system

Abstract

The system according to this embodiment aims to provide an optimal learning plan tailored to the learning progress and needs of employees. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects the learning progress and needs of employees. The analysis unit analyzes the data collected by the collection unit and provides an optimal learning plan for each employee. The provision unit supports learning based on the learning plan provided by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide an optimal learning plan according to the learning progress and needs of employees, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal learning plan according to the learning progress and needs of employees.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects the learning progress and needs of employees. The analysis unit analyzes the data collected by the collection unit and provides an optimal learning plan for each individual employee. The provision unit supports learning based on the learning plan provided by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal learning plan tailored to the learning progress and needs of employees. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides a vocational training system that uses robots to provide customized vocational training programs for corporations. This vocational training system collects employees' learning progress and needs, and an AI analyzes the collected data to provide an optimal learning plan for each individual employee. Furthermore, it provides real-time feedback and progress tracking to address the challenge of labor skill shortages faced by corporations. For example, the vocational training system collects employees' learning progress and needs. In this process, it collects detailed data such as what skills employees possess and what skills they wish to acquire. Next, the AI ​​analyzes the collected data to provide an optimal learning plan for each individual employee. For example, the AI ​​analyzes the employee's learning progress and needs and proposes an optimal curriculum. Furthermore, it provides real-time feedback and progress tracking. For example, the AI ​​monitors the employee's learning status in real time and provides feedback as needed. This allows employees to learn at their own pace. This mechanism can address the challenge of labor skill shortages faced by corporations. For example, by having AI support employee skill development, corporate productivity can be improved. Cost reductions can also be expected by shortening the training period. This allows the vocational training system to provide optimal learning plans based on employees' learning progress and needs, thereby supporting their learning.

[0029] The vocational training system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects the learning progress and needs of employees. The data collection unit collects detailed data, for example, what skills employees possess and what skills they wish to acquire. The data collection unit can collect data such as the number of completed assignments and test scores to measure the learning progress of employees. The data collection unit can collect information such as the desire for skill improvement and the acquisition of specific knowledge to collect employee needs. The analysis unit analyzes the data collected by the data collection unit and provides an optimal learning plan for each individual employee. The analysis unit, for example, analyzes the learning progress and needs of employees based on the collected data and proposes an optimal curriculum. The analysis unit can use AI to analyze the learning progress and needs of employees and provide an optimal learning plan. The data provision unit supports learning based on the learning plan provided by the analysis unit. The data provision unit, for example, provides real-time feedback to employees. The data provision unit can monitor the learning status of employees in real time and provide feedback as needed. The service provider can track employees' learning progress and provide appropriate support. This allows the vocational training system, according to the embodiment, to provide an optimal learning plan based on employees' learning progress and needs, thereby supporting their learning.

[0030] The data collection department collects employee learning progress and needs. Specifically, it collects detailed data such as what skills employees possess and what skills they wish to acquire. The data collection department measures learning progress by recording the number of assignments completed, test scores, and study time. It can also collect information on employees' skill development aspirations and the acquisition of specific knowledge through surveys and interviews. Furthermore, the data collection department collects employee work performance data and evaluations from supervisors to comprehensively assess employees' current skill levels. This allows the data collection department to accurately understand employees' learning needs and progress, and to provide foundational data for providing optimal learning plans for individual employees. The collected data is stored in a secure database and managed so that the analysis department can access it. The data collection department can regularly update the data to respond to changes in employee learning progress and needs. This allows the data collection department to continuously monitor employees' learning status and provide learning support based on the latest information.

[0031] The analytics department analyzes data collected by the data collection department to provide individualized learning plans for each employee. Specifically, it analyzes employees' learning progress and needs based on the collected data and proposes the most suitable curriculum. The analytics department can use AI to analyze employees' learning progress and needs and provide optimal learning plans. For example, AI can analyze an employee's past learning history and test scores to identify areas where the employee excels and areas where improvement is needed. Furthermore, AI can consider the employee's learning style and pace and propose the most suitable learning methods and materials for each individual employee. The analytics department can track employees' learning progress in real time and modify learning plans as needed. For example, if an employee is struggling with a particular task, the analytics department can support the employee's learning by providing additional support and supplementary materials. The analytics department can also evaluate employees' learning outcomes and determine the appropriate timing for moving on to the next step. This allows the analytics department to provide flexible learning support tailored to employees' learning needs and effectively support their skill development.

