System

Abstract

An object of the system according to the embodiment is to appropriately manage the progress of training and the skill level of an employee and to perform necessary follow-up individually.SOLUTION: A system includes a recording unit, a follow-up unit, and a notification unit. The recording part records the training progress and the skill level of the employee. A follow-up part individually performs necessary follow-up based on the training progress and the skill level recorded by the recording part. The notification part performs follow-up at any time on the basis of the update of the training contents.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Previous technology did not adequately manage employee training progress and skill levels, making it difficult to provide individual follow-up.

[0005] The system according to the embodiment aims to appropriately manage the training progress and skill level of employees and provide necessary follow-up on an individual basis. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, a follow-up unit, and a notification unit. The recording unit records the training progress and skill level of employees. The follow-up unit performs individual follow-up as needed based on the training progress and skill level recorded by the recording unit. The notification unit performs follow-up as needed based on updates to the training content. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately manage the training progress and skill level of employees and provide necessary follow-up on an individual basis. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The employee training system according to the embodiment of the present invention uses AI to record the training progress and skill level of each employee, provide necessary follow-up individually, and follow up as needed based on updates to the training content. This allows the employee training system to create an environment in which all employees can perform as highly skilled top guns.

[0029] The employee training system according to the embodiment includes a recording unit, a follow-up unit, and a notification unit. The recording unit records the training progress and skill level of employees. For example, the recording unit stores the training content and test results of employees in a database to evaluate each employee's skill level. For example, the recording unit digitally records the training content of employees and stores the test results in a database. The recording unit can also analyze the training content and test results to evaluate the employee's skill level. The follow-up unit provides individual follow-up based on the training progress and skill level recorded by the recording unit. For example, for employees lacking a specific skill, the follow-up unit provides additional training or materials to compensate for that skill. The follow-up unit suggests individual follow-up based on the employee's skill level. The follow-up unit can also provide an appropriate training program to improve the employee's skill level. The notification unit provides follow-up as needed based on updates to the training content. For example, when new skills or knowledge are added, the notification unit notifies the employee of the content and provides the necessary training. The notification unit notifies the employee based on updates to the training content, for example. The notification unit can also send notifications at appropriate times so that employees can obtain the latest information. As a result, the employee training system according to the embodiment can create an environment in which all employees can perform as highly skilled top guns. For example, employees can check their skill levels and improve them by taking the necessary training. Furthermore, AI can provide follow-up support as training content is updated, allowing employees to acquire the latest knowledge and skills.

[0030] The recording unit can analyze training progress data in real time and immediately detect and notify any delays in progress. The recording unit, for example, uses generative AI to analyze employee training progress data in real time and immediately detect and notify any delays in progress. For example, if an employee has not completed scheduled training, the AI ​​automatically issues an alert and notifies their supervisor or training manager. The recording unit, for example, analyzes data in real time and uses an algorithm to detect delays in progress. In addition, if the recording unit detects a delay in progress, it can also select an appropriate notification method. This makes it possible to analyze employee training progress data in real time and immediately detect and notify any delays in progress, enabling rapid response.

[0031] When assessing skill levels, the recording unit can also take into account past work performance and project results, enabling a more comprehensive skill assessment. For example, the recording unit uses generative AI to extract employees' past work performance and project results from a database and integrates them with training progress data to perform a comprehensive skill assessment. For example, project success rates and work efficiency can be included as evaluation criteria. For example, the recording unit uses an algorithm to consider past work performance and project results. The recording unit can also combine multiple evaluation criteria to perform a comprehensive skill assessment. This allows for a more comprehensive skill assessment by taking into account past work performance and project results.

[0032] The follow-up department can automatically generate optimal training programs to compensate for skill deficiencies and provide customized training. The follow-up department, for example, uses generative AI to build a system that automatically generates optimal training programs to compensate for employee skill deficiencies. For example, it identifies skill gaps and proposes appropriate training content. The follow-up department, for example, uses an algorithm to automatically generate training programs to compensate for skill deficiencies. The follow-up department can also select training content according to individual needs to provide customized training. In this way, by automatically generating optimal training programs to compensate for employee skill deficiencies and providing individually customized training, it is possible to efficiently improve employee skills.

