System

Abstract

An object of the system according to the embodiment is to continuously improve learning progress and performance of a AI through joint learning of a new employee and a generated AI.SOLUTION: A system includes a joint learning part, a comprehension degree evaluation part, and a progress improvement part. A joint learning part performs joint learning of the new employee and the generated AI. The understanding level evaluation unit evaluates the understanding level of the generated AI from multiple aspects. The progress improvement part improves the learning progress and performance of the generation AI based on the result of the comprehension degree evaluation part.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] Conventional technologies do not have a well-established process for effectively evaluating and improving AI learning progress and performance, leaving room for improvement.

[0005] The system according to the embodiment aims to continuously improve the learning progress and performance of the AI ​​through collaborative learning between new employees and the generative AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a collaborative learning unit, an understanding evaluation unit, and a progress improvement unit. The collaborative learning unit conducts collaborative learning between new employees and the generating AI. The understanding evaluation unit evaluates the generating AI's understanding from multiple perspectives. The progress improvement unit improves the learning progress and performance of the generating AI based on the results of the understanding evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can continuously improve the learning progress and performance of the AI ​​through collaborative learning between new employees and the generating AI. [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 non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile 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 knowledge asset formation system according to an embodiment of the present invention is a system in which new employees and a generative AI jointly learn, the generative AI's level of understanding is evaluated from multiple perspectives, and learning progress and performance are continuously improved. As a result, the knowledge asset formation system contributes to the development of knowledge and skills throughout the organization, enabling each employee to fully utilize generative AI in preparation for a generative AI-driven society.

[0029] A knowledge asset formation system according to an embodiment includes a collaborative learning unit, a comprehension assessment unit, and a progress improvement unit. The collaborative learning unit conducts collaborative learning between new employees and the generation AI. For example, the new employee teaches the generation AI a business process, and the generation AI proposes an optimal method based on that process. The collaborative learning unit also allows new employees to learn new knowledge and skills from the generation AI. For example, the new employee learns data analysis methods from the generation AI and applies them to their work. The comprehension assessment unit evaluates the generation AI's level of understanding from multiple perspectives. For example, it uses a quiz-style test to check whether the generation AI accurately understands the business process. The comprehension assessment unit also tests the quality of the solutions proposed by the generation AI in actual work and evaluates the results. For example, it actually tests a business process optimization method proposed by the generation AI and evaluates its effectiveness. The progress improvement unit improves the learning progress and performance of the generation AI based on the results of the comprehension assessment unit. For example, if the generation AI does not understand a particular business process, it provides additional learning materials related to that process. Furthermore, if the quality of the solution proposed by the generative AI is low, the progress improvement unit analyzes the cause and improves the generative AI's algorithm. For example, the progress improvement unit adjusts the generative AI's algorithm to improve the accuracy of the proposed solution. As a result, the knowledge asset formation system according to the embodiment forms organizational knowledge assets through collaborative learning between new employees and the generative AI, and by introducing a process for multifaceted evaluation of the generative AI's level of understanding, the system continuously improves the generative AI's learning progress and performance, contributing to the development of knowledge and skills throughout the organization.

[0030] The collaborative learning unit allows new employees to teach the generating AI a business process, with the generating AI visually simulating the process and providing feedback on the optimal procedure in real time. For example, when a new employee explains a business process to the generating AI, the generating AI visualizes the process as a 3D simulation and provides feedback on the optimal procedure in real time. For example, it simulates manufacturing line procedures and suggests efficient procedures. Furthermore, when a new employee teaches the generating AI a business process, the generating AI visualizes the process as a flowchart and provides feedback on the optimal procedure in real time. For example, it shows customer support procedures in a flowchart and suggests efficient ways to respond. Furthermore, when a new employee teaches the generating AI a business process, the collaborative learning unit visualizes the process as an animation and provides feedback on the optimal procedure in real time. For example, it shows logistics procedures in an animation and suggests the optimal delivery route. This allows the generating AI to visually simulate the process and provide feedback on the optimal procedure in real time, thereby improving the learning effectiveness of new employees.

[0031] The collaborative learning unit can automatically record what new employees teach the generative AI so that it can be reviewed later. For example, the collaborative learning unit adds a function that automatically records what new employees teach the generative AI so that it can be reviewed later. For example, explanations of business processes can be recorded in audio and text so that they can be played back later. The collaborative learning unit also adds a function that automatically records what new employees teach the generative AI so that it can be reviewed later. For example, explanations of business processes can be recorded as videos so that they can be viewed later. The collaborative learning unit also adds a function that automatically records what new employees teach the generative AI so that it can be reviewed later. For example, explanations of business processes can be recorded in slide format so that they can be viewed later. This automatically records what new employees teach the generative AI so that it can be reviewed later, improving the learning effect.

[0032] The collaborative learning department allows other employees to participate in the collaborative learning between new employees and the generative AI, thereby promoting knowledge sharing throughout the team. For example, the collaborative learning department may use an online conference system to hold learning sessions that everyone can attend. The collaborative learning department may also allow other employees to participate in the collaborative learning between new employees and the generative AI, thereby promoting knowledge sharing throughout the team. For example, the content of the collaborative learning may be published on an internal portal site so that all employees can access it. The collaborative learning department may also allow other employees to participate in the collaborative learning between new employees and the generative AI, thereby promoting knowledge sharing throughout the team. For example, the content of the collaborative learning may be regularly presented at internal study sessions, providing opportunities for all employees to learn. This allows other employees to participate in the collaborative learning between new employees and the generative AI, thereby promoting knowledge sharing throughout the team.

