system

Abstract

The system according to this embodiment aims to identify the training content necessary for improving employees' skills and to provide an appropriate on-the-job training (OJT) program. [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 data on the employee's work content or skill level. The analysis unit analyzes the data collected by the collection unit and identifies the training content necessary for improving the employee's skills. The generation unit generates an OJT program based on the training content identified by the analysis unit. The provision unit provides the OJT program generated by the generation unit to the employee.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the character of the chatbot, 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, the training content necessary for improving the skills of employees has not been sufficiently identified efficiently, and an appropriate OJT program has not been provided.

[0005] The system according to the embodiment aims to identify the training content necessary for improving the skills of employees and provide an appropriate OJT program.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a data provision unit. The data collection unit collects data related to the work content or skill level of employees. The analysis unit analyzes the data collected by the data collection unit and identifies the training content necessary for improving the skills of employees. The data generation unit generates an on-the-job training (OJT) program based on the training content identified by the analysis unit. The data provision unit provides the OJT program generated by the data generation unit to the employees. [Effects of the Invention]

[0007] The system according to this embodiment can identify the training content necessary for improving employees' skills and provide an appropriate on-the-job training (OJT) program. [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 controls communication between multiple 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 OJT program generation system according to an embodiment of the present invention is a system that generates and provides an optimal OJT program tailored to the employee's work content and skill level. This system efficiently improves employee skills by collecting data on the employee's work content and skill level, and having a generating AI generate an optimal OJT program. For example, when collecting data on the employee's work content and skill level, detailed data such as what kind of work the employee is doing and what level of skill they possess is collected. Specifically, data such as the projects the employee is in charge of, past work history, and skill evaluations are collected. This makes it possible to understand the employee's work content and skill level. Next, based on the collected data, the generating AI generates an optimal OJT program. The generating AI analyzes the collected data and identifies the training content necessary to improve the employee's skills. For example, it generates training content necessary for the employee to improve specific skills, or practical training programs tailored to the work content. This efficiently improves the employee's skills. The generated OJT program is provided to the employee. The employee can receive training according to the generated OJT program. For example, this may include online training courses or practical training through actual work. This allows employees to receive specific training to improve their skills. This system efficiently promotes employee skill development. Employees can expect skill improvement because they receive training tailored to their job duties and skill level. Furthermore, for companies, improved employee skills lead to increased operational efficiency and productivity. For example, by acquiring new skills, employees can broaden their scope of work and handle more advanced tasks. This is expected to improve the overall competitiveness of the company. In this way, the OJT program generation system can efficiently promote employee skill development.

[0029] The OJT program generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data related to the employee's work content or skill level. The employee's work content includes, but is not limited to, technical work, management work, sales work, etc. The collection unit also collects data such as the projects the employee is in charge of, past work history, and skill evaluations. For example, the collection unit can collect the history of projects the employee has participated in in the past and prioritize the collection of relevant data. The collection unit can also collect employee skill evaluation data and understand the employee's skill level. The analysis unit analyzes the data collected by the collection unit and identifies the training content necessary to improve the employee's skills. For example, the analysis unit can perform a skill gap analysis based on the collected data to identify the training content necessary to improve the employee's skills. The analysis unit can also identify the training content necessary to improve the employee's skills by referring to expert opinions. The generation unit generates an OJT program based on the training content identified by the analysis unit. The generation unit generates an optimal OJT program using, for example, a generation AI. The generation AI can generate OJT programs optimized for improving employee skills using technologies such as natural language generation, image generation, and speech generation. The provisioning unit provides the OJT programs generated by the generation unit to employees. The provisioning unit provides the OJT programs, for example, through an online platform. The online platform includes, but is not limited to, a learning management system or a video conferencing system. As a result, the OJT program generation system according to this embodiment can efficiently improve employee skills.

[0030] The data collection unit can collect employee project history or skill evaluation data. For example, the data collection unit can collect the history of projects that employees have participated in in the past. For example, the data collection unit can collect data such as project type, duration, and results. The data collection unit can also collect employee skill evaluation data. For example, the data collection unit can collect data such as evaluation criteria, evaluators, and evaluation timing. By collecting employee project history and skill evaluation data, it is possible to generate more detailed, data-driven on-the-job training (OJT) programs. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee project history data into a generating AI and have the generating AI perform an analysis of the project history.

[0031] The analysis unit can identify the training content necessary to improve employees' skills based on the collected data. For example, the analysis unit can perform a skills gap analysis based on the collected data to identify the training content necessary to improve employees' skills. For example, the analysis unit can analyze the difference between an employee's current skill level and their target skill level and identify the training content needed to bridge that gap. The analysis unit can also identify the training content necessary to improve employees' skills by referring to expert opinions. For example, the analysis unit can collect expert opinions and use them to identify training content. Furthermore, the analysis unit can also identify training content based on employee feedback. For example, the analysis unit can analyze an employee's past training history and feedback to identify the necessary training content. In this way, by identifying training content based on the collected data, the system can provide training that is optimal for improving employees' skills. 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 collected data into a generating AI and have the generating AI perform a skills gap analysis.