[0033] The follow-up unit can select a teaching material format according to the learning style and preferences. The follow-up unit, for example, uses generative AI to build a system that selects a teaching material format according to the employee's learning style and preferences. For example, it suggests the optimal format from video, text, interactive content, etc. The follow-up unit, for example, uses an algorithm to select a teaching material format according to the learning style and preferences. The follow-up unit can also provide an appropriate teaching material format to improve the learning effectiveness of employees. This makes it possible to improve the effectiveness of follow-up by selecting a teaching material format according to the employee's learning style and preferences.

[0034] The follow-up department can share the content of the follow-up with other employees and improve the skills of the entire team. The follow-up department, for example, uses generative AI to build a system for sharing the content of the follow-up with other employees. For example, it provides a platform for sharing the results of the follow-up and the learnings. The follow-up department, for example, uses a tool for sharing the content of the follow-up. The follow-up department can also select an appropriate sharing method to improve the skills of the entire team. In this way, the skill level of the entire organization can be improved by sharing the content of the follow-up with other employees and improving the skills of the entire team.

[0035] The follow-up department can use the generative AI to quantitatively evaluate the effectiveness of follow-up and identify the most effective follow-up method. The follow-up department, for example, uses the generative AI to build a system that quantitatively evaluates the effectiveness of follow-up. For example, it measures the skill improvement rate and training completion rate after follow-up. The follow-up department, for example, uses an algorithm for performing quantitative evaluation. The follow-up department can also combine multiple evaluation criteria to identify the most effective follow-up method. This makes it possible to quantitatively evaluate the effectiveness of follow-up and identify the most effective follow-up method, thereby maximizing the efficiency and effectiveness of follow-up.

[0036] The notification unit can automatically analyze updated training content and notify each employee at the optimal time. The notification unit, for example, uses generative AI to automatically analyze updated training content and build a system that notifies each employee at the optimal time. For example, it notifies changes to training content and new information in real time. The notification unit, for example, uses an algorithm to analyze updated training content. The notification unit can also take into account employees' schedules and work progress in order to notify at the optimal time. This allows updated training content to be automatically analyzed and notified at the optimal time for each employee, allowing employees to quickly obtain the latest information.

[0037] The notification unit can automatically reconfigure existing training programs based on the updated content to reflect the latest content. The notification unit, for example, uses a generation AI to build a system that automatically reconfigures existing training programs based on the updated training content. For example, it automatically generates training programs that include new technologies and knowledge. The notification unit, for example, uses an algorithm for reconfiguration. The notification unit can also appropriately update existing training programs to reflect the latest content. This allows existing training programs to be automatically reconfigured based on the updated content to reflect the latest content, allowing employees to always learn the latest information.

[0038] The notification department can share the updated training content with other departments and teams to improve skills across the company. The notification department, for example, uses generative AI to build a system for sharing the updated training content with other departments and teams. For example, it notifies the entire company of changes to the training content and new information. The notification department, for example, uses a sharing tool. The notification department can also select an appropriate sharing method to improve skills across the company. In this way, the updated training content can be shared with other departments and teams to improve skills across the company, thereby improving the skill level of the entire organization.

[0039] The notification department can use the generation AI to test the level of comprehension of the updated content and provide additional follow-up to employees with low levels of comprehension. The notification department, for example, uses the generation AI to build a system that tests the level of comprehension of the updated training content. For example, it conducts online tests or quizzes to evaluate the level of comprehension. The notification department can also use an algorithm for evaluating the level of comprehension. The notification department can also select an appropriate method for providing additional follow-up to employees with low levels of comprehension. In this way, by testing the level of comprehension of the updated content and providing additional follow-up to employees with low levels of comprehension, it is possible to improve the level of comprehension of employees.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The employee training system can also be equipped with a gamification section. The gamification section can increase motivation for training by awarding points and badges based on employees' training progress and skill level. For example, employees who complete a specific training course can be awarded a badge and allowed to compete with other employees on a leaderboard. It is also possible to allow employees to earn points and receive special benefits and rewards. This can increase employees' motivation for training and create an environment in which they actively participate in training.

[0042] The employee training system can also be equipped with a virtual reality (VR) section, which allows employees to receive training in a virtual space that simulates an actual work environment. For example, VR simulations can be provided to help employees learn factory operating procedures and dangerous work safely. It can also provide customer service simulations to improve sales skills. This allows employees to receive training in an environment that closely resembles their actual work environment, enabling them to acquire practical skills.

