system

Abstract

The system according to this embodiment aims to provide an optimal learning program tailored to the skill level and learning objectives of employees. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a learning unit, and a monitoring unit. The reception unit inputs the employee's skill level and learning objectives. The generation unit generates a learning program based on the information input by the reception unit. The learning unit proceeds with learning based on the learning program generated by the generation unit. The monitoring unit monitors the progress of learning carried out by the learning unit and adjusts the learning plan as necessary.

Description

Technical Field

[0006] , , , ,

[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, the method including receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently provide an optimal learning program according to the skill level and learning goals of employees.

[0005] The system according to the embodiment aims to provide an optimal learning program according to the skill level and learning goals of employees.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a learning unit, and a monitoring unit. The reception unit inputs the employee's skill level and learning objectives. The generation unit generates a learning program based on the information input by the reception unit. The learning unit proceeds with learning based on the learning program generated by the generation unit. The monitoring unit monitors the progress of learning carried out by the learning unit and adjusts the learning plan as necessary. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal learning program tailored to the skill level and learning objectives of employees. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The reskilling and upskilling system according to an embodiment of the present invention is a system that cultivates internal human resources and expands the scope of work by providing reskilling (re-skilling training) and upskilling (skill improvement) to employees of a company. This system utilizes an e-learning platform to provide a program that allows employees to acquire the necessary skills in a short period of time. This program utilizes generative AI to improve employee skills and productivity and streamline operations. For example, an employee accesses the e-learning platform and selects the skills they need. These may include IT skills or management skills. At this time, the employee can input their skill level and learning goals. This information is input into the generative AI. Next, the generative AI analyzes the input information and proposes the most suitable learning program for the employee. Based on the employee's skill level and learning goals, the generative AI selects the most suitable learning content and generates a learning plan. For example, if an employee wants to improve their IT skills, the generative AI proposes learning content such as programming and data analysis. Based on the generated learning program, the employee proceeds with learning on the e-learning platform. This may include video lectures and interactive exercises. Employees can learn at their own pace and monitor their progress and understanding. Furthermore, the generative AI monitors employees' learning progress in real time and adjusts the learning plan as needed. For example, if an employee gets stuck on a particular topic, the generative AI suggests additional learning content. Also, once an employee achieves their goals, it generates a learning plan to move on to the next step. This allows employees to acquire skills efficiently and broaden their scope of work. For example, by acquiring new IT skills, employees will have more opportunities to participate in new projects and improve work efficiency. Also, by improving management skills, employees can demonstrate leadership and increase team productivity. In this way, a reskilling and upskilling mechanism utilizing generative AI allows companies to effectively develop internal talent and broaden their scope of work even when new hiring is difficult.This improves a company's competitiveness and increases employee motivation and engagement. It also improves employees' work-life balance, leading to reduced turnover and lower recruitment costs. Thus, reskilling and upskilling systems can efficiently improve employee skills and enhance a company's competitiveness.

[0029] The reskilling and upskilling system according to the embodiment comprises a reception unit, a generation unit, a learning unit, and a monitoring unit. The reception unit inputs the employee's skill level and learning objectives. Employees can, for example, input their own skill level and learning objectives. The reception unit provides, for example, an interface for employees to input their own skill level and learning objectives. The generation unit generates a learning program based on the information input by the reception unit. The generation unit, for example, uses a generation AI to select optimal learning content based on the employee's skill level and learning objectives and generates a learning plan. For example, if an employee wants to improve their IT skills, the generation AI suggests learning content such as programming or data analysis. For example, if an employee wants to improve their management skills, the generation AI suggests learning content such as leadership or project management. The learning unit proceeds with learning based on the learning program generated by the generation unit. The learning unit provides, for example, learning content such as video lectures or interactive exercises. The learning unit provides, for example, an interface for employees to check their progress and understanding so that they can learn at their own pace. The learning unit can, for example, provide additional learning content if an employee struggles with a particular topic. The monitoring unit monitors the progress of learning conducted by the learning unit and adjusts the learning plan as needed. The monitoring unit, for example, monitors an employee's learning progress in real time and suggests additional learning content if they struggle with a particular topic. The monitoring unit, for example, generates a learning plan for the next step if the employee achieves their goals. Thus, the reskilling and upskilling system according to this embodiment enables efficient skill acquisition by providing an optimal learning program based on the employee's skill level and learning goals, and by monitoring and adjusting learning progress.

[0030] The reception desk inputs employees' skill levels and learning goals. Employees can, for example, input their own skill levels and learning goals. The reception desk provides an interface for employees to input their skill levels and learning goals. Specifically, the reception desk provides a user-friendly web portal and mobile application that allows employees to easily input their skill levels and learning goals. The interface is designed to be intuitive for employees to use, using dropdown menus, checkboxes, and text input fields. For example, when employees select their current skill level, options such as beginner, intermediate, and advanced are provided, and for learning goals, a text field is available where they can freely input specific skill and knowledge acquisition goals. The reception desk also allows employees to input historical information such as past training and qualifications, and collects data based on this information to generate more accurate learning plans. Furthermore, the reception desk has security features to safely manage the information entered by employees, and thoroughly protects personal information by encrypting data and controlling access. This allows the reception department to accurately and efficiently collect employee skill levels and learning objectives, and provide the information necessary to generate learning programs, which is the next step.

[0031] The generation unit generates learning programs based on information entered by the reception unit. For example, using a generation AI, the generation unit selects optimal learning content based on the employee's skill level and learning objectives, and generates a learning plan. Specifically, the generation AI analyzes the employee's input information and automatically selects the most suitable learning content for their individual needs. For example, if an employee wants to improve their IT skills, the generation AI suggests learning content such as programming and data analysis. The generation AI uses natural language processing technology to understand the employee's input and searches for and selects relevant learning resources. Similarly, if an employee wants to improve their management skills, the generation AI suggests learning content such as leadership and project management. The generation AI has an algorithm for generating optimal learning plans based on past learning data and success stories, and can flexibly adjust the plan according to the employee's learning style and progress. Furthermore, the generation unit has an interface for providing the generated learning plan to employees, allowing them to review their plan and request modifications or additions as needed. This enables the generation unit to provide optimal learning programs based on employees' skill levels and learning objectives, supporting efficient skill acquisition.