[0032] The service provider supports learning based on the learning plan provided by the analysis department. Specifically, it provides real-time feedback to employees. The service provider can monitor employees' learning progress in real time and provide feedback as needed. For example, when an employee completes an assignment, it provides immediate feedback, pointing out correct and incorrect answers. It can also provide additional resources and materials necessary for employees to acquire specific skills. The service provider can track employees' learning progress and provide appropriate support. For example, it can check whether employees are following the learning plan and provide additional support and motivation if they are falling behind. Furthermore, the service provider can evaluate employees' learning outcomes and determine the appropriate timing for moving to the next step. The service provider can provide flexible learning support tailored to employees' learning needs and effectively support their skill development. This allows the service provider to provide optimal learning plans based on employees' learning progress and needs, and support their learning. In addition, the service provider can collect employee feedback and use it to improve learning plans. For example, it can understand how employees feel about specific materials and learning methods and revise the learning plan as needed. This allows the service provider to improve employees' learning experiences and provide more effective learning support.

[0033] The service provider can provide real-time feedback. For example, it can monitor employees' learning progress in real time and provide feedback as needed. The service provider can immediately address any problems or questions employees may encounter during their learning. The service provider can provide appropriate feedback according to the employee's learning progress. For example, when an employee completes a specific task, the service provider can evaluate their performance and provide advice for the next step. If an employee encounters difficulties during their learning, the service provider can identify the cause and suggest solutions. By providing real-time feedback, the service provider can enhance the effectiveness of employee learning.

[0034] The service provider can track progress. For example, the service provider can monitor employees' learning status in real time and track their progress. The service provider can understand how far employees are progressing in their learning and provide appropriate support. The service provider can provide advice on how to move to the next step according to the employee's learning progress. For example, when an employee completes a specific task, the service provider can evaluate the results and provide advice on how to move to the next step. If an employee encounters difficulties during learning, the service provider can identify the cause and propose solutions. In this way, by tracking progress, the service provider can understand the employee's learning status and provide appropriate support.

[0035] The data collection unit can collect information on employees' skills and learning needs. For example, it can collect detailed data on what skills employees possess and what skills they wish to acquire. The data collection unit can collect information such as technical skills and soft skills to assess employees' abilities. To gather information on employees' learning needs, it can collect information on acquiring new technologies and the knowledge required for specific tasks. The data collection unit can use methods such as surveys and interviews to collect information on employees' skills and learning needs. This allows for the provision of optimal learning plans for individual employees by gathering information on their skills and learning needs.

[0036] The analysis department can analyze collected data and propose an optimal curriculum. For example, the analysis department can analyze employees' learning progress and needs based on collected data and propose an optimal curriculum. The analysis department can use AI to analyze employees' learning progress and needs and propose an optimal curriculum. The analysis department can propose an optimal curriculum considering the selection of learning materials according to employees' learning goals and the pace of learning. For example, the analysis department can propose a curriculum according to employees' skill levels and adjust the pace of learning. The analysis department can propose a curriculum for acquiring specific knowledge and skills according to employees' learning needs. In this way, by analyzing collected data and proposing an optimal curriculum, the effectiveness of employee learning can be improved.

[0037] The data collection unit can analyze an employee's past learning history and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on the learning methods the employee has preferred to use in the past (online, in-person, etc.). The data collection unit can prioritize selecting learning methods in which the employee has achieved high results in the past. The data collection unit can select a data collection method that is effective for a specific time period based on the employee's past learning history. This enables efficient data collection by selecting the optimal data collection method through analysis of the employee's past learning history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the employee's past learning history data into a generating AI and have the generating AI select the optimal data collection method.

[0038] The data collection unit can filter learning needs based on an employee's current project and position. For example, the data collection unit can prioritize collecting skills related to the current project. The data collection unit can filter and collect skills required according to the position. The data collection unit can collect necessary learning needs according to the progress of the project. This allows for the collection of highly relevant learning needs by filtering based on an employee's current project and position. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee project and position data into a generating AI and have the generating AI perform the filtering.

[0039] The data collection unit can prioritize collecting highly relevant needs by considering the employee's geographical location when collecting learning needs. For example, if an employee is working remotely, the data collection unit can prioritize collecting learning needs that can be completed online. If an employee is in the office, the data collection unit can prioritize collecting in-person training. If an employee is traveling, the data collection unit can prioritize collecting learning needs available at their travel destination. This allows for the collection of highly relevant learning needs by considering the employee's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee geographical location data into a generating AI and have the generating AI perform the collection of highly relevant needs.