[0043] The employee training system can also include a social interaction section, which provides a platform for employees to discuss and share information about the training content. For example, employees can ask questions and exchange opinions about the training content through online forums and chat functions. It can also provide opportunities for group projects and collaborative learning. This promotes communication between employees and deepens their understanding of the training content.

[0044] The employee training system can further include a personalized recommendation unit. The personalized recommendation unit recommends the most appropriate training programs and learning materials for each employee based on their past training history and skill level. For example, it can recommend additional training to improve a specific skill or related specialized books. It can also suggest training programs based on the employee's interests and career goals. This allows employees to receive the training that is most appropriate for them and improve their skills efficiently.

[0045] The employee training system can further be equipped with a feedback collection section. The feedback collection section collects feedback from employees after the training is completed and is used to improve the training program. For example, it can conduct surveys on the training content, instructor evaluations, and training effectiveness. It can also collect any problems or areas for improvement identified by employees and reflect them in the next training session. This can improve the quality of the training program and increase employee satisfaction.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The recording unit records the employee's training progress and skill level. For example, it stores the training content and test results of the employee in a database to evaluate each employee's skill level. The recording unit digitally records the training content of the employee and stores the test results in a database. The recording unit can also analyze the training content and test results to evaluate the employee's skill level. Step 2: The Follow-up Department will provide necessary follow-up for each employee based on the training progress and skill level recorded by the Recording Department. For example, if an employee lacks a specific skill, they will be provided with additional training and materials to compensate for that skill. The Follow-up Department can also suggest necessary follow-up for each employee based on their skill level and provide an appropriate training program. Step 3: The notification department will follow up on updates to the training content as they come in. For example, if new skills or knowledge are added, the notification department will notify employees of the content and provide the necessary training. The notification department can also notify employees at the appropriate time so that they can obtain the latest information.

[0048] (Example 2) The employee training system according to the embodiment of the present invention uses AI to record the training progress and skill level of each employee, provide necessary follow-up individually, and follow up as needed based on updates to the training content. This allows the employee training system to create an environment in which all employees can perform as highly skilled top guns.

[0049] The employee training system according to the embodiment includes a recording unit, a follow-up unit, and a notification unit. The recording unit records the training progress and skill level of employees. For example, the recording unit stores the training content and test results of employees in a database to evaluate each employee's skill level. For example, the recording unit digitally records the training content of employees and stores the test results in a database. The recording unit can also analyze the training content and test results to evaluate the employee's skill level. The follow-up unit provides individual follow-up based on the training progress and skill level recorded by the recording unit. For example, for employees lacking a specific skill, the follow-up unit provides additional training or materials to compensate for that skill. The follow-up unit suggests individual follow-up based on the employee's skill level. The follow-up unit can also provide an appropriate training program to improve the employee's skill level. The notification unit provides follow-up as needed based on updates to the training content. For example, when new skills or knowledge are added, the notification unit notifies the employee of the content and provides the necessary training. The notification unit notifies the employee based on updates to the training content, for example. The notification unit can also send notifications at appropriate times so that employees can obtain the latest information. As a result, the employee training system according to the embodiment can create an environment in which all employees can perform as highly skilled top guns. For example, employees can check their skill levels and improve them by taking the necessary training. Furthermore, AI can provide follow-up support as training content is updated, allowing employees to acquire the latest knowledge and skills.

[0050] The recording unit can analyze training progress data in real time and immediately detect and notify any delays in progress. The recording unit, for example, uses generative AI to analyze employee training progress data in real time and immediately detect and notify any delays in progress. For example, if an employee has not completed scheduled training, the AI ​​automatically issues an alert and notifies their supervisor or training manager. The recording unit, for example, analyzes data in real time and uses an algorithm to detect delays in progress. In addition, if the recording unit detects a delay in progress, it can also select an appropriate notification method. This makes it possible to analyze employee training progress data in real time and immediately detect and notify any delays in progress, enabling rapid response.

[0051] When assessing skill levels, the recording unit can also take into account past work performance and project results, enabling a more comprehensive skill assessment. For example, the recording unit uses generative AI to extract employees' past work performance and project results from a database and integrates them with training progress data to perform a comprehensive skill assessment. For example, project success rates and work efficiency can be included as evaluation criteria. For example, the recording unit uses an algorithm to consider past work performance and project results. The recording unit can also combine multiple evaluation criteria to perform a comprehensive skill assessment. This allows for a more comprehensive skill assessment by taking into account past work performance and project results.

