system

Abstract

The system according to this embodiment aims to improve the efficiency and effectiveness of training for customer center staff. [Solution] The system according to the embodiment comprises a generation unit, a simulation unit, a feedback unit, and a guidance unit. The generation unit creates a scenario-based training program. The simulation unit performs a simulation based on the training program created by the generation unit. The feedback unit provides real-time feedback on the simulation performed by the simulation unit. The guidance unit provides individual guidance based on the feedback provided by the feedback unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the efficiency and effectiveness of training for the staff of the customer center have not been sufficiently improved, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency and effectiveness of training for the staff of the customer center.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a simulation unit, a feedback unit, and a guidance unit. The generation unit creates a scenario-based training program. The simulation unit performs a simulation based on the training program created by the generation unit. The feedback unit provides real-time feedback on the simulation performed by the simulation unit. The guidance unit provides individual guidance based on the feedback provided by the feedback unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve the efficiency and effectiveness of training for customer center staff. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The training system according to an embodiment of the present invention is a system that provides practical training to new and existing staff at a customer center using generative AI. This training system uses generative AI to create scenario-based training programs. This program simulates various customer support scenarios and is designed so that staff can receive training in an environment close to actual work. For example, it simulates how to respond to customer inquiries and the procedures for handling complaints. Next, the generative AI provides real-time feedback during training. As staff progress through the simulation, the generative AI evaluates their responses and provides real-time suggestions for improvement and advice. This allows staff to review their responses on the spot and improve their skills. Furthermore, the generative AI also provides individualized instruction. It provides optimal training content according to each staff member's skill level and learning progress. For example, new staff members are instructed on basic response methods, while existing staff members are provided with training to hone more advanced response skills. This system significantly improves the efficiency and effectiveness of training and promotes staff skill development. As a result, an improvement in customer satisfaction can be expected. In addition, training time and costs are reduced, contributing to strengthening the company's competitiveness. Thus, the training system aims to improve the skills of staff.

[0029] The training system according to the embodiment comprises a generation unit, a simulation unit, a feedback unit, and a teaching unit. The generation unit creates a scenario-based training program. The generation unit generates customer support scenarios, for example, using a generation AI. The generation unit generates scenarios that simulate, for example, how to respond to customer inquiries and the procedures for handling complaints. The simulation unit performs simulations based on the training program created by the generation unit. The simulation unit performs simulations based on the generated scenarios so that staff can receive training in an environment close to actual work. The simulation unit provides, for example, a simulated environment of a customer service system or an environment that uses actual customer data. The feedback unit provides real-time feedback on the simulation performed by the simulation unit. The feedback unit evaluates the staff's responses and provides real-time suggestions for improvement and advice. The feedback unit evaluates the staff's responses based on evaluation criteria such as response time, customer satisfaction, and resolution rate. The teaching unit provides individualized instruction based on the feedback provided by the feedback unit. The teaching unit provides optimal training content according to each staff member's skill level and learning progress. The training department, for example, focuses on teaching new staff basic response methods and provides existing staff with training to hone more advanced response skills. In this way, the training system according to this embodiment can aim to improve the skills of the staff.

[0030] The generation unit creates scenario-based training programs. For example, it generates customer support scenarios using generative AI. The generative AI utilizes natural language processing technology to analyze the details of customer inquiries and complaints and generates scenarios for the optimal response. Specifically, the generative AI learns from past inquiry data and complaint handling history, and automatically creates scenarios that address frequently occurring problems and customer needs. For example, if a customer inquires about a product defect, the generative AI will create a detailed scenario of how staff should respond based on the type of defect and past resolution methods. It also provides scenarios for complaint handling procedures, offering appropriate responses tailored to the customer's emotions and situation. This allows the generation unit to provide realistic and practical training programs that enable staff to handle various situations they face in their actual work. Furthermore, the generation unit can regularly update the generated scenarios to maintain training programs that respond to the latest customer needs and market trends. This ensures that staff always acquire the latest information and skills, improving the quality of customer service.

[0031] The simulation unit performs simulations based on training programs created by the generation unit. For example, the simulation unit performs simulations based on generated scenarios to allow staff to receive training in an environment similar to actual work. Specifically, the simulation unit builds a simulated environment of a customer service system, allowing staff to perform operations and responses similar to those in actual work. For example, the simulation unit prepares a virtual customer database, allowing staff to practice searching for customer information and responding appropriately to inquiries. Furthermore, by providing an environment that uses actual customer data, staff can receive training based on realistic data. In this way, the simulation unit supports staff in effectively acquiring the skills and knowledge necessary for actual work. In addition, the simulation unit can monitor the progress of training and staff performance in real time and adjust scenarios and training content as needed. In this way, the simulation unit can maximize support for staff skill improvement and enhance the effectiveness of training.

[0032] The Feedback Department provides real-time feedback on simulations conducted by the Simulation Department. For example, the Feedback Department evaluates staff responses and provides real-time suggestions for improvement and advice. Specifically, it evaluates staff responses based on evaluation criteria such as response time, customer satisfaction, and resolution rate. For instance, it assesses whether staff responded quickly and appropriately to customer inquiries and points out areas for improvement. Regarding customer satisfaction, it evaluates based on customer feedback and survey results, and provides specific advice on how staff can improve customer satisfaction. Furthermore, regarding resolution rates, it evaluates how effectively staff resolved inquiries and complaints, analyzes the causes of unresolved issues, and proposes improvement measures. This allows the Feedback Department to provide staff with concrete guidelines for objectively reviewing their own responses and improving their skills. Additionally, the Feedback Department centrally manages staff training history and past feedback, enabling it to provide optimal feedback to each individual staff member. This allows the Feedback Department to continuously support staff growth and maximize the effectiveness of training.

[0033] The training department provides individualized instruction based on feedback provided by the feedback department. For example, the training department provides optimal training content according to each staff member's skill level and learning progress. Specifically, the training department focuses on teaching new staff members basic response methods and provides training to existing staff members to hone more advanced response skills. For example, new staff members are carefully taught basic customer service manners and procedures for handling basic inquiries. On the other hand, existing staff members receive training to acquire complex complaint handling and advanced problem-solving skills. The training department also provides customized training programs tailored to each staff member's individual weaknesses and areas for improvement. For example, if a particular staff member has issues with response time, the training will focus on improving that staff member's response speed. Furthermore, the training department conducts regular evaluations and provides feedback to continuously support staff growth, and reviews training content as needed. This allows the training department to ensure that staff members always acquire the latest skills and improve the quality of customer service. In addition, the training department can promote information sharing and cooperation among staff members, thereby improving the overall skills of the team. This allows the leadership to improve the performance of not only individual staff members, but the entire team.