[0032] The Learning Department facilitates learning based on the learning programs generated by the Generation Department. The Learning Department provides learning content such as video lectures and interactive exercises. Specifically, it provides interfaces to track progress and understanding, allowing employees to learn at their own pace. For example, video lectures include expert explanations and practical demonstrations, presented in a visually easy-to-understand format. Interactive exercises are designed to allow employees to test what they've learned and receive immediate feedback. The Learning Department can also provide additional learning content if an employee struggles with a particular topic. For example, if an employee is having difficulty understanding a specific programming language, the Learning Department provides additional materials and practice exercises specific to that language. Furthermore, the Learning Department tracks employee learning progress in real time and suggests the next steps based on their understanding. This allows employees to learn at their own pace and acquire skills efficiently. In addition, the Learning Department provides forums and chat functions to facilitate communication among employees, allowing them to ask questions and exchange opinions about their learning. This allows the learning department to provide support to employees to effectively advance their learning and improve the efficiency of skill acquisition.

[0033] The monitoring department monitors the progress of learning conducted by the learning department and adjusts the learning plan as needed. For example, the monitoring department monitors employees' learning progress in real time and suggests additional learning content if an employee is struggling with a particular topic. Specifically, the monitoring department collects and analyzes employee learning data and provides a dashboard to evaluate progress and understanding. The dashboard visually displays the employee's learning progress, allowing users to see at a glance which topics they are struggling with. The monitoring department also generates a learning plan for the next step once an employee has achieved their goals. For example, if an employee has acquired a particular skill, the monitoring department suggests advanced courses or new topics related to that skill. Furthermore, the monitoring department collects employee feedback to help improve the learning plan. Information such as how employees feel about the learning plan, which parts were particularly helpful, and which parts could be improved is collected and reflected in the next learning plan. This allows the monitoring department to continuously improve the employee learning experience and support more effective learning. Furthermore, the monitoring department can use AI to analyze learning data and understand employees' learning patterns and tendencies, enabling them to provide customized learning plans tailored to individual needs. This allows the monitoring department to effectively monitor employees' learning progress and adjust learning plans as needed, thereby supporting efficient skill acquisition.

[0034] The generation unit can select the most suitable learning content and generate a learning plan based on the employee's skill level and learning objectives. For example, if an employee wants to improve their IT skills, the generation AI will suggest learning content such as programming and data analysis. If an employee wants to improve their management skills, the generation AI will suggest learning content such as leadership and project management. This enables efficient skill acquisition by providing an optimal learning plan tailored to the employee's skill level and learning objectives.

[0035] The learning department can provide learning content, including video lectures and interactive exercises. For example, the learning department can provide video lectures. For example, the learning department can provide interactive exercises. For example, the learning department can provide interfaces to check progress and understanding, allowing employees to learn at their own pace. This allows for improved employee learning effectiveness by providing diverse learning content.

[0036] The monitoring unit can monitor employees' learning progress in real time and suggest additional learning content if an employee gets stuck on a particular topic. For example, the monitoring unit can monitor employees' learning progress in real time. For example, if an employee gets stuck on a particular topic, the monitoring unit can suggest additional learning content. For example, if an employee achieves their goal, the monitoring unit can generate a learning plan for the next step. This enhances the effectiveness of learning by monitoring learning progress in real time and providing additional learning content as needed.

[0037] The monitoring unit can generate learning plans for employees to take the next step once they achieve their goals. For example, if an employee achieves their goal, the monitoring unit can generate a learning plan for them to take the next step. For example, the monitoring unit can generate a learning plan for an employee to acquire new skills. This supports continuous skill development by clearly defining the next steps after achieving goals.

[0038] The reception desk can analyze an employee's past learning history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the skill levels and learning goals that the employee has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the employee has used in the past. For example, the reception desk can predict and suggest the skill levels and learning goals to be used at a specific time of day based on the employee's past learning history. This enables efficient input by suggesting the optimal input method based on past learning history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the employee's past learning history data into AI and have the AI ​​suggest the optimal input method.

[0039] The reception desk can filter the input of skill levels and learning objectives based on the employee's current projects and work content. For example, the reception desk can prioritize displaying skills related to the project the employee is currently working on. For example, the reception desk can suggest highly relevant learning objectives based on the employee's work content. For example, the reception desk can filter and display the necessary skills according to the progress of the employee's project. This enables efficient learning by prioritizing the input of skills and learning objectives related to the current project and work content. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input employee project data into AI and have the AI ​​perform filtering of relevant skills and learning objectives.

[0040] The reception desk can prioritize inputting highly relevant skills by considering the employee's geographical location when inputting skill levels and learning objectives. For example, if an employee works in a specific region, the reception desk will prioritize inputting skills that are in high demand in that region. For example, the reception desk will suggest highly relevant skills based on the employee's work location. For example, if an employee works remotely, the reception desk will prioritize inputting skills suitable for remote work. This allows for the acquisition of skills appropriate to the region by prioritizing the input of skills and learning objectives based on geographical location information. 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 the employee's geographical location information into AI and have the AI ​​suggest highly relevant skills.

[0041] The reception desk can analyze an employee's social media activity and suggest relevant skills when they input their skill level and learning goals. For example, the reception desk can suggest relevant skills based on topics the employee is interested in on social media. For example, the reception desk can analyze the content of an employee's social media posts and suggest necessary skills. For example, the reception desk can suggest relevant skills based on the activities of experts and influencers the employee follows. This allows employees to acquire skills that match their interests by providing skill suggestions based on their social media activity. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input employee social media activity data into an AI and have the AI ​​suggest relevant skills.

[0042] The generation unit can adjust the level of detail in learning content based on the employee's importance when generating learning programs. For example, the generation unit generates learning programs that include detailed explanations and practice problems for important skills. For example, the generation unit generates learning programs that include concise explanations and basic practice problems for less important skills. The generation unit adjusts the level of detail in learning content based on the employee's job title and duties, for example. This enables efficient learning by providing learning content with a level of detail appropriate to importance. 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 employee importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in learning content.

[0043] The generation unit can apply different learning algorithms depending on the employee's category when generating learning programs. For example, the generation unit applies a learning algorithm that starts from the basics to a learning program for beginners. For example, the generation unit applies an algorithm that includes advanced content to a learning program for intermediate learners. For example, the generation unit applies an algorithm that includes specialized content to a learning program for advanced learners. This enables efficient learning by applying a learning algorithm appropriate to the 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 employee category data into a generation AI and have the generation AI execute the application of the learning algorithm.

[0044] The generation unit can determine the priority of learning programs based on employee submission dates when generating learning programs. For example, the generation unit may prioritize generating learning programs with approaching deadlines. For example, it may postpone generating learning programs with later submission dates. For example, the generation unit may adjust the order of learning programs based on submission dates. This enables efficient learning by providing a priority of learning programs based on submission dates. 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 employee submission date data into a generation AI and have the generation AI determine the priority of learning programs.

