system

Abstract

The system according to this embodiment aims to provide personalized learning materials tailored to the educational status of each employee. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects employee learning data. The analysis unit analyzes the learning data collected by the collection unit. The generation unit generates personalized learning materials in a virtual reality space based on the analysis results obtained by the analysis unit. The provision unit provides the learning materials generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently provide personalized teaching materials according to the educational status of each employee.

[0005] The system according to the embodiment aims to provide personalized teaching materials according to the educational status of each employee.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects employee learning data. The analysis unit analyzes the learning data collected by the collection unit. The generation unit generates personalized learning materials in a virtual reality space based on the analysis results obtained by the analysis unit. The provision unit provides the learning materials generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized learning materials tailored to the educational status of each employee. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI system according to an embodiment of the present invention is a system that generates custom training materials for employee education in conjunction with AR / VR technology. This AI system is a mechanism that generates personalized training materials in a virtual reality space tailored to the training status of each employee, from new employee training to skill improvement and retraining. First, the AI ​​system collects employee learning data. The learning data includes the employee's level of understanding and progress, and past learning history. For example, the progress of new employee training and the results of training for skill improvement are collected. This data is input into the AI ​​system. Next, the AI ​​system analyzes the collected learning data. The AI ​​analyzes the employee's level of understanding and progress and identifies the optimal learning content for each individual employee. For example, for an employee lacking a particular skill, training materials to strengthen that skill are generated. Furthermore, the AI ​​system generates personalized training materials in a virtual reality space based on the analysis results. The generated training materials are provided to employees using AR / VR technology. For example, practical skills training can be conducted in the virtual reality space. This allows employees to acquire skills in an environment similar to their actual work. This mechanism makes it possible to provide training programs tailored to each individual employee. Even large organizations can build training programs that flexibly respond to individual needs. Furthermore, it enables the acquisition of practical skills on the job, contributing to employee capability development. For example, large companies can efficiently develop human resources by generating customized training materials tailored to each employee's learning situation, from new employee training to skill enhancement and retraining. Companies specializing in human resource development can enhance their competitiveness by providing training programs tailored to the needs of individual employees. In this way, AI systems integrated with AR / VR technology can optimize employee learning and support capability development. Thus, AI systems can optimize employee learning and support capability development.

[0029] The AI ​​system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects employee learning data. Learning data includes, but is not limited to, an employee's level of understanding, progress, and past learning history. The collection unit evaluates an employee's level of understanding based on test results or quiz scores, for example. The collection unit can also evaluate progress based on learning progress and the number of completed tasks. Furthermore, the collection unit can collect past learning history based on courses taken in the past and skills acquired. For example, the collection unit retrieves an employee's learning history from a database and inputs it into the AI ​​system. The analysis unit analyzes the learning data collected by the collection unit. The analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit uses statistical analysis to analyze an employee's learning data and evaluate their level of understanding and progress. The analysis unit can also use machine learning algorithms to analyze an employee's learning data and identify the most suitable learning content for each individual employee. For example, the analysis unit identifies learning content using an AI model that takes employee learning data as input and outputs optimal learning content. The generation unit generates personalized learning materials in a virtual reality space based on the analysis results obtained by the analysis unit. Generation is performed using, for example, a generative AI, but is not limited to such examples. For example, the generation unit uses a generative AI to generate personalized learning materials based on employee learning data. The generation unit can also use a generative AI to generate learning materials for practical skills training in a virtual reality space. For example, the generation unit uses a generative AI to generate simulation learning materials for practical skills training on the job. The delivery unit provides the learning materials generated by the generation unit. Delivery is performed using, for example, AR / VR technology, but is not limited to such examples. For example, the delivery unit provides the generated learning materials to employees using AR or VR devices. The delivery unit can also provide the generated learning materials through a web application or mobile application. For example, the delivery unit provides the generated learning materials to employees through a web application, enabling them to learn in a virtual reality space.This enables the AI ​​system according to the embodiment to provide personalized training programs for each employee.

[0030] The data collection unit collects employee learning data. This learning data includes, but is not limited to, employee comprehension, progress, and past learning history. For example, the data collection unit evaluates employee comprehension based on test results and quiz scores. Specifically, when employees take online tests or quizzes, the results are automatically collected and used to evaluate comprehension. Test results include detailed data such as correct answer rate and response time, allowing for a multifaceted evaluation of employee comprehension. The data collection unit can also evaluate progress based on learning progress and the number of completed assignments. For example, when employees take online courses, the completion status of each module and the submission status of assignments are recorded in real time. This allows for an accurate understanding of how far employees are progressing in their learning. Furthermore, the data collection unit can collect past learning history based on previously taken courses and acquired skills. For example, information on training courses previously taken and certifications acquired by employees is stored in a database, and this information is collected and input into the AI ​​system. This allows for a comprehensive understanding of employee learning history and provides foundational data to address individual learning needs. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the learning data collected by the data collection department. This analysis is performed using, but is not limited to, statistical analysis and machine learning algorithms. Specifically, it uses statistical analysis to analyze employee learning data and evaluate understanding and progress. For example, it can statistically analyze employee test results and quiz scores to identify the overall distribution of understanding and biases in understanding in specific areas. The analysis department can also use machine learning algorithms to analyze employee learning data and identify the optimal learning content for individual employees. For example, it can use an AI model that takes employee learning data as input and outputs the optimal learning content. Specifically, a machine learning algorithm recommends the optimal learning content based on the employee's past learning history, understanding, and progress. This AI model learns employee learning patterns and tendencies and is continuously improved to provide content best suited to individual needs. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term learning effectiveness and trend analysis. For example, it can evaluate the effectiveness of specific learning programs based on past learning data to improve future learning plans. It can also use anomaly detection algorithms to detect unusual learning patterns and abnormal data, enabling early intervention. This allows the analysis unit to not only grasp the situation in real time, but also to evaluate long-term learning effects and detect anomalies, thereby improving the reliability and effectiveness of the entire system.

[0032] The generation unit generates personalized learning materials in a virtual reality space based on the analysis results obtained by the analysis unit. Generation is performed using, for example, a generation AI, but is not limited to this example. Specifically, the generation AI is used to generate personalized learning materials based on employee learning data. For example, it can generate learning materials with the optimal difficulty level and content according to the employee's level of understanding and progress. The generation AI uses natural language processing technology to automatically generate text and questions that match the employee's learning needs. The generation unit can also use the generation AI to generate learning materials for practical skills training in a virtual reality space. For example, it can use the generation AI to generate simulation learning materials for practical skills training on the job. This allows employees to receive training in a virtual reality space in an environment that closely resembles their actual work. Furthermore, the generation unit can use the generation AI to continuously improve the learning materials based on employee feedback. For example, it can collect feedback after employees have used the learning materials, and the generation AI analyzes this feedback to improve the content and format of the learning materials. This allows the generation unit to always provide high-quality learning materials that incorporate the latest information and technology. The generation unit centrally manages these learning materials and can collaborate with other systems and departments as needed. For example, the generated teaching materials are stored on a cloud server and made accessible to the distribution unit. This allows the generation unit to generate teaching materials efficiently and effectively, improving the overall performance of the system.