[0032] The generation unit can generate OJT programs using a generation AI. For example, the generation unit generates an optimal OJT program using the generation AI. The generation AI can generate an optimal OJT program for improving employee skills using technologies such as natural language generation, image generation, and speech generation. For example, the generation AI generates training content tailored to the employee's job duties and skill level. The generation AI analyzes data on the employee's job duties and skill level to identify the training content necessary for skill improvement. For example, the generation AI generates training content necessary for employees to improve specific skills, as well as practical training programs tailored to their job duties. This allows for the efficient generation of optimal OJT programs for employee skill improvement using the generation AI. Some or all of the above-described processes in the generation unit may be performed using, for example, the generation AI, or without it. For example, the generation unit can input data on the employee's job duties and skill level into the generation AI and have the generation AI generate the OJT program.

[0033] The service provider can provide on-the-job training (OJT) programs through an online platform. For example, the service provider can provide OJT programs through an online platform. This online platform includes, but is not limited to, learning management systems and video conferencing systems. For example, the service provider can use a learning management system to enable employees to receive training anytime, anywhere. The service provider can also provide real-time training using a video conferencing system. For example, the service provider can provide expert training sessions through a video conferencing system. Furthermore, the service provider can implement a hybrid delivery model. For example, the service provider can provide a combination of online and in-person training. This allows employees to receive training anytime, anywhere by providing OJT programs through an online platform. Some or all of the above processes in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input employee training history into a generating AI and have the generating AI execute the optimal delivery method.

[0034] The data collection unit can analyze an employee's past work history and select the optimal data collection method. For example, the data collection unit can analyze the history of projects an employee has participated in and prioritize the collection of relevant data. For example, the data collection unit can analyze data such as project type, duration, and results to select the optimal data collection method. The data collection unit can also collect data related to specific skills from an employee's past work history. For example, the data collection unit can select an efficient data collection method based on an employee's past work history. This allows for the selection of a more efficient data collection method by analyzing an employee's past work history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee past work history data into a generating AI and have the generating AI select a data collection method.

[0035] The data collection unit can filter data based on an employee's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the projects an employee is currently working on. For example, the data collection unit can filter and collect relevant data based on an employee's areas of interest. The data collection unit can also select and collect necessary data according to the employee's current work. For example, the data collection unit can filter data based on an employee's current projects and areas of interest. This allows for the collection of highly relevant data by filtering data based on an 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, for example, or not using AI. For example, the data collection unit can input an employee's current project data into a generating AI and have the generating AI perform data filtering.

[0036] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of employees during data collection. For example, if an employee works in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect highly relevant data based on the employee's current location. Furthermore, if an employee is on the move, the data collection unit can collect data related to their destination. For example, the data collection unit prioritizes the collection of highly relevant data by considering the employee's geographical location information. This allows for the priority collection of highly relevant data by considering the employee's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the employee's geographical location information into a generating AI and have the generating AI perform the data collection.

[0037] The data collection unit can analyze employees' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of employees' social media activities and collect relevant data. The data collection unit can also collect data based on topics that employees are interested in on social media. Furthermore, the data collection unit can analyze employees' social media networks and collect relevant data. For example, the data collection unit can analyze employees' social media activity and collect relevant data. This allows for the efficient collection of relevant data by analyzing employees' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee social media activity data into a generating AI and have the generating AI perform the data collection.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. Furthermore, the analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the data. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply a statistical analysis algorithm to numerical data. Furthermore, it can apply an image analysis algorithm to image data. For example, the analysis unit applies different analysis algorithms depending on the data category. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0040] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, the analysis unit can prioritize the most recent data while also referring to past data. Furthermore, the analysis unit can adjust the priority of analysis in stages according to the data collection timing. For example, the analysis unit can determine the priority of analysis based on the data collection timing. This allows for the prioritization of analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the data. For example, the analysis unit can adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the 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 relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0042] The generation unit can identify in detail the training content necessary for improving an employee's skills during the generation process. For example, the generation unit can identify the necessary training content based on the employee's current skill level. Furthermore, the generation unit can identify the necessary training content according to the employee's job duties. In addition, the generation unit can identify the necessary training content by referring to the employee's past training history. For example, the generation unit can identify in detail the training content necessary for improving an employee's skills during the generation process. This allows for the provision of a more effective on-the-job training (OJT) program by precisely identifying the training content necessary for improving an employee's skills. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input employee skill data into a generation AI and have the generation AI perform the identification of training content.

[0043] The generation unit can generate different training programs depending on the employee's job duties during the generation process. For example, if an employee is in a technical position, the generation unit can generate a training program aimed at improving their technical skills. If an employee is in a sales position, the generation unit can generate a training program aimed at improving their sales skills. Furthermore, if an employee is in a management position, the generation unit can generate a training program aimed at improving their leadership skills. For example, the generation unit generates different training programs depending on the employee's job duties during the generation process. This allows for more practical skill improvement by generating training programs tailored to the employee's job duties. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input employee job content data into a generation AI and have the generation AI execute the generation of training programs.