[0033] The collaborative learning department can share what new employees teach one generative AI with other generative AIs, thereby maintaining consistency of knowledge across the organization. For example, the collaborative learning department can share what new employees teach one generative AI with other generative AIs, thereby maintaining consistency of knowledge across the organization. For example, a cloud-based knowledge sharing platform can be used to ensure that all generative AIs have access to it. The collaborative learning department can also share what new employees teach one generative AI with other generative AIs, thereby maintaining consistency of knowledge across the organization. For example, a system can be introduced that automatically synchronizes information between generative AIs. The collaborative learning department can also share what new employees teach one generative AI with other generative AIs, thereby maintaining consistency of knowledge across the organization. For example, the knowledge base of the generative AI can be regularly updated to ensure that all generative AIs have the latest information. In this way, what new employees teach one generative AI can be shared with other generative AIs, thereby maintaining consistency of knowledge across the organization.

[0034] The comprehension evaluation unit allows the generation AI to self-evaluate its own level of understanding and compare the result with human evaluation. For example, the generation AI self-evaluates its own level of understanding and compares the result with human evaluation. For example, the generation AI scores its own level of understanding and compares the score with human evaluation. The comprehension evaluation unit also allows the generation AI to self-evaluate its own level of understanding and compare the result with human evaluation. For example, the generation AI tests its own level of understanding and compares the result with human evaluation. The comprehension evaluation unit also allows the generation AI to self-evaluate its own level of understanding and compare the result with human evaluation. For example, the generation AI provides feedback on its own level of understanding and compares the feedback with human evaluation. In this way, the generation AI self-evaluates its own level of understanding and compares the result with human evaluation, thereby improving the accuracy of the comprehension.

[0035] The comprehension evaluation unit can test the content learned by the generative AI in actual business scenarios and evaluate its performance. For example, the comprehension evaluation unit tests the content learned by the generative AI in actual business scenarios and evaluates its performance. For example, it actually executes a business process proposed by the generative AI and evaluates the results. The comprehension evaluation unit also tests the content learned by the generative AI in actual business scenarios and evaluates its performance. For example, it actually tries out a solution proposed by the generative AI and evaluates its effectiveness. The comprehension evaluation unit also tests the content learned by the generative AI in actual business scenarios and evaluates its performance. For example, it actually implements an improvement measure proposed by the generative AI and evaluates the results. In this way, by testing the content learned by the generative AI in actual business scenarios and evaluating its performance, it is possible to confirm its effectiveness in practice.

[0036] The comprehension evaluation unit can evaluate the comprehension level of the generative AI in different business processes and scenarios, thereby achieving a multifaceted evaluation. The comprehension evaluation unit, for example, evaluates the comprehension level of the generative AI in different business processes and scenarios, thereby achieving a multifaceted evaluation. For example, the comprehension level of the generative AI is evaluated in both manufacturing operations and customer support operations. The comprehension evaluation unit also evaluates the comprehension level of the generative AI in different business processes and scenarios, thereby achieving a multifaceted evaluation. For example, the comprehension level of the generative AI is evaluated in both sales processes and marketing processes. The comprehension evaluation unit also evaluates the comprehension level of the generative AI in different business processes and scenarios, thereby achieving a multifaceted evaluation. For example, the comprehension level of the generative AI is evaluated in both logistics processes and financial processes. In this way, the comprehension level of the generative AI is evaluated in different business processes and scenarios, thereby achieving a multifaceted evaluation, thereby improving the overall comprehension level of the generative AI.

[0037] The comprehension evaluation unit can share the comprehension evaluation results of the generative AI with other generative AIs to promote mutual learning. The comprehension evaluation unit, for example, shares the comprehension evaluation results of the generative AI with other generative AIs to promote mutual learning. For example, a cloud-based platform is used to allow all generative AIs to access the evaluation results. The comprehension evaluation unit also shares the comprehension evaluation results of the generative AI with other generative AIs to promote mutual learning. For example, a system is introduced that automatically synchronizes evaluation results between generative AIs. The comprehension evaluation unit also shares the comprehension evaluation results of the generative AI with other generative AIs to promote mutual learning. For example, the evaluation results of the generative AI are regularly updated so that all generative AIs have the latest information. In this way, the comprehension evaluation results of the generative AI are shared with other generative AIs to promote mutual learning, thereby improving the learning effect of the entire generative AI.

[0038] The progress improvement unit visualizes the learning progress of the generating AI and displays it in a graph or chart, allowing the progress to be intuitively understood. The progress improvement unit, for example, visualizes the learning progress of the generating AI and displays it in a graph or chart, allowing the progress to be intuitively understood. For example, the degree of achievement of the learning content is displayed in a bar graph. The progress improvement unit also visualizes the learning progress of the generating AI and displays it in a graph or chart, allowing the progress to be intuitively understood. For example, the progress improvement unit displays the transition of learning time in a line graph. The progress improvement unit also visualizes the learning progress of the generating AI and displays it in a graph or chart, allowing the progress to be intuitively understood. For example, the degree of understanding of the learning content is displayed in a pie chart. In this way, the learning progress of the generating AI can be visualized and displayed in a graph or chart, allowing the progress to be intuitively understood.