[0034] The generation unit can generate customer support scenarios using a generation AI. For example, the generation unit uses the generation AI to generate scenarios that simulate how to respond to customer inquiries and the procedures for handling complaints. For example, the generation unit allows the generation AI to automatically generate customer support scenarios, enabling staff to receive training in an environment close to actual work. This allows customer support scenarios to be automatically generated using the generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit inputs prompts to the generation AI, and the generation AI generates a scenario.

[0035] The simulation unit can perform simulations based on generated scenarios to enable staff to receive training in an environment similar to their actual work. For example, the simulation unit can provide a simulated environment for a customer service system or an environment using actual customer data. The simulation unit can, for example, construct a simulated environment for a customer service system, enabling staff to receive training in an environment similar to their actual work. The simulation unit can also provide an environment using actual customer data, thereby enabling training in an environment similar to their actual work. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can perform simulations based on scenarios generated by a generation AI to enable staff to receive training in an environment similar to their actual work.

[0036] The feedback department can evaluate staff responses and provide real-time suggestions for improvement and advice. For example, the feedback department evaluates staff responses and provides real-time suggestions for improvement and advice. For example, the feedback department evaluates staff responses based on evaluation criteria such as response time, customer satisfaction, and resolution rate. For example, the feedback department uses generative AI to evaluate staff responses and provides real-time feedback. This allows for real-time evaluation of staff responses and the provision of suggestions for improvement and advice. Some or all of the above-described processes in the feedback department may be performed using generative AI, or without it. For example, the feedback department inputs staff response data into the generative AI, which then performs the evaluation and provides feedback.

[0037] The training department can provide optimal training content tailored to each staff member's skill level and learning progress. For example, the training department might focus on teaching new staff members basic response methods and provide training to existing staff members to hone more advanced response skills. The training department can customize training content according to each staff member's skill level and learning progress. This ensures that each staff member receives the most suitable training. Some or all of the above processes in the training department may be performed using AI, or not. For example, the training department could input staff members' skill levels and learning progress into a generating AI, which would then provide the optimal training content.

[0038] The generation unit can generate scenarios that simulate how to respond to customer inquiries and the procedures for handling complaints. For example, the generation unit uses a generation AI to generate scenarios that simulate how to respond to customer inquiries and the procedures for handling complaints. For example, the generation unit allows the generation AI to automatically generate customer response scenarios, enabling staff to receive training in an environment close to actual work. This makes it possible to generate scenarios that simulate customer inquiries and complaint handling procedures. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without using a generation AI. For example, the generation unit inputs a prompt to the generation AI, and the generation AI generates a scenario.

[0039] The generation unit can analyze past training data and automatically generate the most effective scenarios. For example, the generation unit uses a generation AI to analyze past training data and automatically generate the most effective scenarios. For example, the generation unit uses a generation AI to analyze past training data and prioritizes generating scenarios with a high success rate. The generation unit can also use a generation AI to extract and generate scenarios that are effective for specific skill sets from past training data. Furthermore, the generation unit can use a generation AI to generate scenarios that reinforce staff weaknesses based on past training data. This allows for the generation of effective scenarios based on past training data. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs past training data into a generation AI, and the generation AI generates the most effective scenarios.

[0040] The generation unit can customize scenarios according to specific business situations and customer types during scenario generation. For example, the generation unit can use a generation AI to customize scenarios according to specific business situations and customer types. For example, the generation unit can have the generation AI customize scenarios according to specific business situations to provide training that closely resembles actual work. The generation unit can also have the generation AI customize scenarios according to customer types and teach different response methods. Furthermore, the generation unit can have the generation AI customize scenarios according to specific complaint handling situations to enhance the response capabilities of staff. This enables the generation of scenarios tailored to specific business situations and customer types. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the generation unit can input data on specific business situations and customer types into the generation AI, and the generation AI will customize the scenarios.

[0041] The generation unit can provide diverse training by incorporating customer support scenarios from different industries during scenario generation. For example, the generation unit can use a generation AI to incorporate customer support scenarios from different industries during scenario generation to provide diverse training. For example, the generation unit can use a generation AI to incorporate customer support scenarios from different industries and train staff in diverse response skills. The generation unit can also use a generation AI to incorporate complaint handling scenarios from different industries to strengthen staff's response capabilities. Furthermore, the generation unit can use a generation AI to incorporate inquiry handling scenarios from different industries to cultivate staff's flexible response capabilities. In this way, diverse training can be provided by incorporating scenarios from different industries. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs data on customer support scenarios from different industries into the generation AI, and the generation AI generates the scenarios.

[0042] The generation unit can provide individually optimized scenarios by referencing the staff's past performance data during scenario generation. For example, the generation unit can use a generation AI to provide individually optimized scenarios by referencing the staff's past performance data during scenario generation. For example, the generation unit can use a generation AI to analyze the staff's past performance data and generate individually optimized scenarios. The generation unit can also use a generation AI to generate scenarios that reinforce the staff's past weaknesses and provide individually optimized training. Furthermore, the generation unit can use a generation AI to generate individually optimized scenarios based on the staff's past successes. This allows the generation unit to provide optimized scenarios based on the staff's past performance data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the staff's past performance data into a generation AI, and the generation AI can generate the optimal scenario.

[0043] The simulation unit can automatically generate unexpected situations that occur in real time during the simulation, thereby training response capabilities. For example, the simulation unit can use a generative AI to automatically generate unexpected situations that occur in real time during the simulation, thereby training response capabilities. For example, the simulation unit can use a generative AI to automatically generate unexpected customer complaints, thereby training staff response capabilities. The simulation unit can also use a generative AI to automatically generate unexpected system troubles, thereby improving staff problem-solving abilities. Furthermore, the simulation unit can use a generative AI to automatically generate unexpected complex inquiries from customers, thereby strengthening staff response capabilities. In this way, by automatically generating unexpected situations, staff response capabilities can be trained. Some or all of the above-described processes in the simulation unit may be performed using a generative AI, for example, or without using a generative AI. For example, the simulation unit can input data from the simulation into a generative AI, which then automatically generates unexpected situations.

[0044] The simulation unit can improve multitasking capabilities by running multiple scenarios simultaneously during the simulation. For example, the simulation unit can improve multitasking capabilities by using a generative AI to run multiple scenarios simultaneously during the simulation. For example, the simulation unit can improve staff multitasking capabilities by having the generative AI simultaneously handle multiple customer inquiries. The simulation unit can also strengthen staff response capabilities by having the generative AI simultaneously handle multiple complaint processing. Furthermore, the simulation unit can improve staff problem-solving abilities by having the generative AI simultaneously handle multiple system troubles. In this way, the multitasking capabilities of staff can be improved by running multiple scenarios simultaneously. Some or all of the above processing in the simulation unit may be performed using a generative AI, for example, or without using a generative AI. For example, the simulation unit can input multiple scenario data into the generative AI, and the generative AI will run them simultaneously.