[0040] The data collection unit can analyze employees' social media activity and collect relevant needs when collecting learning needs. For example, the data collection unit can collect learning needs based on topics that employees have shown interest in on social media. The data collection unit can analyze posts from industry leaders that employees follow and collect relevant learning needs. The data collection unit can analyze the activities of online communities that employees participate in and collect relevant learning needs. In this way, relevant learning needs can be collected by analyzing employees' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee social media activity data into a generating AI and have the generating AI perform the collection of relevant needs.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the learning needs during the analysis. For example, the analysis unit can perform a detailed analysis for high-importance learning needs. For low-importance learning needs, the analysis unit can perform a concise analysis. The analysis unit can adjust the depth of the analysis in stages according to importance. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the learning needs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input learning need importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of learning needs during analysis. For example, the analysis unit can apply a technical analysis algorithm to learning needs related to technical skills. For learning needs related to soft skills, the analysis unit can apply a behavioral analysis algorithm. For learning needs related to management skills, the analysis unit can apply a leadership analysis algorithm. This enables efficient data analysis by applying different analysis algorithms depending on the category of learning needs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input learning needs category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on when the learning needs were submitted. For example, the analysis unit may prioritize the analysis of recently submitted learning needs. The analysis unit may postpone the analysis of older learning needs. The analysis unit can adjust the priority of analysis in stages according to the submission date. This enables efficient data analysis by determining the priority of analysis based on the submission date of learning needs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the learning need submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of learning needs during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant learning needs. The analysis unit can postpone the analysis of less relevant learning needs. The analysis unit can adjust the order of analysis stepwise according to relevance. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of learning needs. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the relevance of learning needs into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The feedback provider can adjust the level of detail of the feedback based on the learning progress when providing feedback. For example, the provider can provide detailed feedback to employees who are learning quickly, and concise feedback to employees who are learning slowly. The provider can adjust the level of detail of the feedback in stages according to the learning progress. This allows for efficient feedback by adjusting the level of detail of the feedback based on the learning progress. Some or all of the above processing in the feedback provider may be performed using AI, for example, or not using AI. For example, the provider can input learning progress data into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0046] The service provider can apply different feedback algorithms depending on the category of learning content when providing feedback. For example, the service provider can apply a technical feedback algorithm to feedback on technical skills. For feedback on soft skills, the service provider can apply a behavioral analysis feedback algorithm. For feedback on management skills, the service provider can apply a leadership analysis feedback algorithm. This enables efficient feedback by applying different feedback algorithms depending on the category of learning content. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input learning content category data into a generating AI and have the generating AI execute the application of the feedback algorithm.

[0047] The feedback provider can prioritize feedback based on the submission date of learning progress when providing feedback. For example, the provider can prioritize feedback on recently submitted learning progress. The provider can postpone feedback on older submission dates. The provider can adjust the feedback priority in stages according to the submission date. This enables efficient feedback by prioritizing feedback based on the submission date of learning progress. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI. For example, the provider can input learning progress submission date data into a generating AI and have the generating AI determine the feedback priority.

[0048] The feedback provider can adjust the order of feedback based on the relevance of the learning content when providing feedback. For example, the provider can prioritize providing feedback to highly relevant learning content. The provider can postpone providing feedback to less relevant learning content. The provider can adjust the order of feedback in stages according to relevance. This allows for efficient feedback by adjusting the order of feedback based on the relevance of the learning content. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the provider can input data on the relevance of the learning content into a generating AI and have the generating AI perform the adjustment of the feedback order.

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

[0050] The analytics department can consider employees' past learning history and performance data when analyzing their learning progress and needs. For example, it can analyze what learning methods have worked well for employees in the past and incorporate those methods into the current learning plan. It can also identify areas where employees have struggled in the past and provide special support in those areas. Furthermore, it can adjust the pace and difficulty of learning based on employees' past performance data. In this way, by considering employees' past learning history and performance data, it is possible to provide more effective learning plans.

[0051] The service provider can not only monitor employees' learning progress in real time but also provide incentives to maintain learning motivation. For example, the service provider can award badges or points to employees when they complete specific tasks. It can also offer rewards and benefits when employees achieve certain learning goals. Furthermore, the service provider can implement a ranking system to encourage competition among employees. This can increase employee motivation and improve learning effectiveness.

[0052] The service provider can customize progress tracking to suit each employee's learning style. For example, employees with a visual learning style can be provided with progress reports using graphs and charts. Employees with an auditory learning style can be provided with progress reports in the form of audio messages or podcasts. Furthermore, employees with a hands-on learning style can have their progress evaluated through actual projects and tasks. By tracking progress according to each employee's learning style, the learning effect can be maximized.

[0053] The data collection department can consider employees' career goals when gathering information on their skills and learning needs. For example, it can collect information on the career paths employees aspire to in the future and identify the necessary skills and knowledge based on those goals. It can also collect the skills required for employees to advance to specific positions or projects. Furthermore, the data collection department can provide long-term learning plans tailored to employees' career goals. This allows for the provision of more effective learning plans by collecting learning needs based on employees' career goals.