[0039] The progress improvement department can periodically review the learning content of the generative AI and adjust the learning plan as necessary. The progress improvement department, for example, periodically reviews the learning content of the generative AI and adjusts the learning plan as necessary. For example, it conducts monthly reviews and revises the plan according to the learning progress. The progress improvement department also periodically reviews the learning content of the generative AI and adjusts the learning plan as necessary. For example, it conducts weekly reviews and sets new learning goals according to the learning progress. The progress improvement department also periodically reviews the learning content of the generative AI and adjusts the learning plan as necessary. For example, it conducts quarterly reviews and revises the allocation of resources according to the learning progress. In this way, the learning effect is maximized by periodically reviewing the learning content of the generative AI and adjusting the learning plan as necessary.

[0040] The progress improvement unit can compare the learning progress of the generative AI with other generative AIs and share best practices. The progress improvement unit, for example, compares the learning progress of the generative AI with other generative AIs and shares best practices. For example, it compares learning speed and level of understanding and identifies the most effective learning method. The progress improvement unit also compares the learning progress of the generative AI with other generative AIs and shares best practices. For example, it compares the degree of achievement of learning content and shares the most efficient learning plan. The progress improvement unit also compares the learning progress of the generative AI with other generative AIs and shares best practices. For example, it compares learning outcomes and shares the most successful learning cases. In this way, by comparing the learning progress of the generative AI with other generative AIs and sharing best practices, the learning effect is improved.

[0041] The Progress Improvement Department can evaluate the learning progress of the generative AI in different business processes and scenarios to improve its overall performance. For example, the Progress Improvement Department evaluates the learning progress of the generative AI in different business processes and scenarios to improve its overall performance. For example, the Progress Improvement Department evaluates the learning progress of the generative AI in both manufacturing and customer support tasks. The Progress Improvement Department also evaluates the learning progress of the generative AI in different business processes and scenarios to improve its overall performance. For example, the Progress Improvement Department evaluates the learning progress of the generative AI in both sales and marketing processes. The Progress Improvement Department also evaluates the learning progress of the generative AI in different business processes and scenarios to improve its overall performance. For example, the Progress Improvement Department evaluates the learning progress of the generative AI in both logistics and financial processes. In this way, the scope of practical application of the generative AI is expanded by evaluating the learning progress of the generative AI in different business processes and scenarios to improve its overall performance.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The knowledge asset formation system can also be equipped with customization functions according to the individual learning styles of new employees. For example, for new employees who prefer visual learning, the generation AI can provide information using infographics and videos. For new employees who prefer auditory learning, the generation AI can provide information in the form of audio guides or podcasts. For new employees who prefer hands-on learning, the generation AI can provide information through simulations and interactive exercises. In this way, the learning effect can be maximized by providing customization functions according to the individual learning styles of new employees.

[0044] The knowledge asset formation system can also be equipped with a function to monitor new employees' learning progress in real time and issue alerts as necessary. For example, if their learning progress is falling behind, the generation AI can send them a reminder. If specific learning goals are not achieved, the generation AI can provide additional learning materials. Furthermore, if their learning progress is going well, the generation AI can provide positive feedback. This makes it possible to improve learning effectiveness by monitoring new employees' learning progress in real time and issuing alerts as necessary.

[0045] The knowledge asset formation system can also be equipped with the function of analyzing new employees' learning history and optimizing future learning plans. For example, based on past learning history, the generation AI can suggest the optimal learning path for a new employee. It can also analyze learning history to identify strengths and weaknesses in specific areas and adjust the learning plan based on that. Furthermore, based on learning history, the generation AI can recommend learning resources suitable for the new employee. In this way, by analyzing the learning history of new employees and optimizing their future learning plans, it is possible to improve learning effectiveness.

[0046] The knowledge asset formation system can also compare the learning progress of new employees with that of other new employees and provide feedback to foster a sense of competition. For example, learning progress can be displayed in graphs and charts to compare with other new employees. If learning progress is excellent, the generation AI will provide praise. Furthermore, if learning progress is lagging behind, the generation AI will send encouraging messages. In this way, by comparing the learning progress of new employees with that of other new employees and providing feedback to foster a sense of competition, learning effectiveness can be improved.

[0047] The knowledge asset formation system can also be equipped with a function to visualize the learning progress of new employees and provide individual feedback. For example, learning progress can be displayed in graphs and charts to visually show the level of achievement and understanding. The generation AI also provides specific feedback according to the learning progress. Furthermore, if learning progress is falling behind, the generation AI will provide additional learning materials to promote progress. In this way, by visualizing the learning progress of new employees and providing individual feedback, it is possible to improve learning effectiveness.