[0045] The generation unit can adjust the order of learning programs based on employee relevance when generating them. For example, the generation unit prioritizes incorporating highly relevant skills into the learning program. For example, the generation unit postpones incorporating less relevant skills into the learning program. For example, the generation unit adjusts the order of learning programs based on the employee's job duties. This enables efficient learning by providing a learning program order based on relevance. 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 employee relevance data into a generation AI and have the generation AI perform the adjustment of the learning program order.

[0046] The learning department can provide optimal content by referring to an employee's past learning history when delivering learning content. For example, the learning department can provide optimal learning content based on what the employee has learned in the past. For example, the learning department can prioritize providing topics that the employee understands less based on their past learning history. For example, the learning department can analyze an employee's past learning history and provide the most effective learning content. This enables efficient learning by providing optimal learning content based on past learning history. Some or all of the above processes in the learning department may be performed using AI, for example, or not using AI. For example, the learning department can input employee past learning history data into AI and have the AI ​​deliver optimal learning content.

[0047] The learning department can customize learning content based on an employee's current job responsibilities when providing it. For example, the learning department can prioritize providing learning content relevant to an employee's job responsibilities. For example, the learning department can provide necessary learning content according to the progress of an employee's project. For example, the learning department can customize learning content based on an employee's job title and job responsibilities. This enables efficient learning by providing learning content based on current job responsibilities. Some or all of the above processes in the learning department may be performed using AI, for example, or not using AI. For example, the learning department can input employee job responsibilities data into AI and have the AI ​​customize the learning content.

[0048] The learning department can provide optimal learning content by considering the employee's geographical location when delivering learning content. For example, if an employee works in a specific region, the learning department can provide learning content related to skills in high demand in that region. For example, the learning department can provide highly relevant learning content based on the employee's work location. For example, if an employee is working remotely, the learning department can provide learning content suitable for remote work. This makes it possible to acquire skills appropriate for the region by providing learning content based on geographical location information. Some or all of the above processing in the learning department may be performed using AI, for example, or not using AI. For example, the learning department can input the employee's geographical location information into AI and have the AI ​​deliver the optimal learning content.

[0049] The learning department can analyze employees' social media activity and suggest relevant content when providing learning content. For example, the learning department can suggest relevant learning content based on topics that employees are interested in on social media. For example, the learning department can analyze the content of employees' social media posts and suggest necessary learning content. For example, the learning department can suggest relevant learning content based on the activities of experts and influencers that employees follow. This enables employees to acquire skills tailored to their interests by providing learning content based on their social media activity. Some or all of the above processes in the learning department may be performed using AI, for example, or not. For example, the learning department can input employee social media activity data into AI and have the AI ​​suggest relevant learning content.

[0050] The monitoring unit can select the optimal monitoring method by referring to the employee's past learning history when monitoring learning progress. For example, the monitoring unit can select the optimal progress monitoring method based on what the employee has learned in the past. For example, the monitoring unit can focus on monitoring topics where the employee has a low level of understanding based on the employee's past learning history. For example, the monitoring unit can analyze the employee's past learning history and select the most effective progress monitoring method. This enables efficient monitoring of learning progress by providing the optimal monitoring method based on past learning history. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the employee's past learning history data into AI and have the AI ​​select the optimal progress monitoring method.

[0051] The monitoring unit can customize monitoring methods based on the employee's current work content when monitoring learning progress. For example, the monitoring unit may focus on monitoring learning progress related to the employee's work content. For example, the monitoring unit may provide necessary monitoring methods according to the progress of the employee's project. For example, the monitoring unit may customize monitoring methods based on the employee's position and work content. This enables efficient monitoring of learning progress by providing monitoring methods based on current work content. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit may input employee work content data into AI and have the AI ​​perform the customization of monitoring methods.

[0052] The monitoring unit can select the optimal monitoring method when monitoring learning progress, taking into account the employee's geographical location. For example, if an employee works in a specific region, the monitoring unit will focus on monitoring learning progress related to skills in high demand in that region. For example, the monitoring unit will monitor highly relevant learning progress based on the employee's work location. For example, if an employee is working remotely, the monitoring unit will monitor learning progress suitable for remote work. This enables efficient monitoring of learning progress by providing the optimal monitoring method based on geographical location information. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the employee's geographical location information into AI and have the AI ​​select the optimal monitoring method.

[0053] The monitoring unit can analyze employees' social media activity and propose monitoring methods when monitoring learning progress. For example, the monitoring unit can monitor relevant learning progress based on topics that employees show interest in on social media. For example, the monitoring unit can analyze the content of employees' social media posts and propose necessary monitoring methods. For example, the monitoring unit can monitor relevant learning progress based on the activities of experts and influencers that employees follow. This enables efficient monitoring of learning progress by providing monitoring methods based on social media activity. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input employee social media activity data into AI and have the AI ​​propose monitoring methods.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can analyze an employee's past learning history and suggest the optimal input method. For example, it can automatically display as suggestions the skill levels and learning goals that the employee has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the employee has used in the past. Furthermore, it can predict and suggest skill levels and learning goals to be used at specific times based on the employee's past learning history. This enables efficient input by suggesting the optimal input method based on past learning history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the employee's past learning history data into AI and have the AI ​​suggest the optimal input method.

[0056] The reception desk can filter employee input based on their current projects and work content when they enter skill levels and learning objectives. For example, it can prioritize displaying skills related to the project the employee is currently working on. It can also suggest highly relevant learning objectives based on the employee's work content. Furthermore, it can filter and display necessary skills according to the progress of the employee's project. This enables efficient learning by prioritizing the input of skills and learning objectives related to the current project and work content. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input employee project data into AI and have the AI ​​perform filtering of relevant skills and learning objectives.

[0057] The reception desk can prioritize inputting highly relevant skills by considering the employee's geographical location when inputting skill levels and learning objectives. For example, if an employee works in a specific region, skills in high demand in that region can be prioritized. It is also possible to suggest highly relevant skills based on the employee's work location. Furthermore, if an employee is working remotely, skills suitable for remote work can be prioritized. This allows for the acquisition of skills appropriate to the region by prioritizing the input of skills and learning objectives based on geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the employee's geographical location information into the AI ​​and have the AI ​​suggest highly relevant skills.