[0044] The generation unit can generate an optimal training program by considering the employee's geographical location information during the generation process. For example, if an employee works in a specific region, the generation unit will generate a training program relevant to that region. The generation unit can also generate an optimal training program based on the employee's current location. Furthermore, if an employee is on the move, the generation unit can generate a training program relevant to their destination. For example, the generation unit generates an optimal training program by considering the employee's geographical location information during the generation process. This allows for the provision of more relevant training programs by considering the employee's geographical location information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the employee's geographical location information into a generation AI and have the generation AI perform the generation of the training program.

[0045] The generation unit can analyze employees' social media activity and generate relevant training programs during the generation process. For example, the generation unit can analyze the content of employees' social media activities and generate relevant training programs. It can also generate training programs based on topics that employees show interest in on social media. Furthermore, the generation unit can analyze employees' social media networks and generate relevant training programs. For example, the generation unit can analyze employees' social media activity and generate relevant training programs during the generation process. This allows for the provision of more relevant training programs by analyzing employees' social media activity. 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 employee social media activity data into a generation AI and have the generation AI perform the generation of training programs.

[0046] The service provider can select the optimal service delivery method by referring to the employee's past training history at the time of delivery. For example, the service provider can select the optimal service delivery method based on the employee's past training history. The service provider can also select the optimal service delivery method by referring to the training content the employee has received in the past. Furthermore, the service provider can also select the optimal service delivery method by analyzing the employee's past training history. For example, the service provider can select the optimal service delivery method by referring to the employee's past training history at the time of delivery. This allows for the selection of a more effective training delivery method by referring to the employee's past training history. Some or all of the above processes in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input employee training history data into a generating AI and have the generating AI perform the selection of the service delivery method.

[0047] The delivery unit can adjust the timing of delivery based on the employee's current work situation. For example, if an employee is busy, the delivery unit can provide training when their workload has subsided. Alternatively, if an employee is relaxed, the delivery unit can provide training immediately. Furthermore, if an employee is busy before a project deadline, the delivery unit can provide training after the deadline. For example, the delivery unit adjusts the timing of delivery based on the employee's current work situation. This allows for more appropriate timing of training delivery. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not. For example, the delivery unit can input employee work situation data into a generating AI and have the generating AI perform the adjustment of the delivery timing.

[0048] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the employee's device information. For example, if the employee is using a smartphone, the delivery unit can provide mobile-optimized training. If the employee is using a tablet, the delivery unit can provide training optimized for a larger screen. Furthermore, if the employee is using a desktop, the delivery unit can provide training that includes detailed information. For example, the delivery unit selects the optimal delivery method at the time of delivery, taking into account the employee's device information. This allows for the selection of a more appropriate training delivery method by considering the employee's device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input employee device information into a generating AI and have the generating AI perform the selection of the delivery method.

[0049] The service provider can analyze employees' social media activity and provide relevant training programs at the time of delivery. For example, the service provider can analyze the content of employees' social media activities and provide relevant training programs. Furthermore, the service provider can provide training programs based on topics that employees show interest in on social media. In addition, the service provider can analyze employees' social media networks and provide relevant training programs. For example, the service provider can analyze employees' social media activity and provide relevant training programs at the time of delivery. This allows for the provision of more relevant training programs by analyzing employees' social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input employee social media activity data into a generating AI and have the generating AI execute the provision of training programs.

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

[0051] The data collection unit can analyze an employee's past work history and select the optimal data collection method. For example, it can analyze the history of projects an employee has participated in and prioritize the collection of relevant data. For example, the data collection unit can analyze data such as project type, duration, and results to select the optimal data collection method. The data collection unit can also collect data related to specific skills from an employee's past work history. For example, the data collection unit can select an efficient data collection method based on an employee's past work history. This allows for the selection of a more efficient data collection method by analyzing an employee's past work history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee past work history data into a generating AI and have the generating AI select the data collection method.

[0052] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on highly important data and a simplified analysis on less important data. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the 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 importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0053] The generation unit can generate different training programs depending on the employee's job duties during the generation process. For example, if an employee is in a technical position, it can generate a training program aimed at improving their technical skills. If an employee is in a sales position, it can generate a training program aimed at improving their sales skills. Furthermore, if an employee is in a management position, it can generate a training program aimed at improving their leadership skills. This allows for more practical skill improvement by generating training programs tailored to the employee's job duties. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input employee job content data into a generation AI and have the generation AI execute the generation of training programs.

[0054] The delivery unit can select the optimal delivery method by considering the employee's device information at the time of delivery. For example, if an employee is using a smartphone, it can provide mobile-optimized training. If an employee is using a tablet, it can provide training optimized for a larger screen. Furthermore, if an employee is using a desktop, it can provide training with more detailed information. This allows for the selection of a more appropriate training delivery method by considering the employee's device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input employee device information into a generating AI and have the generating AI select the delivery method.