[0045] The simulation unit can incorporate customer interaction scenarios with different language and cultural backgrounds during the simulation. For example, the simulation unit can use generative AI to incorporate customer interaction scenarios with different language and cultural backgrounds during the simulation. For example, the simulation unit can use generative AI to incorporate customer interaction scenarios with customers who speak different languages, thereby improving the multilingual capabilities of staff. The simulation unit can also use generative AI to incorporate customer interaction scenarios with customers from different cultural backgrounds, thereby strengthening the cultural understanding of staff. Furthermore, the simulation unit can use generative AI to incorporate customer interaction scenarios with customers from different countries, thereby improving the international communication capabilities of staff. In this way, by incorporating customer interaction scenarios with different language and cultural backgrounds, diverse communication skills can be cultivated. Some or all of the above-described processes in the simulation unit may be performed using generative AI, for example, or without using generative AI. For example, the simulation unit inputs data on different languages ​​and cultural backgrounds into the generative AI, and the generative AI generates scenarios.

[0046] The simulation unit can provide more realistic scenarios by referring to the staff's past interaction history during the simulation. For example, the simulation unit can provide more realistic scenarios by referring to the staff's past interaction history during the simulation using a generative AI. For example, the simulation unit can provide realistic scenarios by having the generative AI refer to the staff's past interaction history. The simulation unit can also provide realistic scenarios based on the staff's past successes using the generative AI. Furthermore, the simulation unit can also provide realistic scenarios based on the staff's past failures using the generative AI. This allows the simulation unit to provide realistic scenarios based on the staff's past interaction history. Some or all of the above processing in the simulation unit may be performed using a generative AI, for example, or without using a generative AI. For example, the simulation unit inputs the staff's past interaction history data into the generative AI, and the generative AI generates a scenario.

[0047] The feedback unit can provide specific improvement measures during feedback and reflect them in the next simulation. For example, the feedback unit can use a generative AI to provide specific improvement measures during feedback and reflect them in the next simulation. The feedback unit can also use a generative AI to provide specific improvement measures and reflect them in the next simulation. Furthermore, the feedback unit can use a generative AI to provide improvement measures that strengthen the staff's weaknesses and reflect them in the next simulation. In addition, the feedback unit can use a generative AI to provide specific improvement measures based on the staff's success stories and reflect them in the next simulation. This promotes the improvement of staff skills by providing specific improvement measures and reflecting them in the next simulation. Some or all of the above processing in the feedback unit may be performed using a generative AI, for example, or without a generative AI. For example, the feedback unit inputs staff feedback data into a generative AI, and the generative AI provides specific improvement measures.

[0048] The feedback unit can provide individually optimized advice by referring to the staff's past performance data during feedback. For example, the feedback unit can use a generative AI to refer to the staff's past performance data during feedback and provide individually optimized advice. The feedback unit can also use a generative AI to refer to the staff's past performance data and provide individually optimized advice. Furthermore, the feedback unit can use a generative AI to provide individually optimized advice based on the staff's past successes. In addition, the feedback unit can use a generative AI to provide individually optimized advice based on the staff's past failures. This allows for the provision of individually optimized advice based on the staff's past performance data. Some or all of the above processing in the feedback unit may be performed using a generative AI, or without one. For example, the feedback unit inputs the staff's past performance data into a generative AI, and the generative AI provides optimal advice.

[0049] The feedback unit can evaluate staff responses from multiple perspectives using different evaluation criteria during the feedback process. For example, the feedback unit can use generative AI to evaluate staff responses from multiple perspectives using different evaluation criteria. The feedback unit can also use generative AI to evaluate staff responses from multiple perspectives using different evaluation criteria. Furthermore, the feedback unit can use generative AI to evaluate staff responses using criteria such as customer satisfaction, response speed, and problem-solving ability. In addition, the feedback unit can use generative AI to combine different evaluation criteria to evaluate the overall performance of the staff. This allows for a multifaceted evaluation of staff responses by using different evaluation criteria. Some or all of the above-described processes in the feedback unit may be performed using generative AI, or without it. For example, the feedback unit inputs staff response data into generative AI, which then evaluates the responses using different evaluation criteria.

[0050] The feedback unit can, during feedback, refer to the success stories of other staff members and propose specific improvement measures. For example, the feedback unit can use a generative AI to refer to the success stories of other staff members and propose specific improvement measures during feedback. The feedback unit can, for example, have the generative AI refer to the success stories of other staff members and propose specific improvement measures. The feedback unit can also have the generative AI propose improvement measures to strengthen the staff member's weaknesses based on the success stories of other staff members. Furthermore, the feedback unit can have the generative AI propose improvement measures to promote the improvement of the staff member's skills based on the success stories of other staff members. This allows for the proposal of specific improvement measures based on the success stories of other staff members. Some or all of the above processing in the feedback unit may be performed using a generative AI, for example, or without using a generative AI. For example, the feedback unit inputs data on the success stories of other staff members into the generative AI, and the generative AI proposes specific improvement measures.

[0051] The training department can provide optimal training content by referring to the staff's past learning history during training. For example, the training department can use a generative AI to refer to the staff's past learning history during training and provide optimal training content. For example, the training department can have the generative AI refer to the staff's past learning history and provide optimal training content. The training department can also have the generative AI provide optimal training content based on the staff's past success stories. Furthermore, the training department can have the generative AI provide optimal training content based on the staff's past failure stories. This allows the training department to provide optimal training content based on the staff's past learning history. Some or all of the above processes in the training department may be performed using a generative AI, for example, or without using a generative AI. For example, the training department can input the staff's past learning history data into a generative AI, and the generative AI will provide optimal training content.

[0052] The training department can provide focused training on specific skill sets during training sessions. For example, the training department can use generative AI to provide focused training on specific skill sets during training sessions. The training department can also use generative AI to provide focused training on specific skill sets. Furthermore, the training department can use generative AI to provide focused training to reinforce staff weaknesses. In addition, the training department can use generative AI to provide focused training to enhance staff strengths. This allows for the provision of focused training on specific skill sets. Some or all of the above processes in the training department may be performed using generative AI, or not. For example, the training department inputs data on specific skill sets into a generative AI, and the generative AI provides focused training.

[0053] The instruction department can combine different training methods during instruction to facilitate effective learning. For example, the instruction department can use generative AI to combine different training methods during instruction to facilitate effective learning. The instruction department can also use generative AI to combine different training methods to facilitate effective learning. Furthermore, the instruction department can use generative AI to provide different training methods tailored to the staff's learning style. In addition, the instruction department can use generative AI to combine different training methods according to the staff's skill level. This allows for the promotion of effective learning by combining different training methods. Some or all of the above processes in the instruction department may be performed using, for example, generative AI, or without generative AI. For example, the instruction department inputs data on different training methods into the generative AI, and the generative AI provides the optimal combination.