[0058] The generation unit can adjust the level of detail in learning content based on the employee's importance when generating learning programs. For example, it can generate learning programs that include detailed explanations and practice problems for important skills. It can also generate learning programs that include concise explanations and basic practice problems for less important skills. Furthermore, it can adjust the level of detail in learning content based on the employee's job title and duties. This enables efficient learning by providing learning content with a level of detail appropriate to importance. 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 employee importance data into a generation AI and have the generation AI adjust the level of detail in learning content.

[0059] The generation unit can apply different learning algorithms depending on the employee's category when generating learning programs. For example, a learning program for beginners can be applied to an algorithm that teaches from the basics. A learning program for intermediate learners can be applied to an algorithm that includes advanced content. Furthermore, a learning program for advanced learners can be applied to an algorithm that includes specialized content. By applying a learning algorithm according to the category, efficient learning becomes possible. 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 employee category data into a generation AI and have the generation AI execute the application of the learning algorithm.

[0060] The generation unit can determine the priority of learning programs based on employee submission dates when generating learning programs. For example, it can prioritize the generation of learning programs with approaching deadlines. It can also postpone the generation of learning programs with later submission dates. Furthermore, it can adjust the order of learning programs based on submission dates. This enables efficient learning by providing a priority order for learning programs based on submission dates. 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 employee submission date data into a generation AI and have the generation AI determine the priority of learning programs.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk enters the employee's skill level and learning objectives. Employees use an interface to enter their skill level and learning objectives. Step 2: The generation unit generates a learning program based on the information entered by the reception unit. The generation unit uses a generation AI to select the optimal learning content based on the employee's skill level and learning objectives, and generates a learning plan. Step 3: The learning unit proceeds with learning based on the learning program generated by the generation unit. The learning unit provides learning content such as video lectures and interactive exercises, allowing employees to learn at their own pace. Step 4: The monitoring department monitors the progress of learning conducted by the learning department and adjusts the learning plan as needed. The monitoring department monitors employees' learning progress in real time and suggests additional learning content if they get stuck on a particular topic.

[0063] (Example of form 2) The reskilling and upskilling system according to an embodiment of the present invention is a system that cultivates internal human resources and expands the scope of work by providing reskilling (re-skilling training) and upskilling (skill improvement) to employees of a company. This system utilizes an e-learning platform to provide a program that allows employees to acquire the necessary skills in a short period of time. This program utilizes generative AI to improve employee skills and productivity and streamline operations. For example, an employee accesses the e-learning platform and selects the skills they need. These may include IT skills or management skills. At this time, the employee can input their skill level and learning goals. This information is input into the generative AI. Next, the generative AI analyzes the input information and proposes the most suitable learning program for the employee. Based on the employee's skill level and learning goals, the generative AI selects the most suitable learning content and generates a learning plan. For example, if an employee wants to improve their IT skills, the generative AI proposes learning content such as programming and data analysis. Based on the generated learning program, the employee proceeds with learning on the e-learning platform. This may include video lectures and interactive exercises. Employees can learn at their own pace and monitor their progress and understanding. Furthermore, the generative AI monitors employees' learning progress in real time and adjusts the learning plan as needed. For example, if an employee gets stuck on a particular topic, the generative AI suggests additional learning content. Also, once an employee achieves their goals, it generates a learning plan to move on to the next step. This allows employees to acquire skills efficiently and broaden their scope of work. For example, by acquiring new IT skills, employees will have more opportunities to participate in new projects and improve work efficiency. Also, by improving management skills, employees can demonstrate leadership and increase team productivity. In this way, a reskilling and upskilling mechanism utilizing generative AI allows companies to effectively develop internal talent and broaden their scope of work even when new hiring is difficult.This improves a company's competitiveness and increases employee motivation and engagement. It also improves employees' work-life balance, leading to reduced turnover and lower recruitment costs. Thus, reskilling and upskilling systems can efficiently improve employee skills and enhance a company's competitiveness.

[0064] The reskilling and upskilling system according to the embodiment comprises a reception unit, a generation unit, a learning unit, and a monitoring unit. The reception unit inputs the employee's skill level and learning objectives. Employees can, for example, input their own skill level and learning objectives. The reception unit provides, for example, an interface for employees to input their own skill level and learning objectives. The generation unit generates a learning program based on the information input by the reception unit. The generation unit, for example, uses a generation AI to select optimal learning content based on the employee's skill level and learning objectives and generates a learning plan. For example, if an employee wants to improve their IT skills, the generation AI suggests learning content such as programming or data analysis. For example, if an employee wants to improve their management skills, the generation AI suggests learning content such as leadership or project management. The learning unit proceeds with learning based on the learning program generated by the generation unit. The learning unit provides, for example, learning content such as video lectures or interactive exercises. The learning unit provides, for example, an interface for employees to check their progress and understanding so that they can learn at their own pace. The learning unit can, for example, provide additional learning content if an employee struggles with a particular topic. The monitoring unit monitors the progress of learning conducted by the learning unit and adjusts the learning plan as needed. The monitoring unit, for example, monitors an employee's learning progress in real time and suggests additional learning content if they struggle with a particular topic. The monitoring unit, for example, generates a learning plan for the next step if the employee achieves their goals. Thus, the reskilling and upskilling system according to this embodiment enables efficient skill acquisition by providing an optimal learning program based on the employee's skill level and learning goals, and by monitoring and adjusting learning progress.

[0065] The reception desk inputs employees' skill levels and learning goals. Employees can, for example, input their own skill levels and learning goals. The reception desk provides an interface for employees to input their skill levels and learning goals. Specifically, the reception desk provides a user-friendly web portal and mobile application that allows employees to easily input their skill levels and learning goals. The interface is designed to be intuitive for employees to use, using dropdown menus, checkboxes, and text input fields. For example, when employees select their current skill level, options such as beginner, intermediate, and advanced are provided, and for learning goals, a text field is available where they can freely input specific skill and knowledge acquisition goals. The reception desk also allows employees to input historical information such as past training and qualifications, and collects data based on this information to generate more accurate learning plans. Furthermore, the reception desk has security features to safely manage the information entered by employees, and thoroughly protects personal information by encrypting data and controlling access. This allows the reception department to accurately and efficiently collect employee skill levels and learning objectives, and provide the information necessary to generate learning programs, which is the next step.