[0055] The data collection unit can filter data based on an employee's current projects and areas of interest during data collection. For example, it can prioritize collecting data related to the project the employee is currently working on. For example, the data collection unit can filter and collect relevant data based on the employee's areas of interest. The data collection unit can also select and collect necessary data according to the employee's current work. This allows for the collection of highly relevant data by filtering data 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, for example, or not using AI. For example, the data collection unit can input the employee's current project data into a generating AI and have the generating AI perform data filtering.

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

[0057] Step 1: The data collection department collects data on employees' work content or skill levels. Specifically, this includes data on projects employees are currently working on, their past work history, and skill evaluations. For example, they collect data on past projects employees have participated in and their skill evaluations to understand their skill levels. Step 2: The analysis unit analyzes the data collected by the data collection unit to identify the training content necessary to improve employee skills. Specifically, it conducts a skills gap analysis based on the collected data and identifies training content with the help of expert opinions. Step 3: The generation unit generates an OJT program based on the training content identified by the analysis unit. Specifically, it uses a generation AI to generate the optimal OJT program, utilizing technologies such as natural language generation, image generation, and speech generation. Step 4: The providing department provides the OJT program generated by the generating department to the employees. Specifically, the OJT program is provided through an online platform, utilizing a learning management system and video conferencing system.

[0058] (Example of form 2) An OJT program generation system according to an embodiment of the present invention is a system that generates and provides an optimal OJT program tailored to the employee's work content and skill level. This system efficiently improves employee skills by collecting data on the employee's work content and skill level, and having a generating AI generate an optimal OJT program. For example, when collecting data on the employee's work content and skill level, detailed data such as what kind of work the employee is doing and what level of skill they possess is collected. Specifically, data such as the projects the employee is in charge of, past work history, and skill evaluations are collected. This makes it possible to understand the employee's work content and skill level. Next, based on the collected data, the generating AI generates an optimal OJT program. The generating AI analyzes the collected data and identifies the training content necessary to improve the employee's skills. For example, it generates training content necessary for the employee to improve specific skills, or practical training programs tailored to the work content. This efficiently improves the employee's skills. The generated OJT program is provided to the employee. The employee can receive training according to the generated OJT program. For example, this may include online training courses or practical training through actual work. This allows employees to receive specific training to improve their skills. This system efficiently promotes employee skill development. Employees can expect skill improvement because they receive training tailored to their job duties and skill level. Furthermore, for companies, improved employee skills lead to increased operational efficiency and productivity. For example, by acquiring new skills, employees can broaden their scope of work and handle more advanced tasks. This is expected to improve the overall competitiveness of the company. In this way, the OJT program generation system can efficiently promote employee skill development.

[0059] The OJT program generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data related to the employee's work content or skill level. The employee's work content includes, but is not limited to, technical work, management work, sales work, etc. The collection unit also collects data such as the projects the employee is in charge of, past work history, and skill evaluations. For example, the collection unit can collect the history of projects the employee has participated in in the past and prioritize the collection of relevant data. The collection unit can also collect employee skill evaluation data and understand the employee's skill level. The analysis unit analyzes the data collected by the collection unit and identifies the training content necessary to improve the employee's skills. For example, the analysis unit can perform a skill gap analysis based on the collected data to identify the training content necessary to improve the employee's skills. The analysis unit can also identify the training content necessary to improve the employee's skills by referring to expert opinions. The generation unit generates an OJT program based on the training content identified by the analysis unit. The generation unit generates an optimal OJT program using, for example, a generation AI. The generation AI can generate OJT programs optimized for improving employee skills using technologies such as natural language generation, image generation, and speech generation. The provisioning unit provides the OJT programs generated by the generation unit to employees. The provisioning unit provides the OJT programs, for example, through an online platform. The online platform includes, but is not limited to, a learning management system or a video conferencing system. As a result, the OJT program generation system according to this embodiment can efficiently improve employee skills.

[0060] The data collection unit can collect employee project history or skill evaluation data. For example, the data collection unit can collect the history of projects that employees have participated in in the past. For example, the data collection unit can collect data such as project type, duration, and results. The data collection unit can also collect employee skill evaluation data. For example, the data collection unit can collect data such as evaluation criteria, evaluators, and evaluation timing. By collecting employee project history and skill evaluation data, it is possible to generate more detailed, data-driven on-the-job training (OJT) programs. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee project history data into a generating AI and have the generating AI perform an analysis of the project history.