[0033] The provisioning department provides educational materials generated by the generation department. This provision is carried out using, for example, AR / VR technology, but is not limited to such examples. Specifically, generated educational materials are provided to employees using AR or VR devices. For example, by having employees wear AR devices, the educational materials are displayed overlaid on the real-world environment, allowing for more intuitive learning. Using VR devices, an immersive learning experience in a virtual reality space can be provided. Furthermore, the provisioning department can also provide generated educational materials through web and mobile applications. For example, employees can access the materials through a web application and learn on a PC or tablet. Using a mobile application, employees can learn regardless of location. Through these devices and applications, the provisioning department can quickly provide employees with appropriate learning content, maximizing learning effectiveness. Furthermore, the provisioning department can monitor employees' learning progress in real time and provide additional materials and support as needed. For example, if an employee is struggling with a particular task, the provisioning department can detect this and provide additional materials or hints. The provisioning department can also collect employee feedback and continuously improve the quality and content of the provided educational materials. This allows the service provider to offer personalized training programs to each employee, maximizing learning effectiveness.

[0034] The data collection unit can collect learning data such as employees' level of understanding, progress, and past learning history. For example, the data collection unit can evaluate an employee's level of understanding based on test results or quiz scores. It can also evaluate progress based on learning progress and the number of completed tasks. Furthermore, the data collection unit can collect past learning history based on previously taken courses and acquired skills. For example, the data collection unit can retrieve an employee's learning history from a database and input it into an AI system. This enables data collection based on the employee's learning status. 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 an employee's learning history into an AI, which can then collect the data.

[0035] The analysis department can analyze collected learning data and identify the most suitable learning content for each individual employee. For example, the analysis department can analyze learning data using statistical analysis or machine learning algorithms. For instance, it can use statistical analysis to analyze employee learning data and evaluate understanding and progress. It can also use machine learning algorithms to analyze employee learning data and identify the most suitable learning content for each individual employee. For example, the analysis department can use an AI model that takes employee learning data as input and outputs the most suitable learning content to identify the optimal learning content. This makes it possible to provide employees with the most suitable learning content. Some or all of the above-described processes in the analysis department may be performed using AI, or not. For example, the analysis department can input employee learning data into an AI, which can then identify the most suitable learning content.

[0036] The generation unit can generate personalized learning materials in a virtual reality space. For example, the generation unit can use a generation AI to generate personalized learning materials based on employee learning data. The generation unit can also use a generation AI to generate learning materials for practical skills training in a virtual reality space. For example, the generation unit can use a generation AI to generate simulation learning materials for practical skills training on-site. This makes it possible to generate personalized learning materials in a virtual reality space. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input employee learning data into a generation AI, and the generation AI can generate personalized learning materials.

[0037] The distribution unit can provide the generated educational materials to employees using AR / VR technology. For example, the distribution unit can provide the generated educational materials to employees using AR or VR devices. The distribution unit can also provide the generated educational materials through web or mobile applications. For example, the distribution unit can provide the generated educational materials to employees through a web application, allowing employees to learn in a virtual reality space. This enables the provision of educational materials using AR / VR technology. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the generated educational materials into an AI, which can then provide the educational materials.

[0038] The generation unit can generate training materials for conducting practical skills training in a virtual reality space. For example, the generation unit can use a generation AI to generate simulation training materials for conducting practical skills training in a virtual reality space. For example, the generation unit can use a generation AI to generate training materials for conducting practical skills training in a virtual reality space based on employee learning data. The generation unit can also use a generation AI to generate training materials for conducting practical skills training in a virtual reality space. For example, the generation unit can use a generation AI to generate simulation training materials for conducting practical skills training in a virtual reality space. This makes practical skills training possible in a virtual reality space. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit can input employee learning data into a generation AI, and the generation AI can generate training materials for practical skills training.

[0039] 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 may prioritize collecting data on learning methods that the employee has preferred to use in the past. It can also collect data based on learning methods in which the employee has achieved high results in the past. Furthermore, the data collection unit can avoid collecting data on learning methods that the employee has struggled with in the past. For example, the data collection unit can retrieve the employee's learning history from a database and input it into an AI system. This enables optimal data collection based on the employee's past learning history. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the employee's learning history into an AI, which can then select the optimal data collection method.

[0040] The data collection unit can filter the collected training data based on the employee's current projects and areas of interest. For example, the data collection unit can prioritize collecting data related to the project the employee is currently working on. The data collection unit can also collect data related to areas of interest the employee. Furthermore, the data collection unit can filter the data based on areas the employee has shown interest in in the past. For example, the data collection unit can retrieve the employee's project information and areas of interest from a database and input it into the AI ​​system. This enables data collection based on the employee's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the employee's project information and areas of interest into the AI, which can then filter the data.

[0041] The data collection unit can prioritize the collection of highly relevant data based on employees' geographical location information when collecting training data. For example, if an employee is in a specific region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if an employee is on a business trip, the data collection unit can collect data related to their destination. Additionally, if an employee is working remotely, the data collection unit can collect data related to their home area. For example, the data collection unit can obtain employees' geographical location information from GPS data or location services and input it into the AI ​​system. This enables the collection of highly relevant data based on employees' geographical location information. 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 employees' geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze employees' social media activity and collect relevant data when collecting training data. For example, the data collection unit can collect data related to topics that employees have shown interest in on social media. The data collection unit can also collect data based on the content of posts by experts and influencers that employees follow. Furthermore, the data collection unit can analyze the activities of online communities that employees participate in and collect relevant data. For example, the data collection unit can retrieve employees' social media activity from a database and input it into an AI system. This enables data collection based on employees' social media activity. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input employees' social media activity into an AI, which can then collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the training data. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data of moderate importance. For example, the analysis unit can evaluate the importance of the training data based on its impact on business operations and the degree of achievement of learning objectives, and adjust the level of detail of the analysis accordingly. This enables detailed analysis according to the importance of the training data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the training data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the training data. For example, the analysis unit can apply a technical analysis algorithm to data related to technical skills. It can also apply a behavioral analysis algorithm to data related to soft skills. Furthermore, it can apply a process analysis algorithm to data related to business processes. For example, the analysis unit can classify the categories of training data into technical categories and business categories and apply an analysis algorithm appropriate to each category. This enables appropriate analysis according to the category of training data. 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 categories of training data into the AI, and the AI ​​can apply an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on when the training data was collected. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, the analysis unit may postpone the analysis of older data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. For example, the analysis unit may determine priorities based on when the training data was collected and prioritize the analysis of the most recent data. This enables analysis with priorities based on when the training data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the training data collection dates into the AI, and the AI ​​can determine the priorities.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the training data. For example, the analysis unit may prioritize the analysis of data with high relevance. It may also postpone the analysis of data with low relevance. Furthermore, it may analyze data with moderate relevance to a reasonable extent. For example, the analysis unit may evaluate the relevance of the training data based on data correlation and co-occurrence frequency and adjust the order of analysis. This enables analysis in an order based on the relevance of the training data. 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 may input the relevance of the training data into AI, and the AI ​​may adjust the order of analysis.

