning plan, prioritize the plan based on when you expect to achieve your career goals. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a learning plan, adjust the order of the plan based on the relevance of the skill sets. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned progress unit is, We estimate the user's emotions and adjust the progress management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned progress unit is, When managing progress, refer to past progress data to optimize the management algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned progress unit is, When managing progress, customize the management method based on the user's work content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned progress unit is, Estimate user emotions and prioritize progress management based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advance ballast unit is, When managing progress, select the optimal management method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned progress unit is, During progress management, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the importance of the progress. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the category of the progress status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, prioritize the feedback based on when the progress was achieved. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, adjust the order of feedback based on the relevance of the progress. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned project department, It estimates user emotions and adjusts how project-based learning plans are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned project department, When providing project-based learning plans, adjust the level of detail in the plan based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned project department, When providing project-based learning plans, different plan generation algorithms are applied depending on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned project department, It estimates the user's emotions and adjusts the length of project-based learning plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned project department, When providing project-based learning plans, prioritize the plans based on the project completion date. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned project department, When providing project-based learning plans, adjust the order of the plans based on the relevance of the projects. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 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 reception desk accepts employee career goals, current skill sets, job responsibilities, and available time as input. A generation unit analyzes the information received by the reception unit and generates an optimal training or learning plan, A progress unit that manages progress based on the learning plan generated by the generation unit, A feedback unit provides real-time feedback based on the progress status managed by the aforementioned progress unit, The project unit provides a project-based learning plan based on the feedback provided by the aforementioned feedback unit. A system characterized by the following features.

2. The generating unit is We generate optimal training and learning plans by considering employees' career goals, current skill sets, job responsibilities, and available time. The system according to feature 1.

3. The aforementioned progress unit is, Manage progress based on the generated learning plan. The system according to feature 1.

4. The aforementioned feedback unit is Provide real-time feedback based on progress. The system according to feature 1.

5. The aforementioned project department, We provide project-based learning plans and offer real-time feedback and advice. The system according to feature 1.

6. The generating unit is We generate learning plans so that employees can acquire the necessary skills while working on actual projects. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past career goals and skill set history to select the optimal input method. The system according to feature 1.

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