[0061] The analysis unit can identify the training content necessary to improve employees' skills based on the collected data. For example, the analysis unit can perform a skills gap analysis based on the collected data to identify the training content necessary to improve employees' skills. For example, the analysis unit can analyze the difference between an employee's current skill level and their target skill level and identify the training content needed to bridge that gap. The analysis unit can also identify the training content necessary to improve employees' skills by referring to expert opinions. For example, the analysis unit can collect expert opinions and use them to identify training content. Furthermore, the analysis unit can also identify training content based on employee feedback. For example, the analysis unit can analyze an employee's past training history and feedback to identify the necessary training content. In this way, by identifying training content based on the collected data, the system can provide training that is optimal for improving employees' skills. 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 collected data into a generating AI and have the generating AI perform a skills gap analysis.

[0062] The generation unit can generate OJT programs using a generation AI. For example, the generation unit generates an optimal OJT program using the generation AI. The generation AI can generate an optimal OJT program for improving employee skills using technologies such as natural language generation, image generation, and speech generation. For example, the generation AI generates training content tailored to the employee's job duties and skill level. The generation AI analyzes data on the employee's job duties and skill level to identify the training content necessary for skill improvement. For example, the generation AI generates training content necessary for employees to improve specific skills, as well as practical training programs tailored to their job duties. This allows for the efficient generation of optimal OJT programs for employee skill improvement using the generation AI. Some or all of the above-described processes in the generation unit may be performed using, for example, the generation AI, or without it. For example, the generation unit can input data on the employee's job duties and skill level into the generation AI and have the generation AI generate the OJT program.

[0063] The service provider can provide on-the-job training (OJT) programs through an online platform. For example, the service provider can provide OJT programs through an online platform. This online platform includes, but is not limited to, learning management systems and video conferencing systems. For example, the service provider can use a learning management system to enable employees to receive training anytime, anywhere. The service provider can also provide real-time training using a video conferencing system. For example, the service provider can provide expert training sessions through a video conferencing system. Furthermore, the service provider can implement a hybrid delivery model. For example, the service provider can provide a combination of online and in-person training. This allows employees to receive training anytime, anywhere by providing OJT programs through an online platform. Some or all of the above processes in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input employee training history into a generating AI and have the generating AI execute the optimal delivery method.

[0064] The data collection unit can estimate employees' emotions and adjust the timing of data collection based on the estimated emotions. For example, if an employee is stressed, the data collection unit can delay the timing of data collection and collect it when the employee is relaxed. Conversely, if the employee is relaxed, the data collection unit can collect data immediately, efficiently obtaining information. Furthermore, if an employee is busy, the data collection unit can collect data when work has calmed down. For example, the data collection unit estimates employees' emotions and adjusts the timing of data collection based on the estimated emotions. This allows for data collection at a more appropriate time by adjusting the timing of data collection according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes text generation AI (e.g., LLM) and multimodal generation AI.

[0065] The data collection unit can analyze an employee's past work history and select the optimal data collection method. For example, the data collection unit can analyze the history of projects an employee has participated in and prioritize the collection of relevant data. For example, the data collection unit can analyze data such as project type, duration, and results to select the optimal data collection method. The data collection unit can also collect data related to specific skills from an employee's past work history. For example, the data collection unit can select an efficient data collection method based on an employee's past work history. This allows for the selection of a more efficient data collection method by analyzing an employee's past work history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee past work history data into a generating AI and have the generating AI select a data collection method.

[0066] The data collection unit can filter data based on an employee's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the projects an employee is currently working on. For example, the data collection unit can filter and collect relevant data based on an employee's areas of interest. The data collection unit can also select and collect necessary data according to the employee's current work. For example, the data collection unit can filter data based on an employee's current projects and areas of interest. This allows for the collection of highly relevant data by filtering data based on an 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, for example, or not using AI. For example, the data collection unit can input an employee's current project data into a generating AI and have the generating AI perform data filtering.

[0067] The data collection unit can estimate employees' emotions and prioritize the data to be collected based on the estimated emotions. For example, if an employee is stressed, the data collection unit will postpone collecting less important data and prioritize collecting important data. If an employee is relaxed, the data collection unit can collect all data evenly. Furthermore, if an employee is busy, the data collection unit can prioritize collecting the most important data. For example, the data collection unit estimates employees' emotions and prioritizes the data to be collected based on the estimated emotions. This allows for the priority collection of important data by prioritizing data according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into a generative AI and have the generative AI perform the data prioritization.

[0068] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of employees during data collection. For example, if an employee works in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect highly relevant data based on the employee's current location. Furthermore, if an employee is on the move, the data collection unit can collect data related to their destination. For example, the data collection unit prioritizes the collection of highly relevant data by considering the employee's geographical location information. This allows for the priority collection of highly relevant data by considering the employee's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the employee's geographical location information into a generating AI and have the generating AI perform the data collection.

[0069] The data collection unit can analyze employees' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of employees' social media activities and collect relevant data. The data collection unit can also collect data based on topics that employees are interested in on social media. Furthermore, the data collection unit can analyze employees' social media networks and collect relevant data. For example, the data collection unit can analyze employees' social media activity and collect relevant data. This allows for the efficient collection of relevant data by analyzing employees' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee social media activity data into a generating AI and have the generating AI perform the data collection.