[0054] The analytics department can consider employees' learning environments when analyzing collected data. For example, if an employee is working remotely, the analytics department can suggest a curriculum that prioritizes online learning. If an employee is working in the office, it can suggest a curriculum that includes in-person training. Furthermore, if an employee is traveling, it can suggest learning resources available at their destination. This allows for the provision of an optimal curriculum tailored to each employee's learning environment.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection department gathers information on employees' learning progress and needs. For example, it collects detailed data on what skills employees possess and what skills they wish to acquire. The data collection department measures employees' learning progress by collecting data such as the number of completed assignments and test scores. It also gathers information on employee needs, such as their desire for skill development and the acquisition of specific knowledge. Step 2: The analysis unit analyzes the data collected by the data collection unit and provides an optimal learning plan for each individual employee. For example, it analyzes the employee's learning progress and needs based on the collected data and proposes an optimal curriculum. The analysis unit can use AI to analyze the employee's learning progress and needs and provide an optimal learning plan. Step 3: The delivery department supports learning based on the learning plan provided by the analysis department. For example, it provides real-time feedback to employees. The delivery department monitors employees' learning progress in real time and provides feedback as needed. Furthermore, it tracks employees' learning progress and provides appropriate support.

[0057] (Example of form 2) An embodiment of the present invention provides a vocational training system that uses robots to provide customized vocational training programs for corporations. This vocational training system collects employees' learning progress and needs, and an AI analyzes the collected data to provide an optimal learning plan for each individual employee. Furthermore, it provides real-time feedback and progress tracking to address the challenge of labor skill shortages faced by corporations. For example, the vocational training system collects employees' learning progress and needs. In this process, it collects detailed data such as what skills employees possess and what skills they wish to acquire. Next, the AI ​​analyzes the collected data to provide an optimal learning plan for each individual employee. For example, the AI ​​analyzes the employee's learning progress and needs and proposes an optimal curriculum. Furthermore, it provides real-time feedback and progress tracking. For example, the AI ​​monitors the employee's learning status in real time and provides feedback as needed. This allows employees to learn at their own pace. This mechanism can address the challenge of labor skill shortages faced by corporations. For example, by having AI support employee skill development, corporate productivity can be improved. Cost reductions can also be expected by shortening the training period. This allows the vocational training system to provide optimal learning plans based on employees' learning progress and needs, thereby supporting their learning.

[0058] The vocational training system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects the learning progress and needs of employees. The data collection unit collects detailed data, for example, what skills employees possess and what skills they wish to acquire. The data collection unit can collect data such as the number of completed assignments and test scores to measure the learning progress of employees. The data collection unit can collect information such as the desire for skill improvement and the acquisition of specific knowledge to collect employee needs. The analysis unit analyzes the data collected by the data collection unit and provides an optimal learning plan for each individual employee. The analysis unit, for example, analyzes the learning progress and needs of employees based on the collected data and proposes an optimal curriculum. The analysis unit can use AI to analyze the learning progress and needs of employees and provide an optimal learning plan. The data provision unit supports learning based on the learning plan provided by the analysis unit. The data provision unit, for example, provides real-time feedback to employees. The data provision unit can monitor the learning status of employees in real time and provide feedback as needed. The service provider can track employees' learning progress and provide appropriate support. This allows the vocational training system, according to the embodiment, to provide an optimal learning plan based on employees' learning progress and needs, thereby supporting their learning.

[0059] The data collection department collects employee learning progress and needs. Specifically, it collects detailed data such as what skills employees possess and what skills they wish to acquire. The data collection department measures learning progress by recording the number of assignments completed, test scores, and study time. It can also collect information on employees' skill development aspirations and the acquisition of specific knowledge through surveys and interviews. Furthermore, the data collection department collects employee work performance data and evaluations from supervisors to comprehensively assess employees' current skill levels. This allows the data collection department to accurately understand employees' learning needs and progress, and to provide foundational data for providing optimal learning plans for individual employees. The collected data is stored in a secure database and managed so that the analysis department can access it. The data collection department can regularly update the data to respond to changes in employee learning progress and needs. This allows the data collection department to continuously monitor employees' learning status and provide learning support based on the latest information.

[0060] The analytics department analyzes data collected by the data collection department to provide individualized learning plans for each employee. Specifically, it analyzes employees' learning progress and needs based on the collected data and proposes the most suitable curriculum. The analytics department can use AI to analyze employees' learning progress and needs and provide optimal learning plans. For example, AI can analyze an employee's past learning history and test scores to identify areas where the employee excels and areas where improvement is needed. Furthermore, AI can consider the employee's learning style and pace and propose the most suitable learning methods and materials for each individual employee. The analytics department can track employees' learning progress in real time and modify learning plans as needed. For example, if an employee is struggling with a particular task, the analytics department can support the employee's learning by providing additional support and supplementary materials. The analytics department can also evaluate employees' learning outcomes and determine the appropriate timing for moving on to the next step. This allows the analytics department to provide flexible learning support tailored to employees' learning needs and effectively support their skill development.