[0052] The recording unit uses the emotion estimation function to monitor the emotional state of employees during training, detect stress or a decrease in motivation, and provide appropriate support. The recording unit, for example, uses the emotion estimation function to analyze the facial expressions and voice of employees during training and detect stress or a decrease in motivation. For example, the recording unit monitors the emotional state in real time using a camera or microphone. The recording unit, for example, uses an emotion estimation algorithm to evaluate the employee's emotional state. Furthermore, if the recording unit detects stress or a decrease in motivation, it can also suggest an appropriate support method. This makes it possible to improve the effectiveness of employee training by monitoring the emotional state of employees during training, detecting stress or a decrease in motivation, and providing appropriate support.

[0053] The follow-up department can automatically generate optimal training programs to compensate for skill deficiencies and provide customized training. The follow-up department, for example, uses generative AI to build a system that automatically generates optimal training programs to compensate for employee skill deficiencies. For example, it identifies skill gaps and proposes appropriate training content. The follow-up department, for example, uses an algorithm to automatically generate training programs to compensate for skill deficiencies. The follow-up department can also select training content according to individual needs to provide customized training. In this way, by automatically generating optimal training programs to compensate for employee skill deficiencies and providing individually customized training, it is possible to efficiently improve employee skills.

[0054] The follow-up unit can select a teaching material format according to the learning style and preferences. The follow-up unit, for example, uses generative AI to build a system that selects a teaching material format according to the employee's learning style and preferences. For example, it suggests the optimal format from video, text, interactive content, etc. The follow-up unit, for example, uses an algorithm to select a teaching material format according to the learning style and preferences. The follow-up unit can also provide an appropriate teaching material format to improve the learning effectiveness of employees. This makes it possible to improve the effectiveness of follow-up by selecting a teaching material format according to the employee's learning style and preferences.

[0055] The follow-up unit can use the emotion estimation function to monitor the emotional state of the employee during follow-up and provide an incentive to maintain motivation. The follow-up unit, for example, uses the emotion estimation function to analyze the facial expressions and voice of the employee during follow-up and provide an incentive to maintain motivation. For example, the emotional state is monitored in real time using a camera or microphone. The follow-up unit, for example, evaluates the emotional state of the employee using an emotion estimation algorithm. The follow-up unit can also select an appropriate method for providing an incentive to maintain motivation. In this way, by monitoring the emotional state of the employee during follow-up and providing an incentive to maintain motivation, the effectiveness of employee training can be improved.

[0056] The follow-up department can share the content of the follow-up with other employees and improve the skills of the entire team. The follow-up department, for example, uses generative AI to build a system for sharing the content of the follow-up with other employees. For example, it provides a platform for sharing the results of the follow-up and the learnings. The follow-up department, for example, uses a tool for sharing the content of the follow-up. The follow-up department can also select an appropriate sharing method to improve the skills of the entire team. In this way, the skill level of the entire organization can be improved by sharing the content of the follow-up with other employees and improving the skills of the entire team.

[0057] The follow-up department can use the generative AI to quantitatively evaluate the effectiveness of follow-up and identify the most effective follow-up method. The follow-up department, for example, uses the generative AI to build a system that quantitatively evaluates the effectiveness of follow-up. For example, it measures the skill improvement rate and training completion rate after follow-up. The follow-up department, for example, uses an algorithm for performing quantitative evaluation. The follow-up department can also combine multiple evaluation criteria to identify the most effective follow-up method. This makes it possible to quantitatively evaluate the effectiveness of follow-up and identify the most effective follow-up method, thereby maximizing the efficiency and effectiveness of follow-up.

[0058] The follow-up unit can use the emotion estimation function to provide relaxation content to reduce stress and anxiety felt by employees during follow-ups. The follow-up unit, for example, uses the emotion estimation function to monitor the stress and anxiety felt by employees during follow-ups in real time and builds a system to provide relaxation content. For example, the follow-up unit provides music for relaxation or a meditation guide. The follow-up unit can also use an emotion estimation algorithm to evaluate the emotional state of employees. The follow-up unit can also select an appropriate method for providing relaxation content to reduce stress and anxiety. This makes it possible to improve the effectiveness of employee training by providing relaxation content to reduce stress and anxiety felt by employees during follow-ups.