[0070] The analysis unit can estimate the emotions of employees and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit can provide a simple and easy-to-understand analysis result. If an employee is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if an employee is in a hurry, the analysis unit can provide a concise analysis result. For example, the analysis unit estimates the emotions of employees and adjusts the presentation of the analysis based on the estimated emotions. By adjusting the presentation of the analysis according to the emotions of the employees, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input employee emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. Furthermore, the analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the data. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply a statistical analysis algorithm to numerical data. Furthermore, it can apply an image analysis algorithm to image data. For example, the analysis unit applies different analysis algorithms depending on the data category. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0073] The analysis unit can estimate an employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if an employee is in a hurry, the analysis unit can provide a short, concise analysis. If an employee is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if an employee is excited, the analysis unit can provide an analysis with visually stimulating effects. For example, the analysis unit estimates an employee's emotions and adjusts the length of the analysis based on the estimated emotions. By adjusting the length of the analysis according to the employee's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input employee emotion data into a generative AI and have the generative AI perform the adjustment of the analysis length.

[0074] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, the analysis unit can prioritize the most recent data while also referring to past data. Furthermore, the analysis unit can adjust the priority of analysis in stages according to the data collection timing. For example, the analysis unit can determine the priority of analysis based on the data collection timing. This allows for the prioritization of analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the data. For example, the analysis unit can adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the 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 relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0076] The generation unit can estimate an employee's emotions and adjust the content of the OJT program it generates based on the estimated emotions. For example, if an employee is stressed, the generation unit can include training that promotes relaxation. If an employee is relaxed, the generation unit can include training that is challenging. Furthermore, if an employee is excited, the generation unit can include visually stimulating training. For example, the generation unit estimates an employee's emotions and adjusts the content of the OJT program it generates based on the estimated emotions. This allows for more appropriate training by adjusting the content of the OJT program according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input employee emotion data into the generation AI and have the generation AI adjust the content of the OJT program.

[0077] The generation unit can identify in detail the training content necessary for improving an employee's skills during the generation process. For example, the generation unit can identify the necessary training content based on the employee's current skill level. Furthermore, the generation unit can identify the necessary training content according to the employee's job duties. In addition, the generation unit can identify the necessary training content by referring to the employee's past training history. For example, the generation unit can identify in detail the training content necessary for improving an employee's skills during the generation process. This allows for the provision of a more effective on-the-job training (OJT) program by precisely identifying the training content necessary for improving an employee's skills. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input employee skill data into a generation AI and have the generation AI perform the identification of training content.

[0078] The generation unit can generate different training programs depending on the employee's job duties during the generation process. For example, if an employee is in a technical position, the generation unit can generate a training program aimed at improving their technical skills. If an employee is in a sales position, the generation unit can generate a training program aimed at improving their sales skills. Furthermore, if an employee is in a management position, the generation unit can generate a training program aimed at improving their leadership skills. For example, the generation unit generates different training programs depending on the employee's job duties during the generation process. This allows for more practical skill improvement by generating training programs tailored to the employee's job duties. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input employee job content data into a generation AI and have the generation AI execute the generation of training programs.

[0079] The generation unit can estimate an employee's emotions and determine the priority of the OJT (On-the-Job Training) programs to be generated based on the estimated emotions. For example, if an employee is stressed, the generation unit may prioritize relaxing training. If an employee is relaxed, the generation unit may prioritize challenging training. Furthermore, if an employee is excited, the generation unit may prioritize visually stimulating training. For example, the generation unit estimates an employee's emotions and determines the priority of the OJT programs to be generated based on the estimated emotions. This allows for more effective training by prioritizing OJT programs according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input employee emotion data into a generation AI and have the generation AI determine the priority of OJT programs.

[0080] The generation unit can generate an optimal training program by considering the employee's geographical location information during the generation process. For example, if an employee works in a specific region, the generation unit will generate a training program relevant to that region. The generation unit can also generate an optimal training program based on the employee's current location. Furthermore, if an employee is on the move, the generation unit can generate a training program relevant to their destination. For example, the generation unit generates an optimal training program by considering the employee's geographical location information during the generation process. This allows for the provision of more relevant training programs by considering the employee's geographical location information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the employee's geographical location information into a generation AI and have the generation AI perform the generation of the training program.

[0081] The generation unit can analyze employees' social media activity and generate relevant training programs during the generation process. For example, the generation unit can analyze the content of employees' social media activities and generate relevant training programs. It can also generate training programs based on topics that employees show interest in on social media. Furthermore, the generation unit can analyze employees' social media networks and generate relevant training programs. For example, the generation unit can analyze employees' social media activity and generate relevant training programs during the generation process. This allows for the provision of more relevant training programs by analyzing employees' social media activity. 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 employee social media activity data into a generation AI and have the generation AI perform the generation of training programs.