[0054] The training department can improve teamwork by incorporating collaborative training with other staff members during training. For example, the training department can use generative AI to incorporate collaborative training with other staff members during training to improve teamwork. The training department can also use generative AI to incorporate collaborative training with other staff members and improve teamwork. Furthermore, the training department can use generative AI to provide collaborative training that facilitates communication among staff. In addition, the training department can use generative AI to provide collaborative training that promotes cooperation among staff. This allows for improved teamwork by incorporating collaborative training with other staff members. Some or all of the above processes in the training department may be performed using, for example, generative AI, or without generative AI. For example, the training department inputs collaborative training data into the generative AI, and the generative AI provides optimal training.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] Training systems can also utilize virtual reality (VR) technology to improve staff performance. For example, a VR environment can be used to allow staff to experience actual customer support tasks. This allows staff to receive training in a more realistic setting and prepare them for actual work. Furthermore, VR technology allows staff to experience different scenarios and customer interaction situations, thereby strengthening their response capabilities. In addition, the VR environment provides opportunities for self-learning, as staff can reflect on their own interactions and identify areas for improvement.

[0057] Training systems can incorporate gamification elements to maintain staff motivation. For example, a point system can be introduced into the training program, allowing staff to earn points as they progress through the training. This allows staff to approach training in a game-like manner, improving their motivation. A ranking system can also be introduced to encourage competition among staff. Furthermore, awarding badges or titles based on training achievements can enhance staff's sense of accomplishment.

[0058] The training system can also provide role-playing sessions to improve staff communication skills. For example, staff can hone their actual customer service skills by interacting with a virtual customer. Role-playing sessions also allow staff to practice handling different scenarios, fostering flexibility. Furthermore, role-playing sessions can provide opportunities for mutual learning through the exchange of feedback among staff members.

[0059] The training system can also provide self-assessment tools to improve staff self-evaluation abilities. For example, it can provide checklists or questionnaires for staff to reflect on their own actions and conduct self-assessments. This allows staff to understand their strengths and weaknesses and identify areas for improvement. Self-assessment tools can also provide guidelines for staff to track their progress and set goals. Furthermore, self-assessment tools can support staff in creating individualized training plans based on their self-assessment results.

[0060] A training system can also continuously improve its training programs based on staff feedback. For example, staff can provide feedback after completing training, and the training program can be adjusted based on that feedback. Furthermore, staff feedback can be analyzed to identify common challenges and areas for improvement. Additionally, new training modules can be added based on staff feedback, enriching the training program. This allows for the provision of training programs tailored to the needs of the staff.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The generation unit creates a scenario-based training program. For example, it uses a generation AI to generate customer support scenarios, creating scenarios that simulate how to respond to customer inquiries and the procedures for handling complaints. Step 2: The simulation unit performs simulations based on the training programs created by the generation unit. For example, it performs simulations based on the generated scenarios so that staff can receive training in an environment close to actual work, and provides a simulated environment for customer service systems and an environment that uses actual customer data. Step 3: The Feedback Department provides real-time feedback on the simulations performed by the Simulation Department. For example, it evaluates staff responses, provides real-time suggestions for improvement and advice, and assesses staff responses based on evaluation criteria such as response time, customer satisfaction, and resolution rate. Step 4: The training department provides individualized instruction based on the feedback provided by the feedback department. For example, they provide training tailored to each staff member's skill level and learning progress, focusing on basic response methods for new staff members and providing training to hone more advanced response skills for existing staff members.

[0063] (Example of form 2) The training system according to an embodiment of the present invention is a system that provides practical training to new and existing staff at a customer center using generative AI. This training system uses generative AI to create scenario-based training programs. This program simulates various customer support scenarios and is designed so that staff can receive training in an environment close to actual work. For example, it simulates how to respond to customer inquiries and the procedures for handling complaints. Next, the generative AI provides real-time feedback during training. As staff progress through the simulation, the generative AI evaluates their responses and provides real-time suggestions for improvement and advice. This allows staff to review their responses on the spot and improve their skills. Furthermore, the generative AI also provides individualized instruction. It provides optimal training content according to each staff member's skill level and learning progress. For example, new staff members are instructed on basic response methods, while existing staff members are provided with training to hone more advanced response skills. This system significantly improves the efficiency and effectiveness of training and promotes staff skill development. As a result, an improvement in customer satisfaction can be expected. In addition, training time and costs are reduced, contributing to strengthening the company's competitiveness. Thus, the training system aims to improve the skills of staff.

[0064] The training system according to the embodiment comprises a generation unit, a simulation unit, a feedback unit, and a teaching unit. The generation unit creates a scenario-based training program. The generation unit generates customer support scenarios, for example, using a generation AI. The generation unit generates scenarios that simulate, for example, how to respond to customer inquiries and the procedures for handling complaints. The simulation unit performs simulations based on the training program created by the generation unit. The simulation unit performs simulations based on the generated scenarios so that staff can receive training in an environment close to actual work. The simulation unit provides, for example, a simulated environment of a customer service system or an environment that uses actual customer data. The feedback unit provides real-time feedback on the simulation performed by the simulation unit. The feedback unit evaluates the staff's responses and provides real-time suggestions for improvement and advice. The feedback unit evaluates the staff's responses based on evaluation criteria such as response time, customer satisfaction, and resolution rate. The teaching unit provides individualized instruction based on the feedback provided by the feedback unit. The teaching unit provides optimal training content according to each staff member's skill level and learning progress. The training department, for example, focuses on teaching new staff basic response methods and provides existing staff with training to hone more advanced response skills. In this way, the training system according to this embodiment can aim to improve the skills of the staff.

[0065] The generation unit creates scenario-based training programs. For example, it generates customer support scenarios using generative AI. The generative AI utilizes natural language processing technology to analyze the details of customer inquiries and complaints and generates scenarios for the optimal response. Specifically, the generative AI learns from past inquiry data and complaint handling history, and automatically creates scenarios that address frequently occurring problems and customer needs. For example, if a customer inquires about a product defect, the generative AI will create a detailed scenario of how staff should respond based on the type of defect and past resolution methods. It also provides scenarios for complaint handling procedures, offering appropriate responses tailored to the customer's emotions and situation. This allows the generation unit to provide realistic and practical training programs that enable staff to handle various situations they face in their actual work. Furthermore, the generation unit can regularly update the generated scenarios to maintain training programs that respond to the latest customer needs and market trends. This ensures that staff always acquire the latest information and skills, improving the quality of customer service.

[0066] The simulation unit performs simulations based on training programs created by the generation unit. For example, the simulation unit performs simulations based on generated scenarios to allow staff to receive training in an environment similar to actual work. Specifically, the simulation unit builds a simulated environment of a customer service system, allowing staff to perform operations and responses similar to those in actual work. For example, the simulation unit prepares a virtual customer database, allowing staff to practice searching for customer information and responding appropriately to inquiries. Furthermore, by providing an environment that uses actual customer data, staff can receive training based on realistic data. In this way, the simulation unit supports staff in effectively acquiring the skills and knowledge necessary for actual work. In addition, the simulation unit can monitor the progress of training and staff performance in real time and adjust scenarios and training content as needed. In this way, the simulation unit can maximize support for staff skill improvement and enhance the effectiveness of training.