[0048] The knowledge asset formation system can also be equipped with a function that shares the learning progress of new employees with other generation AIs and promotes mutual learning. For example, a cloud-based platform can be used to allow all generation AIs to access the learning progress. A system can also be introduced that automatically synchronizes the learning progress between generation AIs. Furthermore, the learning progress of the generation AIs can be updated regularly so that all generation AIs have the latest information. This allows the learning progress of new employees to be shared with other generation AIs and promote mutual learning, thereby improving learning effectiveness.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The collaborative learning department conducts collaborative learning between new employees and the generative AI. For example, new employees teach the generative AI a business process, and the generative AI proposes the optimal method based on that process. New employees can also learn new knowledge and skills from the generative AI. For example, new employees can learn data analysis methods from the generative AI and use them in their work. Step 2: The understanding evaluation unit evaluates the generative AI's level of understanding from multiple perspectives. For example, a quiz-style test is used to check whether the generative AI accurately understands the business process. The quality of the solutions proposed by the generative AI is also tested in actual business operations and the results are evaluated. For example, a business process optimization method proposed by the generative AI is actually tested and its effectiveness is evaluated. Step 3: The progress improvement unit improves the learning progress and performance of the generating AI based on the results of the understanding assessment unit. For example, if the generating AI does not understand a particular business process, it provides additional learning materials about that process. Also, if the quality of the solutions proposed by the generating AI is low, it analyzes the cause and improves the generating AI's algorithm. For example, it adjusts the generating AI's algorithm to improve the accuracy of the solutions it proposes.

[0051] (Example 2) The knowledge asset formation system according to an embodiment of the present invention is a system in which new employees and a generative AI jointly learn, the generative AI's level of understanding is evaluated from multiple perspectives, and learning progress and performance are continuously improved. As a result, the knowledge asset formation system contributes to the development of knowledge and skills throughout the organization, enabling each employee to fully utilize generative AI in preparation for a generative AI-driven society.

[0052] A knowledge asset formation system according to an embodiment includes a collaborative learning unit, a comprehension assessment unit, and a progress improvement unit. The collaborative learning unit conducts collaborative learning between new employees and the generation AI. For example, the new employee teaches the generation AI a business process, and the generation AI proposes an optimal method based on that process. The collaborative learning unit also allows new employees to learn new knowledge and skills from the generation AI. For example, the new employee learns data analysis methods from the generation AI and applies them to their work. The comprehension assessment unit evaluates the generation AI's level of understanding from multiple perspectives. For example, it uses a quiz-style test to check whether the generation AI accurately understands the business process. The comprehension assessment unit also tests the quality of the solutions proposed by the generation AI in actual work and evaluates the results. For example, it actually tests a business process optimization method proposed by the generation AI and evaluates its effectiveness. The progress improvement unit improves the learning progress and performance of the generation AI based on the results of the comprehension assessment unit. For example, if the generation AI does not understand a particular business process, it provides additional learning materials related to that process. Furthermore, if the quality of the solution proposed by the generative AI is low, the progress improvement unit analyzes the cause and improves the generative AI's algorithm. For example, the progress improvement unit adjusts the generative AI's algorithm to improve the accuracy of the proposed solution. As a result, the knowledge asset formation system according to the embodiment forms organizational knowledge assets through collaborative learning between new employees and the generative AI, and by introducing a process for multifaceted evaluation of the generative AI's level of understanding, the system continuously improves the generative AI's learning progress and performance, contributing to the development of knowledge and skills throughout the organization.

[0053] The collaborative learning unit allows new employees to teach the generating AI a business process, with the generating AI visually simulating the process and providing feedback on the optimal procedure in real time. For example, when a new employee explains a business process to the generating AI, the generating AI visualizes the process as a 3D simulation and provides feedback on the optimal procedure in real time. For example, it simulates manufacturing line procedures and suggests efficient procedures. Furthermore, when a new employee teaches the generating AI a business process, the generating AI visualizes the process as a flowchart and provides feedback on the optimal procedure in real time. For example, it shows customer support procedures in a flowchart and suggests efficient ways to respond. Furthermore, when a new employee teaches the generating AI a business process, the collaborative learning unit visualizes the process as an animation and provides feedback on the optimal procedure in real time. For example, it shows logistics procedures in an animation and suggests the optimal delivery route. This allows the generating AI to visually simulate the process and provide feedback on the optimal procedure in real time, thereby improving the learning effectiveness of new employees.

[0054] The collaborative learning unit can automatically record what new employees teach the generative AI so that it can be reviewed later. For example, the collaborative learning unit adds a function that automatically records what new employees teach the generative AI so that it can be reviewed later. For example, explanations of business processes can be recorded in audio and text so that they can be played back later. The collaborative learning unit also adds a function that automatically records what new employees teach the generative AI so that it can be reviewed later. For example, explanations of business processes can be recorded as videos so that they can be viewed later. The collaborative learning unit also adds a function that automatically records what new employees teach the generative AI so that it can be reviewed later. For example, explanations of business processes can be recorded in slide format so that they can be viewed later. This automatically records what new employees teach the generative AI so that it can be reviewed later, improving the learning effect.