[0061] The service provider supports learning based on the learning plan provided by the analysis department. Specifically, it provides real-time feedback to employees. The service provider can monitor employees' learning progress in real time and provide feedback as needed. For example, when an employee completes an assignment, it provides immediate feedback, pointing out correct and incorrect answers. It can also provide additional resources and materials necessary for employees to acquire specific skills. The service provider can track employees' learning progress and provide appropriate support. For example, it can check whether employees are following the learning plan and provide additional support and motivation if they are falling behind. Furthermore, the service provider can evaluate employees' learning outcomes and determine the appropriate timing for moving to the next step. The service provider can provide flexible learning support tailored to employees' learning needs and effectively support their skill development. This allows the service provider to provide optimal learning plans based on employees' learning progress and needs, and support their learning. In addition, the service provider can collect employee feedback and use it to improve learning plans. For example, it can understand how employees feel about specific materials and learning methods and revise the learning plan as needed. This allows the service provider to improve employees' learning experiences and provide more effective learning support.

[0062] The service provider can provide real-time feedback. For example, it can monitor employees' learning progress in real time and provide feedback as needed. The service provider can immediately address any problems or questions employees may encounter during their learning. The service provider can provide appropriate feedback according to the employee's learning progress. For example, when an employee completes a specific task, the service provider can evaluate their performance and provide advice for the next step. If an employee encounters difficulties during their learning, the service provider can identify the cause and suggest solutions. By providing real-time feedback, the service provider can enhance the effectiveness of employee learning.

[0063] The service provider can track progress. For example, the service provider can monitor employees' learning status in real time and track their progress. The service provider can understand how far employees are progressing in their learning and provide appropriate support. The service provider can provide advice on how to move to the next step according to the employee's learning progress. For example, when an employee completes a specific task, the service provider can evaluate the results and provide advice on how to move to the next step. If an employee encounters difficulties during learning, the service provider can identify the cause and propose solutions. In this way, by tracking progress, the service provider can understand the employee's learning status and provide appropriate support.

[0064] The data collection unit can collect information on employees' skills and learning needs. For example, it can collect detailed data on what skills employees possess and what skills they wish to acquire. The data collection unit can collect information such as technical skills and soft skills to assess employees' abilities. To gather information on employees' learning needs, it can collect information on acquiring new technologies and the knowledge required for specific tasks. The data collection unit can use methods such as surveys and interviews to collect information on employees' skills and learning needs. This allows for the provision of optimal learning plans for individual employees by gathering information on their skills and learning needs.

[0065] The analysis department can analyze collected data and propose an optimal curriculum. For example, the analysis department can analyze employees' learning progress and needs based on collected data and propose an optimal curriculum. The analysis department can use AI to analyze employees' learning progress and needs and propose an optimal curriculum. The analysis department can propose an optimal curriculum considering the selection of learning materials according to employees' learning goals and the pace of learning. For example, the analysis department can propose a curriculum according to employees' skill levels and adjust the pace of learning. The analysis department can propose a curriculum for acquiring specific knowledge and skills according to employees' learning needs. In this way, by analyzing collected data and proposing an optimal curriculum, the effectiveness of employee learning can be improved.

[0066] The data collection unit can estimate employees' emotions and adjust the timing of collecting learning needs based on the estimated emotions. For example, if an employee is stressed, the data collection unit recommends collecting learning needs during breaks or in a relaxed environment to ensure the employee is relaxed. If an employee is focused, the data collection unit can collect learning needs at that time for efficient data collection. If an employee is tired, the data collection unit can postpone the collection and collect it after the employee has refreshed. This allows for efficient data collection by adjusting the timing of collecting learning needs based on employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into a generative AI and have the generative AI perform emotion estimation.

[0067] The data collection unit can analyze an employee's past learning history and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on the learning methods the employee has preferred to use in the past (online, in-person, etc.). The data collection unit can prioritize selecting learning methods in which the employee has achieved high results in the past. The data collection unit can select a data collection method that is effective for a specific time period based on the employee's past learning history. This enables efficient data collection by selecting the optimal data collection method through analysis of the employee's past learning history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the employee's past learning history data into a generating AI and have the generating AI select the optimal data collection method.

[0068] The data collection unit can filter learning needs based on an employee's current project and position. For example, the data collection unit can prioritize collecting skills related to the current project. The data collection unit can filter and collect skills required according to the position. The data collection unit can collect necessary learning needs according to the progress of the project. This allows for the collection of highly relevant learning needs by filtering based on an employee's current project and position. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee project and position data into a generating AI and have the generating AI perform the filtering.