[0067] The Feedback Department provides real-time feedback on simulations conducted by the Simulation Department. For example, the Feedback Department evaluates staff responses and provides real-time suggestions for improvement and advice. Specifically, it evaluates staff responses based on evaluation criteria such as response time, customer satisfaction, and resolution rate. For instance, it assesses whether staff responded quickly and appropriately to customer inquiries and points out areas for improvement. Regarding customer satisfaction, it evaluates based on customer feedback and survey results, and provides specific advice on how staff can improve customer satisfaction. Furthermore, regarding resolution rates, it evaluates how effectively staff resolved inquiries and complaints, analyzes the causes of unresolved issues, and proposes improvement measures. This allows the Feedback Department to provide staff with concrete guidelines for objectively reviewing their own responses and improving their skills. Additionally, the Feedback Department centrally manages staff training history and past feedback, enabling it to provide optimal feedback to each individual staff member. This allows the Feedback Department to continuously support staff growth and maximize the effectiveness of training.

[0068] The training department provides individualized instruction based on feedback provided by the feedback department. For example, the training department provides optimal training content according to each staff member's skill level and learning progress. Specifically, the training department focuses on teaching new staff members basic response methods and provides training to existing staff members to hone more advanced response skills. For example, new staff members are carefully taught basic customer service manners and procedures for handling basic inquiries. On the other hand, existing staff members receive training to acquire complex complaint handling and advanced problem-solving skills. The training department also provides customized training programs tailored to each staff member's individual weaknesses and areas for improvement. For example, if a particular staff member has issues with response time, the training will focus on improving that staff member's response speed. Furthermore, the training department conducts regular evaluations and provides feedback to continuously support staff growth, and reviews training content as needed. This allows the training department to ensure that staff members always acquire the latest skills and improve the quality of customer service. In addition, the training department can promote information sharing and cooperation among staff members, thereby improving the overall skills of the team. This allows the leadership to improve the performance of not only individual staff members, but the entire team.

[0069] The generation unit can generate customer support scenarios using a generation AI. For example, the generation unit uses the generation AI to generate scenarios that simulate how to respond to customer inquiries and the procedures for handling complaints. For example, the generation unit allows the generation AI to automatically generate customer support scenarios, enabling staff to receive training in an environment close to actual work. This allows customer support scenarios to be automatically generated using the generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit inputs prompts to the generation AI, and the generation AI generates a scenario.

[0070] The simulation unit can perform simulations based on generated scenarios to enable staff to receive training in an environment similar to their actual work. For example, the simulation unit can provide a simulated environment for a customer service system or an environment using actual customer data. The simulation unit can, for example, construct a simulated environment for a customer service system, enabling staff to receive training in an environment similar to their actual work. The simulation unit can also provide an environment using actual customer data, thereby enabling training in an environment similar to their actual work. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can perform simulations based on scenarios generated by a generation AI to enable staff to receive training in an environment similar to their actual work.

[0071] The feedback department can evaluate staff responses and provide real-time suggestions for improvement and advice. For example, the feedback department evaluates staff responses and provides real-time suggestions for improvement and advice. For example, the feedback department evaluates staff responses based on evaluation criteria such as response time, customer satisfaction, and resolution rate. For example, the feedback department uses generative AI to evaluate staff responses and provides real-time feedback. This allows for real-time evaluation of staff responses and the provision of suggestions for improvement and advice. Some or all of the above-described processes in the feedback department may be performed using generative AI, or without it. For example, the feedback department inputs staff response data into the generative AI, which then performs the evaluation and provides feedback.

[0072] The training department can provide optimal training content tailored to each staff member's skill level and learning progress. For example, the training department might focus on teaching new staff members basic response methods and provide training to existing staff members to hone more advanced response skills. The training department can customize training content according to each staff member's skill level and learning progress. This ensures that each staff member receives the most suitable training. Some or all of the above processes in the training department may be performed using AI, or not. For example, the training department could input staff members' skill levels and learning progress into a generating AI, which would then provide the optimal training content.

[0073] The generation unit can generate scenarios that simulate how to respond to customer inquiries and the procedures for handling complaints. For example, the generation unit uses a generation AI to generate scenarios that simulate how to respond to customer inquiries and the procedures for handling complaints. For example, the generation unit allows the generation AI to automatically generate customer response scenarios, enabling staff to receive training in an environment close to actual work. This makes it possible to generate scenarios that simulate customer inquiries and complaint handling procedures. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without using a generation AI. For example, the generation unit inputs a prompt to the generation AI, and the generation AI generates a scenario.

[0074] The generation unit can estimate the user's emotions and adjust the difficulty of the scenario based on the estimated emotions. For example, the generation unit can use a generation AI to estimate the user's emotions and adjust the difficulty of the scenario based on the estimated emotions. For example, if the user is stressed, the generation AI can generate an easy scenario to reduce the user's burden. Also, if the user is relaxed, the generation unit can have the generation AI generate a more difficult scenario to promote skill improvement. Furthermore, if the user is excited, the generation unit can have the generation AI generate a challenging scenario to maintain motivation. In this way, the difficulty of the scenario can be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit inputs user emotion data into the generation AI, and the generation AI adjusts the difficulty of the scenario.

[0075] The generation unit can analyze past training data and automatically generate the most effective scenarios. For example, the generation unit uses a generation AI to analyze past training data and automatically generate the most effective scenarios. For example, the generation unit uses a generation AI to analyze past training data and prioritizes generating scenarios with a high success rate. The generation unit can also use a generation AI to extract and generate scenarios that are effective for specific skill sets from past training data. Furthermore, the generation unit can use a generation AI to generate scenarios that reinforce staff weaknesses based on past training data. This allows for the generation of effective scenarios based on past training data. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs past training data into a generation AI, and the generation AI generates the most effective scenarios.

[0076] The generation unit can customize scenarios according to specific business situations and customer types during scenario generation. For example, the generation unit can use a generation AI to customize scenarios according to specific business situations and customer types. For example, the generation unit can have the generation AI customize scenarios according to specific business situations to provide training that closely resembles actual work. The generation unit can also have the generation AI customize scenarios according to customer types and teach different response methods. Furthermore, the generation unit can have the generation AI customize scenarios according to specific complaint handling situations to enhance the response capabilities of staff. This enables the generation of scenarios tailored to specific business situations and customer types. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the generation unit can input data on specific business situations and customer types into the generation AI, and the generation AI will customize the scenarios.

[0077] The generation unit can estimate the user's emotions and adjust the order of scenarios based on the estimated emotions. For example, the generation unit can use a generation AI to estimate the user's emotions and adjust the order of scenarios based on the estimated emotions. For example, if the user is stressed, the generation unit can adjust the order so that the user starts with easier scenarios. Also, if the user is relaxed, the generation unit can have the generation AI place more difficult scenarios first. Furthermore, if the user is excited, the generation unit can have the generation AI place challenging scenarios in the middle to maintain motivation. This allows the order of scenarios to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit inputs user emotion data into a generation AI, and the generation AI adjusts the order of scenarios.