[0059] The notification unit can automatically analyze updated training content and notify each employee at the optimal time. The notification unit, for example, uses generative AI to automatically analyze updated training content and build a system that notifies each employee at the optimal time. For example, it notifies changes to training content and new information in real time. The notification unit, for example, uses an algorithm to analyze updated training content. The notification unit can also take into account employees' schedules and work progress in order to notify at the optimal time. This allows updated training content to be automatically analyzed and notified at the optimal time for each employee, allowing employees to quickly obtain the latest information.

[0060] The notification unit can automatically reconfigure existing training programs based on the updated content to reflect the latest content. The notification unit, for example, uses a generation AI to build a system that automatically reconfigures existing training programs based on the updated training content. For example, it automatically generates training programs that include new technologies and knowledge. The notification unit, for example, uses an algorithm for reconfiguration. The notification unit can also appropriately update existing training programs to reflect the latest content. This allows existing training programs to be automatically reconfigured based on the updated content to reflect the latest content, allowing employees to always learn the latest information.

[0061] The notification unit can use the emotion estimation function to analyze employees' emotional reactions to the updated training content and make improvements to elicit a positive reaction. The notification unit, for example, uses the emotion estimation function to build a system that analyzes employees' emotional reactions to the updated training content in real time. For example, it analyzes facial expressions and voices and calculates an emotion score. The notification unit, for example, uses an emotion estimation algorithm to evaluate employees' emotional reactions. The notification unit can also select an appropriate method for making improvements to elicit a positive reaction. This makes it possible to analyze employees' emotional reactions to the updated training content and make improvements to elicit a positive reaction, thereby improving the effectiveness of the training.

[0062] The notification department can share the updated training content with other departments and teams to improve skills across the company. The notification department, for example, uses generative AI to build a system for sharing the updated training content with other departments and teams. For example, it notifies the entire company of changes to the training content and new information. The notification department, for example, uses a sharing tool. The notification department can also select an appropriate sharing method to improve skills across the company. In this way, the updated training content can be shared with other departments and teams to improve skills across the company, thereby improving the skill level of the entire organization.

[0063] The notification department can use the generation AI to test the level of comprehension of the updated content and provide additional follow-up to employees with low levels of comprehension. The notification department, for example, uses the generation AI to build a system that tests the level of comprehension of the updated training content. For example, it conducts online tests or quizzes to evaluate the level of comprehension. The notification department can also use an algorithm for evaluating the level of comprehension. The notification department can also select an appropriate method for providing additional follow-up to employees with low levels of comprehension. In this way, by testing the level of comprehension of the updated content and providing additional follow-up to employees with low levels of comprehension, it is possible to improve the level of comprehension of employees.

[0064] The notification unit can use the emotion estimation function to monitor employees' emotional reactions to updated training content in real time and adjust the content as needed. The notification unit, for example, uses the emotion estimation function to build a system that monitors employees' emotional reactions to updated training content in real time. For example, it analyzes facial expressions and voices and calculates an emotion score. The notification unit, for example, uses an emotion estimation algorithm to evaluate employees' emotional reactions. The notification unit can also select an appropriate method to adjust the training content as needed. This makes it possible to improve the effectiveness of training by monitoring employees' emotional reactions to updated training content in real time and adjusting the content as needed.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The employee training system can also be equipped with a gamification section. The gamification section can increase motivation for training by awarding points and badges based on employees' training progress and skill level. For example, employees who complete a specific training course can be awarded a badge and allowed to compete with other employees on a leaderboard. It is also possible to allow employees to earn points and receive special benefits and rewards. This can increase employees' motivation for training and create an environment in which they actively participate in training.

[0067] The employee training system can also be equipped with a virtual reality (VR) section, which allows employees to receive training in a virtual space that simulates an actual work environment. For example, VR simulations can be provided to help employees learn factory operating procedures and dangerous work safely. It can also provide customer service simulations to improve sales skills. This allows employees to receive training in an environment that closely resembles their actual work environment, enabling them to acquire practical skills.

[0068] The employee training system can also include a social interaction section, which provides a platform for employees to discuss and share information about the training content. For example, employees can ask questions and exchange opinions about the training content through online forums and chat functions. It can also provide opportunities for group projects and collaborative learning. This promotes communication between employees and deepens their understanding of the training content.

[0069] The employee training system can further include a personalized recommendation unit. The personalized recommendation unit recommends the most appropriate training programs and learning materials for each employee based on their past training history and skill level. For example, it can recommend additional training to improve a specific skill or related specialized books. It can also suggest training programs based on the employee's interests and career goals. This allows employees to receive the training that is most appropriate for them and improve their skills efficiently.