[0066] The generation unit generates learning programs based on information entered by the reception unit. For example, using a generation AI, the generation unit selects optimal learning content based on the employee's skill level and learning objectives, and generates a learning plan. Specifically, the generation AI analyzes the employee's input information and automatically selects the most suitable learning content for their individual needs. For example, if an employee wants to improve their IT skills, the generation AI suggests learning content such as programming and data analysis. The generation AI uses natural language processing technology to understand the employee's input and searches for and selects relevant learning resources. Similarly, if an employee wants to improve their management skills, the generation AI suggests learning content such as leadership and project management. The generation AI has an algorithm for generating optimal learning plans based on past learning data and success stories, and can flexibly adjust the plan according to the employee's learning style and progress. Furthermore, the generation unit has an interface for providing the generated learning plan to employees, allowing them to review their plan and request modifications or additions as needed. This enables the generation unit to provide optimal learning programs based on employees' skill levels and learning objectives, supporting efficient skill acquisition.

[0067] The Learning Department facilitates learning based on the learning programs generated by the Generation Department. The Learning Department provides learning content such as video lectures and interactive exercises. Specifically, it provides interfaces to track progress and understanding, allowing employees to learn at their own pace. For example, video lectures include expert explanations and practical demonstrations, presented in a visually easy-to-understand format. Interactive exercises are designed to allow employees to test what they've learned and receive immediate feedback. The Learning Department can also provide additional learning content if an employee struggles with a particular topic. For example, if an employee is having difficulty understanding a specific programming language, the Learning Department provides additional materials and practice exercises specific to that language. Furthermore, the Learning Department tracks employee learning progress in real time and suggests the next steps based on their understanding. This allows employees to learn at their own pace and acquire skills efficiently. In addition, the Learning Department provides forums and chat functions to facilitate communication among employees, allowing them to ask questions and exchange opinions about their learning. This allows the learning department to provide support to employees to effectively advance their learning and improve the efficiency of skill acquisition.

[0068] The monitoring department monitors the progress of learning conducted by the learning department and adjusts the learning plan as needed. For example, the monitoring department monitors employees' learning progress in real time and suggests additional learning content if an employee is struggling with a particular topic. Specifically, the monitoring department collects and analyzes employee learning data and provides a dashboard to evaluate progress and understanding. The dashboard visually displays the employee's learning progress, allowing users to see at a glance which topics they are struggling with. The monitoring department also generates a learning plan for the next step once an employee has achieved their goals. For example, if an employee has acquired a particular skill, the monitoring department suggests advanced courses or new topics related to that skill. Furthermore, the monitoring department collects employee feedback to help improve the learning plan. Information such as how employees feel about the learning plan, which parts were particularly helpful, and which parts could be improved is collected and reflected in the next learning plan. This allows the monitoring department to continuously improve the employee learning experience and support more effective learning. Furthermore, the monitoring department can use AI to analyze learning data and understand employees' learning patterns and tendencies, enabling them to provide customized learning plans tailored to individual needs. This allows the monitoring department to effectively monitor employees' learning progress and adjust learning plans as needed, thereby supporting efficient skill acquisition.

[0069] The generation unit can select the most suitable learning content and generate a learning plan based on the employee's skill level and learning objectives. For example, if an employee wants to improve their IT skills, the generation AI will suggest learning content such as programming and data analysis. If an employee wants to improve their management skills, the generation AI will suggest learning content such as leadership and project management. This enables efficient skill acquisition by providing an optimal learning plan tailored to the employee's skill level and learning objectives.

[0070] The learning department can provide learning content, including video lectures and interactive exercises. For example, the learning department can provide video lectures. For example, the learning department can provide interactive exercises. For example, the learning department can provide interfaces to check progress and understanding, allowing employees to learn at their own pace. This allows for improved employee learning effectiveness by providing diverse learning content.

[0071] The monitoring unit can monitor employees' learning progress in real time and suggest additional learning content if an employee gets stuck on a particular topic. For example, the monitoring unit can monitor employees' learning progress in real time. For example, if an employee gets stuck on a particular topic, the monitoring unit can suggest additional learning content. For example, if an employee achieves their goal, the monitoring unit can generate a learning plan for the next step. This enhances the effectiveness of learning by monitoring learning progress in real time and providing additional learning content as needed.

[0072] The monitoring unit can generate learning plans for employees to take the next step once they achieve their goals. For example, if an employee achieves their goal, the monitoring unit can generate a learning plan for them to take the next step. For example, the monitoring unit can generate a learning plan for an employee to acquire new skills. This supports continuous skill development by clearly defining the next steps after achieving goals.

[0073] The reception desk can estimate an employee's emotions and adjust the input method for skill levels and learning goals based on the estimated emotions. For example, if an employee is stressed, the reception desk provides a simple interface and minimizes the input steps. For example, if an employee is relaxed, the reception desk provides detailed input options and suggests a customizable input method. For example, if an employee is in a hurry, the reception desk prioritizes voice input to allow for quick input of skill levels and learning goals. This enables smooth input of skill levels and learning goals by providing an input method that is tailored to the employee's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The reception desk can analyze an employee's past learning history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the skill levels and learning goals that the employee has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the employee has used in the past. For example, the reception desk can predict and suggest the skill levels and learning goals to be used at a specific time of day based on the employee's past learning history. This enables efficient input by suggesting the optimal input method based on past learning history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the employee's past learning history data into AI and have the AI ​​suggest the optimal input method.

[0075] The reception desk can filter the input of skill levels and learning objectives based on the employee's current projects and work content. For example, the reception desk can prioritize displaying skills related to the project the employee is currently working on. For example, the reception desk can suggest highly relevant learning objectives based on the employee's work content. For example, the reception desk can filter and display the necessary skills according to the progress of the employee's project. This enables efficient learning by prioritizing the input of skills and learning objectives related to the current project and work content. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input employee project data into AI and have the AI ​​perform filtering of relevant skills and learning objectives.

[0076] The reception desk can estimate an employee's emotions and, based on that estimation, prioritize the skill levels and learning objectives to be entered. For example, if an employee is stressed, the reception desk will prioritize entering high-priority skills. If an employee is relaxed, the reception desk will prioritize entering detailed skill levels and learning objectives. If an employee is in a hurry, the reception desk will prioritize entering the most important skills. This allows for efficient input of skill levels and learning objectives by setting priorities according to the employee's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The reception desk can prioritize inputting highly relevant skills by considering the employee's geographical location when inputting skill levels and learning objectives. For example, if an employee works in a specific region, the reception desk will prioritize inputting skills that are in high demand in that region. For example, the reception desk will suggest highly relevant skills based on the employee's work location. For example, if an employee works remotely, the reception desk will prioritize inputting skills suitable for remote work. This allows for the acquisition of skills appropriate to the region by prioritizing the input of skills and learning objectives based on geographical location information. 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 the employee's geographical location information into AI and have the AI ​​suggest highly relevant skills.