[0069] The data collection unit can estimate employees' emotions and prioritize learning needs to be collected based on those estimated emotions. For example, if an employee is stressed, the data collection unit will prioritize collecting relaxing learning needs. If an employee is motivated, the data collection unit can prioritize collecting challenging learning needs. If an employee is tired, the data collection unit can prioritize collecting easy learning needs. This enables efficient data collection by prioritizing learning needs based on employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into a generative AI and have the generative AI determine the priority of learning needs.

[0070] The data collection unit can prioritize collecting highly relevant needs by considering the employee's geographical location when collecting learning needs. For example, if an employee is working remotely, the data collection unit can prioritize collecting learning needs that can be completed online. If an employee is in the office, the data collection unit can prioritize collecting in-person training. If an employee is traveling, the data collection unit can prioritize collecting learning needs available at their travel destination. This allows for the collection of highly relevant learning needs by considering the employee's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee geographical location data into a generating AI and have the generating AI perform the collection of highly relevant needs.

[0071] The data collection unit can analyze employees' social media activity and collect relevant needs when collecting learning needs. For example, the data collection unit can collect learning needs based on topics that employees have shown interest in on social media. The data collection unit can analyze posts from industry leaders that employees follow and collect relevant learning needs. The data collection unit can analyze the activities of online communities that employees participate in and collect relevant learning needs. In this way, relevant learning needs can be collected by analyzing employees' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee social media activity data into a generating AI and have the generating AI perform the collection of relevant needs.

[0072] The analysis unit can estimate employees' emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit can provide a simple and visually easy-to-understand analysis result. If an employee is relaxed, the analysis unit can provide a detailed analysis result. If an employee is in a hurry, the analysis unit can provide a concise analysis result that gets straight to the point. This allows for efficient data analysis by adjusting the presentation of the analysis based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input employee emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the learning needs during the analysis. For example, the analysis unit can perform a detailed analysis for high-importance learning needs. For low-importance learning needs, the analysis unit can perform a concise analysis. The analysis unit can adjust the depth of the analysis in stages according to importance. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the learning needs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input learning need importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the category of learning needs during analysis. For example, the analysis unit can apply a technical analysis algorithm to learning needs related to technical skills. For learning needs related to soft skills, the analysis unit can apply a behavioral analysis algorithm. For learning needs related to management skills, the analysis unit can apply a leadership analysis algorithm. This enables efficient data analysis by applying different analysis algorithms depending on the category of learning needs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input learning needs category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0075] The analysis unit can estimate the employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the employee is stressed, the analysis unit can provide a short, concise analysis. If the employee is relaxed, the analysis unit can provide a detailed analysis. If the employee is in a hurry, the analysis unit can provide a brief analysis. This allows for efficient data analysis by adjusting the length of the analysis based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input employee emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0076] The analysis unit can determine the priority of analysis based on when the learning needs were submitted. For example, the analysis unit may prioritize the analysis of recently submitted learning needs. The analysis unit may postpone the analysis of older learning needs. The analysis unit can adjust the priority of analysis in stages according to the submission date. This enables efficient data analysis by determining the priority of analysis based on the submission date of learning needs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the learning need submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0077] The analysis unit can adjust the order of analysis based on the relevance of learning needs during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant learning needs. The analysis unit can postpone the analysis of less relevant learning needs. The analysis unit can adjust the order of analysis stepwise according to relevance. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of learning needs. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the relevance of learning needs into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0078] The service provider can estimate an employee's emotions and adjust the way feedback is presented based on the estimated emotions. For example, if an employee is stressed, the service provider will prioritize providing positive feedback. If an employee is relaxed, the service provider can provide detailed feedback. If an employee is in a hurry, the service provider can provide concise feedback. This allows for efficient feedback by adjusting the way feedback is presented based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input employee emotion data into a generative AI and have the generative AI adjust the way feedback is presented.

[0079] The feedback provider can adjust the level of detail of the feedback based on the learning progress when providing feedback. For example, the provider can provide detailed feedback to employees who are learning quickly, and concise feedback to employees who are learning slowly. The provider can adjust the level of detail of the feedback in stages according to the learning progress. This allows for efficient feedback by adjusting the level of detail of the feedback based on the learning progress. Some or all of the above processing in the feedback provider may be performed using AI, for example, or not using AI. For example, the provider can input learning progress data into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0080] The service provider can apply different feedback algorithms depending on the category of learning content when providing feedback. For example, the service provider can apply a technical feedback algorithm to feedback on technical skills. For feedback on soft skills, the service provider can apply a behavioral analysis feedback algorithm. For feedback on management skills, the service provider can apply a leadership analysis feedback algorithm. This enables efficient feedback by applying different feedback algorithms depending on the category of learning content. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input learning content category data into a generating AI and have the generating AI execute the application of the feedback algorithm.