[0070] The employee training system can further be equipped with a feedback collection section. The feedback collection section collects feedback from employees after the training is completed and is used to improve the training program. For example, it can conduct surveys on the training content, instructor evaluations, and training effectiveness. It can also collect any problems or areas for improvement identified by employees and reflect them in the next training session. This can improve the quality of the training program and increase employee satisfaction.

[0071] The follow-up department can use the emotion estimation function to adjust the timing of follow-ups based on the employee's emotional state. For example, if an employee is feeling stressed, the follow-up can be delayed or conducted in a relaxed environment. Also, if an employee is highly motivated, proactive follow-ups can be conducted to encourage further skill improvement. This allows for flexible follow-ups based on the employee's emotional state, maximizing the effectiveness of training.

[0072] The follow-up department can use the emotion estimation function to customize the content of follow-ups based on the employee's emotional state. For example, if an employee is feeling anxious, it can provide support that gives them a sense of security. Or, if an employee is excited, it can provide challenging tasks to harness that energy. This makes it possible to improve the effectiveness of training by providing appropriate follow-ups according to the employee's emotional state.

[0073] The follow-up department can use the emotion estimation function to adjust the frequency of follow-ups based on the employee's emotional state. For example, if an employee is feeling stressed, the frequency of follow-ups can be reduced to allow more time for them to relax. Also, if an employee is highly motivated, the frequency of follow-ups can be increased to provide more proactive support. This makes it possible to maximize the effectiveness of training by setting an appropriate frequency of follow-ups according to the employee's emotional state.

[0074] The follow-up department can use the emotion estimation function to select a follow-up method based on the employee's emotional state. For example, if an employee is feeling stressed, face-to-face follow-up can be conducted to help them relax. On the other hand, if an employee is highly motivated, online follow-up can be conducted to provide efficient support. This makes it possible to improve the effectiveness of training by selecting an appropriate follow-up method based on the employee's emotional state.

[0075] The follow-up department can use the emotion estimation function to adjust the content of follow-up based on the employee's emotional state. For example, if an employee is feeling stressed, it can provide them with content that helps them relax. On the other hand, if an employee is highly motivated, it can provide them with challenging content. This makes it possible to improve the effectiveness of training by providing appropriate follow-up content according to the employee's emotional state.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The recording unit records the employee's training progress and skill level. For example, it stores the training content and test results of the employee in a database to evaluate each employee's skill level. The recording unit digitally records the training content of the employee and stores the test results in a database. The recording unit can also analyze the training content and test results to evaluate the employee's skill level. Step 2: The Follow-up Department will provide necessary follow-up for each employee based on the training progress and skill level recorded by the Recording Department. For example, if an employee lacks a specific skill, they will be provided with additional training and materials to compensate for that skill. The Follow-up Department can also suggest necessary follow-up for each employee based on their skill level and provide an appropriate training program. Step 3: The notification department will follow up on updates to the training content as they come in. For example, if new skills or knowledge are added, the notification department will notify employees of the content and provide the necessary training. The notification department can also notify employees at the appropriate time so that they can obtain the latest information.

[0078] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0079] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0080] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 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.

[0083] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0084] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0085] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0086] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0087] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0088] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0089] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0090] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0091] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0092] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0093] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0098] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 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.

[0113] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0119] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0125] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0128] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0129] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0130] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0131] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0132] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0133] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0134] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0135] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0136] 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.

[0137] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0138] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0139] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0140] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0141] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0142] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0143] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0144] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0145] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A recording department that records employee training progress and skill levels; a follow-up unit that performs necessary follow-up individually based on the training progress and skill level recorded by the recording unit; A notification department that follows up on updates to the training content as they occur. A system characterized by: We will create an environment where all employees can play an active role as top guns with excellent skills.

2. The recording unit Analyze training progress data in real time to instantly detect and notify delays in progress The system of claim 1 .

3. The follow-up unit Automatically generate optimal training programs to fill skill gaps and provide customized training The system of claim 1 .

4. The notification unit The updated training content is automatically analyzed and notified to each employee at the optimal time. The system of claim 1 .

5. The recording unit Using emotion estimation, the emotional state of the employee during training is monitored, and support is provided to detect stress or loss of motivation. The system of claim 1 .

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