[0078] The generation unit can provide diverse training by incorporating customer support scenarios from different industries during scenario generation. For example, the generation unit can use a generation AI to incorporate customer support scenarios from different industries during scenario generation to provide diverse training. For example, the generation unit can use a generation AI to incorporate customer support scenarios from different industries and train staff in diverse response skills. The generation unit can also use a generation AI to incorporate complaint handling scenarios from different industries to strengthen staff's response capabilities. Furthermore, the generation unit can use a generation AI to incorporate inquiry handling scenarios from different industries to cultivate staff's flexible response capabilities. In this way, diverse training can be provided by incorporating scenarios from different industries. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs data on customer support scenarios from different industries into the generation AI, and the generation AI generates the scenarios.

[0079] The generation unit can provide individually optimized scenarios by referencing the staff's past performance data during scenario generation. For example, the generation unit can use a generation AI to provide individually optimized scenarios by referencing the staff's past performance data during scenario generation. For example, the generation unit can use a generation AI to analyze the staff's past performance data and generate individually optimized scenarios. The generation unit can also use a generation AI to generate scenarios that reinforce the staff's past weaknesses and provide individually optimized training. Furthermore, the generation unit can use a generation AI to generate individually optimized scenarios based on the staff's past successes. This allows the generation unit to provide optimized scenarios based on the staff's past performance data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the staff's past performance data into a generation AI, and the generation AI can generate the optimal scenario.

[0080] The simulation unit can estimate the user's emotions and adjust the simulation speed based on the estimated emotions. For example, the simulation unit might use a generative AI to estimate the user's emotions and adjust the simulation speed based on the estimated emotions. For instance, if the user is stressed, the simulation unit might slow down the simulation speed to reduce the user's burden. Conversely, if the user is relaxed, the simulation unit might maintain a normal speed to provide smooth training. Furthermore, if the user is excited, the simulation unit might speed up the simulation speed to provide a challenging environment. This allows the simulation speed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the simulation unit may be performed using, for example, a generative AI, or without one. For example, the simulation unit might input user emotion data into a generative AI, which then adjusts the simulation speed.

[0081] The simulation unit can automatically generate unexpected situations that occur in real time during the simulation, thereby training response capabilities. For example, the simulation unit can use a generative AI to automatically generate unexpected situations that occur in real time during the simulation, thereby training response capabilities. For example, the simulation unit can use a generative AI to automatically generate unexpected customer complaints, thereby training staff response capabilities. The simulation unit can also use a generative AI to automatically generate unexpected system troubles, thereby improving staff problem-solving abilities. Furthermore, the simulation unit can use a generative AI to automatically generate unexpected complex inquiries from customers, thereby strengthening staff response capabilities. In this way, by automatically generating unexpected situations, staff response capabilities can be trained. Some or all of the above-described processes in the simulation unit may be performed using a generative AI, for example, or without using a generative AI. For example, the simulation unit can input data from the simulation into a generative AI, which then automatically generates unexpected situations.

[0082] The simulation unit can improve multitasking capabilities by running multiple scenarios simultaneously during the simulation. For example, the simulation unit can improve multitasking capabilities by using a generative AI to run multiple scenarios simultaneously during the simulation. For example, the simulation unit can improve staff multitasking capabilities by having the generative AI simultaneously handle multiple customer inquiries. The simulation unit can also strengthen staff response capabilities by having the generative AI simultaneously handle multiple complaint processing. Furthermore, the simulation unit can improve staff problem-solving abilities by having the generative AI simultaneously handle multiple system troubles. In this way, the multitasking capabilities of staff can be improved by running multiple scenarios simultaneously. Some or all of the above processing in the simulation unit may be performed using a generative AI, for example, or without using a generative AI. For example, the simulation unit can input multiple scenario data into the generative AI, and the generative AI will run them simultaneously.

[0083] The simulation unit can estimate the user's emotions and adjust the simulation's feedback timing based on the estimated user emotions. For example, the simulation unit might use a generative AI to estimate the user's emotions and adjust the simulation's feedback timing based on the estimated user emotions. For example, if the user is stressed, the simulation unit might delay the feedback timing to reduce the user's burden. It can also maintain normal feedback timing when the user is relaxed, providing smooth training. Furthermore, if the user is excited, the simulation unit might speed up the feedback timing to provide a challenging environment. This allows for adjustment of feedback timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the simulation unit may be performed using, for example, a generative AI, or without one. For example, the simulation unit inputs user emotion data into a generative AI, which then adjusts the feedback timing.

[0084] The simulation unit can incorporate customer interaction scenarios with different language and cultural backgrounds during the simulation. For example, the simulation unit can use generative AI to incorporate customer interaction scenarios with different language and cultural backgrounds during the simulation. For example, the simulation unit can use generative AI to incorporate customer interaction scenarios with customers who speak different languages, thereby improving the multilingual capabilities of staff. The simulation unit can also use generative AI to incorporate customer interaction scenarios with customers from different cultural backgrounds, thereby strengthening the cultural understanding of staff. Furthermore, the simulation unit can use generative AI to incorporate customer interaction scenarios with customers from different countries, thereby improving the international communication capabilities of staff. In this way, by incorporating customer interaction scenarios with different language and cultural backgrounds, diverse communication skills can be cultivated. Some or all of the above-described processes in the simulation unit may be performed using generative AI, for example, or without using generative AI. For example, the simulation unit inputs data on different languages ​​and cultural backgrounds into the generative AI, and the generative AI generates scenarios.

[0085] The simulation unit can provide more realistic scenarios by referring to the staff's past interaction history during the simulation. For example, the simulation unit can provide more realistic scenarios by referring to the staff's past interaction history during the simulation using a generative AI. For example, the simulation unit can provide realistic scenarios by having the generative AI refer to the staff's past interaction history. The simulation unit can also provide realistic scenarios based on the staff's past successes using the generative AI. Furthermore, the simulation unit can also provide realistic scenarios based on the staff's past failures using the generative AI. This allows the simulation unit to provide realistic scenarios based on the staff's past interaction history. Some or all of the above processing in the simulation unit may be performed using a generative AI, for example, or without using a generative AI. For example, the simulation unit inputs the staff's past interaction history data into the generative AI, and the generative AI generates a scenario.

[0086] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, the feedback unit can use generative AI to estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit can make the feedback content concise to reduce the user's burden. Also, if the user is relaxed, the feedback unit can provide detailed feedback to promote skill improvement. Furthermore, if the user is excited, the feedback unit can provide challenging feedback to maintain motivation. In this way, the content of the feedback can be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using generative AI, or not using generative AI. For example, the feedback unit inputs user emotion data into the generative AI, and the generative AI adjusts the content of the feedback.