[0047] The generation unit can adjust the level of detail in the learning materials based on the importance of the learning data during material generation. For example, the generation unit can include detailed explanations in materials based on high-importance data. It can also include simplified explanations in materials based on low-importance data. Furthermore, it can include explanations with an appropriate level of detail in materials based on moderately important data. For example, the generation unit can evaluate the importance of the learning data based on its impact on work and the degree of achievement of learning objectives, and adjust the level of detail in the materials. This makes it possible to generate detailed learning materials according to the importance of the learning data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the learning data into the AI, and the AI ​​can adjust the level of detail in the materials.

[0048] The generation unit can apply different generation algorithms depending on the category of the learning data when generating learning materials. For example, the generation unit can apply a technical generation algorithm to learning materials related to technical skills. It can also apply an action generation algorithm to learning materials related to soft skills. Furthermore, it can apply a process generation algorithm to learning materials related to business processes. For example, the generation unit can classify the categories of the learning data into technical categories or business categories and apply a generation algorithm appropriate to each category. This enables the generation of appropriate learning materials according to the category of the learning data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the categories of the learning data into the AI, and the AI ​​can apply an appropriate generation algorithm.

[0049] The generation unit can determine the priority of learning materials based on the timing of learning data collection when generating learning materials. For example, the generation unit can prioritize generating learning materials based on the latest data. It can also postpone the generation of learning materials based on older data. Furthermore, the generation unit can prioritize generating learning materials based on data collected during a specific period. For example, the generation unit can determine priorities based on the timing of learning data collection and prioritize the generation of learning materials based on the latest data. This makes it possible to generate learning materials with priorities based on the timing of learning data collection. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of learning data collection into the AI, and the AI ​​can determine the priorities.

[0050] The generation unit can adjust the order of learning materials based on the relevance of the training data during material generation. For example, the generation unit can prioritize generating materials based on highly relevant data. It can also postpone the generation of materials based on less relevant data. Furthermore, the generation unit can appropriately generate materials based on moderately relevant data. For example, the generation unit can evaluate the relevance of the training data based on data correlation and co-occurrence frequency and adjust the order of the materials. This makes it possible to generate materials in an order based on the relevance of the training data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the training data into the AI, and the AI ​​can adjust the order of the materials.

[0051] The delivery department can select the optimal delivery method when providing educational materials by referring to the employee's past learning history. For example, the delivery department may prioritize using delivery methods that the employee has preferred in the past. It can also provide materials based on delivery methods in which the employee has achieved high results in the past. Furthermore, the delivery department may avoid delivery methods that the employee has struggled with in the past. For example, the delivery department can retrieve the employee's learning history from a database and input it into an AI system. This enables the delivery of educational materials using the optimal delivery method based on the employee's past learning history. Some or all of the above processes in the delivery department may be performed using AI, or not. For example, the delivery department can input the employee's learning history into an AI, which can then select the optimal delivery method.

[0052] The delivery department can select the optimal delivery method based on the employee's device information when providing educational materials. For example, if an employee is using a smartphone, the delivery department will use a delivery method adapted to the screen size. If an employee is using a tablet, the delivery department can also use a delivery method optimized for larger screens. Furthermore, if an employee is using a desktop computer, the delivery department can use a delivery method that includes detailed information. For example, the delivery department can evaluate the employee's device information based on the device type and usage to select the optimal delivery method. This enables the delivery of educational materials using the most appropriate method based on the employee's device information. Some or all of the above processing in the delivery department may be performed using AI, or not. For example, the delivery department can input employee device information into AI, which can then select the optimal delivery method.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The data collection unit can collect employees' biometric data and monitor their health status. For example, the data collection unit can collect data such as employees' heart rate, blood pressure, and body temperature using sensors. It can also track employees' sleep patterns and exercise levels to assess their health status. Furthermore, the data collection unit can measure employees' stress levels and predict health risks. This enables the provision of appropriate learning programs based on employees' health status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employees' biometric data into an AI, which can then assess their health status.

[0055] The analytics department can estimate employees' learning styles and identify the optimal learning methods. For example, if an employee is a visual learner, the analytics department can recommend learning materials that heavily utilize visual content. If an employee is an auditory learner, the analytics department can also recommend audio or podcast-style learning materials. Furthermore, if an employee is a hands-on learner, the analytics department can recommend hands-on training. This enables the provision of optimal learning methods tailored to each employee's learning style. Some or all of the above processing in the analytics department may be performed using AI, for example, or not. For instance, the analytics department can input employee learning style data into an AI, which can then identify the optimal learning methods.

[0056] The generation unit can update learning materials in real time according to the employee's learning progress. For example, the generation unit can automatically generate new tasks when an employee completes a specific task. It can also provide supplementary materials if an employee is having difficulty understanding something. Furthermore, the generation unit can adjust the content of the learning materials based on employee feedback. This enables flexible provision of learning materials tailored to the employee's learning progress. Some or all of the above processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input employee learning progress data into the AI, which can then update the learning materials.

[0057] The service provider can provide a dashboard that visualizes the learning progress of employees based on their learning history. For example, the service provider can display completed tasks and acquired skills in graphs and charts. The service provider can also update the employee's progress toward their learning goals in real time. Furthermore, the service provider can recommend tasks and skills that employees should work on next. This allows for a clear overview of the employee's learning progress. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input employee learning history data into an AI, which can then generate the dashboard.

[0058] The service provider can create personalized learning schedules based on employee learning data. For example, the service provider can propose learning plans tailored to employees' work schedules and individual paces. It can also create short-term and long-term learning schedules according to employees' learning goals. Furthermore, the service provider can adjust learning schedules based on employee feedback. This enables the provision of flexible learning schedules that meet employee needs. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input employee learning data into an AI, which can then create a learning schedule.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects employee learning data. This learning data includes employee comprehension levels, progress, and past learning history. For example, the data collection unit evaluates employee comprehension levels based on test results and quiz scores, and assesses progress based on learning progress and the number of completed tasks. It also collects past learning history based on previously taken courses and acquired skills. Step 2: The analysis department analyzes the learning data collected by the data collection department. The analysis is performed using statistical analysis and machine learning algorithms. For example, statistical analysis is used to evaluate employees' understanding and progress, and machine learning algorithms are used to identify the most suitable learning content for each individual employee. Step 3: The generation unit generates personalized learning materials in a virtual reality space based on the analysis results obtained by the analysis unit. Generation is performed using generation AI. For example, it generates personalized learning materials based on employee learning data, or simulation materials for practical skills training in a virtual reality space. Step 4: The delivery unit provides the educational materials generated by the generation unit. The delivery is carried out using AR / VR technology. For example, the educational materials generated using AR or VR devices can be provided to employees, and can also be provided through web applications or mobile applications.