[0078] The reception desk can analyze an employee's social media activity and suggest relevant skills when they input their skill level and learning goals. For example, the reception desk can suggest relevant skills based on topics the employee is interested in on social media. For example, the reception desk can analyze the content of an employee's social media posts and suggest necessary skills. For example, the reception desk can suggest relevant skills based on the activities of experts and influencers the employee follows. This allows employees to acquire skills that match their interests by providing skill suggestions based on their social media activity. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input employee social media activity data into an AI and have the AI ​​suggest relevant skills.

[0079] The generation unit can estimate an employee's emotions and adjust the presentation of the learning program based on the estimated emotions. For example, if an employee is relaxed, the generation unit generates a learning program that proceeds at a relaxed pace. If an employee is in a hurry, the generation unit generates a learning program that emphasizes the shortest route. If an employee is excited, the generation unit generates a learning program with visually stimulating effects. This enhances learning effectiveness by providing a learning program presentation that responds to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The generation unit can adjust the level of detail in learning content based on the employee's importance when generating learning programs. For example, the generation unit generates learning programs that include detailed explanations and practice problems for important skills. For example, the generation unit generates learning programs that include concise explanations and basic practice problems for less important skills. The generation unit adjusts the level of detail in learning content based on the employee's job title and duties, for example. This enables efficient learning by providing learning content with a level of detail appropriate to importance. 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 employee importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in learning content.

[0081] The generation unit can apply different learning algorithms depending on the employee's category when generating learning programs. For example, the generation unit applies a learning algorithm that starts from the basics to a learning program for beginners. For example, the generation unit applies an algorithm that includes advanced content to a learning program for intermediate learners. For example, the generation unit applies an algorithm that includes specialized content to a learning program for advanced learners. This enables efficient learning by applying a learning algorithm appropriate to the 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 employee category data into a generation AI and have the generation AI execute the application of the learning algorithm.

[0082] The generation unit can estimate an employee's emotions and adjust the length of the learning program based on the estimated emotions. For example, if an employee is in a hurry, the generation unit generates a short, concise learning program. If an employee is relaxed, the generation unit generates a longer learning program with detailed explanations. If an employee is excited, the generation unit generates a learning program with visually stimulating effects. This enhances learning effectiveness by providing learning program lengths that correspond to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The generation unit can determine the priority of learning programs based on employee submission dates when generating learning programs. For example, the generation unit may prioritize generating learning programs with approaching deadlines. For example, it may postpone generating learning programs with later submission dates. For example, the generation unit may adjust the order of learning programs based on submission dates. This enables efficient learning by providing a priority of learning programs based on submission dates. 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 employee submission date data into a generation AI and have the generation AI determine the priority of learning programs.

[0084] The generation unit can adjust the order of learning programs based on employee relevance when generating them. For example, the generation unit prioritizes incorporating highly relevant skills into the learning program. For example, the generation unit postpones incorporating less relevant skills into the learning program. For example, the generation unit adjusts the order of learning programs based on the employee's job duties. This enables efficient learning by providing a learning program order based on relevance. 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 employee relevance data into a generation AI and have the generation AI perform the adjustment of the learning program order.

[0085] The learning unit can estimate employees' emotions and adjust how learning content is delivered based on those emotions. For example, if an employee is relaxed, the learning unit will provide learning content that progresses at a relaxed pace. If an employee is in a hurry, the learning unit will provide learning content that emphasizes the shortest route. If an employee is excited, the learning unit will provide learning content with visually stimulating effects. This enhances learning effectiveness by providing learning content tailored to employees' emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The learning department can provide optimal content by referring to an employee's past learning history when delivering learning content. For example, the learning department can provide optimal learning content based on what the employee has learned in the past. For example, the learning department can prioritize providing topics that the employee understands less based on their past learning history. For example, the learning department can analyze an employee's past learning history and provide the most effective learning content. This enables efficient learning by providing optimal learning content based on past learning history. Some or all of the above processes in the learning department may be performed using AI, for example, or not using AI. For example, the learning department can input employee past learning history data into AI and have the AI ​​deliver optimal learning content.

[0087] The learning department can customize learning content based on an employee's current job responsibilities when providing it. For example, the learning department can prioritize providing learning content relevant to an employee's job responsibilities. For example, the learning department can provide necessary learning content according to the progress of an employee's project. For example, the learning department can customize learning content based on an employee's job title and job responsibilities. This enables efficient learning by providing learning content based on current job responsibilities. Some or all of the above processes in the learning department may be performed using AI, for example, or not using AI. For example, the learning department can input employee job responsibilities data into AI and have the AI ​​customize the learning content.

[0088] The learning unit can estimate employees' emotions and prioritize learning content based on those emotions. For example, if an employee is stressed, the learning unit will prioritize providing high-priority learning content. If an employee is relaxed, the learning unit will provide detailed learning content. If an employee is in a hurry, the learning unit will prioritize providing the most important learning content. This enables efficient learning by setting priorities according to employees' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The learning department can provide optimal learning content by considering the employee's geographical location when delivering learning content. For example, if an employee works in a specific region, the learning department can provide learning content related to skills in high demand in that region. For example, the learning department can provide highly relevant learning content based on the employee's work location. For example, if an employee is working remotely, the learning department can provide learning content suitable for remote work. This makes it possible to acquire skills appropriate for the region by providing learning content based on geographical location information. Some or all of the above processing in the learning department may be performed using AI, for example, or not using AI. For example, the learning department can input the employee's geographical location information into AI and have the AI ​​deliver the optimal learning content.

[0090] The learning department can analyze employees' social media activity and suggest relevant content when providing learning content. For example, the learning department can suggest relevant learning content based on topics that employees are interested in on social media. For example, the learning department can analyze the content of employees' social media posts and suggest necessary learning content. For example, the learning department can suggest relevant learning content based on the activities of experts and influencers that employees follow. This enables employees to acquire skills tailored to their interests by providing learning content based on their social media activity. Some or all of the above processes in the learning department may be performed using AI, for example, or not. For example, the learning department can input employee social media activity data into AI and have the AI ​​suggest relevant learning content.