[0087] The feedback unit can provide specific improvement measures during feedback and reflect them in the next simulation. For example, the feedback unit can use a generative AI to provide specific improvement measures during feedback and reflect them in the next simulation. The feedback unit can also use a generative AI to provide specific improvement measures and reflect them in the next simulation. Furthermore, the feedback unit can use a generative AI to provide improvement measures that strengthen the staff's weaknesses and reflect them in the next simulation. In addition, the feedback unit can use a generative AI to provide specific improvement measures based on the staff's success stories and reflect them in the next simulation. This promotes the improvement of staff skills by providing specific improvement measures and reflecting them in the next simulation. Some or all of the above processing in the feedback unit may be performed using a generative AI, for example, or without a generative AI. For example, the feedback unit inputs staff feedback data into a generative AI, and the generative AI provides specific improvement measures.

[0088] The feedback unit can provide individually optimized advice by referring to the staff's past performance data during feedback. For example, the feedback unit can use a generative AI to refer to the staff's past performance data during feedback and provide individually optimized advice. The feedback unit can also use a generative AI to refer to the staff's past performance data and provide individually optimized advice. Furthermore, the feedback unit can use a generative AI to provide individually optimized advice based on the staff's past successes. In addition, the feedback unit can use a generative AI to provide individually optimized advice based on the staff's past failures. This allows for the provision of individually optimized advice based on the staff's past performance data. Some or all of the above processing in the feedback unit may be performed using a generative AI, or without one. For example, the feedback unit inputs the staff's past performance data into a generative AI, and the generative AI provides optimal advice.

[0089] The feedback unit can estimate the user's emotions and adjust the format of the feedback based on the estimated emotions. For example, the feedback unit might use generative AI to estimate the user's emotions and adjust the format of the feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit might simplify the feedback format to reduce the user's burden. If the user is relaxed, the feedback unit might provide detailed feedback to promote skill improvement. Furthermore, if the user is excited, the feedback unit might provide challenging feedback to maintain motivation. This allows the feedback format to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the feedback unit may be performed using or without a generative AI. For example, the feedback unit inputs user emotion data into a generative AI, which then adjusts the format of the feedback.

[0090] The feedback unit can evaluate staff responses from multiple perspectives using different evaluation criteria during the feedback process. For example, the feedback unit can use generative AI to evaluate staff responses from multiple perspectives using different evaluation criteria. The feedback unit can also use generative AI to evaluate staff responses from multiple perspectives using different evaluation criteria. Furthermore, the feedback unit can use generative AI to evaluate staff responses using criteria such as customer satisfaction, response speed, and problem-solving ability. In addition, the feedback unit can use generative AI to combine different evaluation criteria to evaluate the overall performance of the staff. This allows for a multifaceted evaluation of staff responses by using different evaluation criteria. Some or all of the above-described processes in the feedback unit may be performed using generative AI, or without it. For example, the feedback unit inputs staff response data into generative AI, which then evaluates the responses using different evaluation criteria.

[0091] The feedback unit can, during feedback, refer to the success stories of other staff members and propose specific improvement measures. For example, the feedback unit can use a generative AI to refer to the success stories of other staff members and propose specific improvement measures during feedback. The feedback unit can, for example, have the generative AI refer to the success stories of other staff members and propose specific improvement measures. The feedback unit can also have the generative AI propose improvement measures to strengthen the staff member's weaknesses based on the success stories of other staff members. Furthermore, the feedback unit can have the generative AI propose improvement measures to promote the improvement of the staff member's skills based on the success stories of other staff members. This allows for the proposal of specific improvement measures based on the success stories of other staff members. Some or all of the above processing in the feedback unit may be performed using a generative AI, for example, or without using a generative AI. For example, the feedback unit inputs data on the success stories of other staff members into the generative AI, and the generative AI proposes specific improvement measures.

[0092] The instruction unit can estimate the user's emotions and adjust the instruction method based on the estimated emotions. For example, the instruction unit can use generative AI to estimate the user's emotions and adjust the instruction method based on the estimated emotions. For example, if the user is stressed, the instruction unit can make the instruction gentler to reduce the user's burden. Also, if the user is relaxed, the instruction unit can maintain a normal instruction method to provide smooth training. Furthermore, if the user is excited, the instruction unit can make the instruction more challenging to maintain motivation. This allows the instruction method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using generative AI, or not using generative AI. For example, the instruction unit inputs user emotion data into the generative AI, and the generative AI adjusts the instruction method.

[0093] The training department can provide optimal training content by referring to the staff's past learning history during training. For example, the training department can use a generative AI to refer to the staff's past learning history during training and provide optimal training content. For example, the training department can have the generative AI refer to the staff's past learning history and provide optimal training content. The training department can also have the generative AI provide optimal training content based on the staff's past success stories. Furthermore, the training department can have the generative AI provide optimal training content based on the staff's past failure stories. This allows the training department to provide optimal training content based on the staff's past learning history. Some or all of the above processes in the training department may be performed using a generative AI, for example, or without using a generative AI. For example, the training department can input the staff's past learning history data into a generative AI, and the generative AI will provide optimal training content.

[0094] The training department can provide focused training on specific skill sets during training sessions. For example, the training department can use generative AI to provide focused training on specific skill sets during training sessions. The training department can also use generative AI to provide focused training on specific skill sets. Furthermore, the training department can use generative AI to provide focused training to reinforce staff weaknesses. In addition, the training department can use generative AI to provide focused training to enhance staff strengths. This allows for the provision of focused training on specific skill sets. Some or all of the above processes in the training department may be performed using generative AI, or not. For example, the training department inputs data on specific skill sets into a generative AI, and the generative AI provides focused training.

[0095] The instruction unit can estimate the user's emotions and adjust the frequency of instruction based on the estimated emotions. For example, the instruction unit can use generative AI to estimate the user's emotions and adjust the frequency of instruction based on the estimated emotions. For example, if the user is stressed, the instruction unit can reduce the frequency of instruction to alleviate the user's burden. The instruction unit can also maintain a normal frequency of instruction if the user is relaxed, providing smooth training. Furthermore, if the user is excited, the instruction unit can increase the frequency of instruction to provide a challenging environment. This allows the instruction frequency to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using generative AI, or not using generative AI. For example, the instruction unit inputs user emotion data into the generative AI, and the generative AI adjusts the frequency of instruction.

[0096] The instruction department can combine different training methods during instruction to facilitate effective learning. For example, the instruction department can use generative AI to combine different training methods during instruction to facilitate effective learning. The instruction department can also use generative AI to combine different training methods to facilitate effective learning. Furthermore, the instruction department can use generative AI to provide different training methods tailored to the staff's learning style. In addition, the instruction department can use generative AI to combine different training methods according to the staff's skill level. This allows for the promotion of effective learning by combining different training methods. Some or all of the above processes in the instruction department may be performed using, for example, generative AI, or without generative AI. For example, the instruction department inputs data on different training methods into the generative AI, and the generative AI provides the optimal combination.