[0061] (Example of form 2) An AI system according to an embodiment of the present invention is a system that generates custom training materials for employee education in conjunction with AR / VR technology. This AI system is a mechanism that generates personalized training materials in a virtual reality space tailored to the training status of each employee, from new employee training to skill improvement and retraining. First, the AI ​​system collects employee learning data. The learning data includes the employee's level of understanding and progress, and past learning history. For example, the progress of new employee training and the results of training for skill improvement are collected. This data is input into the AI ​​system. Next, the AI ​​system analyzes the collected learning data. The AI ​​analyzes the employee's level of understanding and progress and identifies the optimal learning content for each individual employee. For example, for an employee lacking a particular skill, training materials to strengthen that skill are generated. Furthermore, the AI ​​system generates personalized training materials in a virtual reality space based on the analysis results. The generated training materials are provided to employees using AR / VR technology. For example, practical skills training can be conducted in the virtual reality space. This allows employees to acquire skills in an environment similar to their actual work. This mechanism makes it possible to provide training programs tailored to each individual employee. Even large organizations can build training programs that flexibly respond to individual needs. Furthermore, it enables the acquisition of practical skills on the job, contributing to employee capability development. For example, large companies can efficiently develop human resources by generating customized training materials tailored to each employee's learning situation, from new employee training to skill enhancement and retraining. Companies specializing in human resource development can enhance their competitiveness by providing training programs tailored to the needs of individual employees. In this way, AI systems integrated with AR / VR technology can optimize employee learning and support capability development. Thus, AI systems can optimize employee learning and support capability development.

[0062] The AI ​​system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects employee learning data. Learning data includes, but is not limited to, an employee's level of understanding, progress, and past learning history. The collection unit evaluates an employee's level of understanding based on test results or quiz scores, for example. The collection unit can also evaluate progress based on learning progress and the number of completed tasks. Furthermore, the collection unit can collect past learning history based on courses taken in the past and skills acquired. For example, the collection unit retrieves an employee's learning history from a database and inputs it into the AI ​​system. The analysis unit analyzes the learning data collected by the collection unit. The analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit uses statistical analysis to analyze an employee's learning data and evaluate their level of understanding and progress. The analysis unit can also use machine learning algorithms to analyze an employee's learning data and identify the most suitable learning content for each individual employee. For example, the analysis unit identifies learning content using an AI model that takes employee learning data as input and outputs optimal learning content. The generation unit generates personalized learning materials in a virtual reality space based on the analysis results obtained by the analysis unit. Generation is performed using, for example, a generative AI, but is not limited to such examples. For example, the generation unit uses a generative AI to generate personalized learning materials based on employee learning data. The generation unit can also use a generative AI to generate learning materials for practical skills training in a virtual reality space. For example, the generation unit uses a generative AI to generate simulation learning materials for practical skills training on the job. The delivery unit provides the learning materials generated by the generation unit. Delivery is performed using, for example, AR / VR technology, but is not limited to such examples. For example, the delivery unit provides the generated learning materials to employees using AR or VR devices. The delivery unit can also provide the generated learning materials through a web application or mobile application. For example, the delivery unit provides the generated learning materials to employees through a web application, enabling them to learn in a virtual reality space.This enables the AI ​​system according to the embodiment to provide personalized training programs for each employee.

[0063] The data collection unit collects employee learning data. This learning data includes, but is not limited to, employee comprehension, progress, and past learning history. For example, the data collection unit evaluates employee comprehension based on test results and quiz scores. Specifically, when employees take online tests or quizzes, the results are automatically collected and used to evaluate comprehension. Test results include detailed data such as correct answer rate and response time, allowing for a multifaceted evaluation of employee comprehension. The data collection unit can also evaluate progress based on learning progress and the number of completed assignments. For example, when employees take online courses, the completion status of each module and the submission status of assignments are recorded in real time. This allows for an accurate understanding of how far employees are progressing in their learning. Furthermore, the data collection unit can collect past learning history based on previously taken courses and acquired skills. For example, information on training courses previously taken and certifications acquired by employees is stored in a database, and this information is collected and input into the AI ​​system. This allows for a comprehensive understanding of employee learning history and provides foundational data to address individual learning needs. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0064] The analysis department analyzes the learning data collected by the data collection department. This analysis is performed using, but is not limited to, statistical analysis and machine learning algorithms. Specifically, it uses statistical analysis to analyze employee learning data and evaluate understanding and progress. For example, it can statistically analyze employee test results and quiz scores to identify the overall distribution of understanding and biases in understanding in specific areas. The analysis department can also use machine learning algorithms to analyze employee learning data and identify the optimal learning content for individual employees. For example, it can use an AI model that takes employee learning data as input and outputs the optimal learning content. Specifically, a machine learning algorithm recommends the optimal learning content based on the employee's past learning history, understanding, and progress. This AI model learns employee learning patterns and tendencies and is continuously improved to provide content best suited to individual needs. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term learning effectiveness and trend analysis. For example, it can evaluate the effectiveness of specific learning programs based on past learning data to improve future learning plans. It can also use anomaly detection algorithms to detect unusual learning patterns and abnormal data, enabling early intervention. This allows the analysis unit to not only grasp the situation in real time, but also to evaluate long-term learning effects and detect anomalies, thereby improving the reliability and effectiveness of the entire system.

[0065] The generation unit generates personalized learning materials in a virtual reality space based on the analysis results obtained by the analysis unit. Generation is performed using, for example, a generation AI, but is not limited to this example. Specifically, the generation AI is used to generate personalized learning materials based on employee learning data. For example, it can generate learning materials with the optimal difficulty level and content according to the employee's level of understanding and progress. The generation AI uses natural language processing technology to automatically generate text and questions that match the employee's learning needs. The generation unit can also use the generation AI to generate learning materials for practical skills training in a virtual reality space. For example, it can use the generation AI to generate simulation learning materials for practical skills training on the job. This allows employees to receive training in a virtual reality space in an environment that closely resembles their actual work. Furthermore, the generation unit can use the generation AI to continuously improve the learning materials based on employee feedback. For example, it can collect feedback after employees have used the learning materials, and the generation AI analyzes this feedback to improve the content and format of the learning materials. This allows the generation unit to always provide high-quality learning materials that incorporate the latest information and technology. The generation unit centrally manages these learning materials and can collaborate with other systems and departments as needed. For example, the generated teaching materials are stored on a cloud server and made accessible to the distribution unit. This allows the generation unit to generate teaching materials efficiently and effectively, improving the overall performance of the system.