[0055] The collaborative learning unit uses the emotion estimation function to analyze the emotional state of new employees, and if stress is high, the generation AI can suggest relaxation methods. For example, the collaborative learning unit uses the emotion estimation function to analyze the emotional state of new employees in real time, and if stress is high, the generation AI can suggest relaxation methods. For example, it can suggest deep breathing or stretching methods. The collaborative learning unit also uses the emotion estimation function to analyze the emotional state of new employees, and if stress is high, the generation AI can suggest relaxation methods. For example, it can play relaxing music. The collaborative learning unit also uses the emotion estimation function to analyze the emotional state of new employees, and if stress is high, the generation AI can suggest relaxation methods. For example, it can encourage them to take a short break. In this way, by analyzing the emotional state of new employees and suggesting relaxation methods if stress is high, it supports stress management.

[0056] The collaborative learning department allows other employees to participate in the collaborative learning between new employees and the generative AI, thereby promoting knowledge sharing throughout the team. For example, the collaborative learning department may use an online conference system to hold learning sessions that everyone can attend. The collaborative learning department may also allow other employees to participate in the collaborative learning between new employees and the generative AI, thereby promoting knowledge sharing throughout the team. For example, the content of the collaborative learning may be published on an internal portal site so that all employees can access it. The collaborative learning department may also allow other employees to participate in the collaborative learning between new employees and the generative AI, thereby promoting knowledge sharing throughout the team. For example, the content of the collaborative learning may be regularly presented at internal study sessions, providing opportunities for all employees to learn. This allows other employees to participate in the collaborative learning between new employees and the generative AI, thereby promoting knowledge sharing throughout the team.

[0057] The collaborative learning department can share what new employees teach one generative AI with other generative AIs, thereby maintaining consistency of knowledge across the organization. For example, the collaborative learning department can share what new employees teach one generative AI with other generative AIs, thereby maintaining consistency of knowledge across the organization. For example, a cloud-based knowledge sharing platform can be used to ensure that all generative AIs have access to it. The collaborative learning department can also share what new employees teach one generative AI with other generative AIs, thereby maintaining consistency of knowledge across the organization. For example, a system can be introduced that automatically synchronizes information between generative AIs. The collaborative learning department can also share what new employees teach one generative AI with other generative AIs, thereby maintaining consistency of knowledge across the organization. For example, the knowledge base of the generative AI can be regularly updated to ensure that all generative AIs have the latest information. In this way, what new employees teach one generative AI can be shared with other generative AIs, thereby maintaining consistency of knowledge across the organization.

[0058] The collaborative learning unit can use the emotion estimation function to enable the generation AI to provide feedback to increase the motivation of new employees. For example, the collaborative learning unit uses the emotion estimation function to enable the generation AI to provide feedback to increase the motivation of new employees. For example, it sends positive feedback or encouraging messages. The collaborative learning unit also uses the emotion estimation function to enable the generation AI to provide feedback to increase the motivation of new employees. For example, it suggests goal setting that will give them a sense of accomplishment. The collaborative learning unit also uses the emotion estimation function to enable the generation AI to provide feedback to increase the motivation of new employees. For example, it suggests activities to refresh them if their motivation is low. In this way, using the emotion estimation function to provide feedback to increase the motivation of new employees improves the learning effect.

[0059] The comprehension evaluation unit allows the generation AI to self-evaluate its own level of understanding and compare the result with human evaluation. For example, the generation AI self-evaluates its own level of understanding and compares the result with human evaluation. For example, the generation AI scores its own level of understanding and compares the score with human evaluation. The comprehension evaluation unit also allows the generation AI to self-evaluate its own level of understanding and compare the result with human evaluation. For example, the generation AI tests its own level of understanding and compares the result with human evaluation. The comprehension evaluation unit also allows the generation AI to self-evaluate its own level of understanding and compare the result with human evaluation. For example, the generation AI provides feedback on its own level of understanding and compares the feedback with human evaluation. In this way, the generation AI self-evaluates its own level of understanding and compares the result with human evaluation, thereby improving the accuracy of the comprehension.

[0060] The comprehension evaluation unit can test the content learned by the generative AI in actual business scenarios and evaluate its performance. For example, the comprehension evaluation unit tests the content learned by the generative AI in actual business scenarios and evaluates its performance. For example, it actually executes a business process proposed by the generative AI and evaluates the results. The comprehension evaluation unit also tests the content learned by the generative AI in actual business scenarios and evaluates its performance. For example, it actually tries out a solution proposed by the generative AI and evaluates its effectiveness. The comprehension evaluation unit also tests the content learned by the generative AI in actual business scenarios and evaluates its performance. For example, it actually implements an improvement measure proposed by the generative AI and evaluates the results. In this way, by testing the content learned by the generative AI in actual business scenarios and evaluating its performance, it is possible to confirm its effectiveness in practice.

[0061] The comprehension evaluation unit can use the emotion estimation function to evaluate the user's emotional response to a solution proposed by the generation AI and reflect the result in the comprehension evaluation. The comprehension evaluation unit, for example, uses the emotion estimation function to evaluate the user's emotional response to a solution proposed by the generation AI and reflects the result in the comprehension evaluation. For example, if there are many positive user responses, the generation AI's level of understanding is evaluated as high. The comprehension evaluation unit also uses the emotion estimation function to evaluate the user's emotional response to a solution proposed by the generation AI and reflects the result in the comprehension evaluation. For example, if there are many negative user responses, the generation AI's level of understanding is evaluated as low. The comprehension evaluation unit also uses the emotion estimation function to evaluate the user's emotional response to a solution proposed by the generation AI and reflects the result in the comprehension evaluation. For example, the comprehension evaluation unit evaluates the user's emotional response to a solution proposed by the generation AI using the emotion estimation function and reflects the result in the comprehension evaluation. In this way, by using the emotion estimation function to evaluate the user's emotional response to a solution proposed by the generation AI and reflecting the result in the comprehension evaluation, user satisfaction is improved.