[0091] The monitoring unit can estimate employees' emotions and adjust the method of monitoring learning progress based on the estimated emotions. For example, if an employee is stressed, the monitoring unit will provide a simple progress report and refrain from detailed feedback. For example, if an employee is relaxed, the monitoring unit will provide a detailed progress report and feedback. For example, if an employee is in a hurry, the monitoring unit will provide a concise progress report. This can enhance learning effectiveness by providing a method of monitoring learning progress that is tailored to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The monitoring unit can select the optimal monitoring method by referring to the employee's past learning history when monitoring learning progress. For example, the monitoring unit can select the optimal progress monitoring method based on what the employee has learned in the past. For example, the monitoring unit can focus on monitoring topics where the employee has a low level of understanding based on the employee's past learning history. For example, the monitoring unit can analyze the employee's past learning history and select the most effective progress monitoring method. This enables efficient monitoring of learning progress by providing the optimal monitoring method based on past learning history. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the employee's past learning history data into AI and have the AI ​​select the optimal progress monitoring method.

[0093] The monitoring unit can customize monitoring methods based on the employee's current work content when monitoring learning progress. For example, the monitoring unit may focus on monitoring learning progress related to the employee's work content. For example, the monitoring unit may provide necessary monitoring methods according to the progress of the employee's project. For example, the monitoring unit may customize monitoring methods based on the employee's position and work content. This enables efficient monitoring of learning progress by providing monitoring methods based on current work content. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit may input employee work content data into AI and have the AI ​​perform the customization of monitoring methods.

[0094] The monitoring unit can estimate employees' emotions and prioritize learning progress based on those emotions. For example, if an employee is stressed, the monitoring unit will prioritize monitoring high-priority learning progress. If an employee is relaxed, the monitoring unit will prioritize monitoring detailed learning progress. If an employee is in a hurry, the monitoring unit will prioritize monitoring the most important learning progress. This enables efficient monitoring of learning progress by setting priorities according to employees' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The monitoring unit can select the optimal monitoring method when monitoring learning progress, taking into account the employee's geographical location. For example, if an employee works in a specific region, the monitoring unit will focus on monitoring learning progress related to skills in high demand in that region. For example, the monitoring unit will monitor highly relevant learning progress based on the employee's work location. For example, if an employee is working remotely, the monitoring unit will monitor learning progress suitable for remote work. This enables efficient monitoring of learning progress by providing the optimal monitoring method based on geographical location information. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the employee's geographical location information into AI and have the AI ​​select the optimal monitoring method.

[0096] The monitoring unit can analyze employees' social media activity and propose monitoring methods when monitoring learning progress. For example, the monitoring unit can monitor relevant learning progress based on topics that employees show interest in on social media. For example, the monitoring unit can analyze the content of employees' social media posts and propose necessary monitoring methods. For example, the monitoring unit can monitor relevant learning progress based on the activities of experts and influencers that employees follow. This enables efficient monitoring of learning progress by providing monitoring methods based on social media activity. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input employee social media activity data into AI and have the AI ​​propose monitoring methods.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception desk can estimate an employee's emotions and adjust the input method for skill levels and learning goals based on the estimated emotions. For example, if an employee is stressed, a simple interface can be provided, minimizing the input steps. If an employee is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if an employee is in a hurry, voice input can be prioritized, allowing for quick input of skill levels and learning goals. This enables smooth input of skill levels and learning goals by providing input methods that are tailored to the employee's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The reception desk can analyze an employee's past learning history and suggest the optimal input method. For example, it can automatically display as suggestions the skill levels and learning goals that the employee has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the employee has used in the past. Furthermore, it can predict and suggest skill levels and learning goals to be used at specific times based on the employee's past learning history. This enables efficient input by suggesting the optimal input method based on past learning history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the employee's past learning history data into AI and have the AI ​​suggest the optimal input method.

[0100] The reception desk can filter employee input based on their current projects and work content when they enter skill levels and learning objectives. For example, it can prioritize displaying skills related to the project the employee is currently working on. It can also suggest highly relevant learning objectives based on the employee's work content. Furthermore, it can filter and display necessary skills according to the progress of the employee's project. This enables efficient learning by prioritizing the input of skills and learning objectives related to the current project and work content. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input employee project data into AI and have the AI ​​perform filtering of relevant skills and learning objectives.

[0101] The reception desk can estimate an employee's emotions and, based on that estimation, prioritize the skill levels and learning goals to be entered. For example, if an employee is stressed, high-priority skills can be prioritized. If an employee is relaxed, more detailed skill levels and learning goals can be entered. Furthermore, if an employee is in a hurry, the most important skills can be prioritized. This allows for efficient input of skill levels and learning goals by setting priorities according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The reception desk can prioritize inputting highly relevant skills by considering the employee's geographical location when inputting skill levels and learning objectives. For example, if an employee works in a specific region, skills in high demand in that region can be prioritized. It is also possible to suggest highly relevant skills based on the employee's work location. Furthermore, if an employee is working remotely, skills suitable for remote work can be prioritized. This allows for the acquisition of skills appropriate to the region by prioritizing the input of skills and learning objectives based on geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the employee's geographical location information into the AI ​​and have the AI ​​suggest highly relevant skills.

[0103] The generation unit can estimate the employee's emotions and adjust the presentation of the learning program based on the estimated emotions. For example, if the employee is relaxed, it can generate a learning program that proceeds at a relaxed pace. If the employee is in a hurry, it can generate a learning program that emphasizes the shortest route. Furthermore, if the employee is excited, it can generate a learning program with visually stimulating effects. This enhances learning effectiveness by providing a learning program presentation that responds to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The generation unit can adjust the level of detail in learning content based on the employee's importance when generating learning programs. For example, it can generate learning programs that include detailed explanations and practice problems for important skills. It can also generate learning programs that include concise explanations and basic practice problems for less important skills. Furthermore, it can adjust the level of detail in learning content based on the employee's job title and duties. This enables efficient learning by providing learning content with a level of detail appropriate to importance. 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 employee importance data into a generation AI and have the generation AI adjust the level of detail in learning content.

[0105] The generation unit can apply different learning algorithms depending on the employee's category when generating learning programs. For example, a learning program for beginners can be applied to an algorithm that teaches from the basics. A learning program for intermediate learners can be applied to an algorithm that includes advanced content. Furthermore, a learning program for advanced learners can be applied to an algorithm that includes specialized content. By applying a learning algorithm according to the category, efficient learning becomes possible. 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 employee category data into a generation AI and have the generation AI execute the application of the learning algorithm.