[0066] The provisioning department provides educational materials generated by the generation department. This provision is carried out using, for example, AR / VR technology, but is not limited to such examples. Specifically, generated educational materials are provided to employees using AR or VR devices. For example, by having employees wear AR devices, the educational materials are displayed overlaid on the real-world environment, allowing for more intuitive learning. Using VR devices, an immersive learning experience in a virtual reality space can be provided. Furthermore, the provisioning department can also provide generated educational materials through web and mobile applications. For example, employees can access the materials through a web application and learn on a PC or tablet. Using a mobile application, employees can learn regardless of location. Through these devices and applications, the provisioning department can quickly provide employees with appropriate learning content, maximizing learning effectiveness. Furthermore, the provisioning department can monitor employees' learning progress in real time and provide additional materials and support as needed. For example, if an employee is struggling with a particular task, the provisioning department can detect this and provide additional materials or hints. The provisioning department can also collect employee feedback and continuously improve the quality and content of the provided educational materials. This allows the service provider to offer personalized training programs to each employee, maximizing learning effectiveness.

[0067] The data collection unit can collect learning data such as employees' level of understanding, progress, and past learning history. For example, the data collection unit can evaluate an employee's level of understanding based on test results or quiz scores. It can also evaluate progress based on learning progress and the number of completed tasks. Furthermore, the data collection unit can collect past learning history based on previously taken courses and acquired skills. For example, the data collection unit can retrieve an employee's learning history from a database and input it into an AI system. This enables data collection based on the employee's learning status. 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 an employee's learning history into an AI, which can then collect the data.

[0068] The analysis department can analyze collected learning data and identify the most suitable learning content for each individual employee. For example, the analysis department can analyze learning data using statistical analysis or machine learning algorithms. For instance, it can use statistical analysis to analyze employee learning data and evaluate understanding and progress. It can also use machine learning algorithms to analyze employee learning data and identify the most suitable learning content for each individual employee. For example, the analysis department can use an AI model that takes employee learning data as input and outputs the most suitable learning content to identify the optimal learning content. This makes it possible to provide employees with the most suitable learning content. Some or all of the above-described processes in the analysis department may be performed using AI, or not. For example, the analysis department can input employee learning data into an AI, which can then identify the most suitable learning content.

[0069] The generation unit can generate personalized learning materials in a virtual reality space. For example, the generation unit can use a generation AI to generate personalized learning materials based on employee learning data. The generation unit can also use a generation AI to generate learning materials for practical skills training in a virtual reality space. For example, the generation unit can use a generation AI to generate simulation learning materials for practical skills training on-site. This makes it possible to generate personalized learning materials in a virtual reality space. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input employee learning data into a generation AI, and the generation AI can generate personalized learning materials.

[0070] The distribution unit can provide the generated educational materials to employees using AR / VR technology. For example, the distribution unit can provide the generated educational materials to employees using AR or VR devices. The distribution unit can also provide the generated educational materials through web or mobile applications. For example, the distribution unit can provide the generated educational materials to employees through a web application, allowing employees to learn in a virtual reality space. This enables the provision of educational materials using AR / VR technology. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the generated educational materials into an AI, which can then provide the educational materials.

[0071] The generation unit can generate training materials for conducting practical skills training in a virtual reality space. For example, the generation unit can use a generation AI to generate simulation training materials for conducting practical skills training in a virtual reality space. For example, the generation unit can use a generation AI to generate training materials for conducting practical skills training in a virtual reality space based on employee learning data. The generation unit can also use a generation AI to generate training materials for conducting practical skills training in a virtual reality space. For example, the generation unit can use a generation AI to generate simulation training materials for conducting practical skills training in a virtual reality space. This makes practical skills training possible in a virtual reality space. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit can input employee learning data into a generation AI, and the generation AI can generate training materials for practical skills training.

[0072] The data collection unit can estimate employees' emotions and adjust the timing of training data collection based on the estimated emotions. For example, if an employee is stressed, the unit can delay training data collection until the employee is relaxed. Conversely, if an employee is focused, the unit can collect training data at that time to obtain more accurate data. Furthermore, if an employee is tired, the unit can adjust the timing of training data collection to after a break. For example, the unit can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the unit can calculate an emotion score based on changes in facial expression. The unit can also record an employee's voice and estimate their emotions using voice analysis technology. For example, the unit can analyze the tone and speed of their voice to calculate an emotion score. Additionally, the unit can collect employee biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the unit can calculate an emotion score based on fluctuations in heart rate. This enables data collection at the appropriate timing according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using 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 collection unit may be performed using AI or not using AI. For example, the collection unit can input employee emotion data into an AI, which can adjust the timing of data collection.

[0073] 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 may prioritize collecting data on learning methods that the employee has preferred to use in the past. It can also collect data based on learning methods in which the employee has achieved high results in the past. Furthermore, the data collection unit can avoid collecting data on learning methods that the employee has struggled with in the past. For example, the data collection unit can retrieve the employee's learning history from a database and input it into an AI system. This enables optimal data collection based on the employee's past learning history. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the employee's learning history into an AI, which can then select the optimal data collection method.

[0074] The data collection unit can filter the collected training data based on the employee's current projects and areas of interest. For example, the data collection unit can prioritize collecting data related to the project the employee is currently working on. The data collection unit can also collect data related to areas of interest the employee. Furthermore, the data collection unit can filter the data based on areas the employee has shown interest in in the past. For example, the data collection unit can retrieve the employee's project information and areas of interest from a database and input it into the AI ​​system. This enables data collection based on the employee's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the employee's project information and areas of interest into the AI, which can then filter the data.

[0075] The data collection unit can estimate employees' emotions and prioritize the training data to be collected based on those estimated emotions. For example, if an employee is stressed, the unit will prioritize collecting data that promotes relaxation. If an employee is focused, the unit can prioritize collecting data that is difficult to understand. Furthermore, if an employee is tired, the unit can prioritize collecting data that is easy to understand. For example, the unit can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the unit can calculate an emotion score based on changes in facial expression. The unit can also record an employee's voice and estimate their emotions using voice analysis technology. For example, the unit can analyze the tone and speed of their voice and calculate an emotion score. The unit can also collect employees' biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the unit can calculate an emotion score based on fluctuations in heart rate. This enables data collection with priorities tailored to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not using AI. For example, the collection unit can input employee emotion data into an AI, which can then determine the priority of the data.