[0062] The comprehension evaluation unit can evaluate the comprehension level of the generative AI in different business processes and scenarios, thereby achieving a multifaceted evaluation. The comprehension evaluation unit, for example, evaluates the comprehension level of the generative AI in different business processes and scenarios, thereby achieving a multifaceted evaluation. For example, the comprehension level of the generative AI is evaluated in both manufacturing operations and customer support operations. The comprehension evaluation unit also evaluates the comprehension level of the generative AI in different business processes and scenarios, thereby achieving a multifaceted evaluation. For example, the comprehension level of the generative AI is evaluated in both sales processes and marketing processes. The comprehension evaluation unit also evaluates the comprehension level of the generative AI in different business processes and scenarios, thereby achieving a multifaceted evaluation. For example, the comprehension level of the generative AI is evaluated in both logistics processes and financial processes. In this way, the comprehension level of the generative AI is evaluated in different business processes and scenarios, thereby achieving a multifaceted evaluation, thereby improving the overall comprehension level of the generative AI.

[0063] The comprehension evaluation unit can share the comprehension evaluation results of the generative AI with other generative AIs to promote mutual learning. The comprehension evaluation unit, for example, shares the comprehension evaluation results of the generative AI with other generative AIs to promote mutual learning. For example, a cloud-based platform is used to allow all generative AIs to access the evaluation results. The comprehension evaluation unit also shares the comprehension evaluation results of the generative AI with other generative AIs to promote mutual learning. For example, a system is introduced that automatically synchronizes evaluation results between generative AIs. The comprehension evaluation unit also shares the comprehension evaluation results of the generative AI with other generative AIs to promote mutual learning. For example, the evaluation results of the generative AI are regularly updated so that all generative AIs have the latest information. In this way, the comprehension evaluation results of the generative AI are shared with other generative AIs to promote mutual learning, thereby improving the learning effect of the entire generative AI.

[0064] The comprehension evaluation unit can use the emotion estimation function to collect the emotional reactions of the entire team to the solution proposed by the generation AI and reflect them in the evaluation. The comprehension evaluation unit, for example, uses the emotion estimation function to collect the emotional reactions of the entire team to the solution proposed by the generation AI and reflects them in the evaluation. For example, it evaluates the understanding of the generation AI based on the emotion scores of all team members. The comprehension evaluation unit also uses the emotion estimation function to collect the emotional reactions of the entire team to the solution proposed by the generation AI and reflects them in the evaluation. For example, if there are many positive reactions, it evaluates the generation AI's level of understanding as high. The comprehension evaluation unit also uses the emotion estimation function to collect the emotional reactions of the entire team to the solution proposed by the generation AI and reflects them in the evaluation. For example, if there are many negative reactions, it evaluates the generation AI's level of understanding as low. In this way, by using the emotion estimation function to collect the emotional reactions of the entire team to the solution proposed by the generation AI and reflecting them in the evaluation, the satisfaction of the entire team is improved.

[0065] The progress improvement unit visualizes the learning progress of the generating AI and displays it in a graph or chart, allowing the progress to be intuitively understood. The progress improvement unit, for example, visualizes the learning progress of the generating AI and displays it in a graph or chart, allowing the progress to be intuitively understood. For example, the degree of achievement of the learning content is displayed in a bar graph. The progress improvement unit also visualizes the learning progress of the generating AI and displays it in a graph or chart, allowing the progress to be intuitively understood. For example, the progress improvement unit displays the transition of learning time in a line graph. The progress improvement unit also visualizes the learning progress of the generating AI and displays it in a graph or chart, allowing the progress to be intuitively understood. For example, the degree of understanding of the learning content is displayed in a pie chart. In this way, the learning progress of the generating AI can be visualized and displayed in a graph or chart, allowing the progress to be intuitively understood.

[0066] The progress improvement department can periodically review the learning content of the generative AI and adjust the learning plan as necessary. The progress improvement department, for example, periodically reviews the learning content of the generative AI and adjusts the learning plan as necessary. For example, it conducts monthly reviews and revises the plan according to the learning progress. The progress improvement department also periodically reviews the learning content of the generative AI and adjusts the learning plan as necessary. For example, it conducts weekly reviews and sets new learning goals according to the learning progress. The progress improvement department also periodically reviews the learning content of the generative AI and adjusts the learning plan as necessary. For example, it conducts quarterly reviews and revises the allocation of resources according to the learning progress. In this way, the learning effect is maximized by periodically reviewing the learning content of the generative AI and adjusting the learning plan as necessary.