[0106] The generation unit can estimate the employee's emotions and adjust the length of the learning program based on the estimated emotions. For example, if the employee is in a hurry, it can generate a short, concise learning program. If the employee is relaxed, it can generate a longer learning program with detailed explanations. Furthermore, if the employee is excited, it can generate a learning program with visually stimulating effects. This enhances learning effectiveness by providing learning program lengths that match the employee's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The generation unit can determine the priority of learning programs based on employee submission dates when generating learning programs. For example, it can prioritize the generation of learning programs with approaching deadlines. It can also postpone the generation of learning programs with later submission dates. Furthermore, it can adjust the order of learning programs based on submission dates. This enables efficient learning by providing a priority order for learning programs based on submission dates. 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 employee submission date data into a generation AI and have the generation AI determine the priority of learning programs.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk enters the employee's skill level and learning objectives. Employees use an interface to enter their skill level and learning objectives. Step 2: The generation unit generates a learning program based on the information entered by the reception unit. The generation unit uses a generation AI to select the optimal learning content based on the employee's skill level and learning objectives, and generates a learning plan. Step 3: The learning unit proceeds with learning based on the learning program generated by the generation unit. The learning unit provides learning content such as video lectures and interactive exercises, allowing employees to learn at their own pace. Step 4: The monitoring department monitors the progress of learning conducted by the learning department and adjusts the learning plan as needed. The monitoring department monitors employees' learning progress in real time and suggests additional learning content if they get stuck on a particular topic.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the reception unit, generation unit, learning unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit provides an interface for inputting employee skill levels and learning objectives using the reception device 38 of the smart device 14. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates an optimal learning program using a generation AI. The learning unit provides video lectures and interactive exercises using the output device 40 of the smart device 14. The monitoring unit is implemented in the specific processing unit 290 of the data processing unit 12 and monitors employee learning progress in real time, adjusting the learning plan as needed. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the reception unit, generation unit, learning unit, and monitoring unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit provides an interface for inputting the employee's skill level and learning objectives using the microphone 238 of the smart glasses 214. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates an optimal learning program using a generation AI. The learning unit provides, for example, video lectures and interactive exercises using the speaker 240 of the smart glasses 214. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and monitors the employee's learning progress in real time and adjusts the learning plan as needed. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the reception unit, generation unit, learning unit, and monitoring unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the reception unit provides an interface for inputting the employee's skill level and learning objectives using the microphone 238 of the headset terminal 314. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates an optimal learning program using a generation AI. The learning unit provides, for example, video lectures and interactive exercises using the display 343 of the headset terminal 314. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and monitors the employee's learning progress in real time and adjusts the learning plan as needed. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the reception unit, generation unit, learning unit, and monitoring unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit provides an interface for inputting the employee's skill level and learning objectives using the microphone 238 of the robot 414. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates an optimal learning program using a generation AI. The learning unit provides, for example, video lectures and interactive exercises using the speaker 240 of the robot 414. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and monitors the employee's learning progress in real time and adjusts the learning plan as needed. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) The reception desk is where employees' skill levels and learning goals are entered, A generation unit generates a learning program based on the information input by the reception unit, A learning unit that proceeds with learning based on the learning program generated by the generation unit, The system includes a monitoring unit that monitors the progress of learning conducted by the aforementioned learning unit and adjusts the learning plan as needed. A system characterized by the following features. (Note 2) The generating unit is Select the most suitable learning content and generate a learning plan based on employees' skill levels and learning objectives. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, We provide learning content including video lectures and interactive exercises. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned monitoring unit, Monitor employees' learning progress in real time and suggest additional learning content if they get stuck on a particular topic. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned monitoring unit, Generate learning plans for employees to take the next step once they achieve their goals. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates employee sentiment and adjusts the input methods for skill levels and learning objectives based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze employees' past learning history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering skill levels and learning objectives, filtering is performed based on the employee's current projects and job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates employee sentiment and determines the priority of input skill levels and learning objectives based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering skill levels and learning objectives, the system prioritizes inputting highly relevant skills by considering the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When employees input their skill levels and learning goals, the system analyzes their social media activity and suggests relevant skills. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is The system estimates employee emotions and adjusts the presentation of the learning program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating learning programs, adjust the level of detail in the learning content based on the importance of each employee. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating the learning program, different learning algorithms are applied depending on the employee category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is The system estimates employee emotions and adjusts the length of the learning program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating learning programs, prioritize them based on when employees submit them. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating learning programs, the order of the learning programs is adjusted based on employee relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, The system estimates employee sentiment and adjusts how learning content is delivered based on that estimation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, When providing learning content, we refer to employees' past learning history to provide the most suitable content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, When providing learning content, customize the content based on the employee's current job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, The system estimates employee sentiment and prioritizes learning content based on that sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning unit, When providing learning content, we will consider the geographical location of employees to provide the most suitable content. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, When providing learning content, we analyze employees' social media activity and suggest relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned monitoring unit, Estimate employee sentiment and adjust learning progress monitoring methods based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned monitoring unit, When monitoring learning progress, the optimal monitoring method is selected by referring to the employee's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned monitoring unit, When monitoring learning progress, customize the monitoring methods based on the employee's current work content. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit, The system estimates employee emotions and prioritizes learning progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned monitoring unit, When monitoring learning progress, the optimal monitoring method is selected considering the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned monitoring unit, When monitoring learning progress, we analyze employees' social media activity and propose monitoring methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 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 is where employees' skill levels and learning goals are entered, A generation unit generates a learning program based on the information input by the reception unit, A learning unit that proceeds with learning based on the learning program generated by the generation unit, The system includes a monitoring unit that monitors the progress of learning conducted by the aforementioned learning unit and adjusts the learning plan as needed. A system characterized by the following features.

2. The generating unit is Select the most suitable learning content and generate a learning plan based on employees' skill levels and learning objectives. The system according to feature 1.

3. The aforementioned learning unit, We provide learning content including video lectures and interactive exercises. The system according to feature 1.

4. The aforementioned monitoring unit, Monitor employees' learning progress in real time and suggest additional learning content if they get stuck on a particular topic. The system according to feature 1.

5. The aforementioned monitoring unit, Generate learning plans for employees to take the next step once they achieve their goals. The system according to feature 1.

6. The aforementioned reception unit is The system estimates employee sentiment and adjusts the input methods for skill levels and learning objectives based on the estimated sentiment. The system according to feature 1.

7. The aforementioned reception unit is We analyze employees' past learning history and suggest the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering skill levels and learning objectives, filtering is performed based on the employee's current projects and job responsibilities. The system according to feature 1.

9. The aforementioned reception unit is The system estimates employee sentiment and determines the priority of input skill levels and learning objectives based on the estimated employee sentiment. The system according to feature 1.

10. The aforementioned reception unit is When entering skill levels and learning objectives, the system prioritizes inputting highly relevant skills by considering the employee's geographical location. The system according to feature 1.

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