[0076] The data collection unit can prioritize the collection of highly relevant data based on employees' geographical location information when collecting training data. For example, if an employee is in a specific region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if an employee is on a business trip, the data collection unit can collect data related to their destination. Additionally, if an employee is working remotely, the data collection unit can collect data related to their home area. For example, the data collection unit can obtain employees' geographical location information from GPS data or location services and input it into the AI ​​system. This enables the collection of highly relevant data based on employees' geographical location information. 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 employees' geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze employees' social media activity and collect relevant data when collecting training data. For example, the data collection unit can collect data related to topics that employees have shown interest in on social media. The data collection unit can also collect data based on the content of posts by experts and influencers that employees follow. Furthermore, the data collection unit can analyze the activities of online communities that employees participate in and collect relevant data. For example, the data collection unit can retrieve employees' social media activity from a database and input it into an AI system. This enables data collection based on employees' social media activity. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input employees' social media activity into an AI, which can then collect relevant data.

[0078] The analysis department can estimate employees' emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is tense, the analysis department will use a simple and highly visual presentation. If an employee is relaxed, the analysis department can use a presentation that includes detailed information. Furthermore, if an employee is in a hurry, the analysis department can use a concise presentation that gets straight to the point. For example, the analysis department can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the analysis department can calculate an emotion score based on changes in facial expression. The analysis department can also record an employee's voice and estimate their emotions using voice analysis technology. For example, the analysis department can analyze the tone and speed of the voice and calculate an emotion score. The analysis department can also collect employees' biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis department can calculate an emotion score based on fluctuations in heart rate. This enables analysis using an appropriate presentation method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input employee emotion data into an AI, which can then adjust the way it is expressed.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the training data. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data of moderate importance. For example, the analysis unit can evaluate the importance of the training data based on its impact on business operations and the degree of achievement of learning objectives, and adjust the level of detail of the analysis accordingly. This enables detailed analysis according to the importance of the training data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the training data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of the training data. For example, the analysis unit can apply a technical analysis algorithm to data related to technical skills. It can also apply a behavioral analysis algorithm to data related to soft skills. Furthermore, it can apply a process analysis algorithm to data related to business processes. For example, the analysis unit can classify the categories of training data into technical categories and business categories and apply an analysis algorithm appropriate to each category. This enables appropriate analysis according to the category of training data. 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 categories of training data into the AI, and the AI ​​can apply an appropriate analysis algorithm.

[0081] The analysis department can estimate an employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if an employee is tense, the analysis department can perform a short, concise analysis. If an employee is relaxed, the analysis department can perform a detailed analysis. Furthermore, if an employee is in a hurry, the analysis department can perform a brief analysis. For example, the analysis department can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the analysis department can calculate an emotion score based on changes in facial expression. The analysis department can also record an employee's voice and estimate their emotions using voice analysis technology. For example, the analysis department can analyze the tone and speed of the voice and calculate an emotion score. The analysis department can also collect an employee's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis department can calculate an emotion score based on fluctuations in heart rate. This enables analysis of an appropriate length according to the employee's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input employee sentiment data into the AI, which can then adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on when the training data was collected. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, the analysis unit may postpone the analysis of older data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. For example, the analysis unit may determine priorities based on when the training data was collected and prioritize the analysis of the most recent data. This enables analysis with priorities based on when the training data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the training data collection dates into the AI, and the AI ​​can determine the priorities.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the training data. For example, the analysis unit may prioritize the analysis of data with high relevance. It may also postpone the analysis of data with low relevance. Furthermore, it may analyze data with moderate relevance to a reasonable extent. For example, the analysis unit may evaluate the relevance of the training data based on data correlation and co-occurrence frequency and adjust the order of analysis. This enables analysis in an order based on the relevance of the training data. 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 may input the relevance of the training data into AI, and the AI ​​may adjust the order of analysis.

[0084] The generation unit can estimate an employee's emotions and adjust the presentation of the generated training materials based on the estimated emotions. For example, if an employee is relaxed, the generation unit can generate training materials that proceed at a relaxed pace. If an employee is in a hurry, the generation unit can also generate training materials that emphasize the shortest route. Furthermore, if an employee is excited, the generation unit can generate training materials with visually stimulating effects. For example, the generation unit can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expression. The generation unit can also record an employee's voice and estimate their emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of their voice and calculate an emotion score. The generation unit can also collect an employee's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate. This makes it possible to generate training materials with appropriate presentation methods according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 generation unit may be performed using AI or not using AI. For example, the generation unit can input employee emotion data into the AI, which can then adjust the way it is expressed.

[0085] The generation unit can adjust the level of detail in the learning materials based on the importance of the learning data during material generation. For example, the generation unit can include detailed explanations in materials based on high-importance data. It can also include simplified explanations in materials based on low-importance data. Furthermore, it can include explanations with an appropriate level of detail in materials based on moderately important data. For example, the generation unit can evaluate the importance of the learning data based on its impact on work and the degree of achievement of learning objectives, and adjust the level of detail in the materials. This makes it possible to generate detailed learning materials according to the importance of the learning data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the learning data into the AI, and the AI ​​can adjust the level of detail in the materials.

[0086] The generation unit can apply different generation algorithms depending on the category of the learning data when generating learning materials. For example, the generation unit can apply a technical generation algorithm to learning materials related to technical skills. It can also apply an action generation algorithm to learning materials related to soft skills. Furthermore, it can apply a process generation algorithm to learning materials related to business processes. For example, the generation unit can classify the categories of the learning data into technical categories or business categories and apply a generation algorithm appropriate to each category. This enables the generation of appropriate learning materials according to the category of the learning data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the categories of the learning data into the AI, and the AI ​​can apply an appropriate generation algorithm.

[0087] The generation unit can estimate an employee's emotions and adjust the length of the generated training material based on the estimated emotions. For example, if an employee is in a hurry, the generation unit can generate short, concise training material. If an employee is relaxed, the generation unit can generate longer training material with detailed explanations. Furthermore, if an employee is excited, the generation unit can generate training material with visually stimulating effects. For example, the generation unit can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expression. The generation unit can also record an employee's voice and estimate their emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of their voice and calculate an emotion score. The generation unit can also collect an employee's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate. This makes it possible to generate training materials of appropriate length according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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-described processes in the generation unit may be performed using AI or not using AI. For example, the generation unit can input employee emotion data into the AI, which can then adjust the length of the training material.

[0088] The generation unit can determine the priority of learning materials based on the timing of learning data collection when generating learning materials. For example, the generation unit can prioritize generating learning materials based on the latest data. It can also postpone the generation of learning materials based on older data. Furthermore, the generation unit can prioritize generating learning materials based on data collected during a specific period. For example, the generation unit can determine priorities based on the timing of learning data collection and prioritize the generation of learning materials based on the latest data. This makes it possible to generate learning materials with priorities based on the timing of learning data collection. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of learning data collection into the AI, and the AI ​​can determine the priorities.