[0067] The progress improvement unit can use the emotion estimation function to analyze the user's emotional response to the learning progress of the generation AI and reinforce positive feedback. For example, the progress improvement unit uses the emotion estimation function to analyze the user's emotional response to the learning progress of the generation AI and reinforce positive feedback. For example, if the user is satisfied, it reflects that feedback to the generation AI. The progress improvement unit also uses the emotion estimation function to analyze the user's emotional response to the learning progress of the generation AI and reinforce positive feedback. For example, if the user is happy, it communicates that emotion to the generation AI. The progress improvement unit also uses the emotion estimation function to analyze the user's emotional response to the learning progress of the generation AI and reinforce positive feedback. For example, if the user is grateful, it feeds that emotion back to the generation AI. In this way, the learning effect is improved by using the emotion estimation function to analyze the user's emotional response to the learning progress of the generation AI and reinforce positive feedback.

[0068] The progress improvement unit can compare the learning progress of the generative AI with other generative AIs and share best practices. The progress improvement unit, for example, compares the learning progress of the generative AI with other generative AIs and shares best practices. For example, it compares learning speed and level of understanding and identifies the most effective learning method. The progress improvement unit also compares the learning progress of the generative AI with other generative AIs and shares best practices. For example, it compares the degree of achievement of learning content and shares the most efficient learning plan. The progress improvement unit also compares the learning progress of the generative AI with other generative AIs and shares best practices. For example, it compares learning outcomes and shares the most successful learning cases. In this way, by comparing the learning progress of the generative AI with other generative AIs and sharing best practices, the learning effect is improved.

[0069] The Progress Improvement Department can evaluate the learning progress of the generative AI in different business processes and scenarios to improve its overall performance. For example, the Progress Improvement Department evaluates the learning progress of the generative AI in different business processes and scenarios to improve its overall performance. For example, the Progress Improvement Department evaluates the learning progress of the generative AI in both manufacturing and customer support tasks. The Progress Improvement Department also evaluates the learning progress of the generative AI in different business processes and scenarios to improve its overall performance. For example, the Progress Improvement Department evaluates the learning progress of the generative AI in both sales and marketing processes. The Progress Improvement Department also evaluates the learning progress of the generative AI in different business processes and scenarios to improve its overall performance. For example, the Progress Improvement Department evaluates the learning progress of the generative AI in both logistics and financial processes. In this way, the scope of practical application of the generative AI is expanded by evaluating the learning progress of the generative AI in different business processes and scenarios to improve its overall performance.

[0070] The progress improvement unit can use the emotion estimation function to collect the emotional reactions of the entire team to the learning progress of the generative AI and reflect them in the learning plan. The progress improvement unit, for example, uses the emotion estimation function to collect the emotional reactions of the entire team to the learning progress of the generative AI and reflect them in the learning plan. For example, the learning plan is adjusted based on the emotion scores of all team members. The progress improvement unit also uses the emotion estimation function to collect the emotional reactions of the entire team to the learning progress of the generative AI and reflect them in the learning plan. For example, if there are many positive reactions, the learning method is continued. The progress improvement unit also uses the emotion estimation function to collect the emotional reactions of the entire team to the learning progress of the generative AI and reflect them in the learning plan. For example, if there are many negative reactions, the learning method is reviewed. In this way, by using the emotion estimation function to collect the emotional reactions of the entire team to the learning progress of the generative AI and reflecting them in the learning plan, the learning effect of the entire team is improved.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The knowledge asset formation system can also be equipped with customization functions according to the individual learning styles of new employees. For example, for new employees who prefer visual learning, the generation AI can provide information using infographics and videos. For new employees who prefer auditory learning, the generation AI can provide information in the form of audio guides or podcasts. For new employees who prefer hands-on learning, the generation AI can provide information through simulations and interactive exercises. In this way, the learning effect can be maximized by providing customization functions according to the individual learning styles of new employees.

[0073] The knowledge asset formation system can also be equipped with a function to monitor new employees' learning progress in real time and issue alerts as necessary. For example, if their learning progress is falling behind, the generation AI can send them a reminder. If specific learning goals are not achieved, the generation AI can provide additional learning materials. Furthermore, if their learning progress is going well, the generation AI can provide positive feedback. This makes it possible to improve learning effectiveness by monitoring new employees' learning progress in real time and issuing alerts as necessary.

[0074] The knowledge asset formation system can also be equipped with the function of analyzing new employees' learning history and optimizing future learning plans. For example, based on past learning history, the generation AI can suggest the optimal learning path for a new employee. It can also analyze learning history to identify strengths and weaknesses in specific areas and adjust the learning plan based on that. Furthermore, based on learning history, the generation AI can recommend learning resources suitable for the new employee. In this way, by analyzing the learning history of new employees and optimizing their future learning plans, it is possible to improve learning effectiveness.

[0075] The knowledge asset formation system can also analyze the emotional state of new employees and provide feedback to maintain their motivation. For example, the emotion estimation function can be used to analyze the emotional state of new employees in real time, and if their motivation is low, the generation AI can send them an encouraging message. Furthermore, if their emotional state is analyzed and they are experiencing high levels of stress, the generation AI can suggest ways to relax. Furthermore, if their emotional state is analyzed and they are experiencing a lot of positive emotions, the generation AI can provide words of praise. This makes it possible to improve learning effectiveness by analyzing the emotional state of new employees and providing feedback to maintain their motivation.