[0082] The service provider can estimate an employee's emotions and adjust the delivery method of the OJT program based on the estimated emotions. For example, if an employee is feeling stressed, the service provider can provide training in a relaxing environment. If an employee is relaxed, the service provider can provide training in a challenging environment. Furthermore, if an employee is excited, the service provider can provide training in a visually stimulating environment. For example, the service provider estimates an employee's emotions and adjusts the delivery method of the OJT program based on the estimated emotions. By adjusting the delivery method of the OJT program according to the employee's emotions, more effective training can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The service provider can select the optimal service delivery method by referring to the employee's past training history at the time of delivery. For example, the service provider can select the optimal service delivery method based on the employee's past training history. The service provider can also select the optimal service delivery method by referring to the training content the employee has received in the past. Furthermore, the service provider can also select the optimal service delivery method by analyzing the employee's past training history. For example, the service provider can select the optimal service delivery method by referring to the employee's past training history at the time of delivery. This allows for the selection of a more effective training delivery method by referring to the employee's past training history. Some or all of the above processes in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input employee training history data into a generating AI and have the generating AI perform the selection of the service delivery method.

[0084] The delivery unit can adjust the timing of delivery based on the employee's current work situation. For example, if an employee is busy, the delivery unit can provide training when their workload has subsided. Alternatively, if an employee is relaxed, the delivery unit can provide training immediately. Furthermore, if an employee is busy before a project deadline, the delivery unit can provide training after the deadline. For example, the delivery unit adjusts the timing of delivery based on the employee's current work situation. This allows for more appropriate timing of training delivery. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not. For example, the delivery unit can input employee work situation data into a generating AI and have the generating AI perform the adjustment of the delivery timing.

[0085] The service provider can estimate an employee's emotions and determine the order in which OJT (On-the-Job Training) programs are delivered based on the estimated emotions. For example, if an employee is feeling stressed, the service provider can deliver relaxing training first. If an employee is relaxed, the service provider can deliver challenging training first. Furthermore, if an employee is excited, the service provider can deliver visually stimulating training first. For example, the service provider estimates an employee's emotions and determines the order in which OJT programs are delivered based on the estimated emotions. This allows for more effective training by determining the order of OJT programs according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input employee emotion data into a generative AI and have the generative AI determine the order of delivery.

[0086] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the employee's device information. For example, if the employee is using a smartphone, the delivery unit can provide mobile-optimized training. If the employee is using a tablet, the delivery unit can provide training optimized for a larger screen. Furthermore, if the employee is using a desktop, the delivery unit can provide training that includes detailed information. For example, the delivery unit selects the optimal delivery method at the time of delivery, taking into account the employee's device information. This allows for the selection of a more appropriate training delivery method by considering the employee's device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input employee device information into a generating AI and have the generating AI perform the selection of the delivery method.

[0087] The service provider can analyze employees' social media activity and provide relevant training programs at the time of delivery. For example, the service provider can analyze the content of employees' social media activities and provide relevant training programs. Furthermore, the service provider can provide training programs based on topics that employees show interest in on social media. In addition, the service provider can analyze employees' social media networks and provide relevant training programs. For example, the service provider can analyze employees' social media activity and provide relevant training programs at the time of delivery. This allows for the provision of more relevant training programs by analyzing employees' social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input employee social media activity data into a generating AI and have the generating AI execute the provision of training programs. === Hard Collateral 1-1 === 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 uses the camera 42 and microphone 38B of the smart device 14 to collect data on employees' work content and skill levels, and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the training content necessary for improving employees' skills. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates an optimal OJT program using generation AI. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, and provides the OJT program to employees through an online platform. === Hard Collateral 1-2 === 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 uses the camera 42 and microphone 238 of the smart glasses 214 to collect data on the employee's work content and skill level, and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the training content necessary to improve the employee's skills. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates an optimal OJT program using generation AI. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides the OJT program to the employee through an online platform. === Hard Collateral 1-3 === 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 uses the camera 42 and microphone 238 of the headset terminal 314 to collect data on the employee's work content and skill level, and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the training content necessary for improving the employee's skills. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates an optimal OJT program using a generation AI. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides the OJT program to the employee through an online platform. === Hard Collateral 1-4 === 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 uses the camera 42 and microphone 238 of the robot 414 to collect data on the employee's work content and skill level, and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the training content necessary to improve the employee's skills. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates an optimal OJT program using a generation AI. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the OJT program to the employee through an online platform.