[0089] The generation unit can adjust the order of learning materials based on the relevance of the training data during material generation. For example, the generation unit can prioritize generating materials based on highly relevant data. It can also postpone the generation of materials based on less relevant data. Furthermore, the generation unit can appropriately generate materials based on moderately relevant data. For example, the generation unit can evaluate the relevance of the training data based on data correlation and co-occurrence frequency and adjust the order of the materials. This makes it possible to generate materials in an order based on the relevance of the training data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the training data into the AI, and the AI ​​can adjust the order of the materials.

[0090] The delivery department can estimate employees' emotions and adjust the delivery method of training materials based on those estimated emotions. For example, if an employee is nervous, the delivery department can use a simple and highly visual delivery method. If an employee is relaxed, the delivery department can use a delivery method that includes detailed information. Furthermore, if an employee is in a hurry, the delivery department can use a concise delivery method that gets straight to the point. For example, the delivery department can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the delivery department can calculate an emotion score based on changes in facial expression. The delivery department can also record an employee's voice and estimate their emotions using voice analysis technology. For example, the delivery department can analyze the tone and speed of their voice and calculate an emotion score. The delivery department can also collect employees' biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the delivery department can calculate an emotion score based on fluctuations in heart rate. This makes it possible to deliver training materials using an appropriate delivery method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using 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 delivery unit may be performed using AI or not using AI. For example, the delivery unit can input employee emotion data into the AI, which can then adjust the delivery method.

[0091] The delivery department can select the optimal delivery method when providing educational materials by referring to the employee's past learning history. For example, the delivery department may prioritize using delivery methods that the employee has preferred in the past. It can also provide materials based on delivery methods in which the employee has achieved high results in the past. Furthermore, the delivery department may avoid delivery methods that the employee has struggled with in the past. For example, the delivery department can retrieve the employee's learning history from a database and input it into an AI system. This enables the delivery of educational materials using the optimal delivery method based on the employee's past learning history. Some or all of the above processes in the delivery department may be performed using AI, or not. For example, the delivery department can input the employee's learning history into an AI, which can then select the optimal delivery method.

[0092] The system can estimate employees' emotions and adjust the order in which educational materials are provided based on those emotions. For example, if an employee is tense, the system will prioritize providing relaxing materials. It can also prioritize providing more challenging materials if an employee is focused. Furthermore, if an employee is tired, it can prioritize providing easier materials. For instance, the system can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expression. The system can also record an employee's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of their voice to calculate an emotion score. Additionally, the system can collect employee biometric data (heart rate and skin electrical activity) using sensors and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This enables the provision of educational materials in an appropriate order according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using 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 serving unit may be performed using AI or not using AI. For example, the serving unit can input employee emotion data into the AI, which can then adjust the serving order.

[0093] The delivery department can select the optimal delivery method based on the employee's device information when providing educational materials. For example, if an employee is using a smartphone, the delivery department will use a delivery method adapted to the screen size. If an employee is using a tablet, the delivery department can also use a delivery method optimized for larger screens. Furthermore, if an employee is using a desktop computer, the delivery department can use a delivery method that includes detailed information. For example, the delivery department can evaluate the employee's device information based on the device type and usage to select the optimal delivery method. This enables the delivery of educational materials using the most appropriate method based on the employee's device information. Some or all of the above processing in the delivery department may be performed using AI, or not. For example, the delivery department can input employee device information into AI, which can then select the optimal delivery method.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The data collection unit can collect employees' biometric data and monitor their health status. For example, the data collection unit can collect data such as employees' heart rate, blood pressure, and body temperature using sensors. It can also track employees' sleep patterns and exercise levels to assess their health status. Furthermore, the data collection unit can measure employees' stress levels and predict health risks. This enables the provision of appropriate learning programs based on employees' health status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employees' biometric data into an AI, which can then assess their health status.

[0096] The analytics department can estimate employees' learning styles and identify the optimal learning methods. For example, if an employee is a visual learner, the analytics department can recommend learning materials that heavily utilize visual content. If an employee is an auditory learner, the analytics department can also recommend audio or podcast-style learning materials. Furthermore, if an employee is a hands-on learner, the analytics department can recommend hands-on training. This enables the provision of optimal learning methods tailored to each employee's learning style. Some or all of the above processing in the analytics department may be performed using AI, for example, or not. For instance, the analytics department can input employee learning style data into an AI, which can then identify the optimal learning methods.

[0097] The generation unit can update learning materials in real time according to the employee's learning progress. For example, the generation unit can automatically generate new tasks when an employee completes a specific task. It can also provide supplementary materials if an employee is having difficulty understanding something. Furthermore, the generation unit can adjust the content of the learning materials based on employee feedback. This enables flexible provision of learning materials tailored to the employee's learning progress. Some or all of the above processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input employee learning progress data into the AI, which can then update the learning materials.

[0098] The service provider can provide a dashboard that visualizes the learning progress of employees based on their learning history. For example, the service provider can display completed tasks and acquired skills in graphs and charts. The service provider can also update the employee's progress toward their learning goals in real time. Furthermore, the service provider can recommend tasks and skills that employees should work on next. This allows for a clear overview of the employee's learning progress. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input employee learning history data into an AI, which can then generate the dashboard.

[0099] The service provider can create personalized learning schedules based on employee learning data. For example, the service provider can propose learning plans tailored to employees' work schedules and individual paces. It can also create short-term and long-term learning schedules according to employees' learning goals. Furthermore, the service provider can adjust learning schedules based on employee feedback. This enables the provision of flexible learning schedules that meet employee needs. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input employee learning data into an AI, which can then create a learning schedule.

[0100] The data collection unit can estimate employees' emotions and adjust the learning environment based on those estimated emotions. For example, if an employee is feeling stressed, the unit can provide a relaxing environment. It can also provide an environment that helps maintain concentration if an employee is focused. Furthermore, if an employee is tired, the unit can provide an environment that encourages breaks. For example, the unit can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the unit can calculate an emotion score based on changes in facial expression. The unit can also record an employee's voice and estimate their emotions using voice analysis technology. For example, the unit can analyze the tone and speed of their voice to calculate an emotion score. Additionally, the unit can collect employee biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the unit can calculate an emotion score based on fluctuations in heart rate. This enables the provision of an appropriate learning environment tailored to each employee's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input employee sentiment data into the AI, which can then adjust its learning environment.