[0081] The service provider can estimate an employee's emotions and adjust the length of the feedback based on the estimated emotions. For example, if an employee is stressed, the service provider can provide short, concise feedback. If an employee is relaxed, the service provider can provide detailed feedback. If an employee is in a hurry, the service provider can provide brief feedback. This allows for efficient feedback by adjusting the length of the feedback based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input employee emotion data into a generative AI and have the generative AI adjust the length of the feedback.

[0082] The feedback provider can prioritize feedback based on the submission date of learning progress when providing feedback. For example, the provider can prioritize feedback on recently submitted learning progress. The provider can postpone feedback on older submission dates. The provider can adjust the feedback priority in stages according to the submission date. This enables efficient feedback by prioritizing feedback based on the submission date of learning progress. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI. For example, the provider can input learning progress submission date data into a generating AI and have the generating AI determine the feedback priority.

[0083] The feedback provider can adjust the order of feedback based on the relevance of the learning content when providing feedback. For example, the provider can prioritize providing feedback to highly relevant learning content. The provider can postpone providing feedback to less relevant learning content. The provider can adjust the order of feedback in stages according to relevance. This allows for efficient feedback by adjusting the order of feedback based on the relevance of the learning content. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the provider can input data on the relevance of the learning content into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The analytics department can consider employees' past learning history and performance data when analyzing their learning progress and needs. For example, it can analyze what learning methods have worked well for employees in the past and incorporate those methods into the current learning plan. It can also identify areas where employees have struggled in the past and provide special support in those areas. Furthermore, it can adjust the pace and difficulty of learning based on employees' past performance data. In this way, by considering employees' past learning history and performance data, it is possible to provide more effective learning plans.

[0086] The service provider can not only monitor employees' learning progress in real time but also provide incentives to maintain learning motivation. For example, the service provider can award badges or points to employees when they complete specific tasks. It can also offer rewards and benefits when employees achieve certain learning goals. Furthermore, the service provider can implement a ranking system to encourage competition among employees. This can increase employee motivation and improve learning effectiveness.

[0087] The service provider can customize progress tracking to suit each employee's learning style. For example, employees with a visual learning style can be provided with progress reports using graphs and charts. Employees with an auditory learning style can be provided with progress reports in the form of audio messages or podcasts. Furthermore, employees with a hands-on learning style can have their progress evaluated through actual projects and tasks. By tracking progress according to each employee's learning style, the learning effect can be maximized.

[0088] The data collection department can consider employees' career goals when gathering information on their skills and learning needs. For example, it can collect information on the career paths employees aspire to in the future and identify the necessary skills and knowledge based on those goals. It can also collect the skills required for employees to advance to specific positions or projects. Furthermore, the data collection department can provide long-term learning plans tailored to employees' career goals. This allows for the provision of more effective learning plans by collecting learning needs based on employees' career goals.

[0089] The analytics department can consider employees' learning environments when analyzing collected data. For example, if an employee is working remotely, the analytics department can suggest a curriculum that prioritizes online learning. If an employee is working in the office, it can suggest a curriculum that includes in-person training. Furthermore, if an employee is traveling, it can suggest learning resources available at their destination. This allows for the provision of an optimal curriculum tailored to each employee's learning environment.

[0090] The data collection unit can estimate employees' emotions and adjust the timing of collecting learning needs based on those emotions. For example, if an employee is stressed, the unit recommends collecting learning needs during breaks or in a relaxed environment to ensure a more relaxed state. If an employee is focused, the unit can collect learning needs at that time, enabling efficient data collection. Furthermore, if an employee is tired, the collection timing can be postponed until the employee has refreshed. This allows for efficient data collection by adjusting the timing of learning needs collection based on employees' emotions.

[0091] The data collection unit can estimate employees' emotions and prioritize the learning needs to be collected based on those estimated emotions. For example, if an employee is stressed, it will prioritize collecting relaxing learning needs. If an employee is motivated, the data collection unit can prioritize collecting challenging learning needs. Furthermore, if an employee is tired, it can prioritize collecting easy learning needs. This allows for efficient data collection by prioritizing learning needs based on employees' emotions.

[0092] The analysis unit can estimate employees' emotions and adjust the presentation of the analysis based on those estimated emotions. For example, if an employee is stressed, it can provide a simple and visually easy-to-understand analysis. If an employee is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if an employee is in a hurry, it can provide a concise analysis that gets straight to the point. By adjusting the presentation of the analysis based on employees' emotions, efficient data analysis becomes possible.