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

[0089] The analysis unit can estimate employees' emotions and determine analysis priorities based on those estimated emotions. For example, if an employee is stressed, less important data can be postponed, and important data can be prioritized. If an employee is relaxed, all data can be analyzed equally. Furthermore, if an employee is busy, the most important data can be prioritized. In this way, by determining analysis priorities according to employees' emotions, important data can be analyzed preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The service provider can estimate the employee's emotions and adjust the delivery method of the OJT program based on the estimated emotions. For example, if an employee is stressed, training can be provided in a relaxing environment. If the employee is relaxed, training can be provided in a challenging environment. Furthermore, if the employee is excited, training can be provided in a visually stimulating environment. By adjusting the delivery method of the OJT program according to the employee's emotions, more effective training can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The data collection unit can estimate employees' emotions and adjust the timing of data collection based on the estimated emotions. For example, if an employee is stressed, data collection can be delayed until they are relaxed. If an employee is relaxed, data can be collected immediately for efficient information acquisition. Furthermore, if an employee is busy, data can be collected when their workload has subsided. By adjusting the timing of data collection according to employees' emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The generation unit can estimate an employee's emotions and adjust the content of the OJT program it generates based on the estimated emotions. For example, if an employee is stressed, it can include training that promotes relaxation. If the employee is relaxed, it can include training that is challenging. Furthermore, if the employee is excited, it can include visually stimulating training. This allows for more appropriate training by adjusting the OJT program content according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The training system can estimate an employee's emotions and determine the order in which OJT (On-the-Job Training) programs are delivered based on those estimates. For example, if an employee is stressed, relaxing training can be provided first. If the employee is relaxed, challenging training can be provided first. Furthermore, if the employee is excited, visually stimulating training can be provided first. This allows for more effective training by determining the order of OJT programs according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The data collection unit can analyze an employee's past work history and select the optimal data collection method. For example, it can analyze the history of projects an employee has participated in and prioritize the collection of relevant data. For example, the data collection unit can analyze data such as project type, duration, and results to select the optimal data collection method. The data collection unit can also collect data related to specific skills from an employee's past work history. For example, the data collection unit can select an efficient data collection method based on an employee's past work history. This allows for the selection of a more efficient data collection method by analyzing an employee's past work history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee past work history data into a generating AI and have the generating AI select the data collection method.

[0095] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on highly important data and a simplified analysis on less important data. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the 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 importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0096] The generation unit can generate different training programs depending on the employee's job duties during the generation process. For example, if an employee is in a technical position, it can generate a training program aimed at improving their technical skills. If an employee is in a sales position, it can generate a training program aimed at improving their sales skills. Furthermore, if an employee is in a management position, it can generate a training program aimed at improving their leadership skills. This allows for more practical skill improvement by generating training programs tailored to the employee's job duties. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input employee job content data into a generation AI and have the generation AI execute the generation of training programs.

[0097] The delivery unit can select the optimal delivery method by considering the employee's device information at the time of delivery. For example, if an employee is using a smartphone, it can provide mobile-optimized training. If an employee is using a tablet, it can provide training optimized for a larger screen. Furthermore, if an employee is using a desktop, it can provide training with more detailed information. This allows for the selection of a more appropriate training delivery method by considering the employee's device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input employee device information into a generating AI and have the generating AI select the delivery method.

[0098] The data collection unit can filter data based on an employee's current projects and areas of interest during data collection. For example, it can prioritize collecting data related to the project the employee is currently working on. For example, the data collection unit can filter and collect relevant data based on the employee's areas of interest. The data collection unit can also select and collect necessary data according to the employee's current work. This allows for the collection of highly relevant data by filtering data 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, for example, or not using AI. For example, the data collection unit can input the employee's current project data into a generating AI and have the generating AI perform data filtering.

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

[0100] Step 1: The data collection department collects data on employees' work content or skill levels. Specifically, this includes data on projects employees are currently working on, their past work history, and skill evaluations. For example, they collect data on past projects employees have participated in and their skill evaluations to understand their skill levels. Step 2: The analysis unit analyzes the data collected by the data collection unit to identify the training content necessary to improve employee skills. Specifically, it conducts a skills gap analysis based on the collected data and identifies training content with the help of expert opinions. Step 3: The generation unit generates an OJT program based on the training content identified by the analysis unit. Specifically, it uses a generation AI to generate the optimal OJT program, utilizing technologies such as natural language generation, image generation, and speech generation. Step 4: The providing department provides the OJT program generated by the generating department to the employees. Specifically, the OJT program is provided through an online platform, utilizing a learning management system and video conferencing system.

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

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

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

[0104] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0120] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0136] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0172] [Explanation of symbols]

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

Claims

1. A system comprising: a collection unit that collects data on the work content or skill level of employees; an analysis unit that analyzes the data collected by the collection unit and identifies the training content necessary for improving the skills of employees; a generation unit that generates an OJT program based on the training content identified by the analysis unit; and a provision unit that provides the OJT program generated by the generation unit to employees.

2. The system according to claim 1, characterized in that the collection unit collects employee project history or skill evaluation data.

3. The aforementioned analysis unit, Based on the collected data, we identify the training content necessary to improve employee skills. The system according to feature 1.

4. The system according to claim 1, characterized in that the generation unit generates an OJT program using generation AI.

5. The aforementioned supply unit is, We provide on-the-job training (OJT) programs through an online platform. The system according to feature 1.

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

7. The system according to claim 1, characterized in that the collection unit analyzes the past work history of employees and selects a data collection method.

8. The system according to claim 1, characterized in that the collection unit filters data based on the employee's current project or area of ​​interest when collecting data.

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