[0101] The analytics department can estimate employees' emotions and adjust the difficulty level of learning content based on those emotions. For example, if an employee is relaxed, the analytics department can provide more challenging content. Conversely, if an employee is stressed, it can provide less challenging content. Furthermore, if an employee is focused, it can provide content of appropriate difficulty. For example, the analytics department can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the analytics department can calculate an emotion score based on changes in facial expression. The analytics department can also record an employee's voice and estimate their emotions using voice analysis technology. For example, the analytics department can analyze the tone and speed of their voice and calculate an emotion score. The analytics department can also collect employees' biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analytics department can calculate an emotion score based on fluctuations in heart rate. This makes it possible to provide learning content at an appropriate difficulty level according to the employee's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input employee sentiment data into an AI, which can then adjust the difficulty level of the learning content.

[0102] The generation unit can estimate an employee's emotions and adjust the format of the learning content based on the estimated emotions. For example, if an employee is relaxed, the generation unit can provide video content. It can also provide text content if the employee is focused. Furthermore, if an employee is tired, the generation unit can provide interactive game content. For example, the generation unit can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expression. The generation unit can also record an employee's voice and estimate their emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of their voice to calculate an emotion score. The generation unit can also collect employee biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate. This enables the provision of learning content in an appropriate format according to the employee's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the generation unit may be performed using AI, or not using AI. For example, the generation unit may input employee sentiment data into the AI, which can then adjust the format of the learning content.

[0103] The service provider can estimate employees' emotions and adjust the timing of learning content delivery based on those estimated emotions. For example, the service provider can deliver learning content when an employee is relaxed. It can also deliver learning content at a time when an employee is feeling stressed, or at a time when an employee is concentrating. For instance, the service provider can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expression. The service provider can also record an employee's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of their voice to calculate an emotion score. Furthermore, the service provider can collect employee biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This enables the delivery of learning content at the appropriate time according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the delivery unit may be performed using AI, or not using AI. For example, the delivery unit can input employee sentiment data into the AI, which can then adjust the timing of the delivery.

[0104] The delivery department can estimate employees' emotions and adjust the delivery method of learning content based on the estimated emotions. For example, if an employee is stressed, the delivery department can use a simple and highly visual delivery method. If an employee is relaxed, the delivery department can use a delivery method that includes detailed information. Furthermore, if an employee is in a hurry, the delivery department can use a concise delivery method that gets straight to the point. For example, the delivery department can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the delivery department can calculate an emotion score based on changes in facial expression. The delivery department can also record an employee's voice and estimate their emotions using voice analysis technology. For example, the delivery department can analyze the tone and speed of their voice and calculate an emotion score. The delivery department can also collect employees' biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the delivery department can calculate an emotion score based on fluctuations in heart rate. This makes it possible to deliver learning content in an appropriate delivery method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using 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 delivery unit may be performed using AI or not using AI. For example, the delivery unit can input employee emotion data into the AI, which can then adjust the delivery method.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The data collection unit collects employee learning data. This learning data includes employee comprehension levels, progress, and past learning history. For example, the data collection unit evaluates employee comprehension levels based on test results and quiz scores, and assesses progress based on learning progress and the number of completed tasks. It also collects past learning history based on previously taken courses and acquired skills. Step 2: The analysis department analyzes the learning data collected by the data collection department. The analysis is performed using statistical analysis and machine learning algorithms. For example, statistical analysis is used to evaluate employees' understanding and progress, and machine learning algorithms are used to identify the most suitable learning content for each individual employee. Step 3: The generation unit generates personalized learning materials in a virtual reality space based on the analysis results obtained by the analysis unit. Generation is performed using generation AI. For example, it generates personalized learning materials based on employee learning data, or simulation materials for practical skills training in a virtual reality space. Step 4: The delivery unit provides the educational materials generated by the generation unit. The delivery is carried out using AR / VR technology. For example, the educational materials generated using AR or VR devices can be provided to employees, and can also be provided through web applications or mobile applications.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects employee learning data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected learning data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates personalized learning materials in a virtual reality space. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the generated learning materials to employees using AR / VR technology. 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.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects employee learning data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected learning data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates personalized learning materials in a virtual reality space. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the generated learning materials to employees using AR / VR technology. 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.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects employee learning data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected learning data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates personalized learning materials in a virtual reality space. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides the generated learning materials to employees using AR / VR technology. 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.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects employee learning data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected learning data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates personalized learning materials in a virtual reality space. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generated learning materials to employees using AR / VR technology. 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) The data collection department collects employee learning data, An analysis unit analyzes the learning data collected by the aforementioned collection unit, A generation unit generates personalized educational materials in a virtual reality space based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides educational materials generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect learning data such as employees' level of understanding, progress, and past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We analyze the collected learning data to identify the most suitable learning content for each individual employee. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate personalized learning materials in a virtual reality space. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The generated training materials are provided to employees using AR / VR technology. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate training materials for conducting practical skills training in a virtual reality environment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate employee emotions and adjust the timing of training data collection based on the estimated employee emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned collection unit is When collecting training data, filter it based on employees' current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates employee sentiment and prioritizes the training data to collect based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting training data, the system prioritizes collecting highly relevant data based on employees' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting training data, analyze employees' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate employee sentiment and adjust the presentation of the analysis based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is Adjust the level of detail of the analysis based on the importance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is Apply different analysis algorithms depending on the category of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is Estimate employee sentiment and adjust the length of the analysis based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is Prioritize analysis based on when the training data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is Adjust the order of analysis based on the relevance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate employees' emotions and adjust the way training materials are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating learning materials, adjust the level of detail based on the importance of the learning data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating learning materials, different generation algorithms are applied depending on the category of the learning data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates employee emotions and adjusts the length of the training materials generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating learning materials, the priority of the materials is determined based on when the learning data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating learning materials, adjust the order of the materials based on the relevance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate employees' emotions and adjust the way training materials are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing educational materials, the optimal delivery method is selected by referring to the employee's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates employees' emotions and adjusts the order in which training materials are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing educational materials, the optimal delivery method is selected based on the employee's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects employee learning data, An analysis unit analyzes the learning data collected by the aforementioned collection unit, A generation unit generates personalized educational materials in a virtual reality space based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides educational materials generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect learning data such as employees' level of understanding, progress, and past learning history. The system according to feature 1.

3. The aforementioned analysis unit is We analyze the collected learning data to identify the most suitable learning content for each individual employee. The system according to feature 1.

4. The generating unit is Generate personalized learning materials in a virtual reality space. The system according to feature 1.

5. The aforementioned supply unit is, The generated training materials are provided to employees using AR / VR technology. The system according to feature 1.

6. The generating unit is Generate training materials for conducting practical skills training in a virtual reality environment. The system according to feature 1.

7. The aforementioned collection unit is We estimate employee emotions and adjust the timing of training data collection based on the estimated employee emotions. The system according to feature 1.

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

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