[0076] The knowledge asset formation system can also analyze the emotional state of new employees and make suggestions to optimize the learning environment. For example, the emotional state of new employees can be analyzed in real time using the emotion estimation function, and if their concentration is declining, the generation AI can suggest a quiet environment. The generation AI can also analyze their emotional state and, if fatigue is accumulating, encourage them to take a short break. Furthermore, if stress is high, the generation AI can play relaxing music. In this way, by analyzing the emotional state of new employees and making suggestions to optimize the learning environment, learning effectiveness can be improved.

[0077] The knowledge asset formation system can also compare the learning progress of new employees with that of other new employees and provide feedback to foster a sense of competition. For example, learning progress can be displayed in graphs and charts to compare with other new employees. If learning progress is excellent, the generation AI will provide praise. Furthermore, if learning progress is lagging behind, the generation AI will send encouraging messages. In this way, by comparing the learning progress of new employees with that of other new employees and providing feedback to foster a sense of competition, learning effectiveness can be improved.

[0078] The knowledge asset formation system can also analyze the emotional state of new employees and make suggestions to adjust the learning content. For example, the emotional state of new employees can be analyzed in real time using the emotion estimation function, and if interest is declining, the generation AI can suggest a new learning topic. The generation AI can also analyze the emotional state and make suggestions to reduce the learning content if fatigue is accumulating. Furthermore, if motivation is high, the generation AI can provide challenging tasks. In this way, by analyzing the emotional state of new employees and making suggestions to adjust the learning content, it is possible to improve learning effectiveness.

[0079] The knowledge asset formation system can also be equipped with a function to visualize the learning progress of new employees and provide individual feedback. For example, learning progress can be displayed in graphs and charts to visually show the level of achievement and understanding. The generation AI also provides specific feedback according to the learning progress. Furthermore, if learning progress is falling behind, the generation AI will provide additional learning materials to promote progress. In this way, by visualizing the learning progress of new employees and providing individual feedback, it is possible to improve learning effectiveness.

[0080] The knowledge asset formation system can also analyze the emotional state of new employees and make suggestions to optimize the timing of their studies. For example, the emotional state of new employees can be analyzed in real time using the emotion estimation function, and suggestions can be made to study during times when they are most likely to concentrate. In addition, by analyzing their emotional state and finding that fatigue is building up, the generation AI can encourage them to take a break. Furthermore, by analyzing their emotional state and finding that stress is high, the generation AI can suggest activities that will help them relax. In this way, by analyzing the emotional state of new employees and making suggestions to optimize the timing of their studies, it is possible to improve learning effectiveness.

[0081] The knowledge asset formation system can also be equipped with a function that shares the learning progress of new employees with other generation AIs and promotes mutual learning. For example, a cloud-based platform can be used to allow all generation AIs to access the learning progress. A system can also be introduced that automatically synchronizes the learning progress between generation AIs. Furthermore, the learning progress of the generation AIs can be updated regularly so that all generation AIs have the latest information. This allows the learning progress of new employees to be shared with other generation AIs and promote mutual learning, thereby improving learning effectiveness.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The collaborative learning department conducts collaborative learning between new employees and the generative AI. For example, new employees teach the generative AI a business process, and the generative AI proposes the optimal method based on that process. New employees can also learn new knowledge and skills from the generative AI. For example, new employees can learn data analysis methods from the generative AI and use them in their work. Step 2: The understanding evaluation unit evaluates the generative AI's level of understanding from multiple perspectives. For example, a quiz-style test is used to check whether the generative AI accurately understands the business process. The quality of the solutions proposed by the generative AI is also tested in actual business operations and the results are evaluated. For example, a business process optimization method proposed by the generative AI is actually tested and its effectiveness is evaluated. Step 3: The progress improvement unit improves the learning progress and performance of the generating AI based on the results of the understanding assessment unit. For example, if the generating AI does not understand a particular business process, it provides additional learning materials about that process. Also, if the quality of the solutions proposed by the generating AI is low, it analyzes the cause and improves the generating AI's algorithm. For example, it adjusts the generating AI's algorithm to improve the accuracy of the solutions it proposes.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 collaborative learning department where new employees and generative AI learn together, an understanding evaluation unit that evaluates the understanding of the generating AI from multiple perspectives; and a progress improvement unit that improves the learning progress and performance of the generation AI based on the results of the understanding evaluation unit. A system characterized by:

2. The collaborative learning department When a new employee teaches the AI ​​a business process, the AI ​​visually simulates that process and provides feedback on the optimal procedure in real time.

2. The system of claim 1.

3. The understanding assessment section The generative AI self-evaluates its own level of understanding and compares the results with human evaluations.

2. The system of claim 1.

4. The Progress Improvement Department Visualize the learning progress of generative AI and display it in graphs and charts to intuitively understand the progress.

2. The system of claim 1.

5. The collaborative learning department Analyzing the emotional state of new employees, generative AI suggests ways to relax if stress levels are high 2. The system of claim 1.

6. The understanding assessment section Evaluate the user's emotional response to the solution proposed by the generative AI and reflect the results in the understanding assessment.

2. The system of claim 1.

7. The Progress Improvement Department Analyze users' emotional reactions to the generative AI's learning progress and reinforce positive feedback 2. The system of claim 1.

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