[0093] The analysis unit can estimate employees' emotions and adjust the length of the analysis based on those estimates. For example, if an employee is stressed, it provides a short, concise analysis. If an employee is relaxed, it can provide a detailed analysis. Furthermore, if an employee is in a hurry, it can provide a brief analysis. By adjusting the length of the analysis based on employees' emotions, efficient data analysis becomes possible.

[0094] The feedback system can estimate an employee's emotions and adjust the way feedback is presented based on that estimation. For example, if an employee is stressed, positive feedback will be prioritized. If an employee is relaxed, detailed feedback can be provided. Furthermore, if an employee is in a hurry, concise feedback can be provided. This allows for more efficient feedback by adjusting the presentation based on the employee's emotions.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The data collection department gathers information on employees' learning progress and needs. For example, it collects detailed data on what skills employees possess and what skills they wish to acquire. The data collection department measures employees' learning progress by collecting data such as the number of completed assignments and test scores. It also gathers information on employee needs, such as their desire for skill development and the acquisition of specific knowledge. Step 2: The analysis unit analyzes the data collected by the data collection unit and provides an optimal learning plan for each individual employee. For example, it analyzes the employee's learning progress and needs based on the collected data and proposes an optimal curriculum. The analysis unit can use AI to analyze the employee's learning progress and needs and provide an optimal learning plan. Step 3: The delivery department supports learning based on the learning plan provided by the analysis department. For example, it provides real-time feedback to employees. The delivery department monitors employees' learning progress in real time and provides feedback as needed. Furthermore, it tracks employees' learning progress and provides appropriate support.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect employee learning progress and needs. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and provide an optimal learning plan. The data provision unit is implemented in the control unit 46A of the smart device 14, for example, to provide real-time feedback and progress tracking. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect employee learning progress and needs. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to provide an optimal learning plan. The data provision unit is implemented, for example, in the control unit 46A of the smart glasses 214, which provides real-time feedback and progress tracking. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect employee learning progress and needs. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and provide an optimal learning plan. The data provision unit is implemented in the control unit 46A of the headset terminal 314, for example, to provide real-time feedback and progress tracking. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect employee learning progress and needs. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to provide an optimal learning plan. The data provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides real-time feedback and progress tracking. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) The collection department collects information on employees' learning progress and needs, The analysis unit analyzes the data collected by the aforementioned collection unit and provides an optimal learning plan for each employee, The system includes a provisioning unit that supports learning based on the learning plan provided by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Provide real-time feedback The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Track progress The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect information on employees' skills and learning needs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We analyze the collected data and propose the optimal curriculum. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate employee sentiment and adjust the timing of collecting learning needs based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze employees' past learning history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting learning needs, filter them based on the employee's current project and role. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Estimate employee sentiment and prioritize learning needs to be collected based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting learning needs, prioritize collecting highly relevant needs by considering employees' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering learning needs, analyze employees' social media activity to collect relevant needs. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, We estimate the emotions of employees and adjust the representation of the analysis based on the estimated emotions of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the learning needs. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of learning needs. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates employee sentiment and adjusts the length of the analysis based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the analysis priorities are determined based on when the learning needs were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of learning needs. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates employee emotions and adjusts the way feedback is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing feedback, adjust the level of detail based on the learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing feedback, different feedback algorithms are applied depending on the category of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates employee emotions and adjusts the length of feedback based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing feedback, we will prioritize the feedback based on when the learning progress is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing feedback, adjust the order of feedback based on the relevance of the learned content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 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 collection department collects information on employees' learning progress and needs, The analysis unit analyzes the data collected by the aforementioned collection unit and provides an optimal learning plan for each employee, The system includes a provisioning unit that supports learning based on the learning plan provided by the analysis unit. A system characterized by the following features.

2. The aforementioned supply unit is, Provide real-time feedback The system according to feature 1.

3. The aforementioned supply unit is, Track progress The system according to feature 1.

4. The aforementioned collection unit is Collect information on employees' skills and learning needs. The system according to feature 1.

5. The aforementioned analysis unit, We analyze the collected data and propose the optimal curriculum. The system according to feature 1.

6. The aforementioned collection unit is We estimate employee sentiment and adjust the timing of collecting learning needs based on the estimated sentiment. The system according to feature 1.

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

8. The aforementioned collection unit is When collecting learning needs, filter them based on the employee's current project and role. The system according to feature 1.

9. The aforementioned collection unit is Estimate employee sentiment and prioritize learning needs to be collected based on the estimated employee sentiment. The system according to feature 1.

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