[0097] The training department can improve teamwork by incorporating collaborative training with other staff members during training. For example, the training department can use generative AI to incorporate collaborative training with other staff members during training to improve teamwork. The training department can also use generative AI to incorporate collaborative training with other staff members and improve teamwork. Furthermore, the training department can use generative AI to provide collaborative training that facilitates communication among staff. In addition, the training department can use generative AI to provide collaborative training that promotes cooperation among staff. This allows for improved teamwork by incorporating collaborative training with other staff members. Some or all of the above processes in the training department may be performed using, for example, generative AI, or without generative AI. For example, the training department inputs collaborative training data into the generative AI, and the generative AI provides optimal training.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] Training systems can also utilize virtual reality (VR) technology to improve staff performance. For example, a VR environment can be used to allow staff to experience actual customer support tasks. This allows staff to receive training in a more realistic setting and prepare them for actual work. Furthermore, VR technology allows staff to experience different scenarios and customer interaction situations, thereby strengthening their response capabilities. In addition, the VR environment provides opportunities for self-learning, as staff can reflect on their own interactions and identify areas for improvement.

[0100] Training systems can incorporate gamification elements to maintain staff motivation. For example, a point system can be introduced into the training program, allowing staff to earn points as they progress through the training. This allows staff to approach training in a game-like manner, improving their motivation. A ranking system can also be introduced to encourage competition among staff. Furthermore, awarding badges or titles based on training achievements can enhance staff's sense of accomplishment.

[0101] The training system can also provide relaxation modules to support staff stress management. For example, providing relaxation exercises or meditation sessions between training sessions can reduce staff stress. Relaxation modules can also provide relaxing music or nature sounds for staff. Furthermore, relaxation modules can allow staff to check their stress levels and choose appropriate relaxation methods.

[0102] The training system can also provide role-playing sessions to improve staff communication skills. For example, staff can hone their actual customer service skills by interacting with a virtual customer. Role-playing sessions also allow staff to practice handling different scenarios, fostering flexibility. Furthermore, role-playing sessions can provide opportunities for mutual learning through the exchange of feedback among staff members.

[0103] The training system can also provide self-assessment tools to improve staff self-evaluation abilities. For example, it can provide checklists or questionnaires for staff to reflect on their own actions and conduct self-assessments. This allows staff to understand their strengths and weaknesses and identify areas for improvement. Self-assessment tools can also provide guidelines for staff to track their progress and set goals. Furthermore, self-assessment tools can support staff in creating individualized training plans based on their self-assessment results.

[0104] The training system can also estimate staff emotions and adjust training content based on those estimates. For example, if staff are stressed, the training content can be reduced and a relaxing environment provided. Conversely, if staff are relaxed, more challenging training content can be offered to promote skill improvement. Furthermore, if staff are excited, the training content can be adjusted to maintain motivation. This allows for training content tailored to the emotions of the staff.

[0105] A training system can also continuously improve its training programs based on staff feedback. For example, staff can provide feedback after completing training, and the training program can be adjusted based on that feedback. Furthermore, staff feedback can be analyzed to identify common challenges and areas for improvement. Additionally, new training modules can be added based on staff feedback, enriching the training program. This allows for the provision of training programs tailored to the needs of the staff.

[0106] The training system can also estimate staff emotions and adjust the training pace based on those estimates. For example, if staff are stressed, the training pace can be slowed to reduce their burden. Conversely, if staff are relaxed, the training pace can be kept normal to provide a smooth training experience. Furthermore, if staff are excited, the training pace can be increased to provide a more challenging environment. This allows for training paces tailored to the staff's emotions.

[0107] The training system can also estimate staff emotions and adjust the content of feedback based on those estimates. For example, if a staff member is stressed, the feedback can be concise to reduce their burden. Conversely, if a staff member is relaxed, detailed feedback can be provided to promote skill improvement. Furthermore, if a staff member is excited, challenging feedback can be provided to maintain motivation. This allows for the delivery of feedback tailored to the staff member's emotional state.

[0108] The training system can also estimate staff emotions and adjust training frequency based on those estimates. For example, if staff are stressed, the training frequency can be reduced to lessen their burden. Conversely, if staff are relaxed, the training frequency can be kept normal to provide smooth training. Furthermore, if staff are excited, the training frequency can be increased to provide a more challenging environment. This allows for training frequency tailored to the staff's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The generation unit creates a scenario-based training program. For example, it uses a generation AI to generate customer support scenarios, creating scenarios that simulate how to respond to customer inquiries and the procedures for handling complaints. Step 2: The simulation unit performs simulations based on the training programs created by the generation unit. For example, it performs simulations based on the generated scenarios so that staff can receive training in an environment close to actual work, and provides a simulated environment for customer service systems and an environment that uses actual customer data. Step 3: The Feedback Department provides real-time feedback on the simulations performed by the Simulation Department. For example, it evaluates staff responses, provides real-time suggestions for improvement and advice, and assesses staff responses based on evaluation criteria such as response time, customer satisfaction, and resolution rate. Step 4: The training department provides individualized instruction based on the feedback provided by the feedback department. For example, they provide training tailored to each staff member's skill level and learning progress, focusing on basic response methods for new staff members and providing training to hone more advanced response skills for existing staff members.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the generation unit, simulation unit, feedback unit, and instruction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates customer support scenarios using generation AI. The simulation unit is implemented by the control unit 46A of the smart device 14 and performs simulations based on the generated scenarios so that staff can receive training in an environment close to actual work. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the staff's responses and provides improvements and advice in real time. The instruction unit is implemented by the control unit 46A of the smart device 14 and provides optimal training content according to each staff member's skill level and learning progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the generation unit, simulation unit, feedback unit, and instruction unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates customer support scenarios using generation AI. The simulation unit is implemented by the control unit 46A of the smart glasses 214 and performs simulations based on the generated scenarios so that staff can receive training in an environment close to actual work. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the staff's responses and provides improvements and advice in real time. The instruction unit is implemented by the control unit 46A of the smart glasses 214 and provides optimal training content according to each staff member's skill level and learning progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the generation unit, simulation unit, feedback unit, and instruction unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates customer support scenarios using generation AI. The simulation unit is implemented by the control unit 46A of the headset terminal 314 and performs simulations based on the generated scenarios so that staff can receive training in an environment close to actual work. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the staff's responses and provides improvements and advice in real time. The instruction unit is implemented by the control unit 46A of the headset terminal 314 and provides optimal training content according to each staff member's skill level and learning progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the generation unit, simulation unit, feedback unit, and instruction unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates customer support scenarios using generation AI. The simulation unit is implemented by the control unit 46A of the robot 414, which performs simulations based on the generated scenarios so that staff can receive training in an environment close to actual work. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which evaluates the staff's responses and provides improvements and advice in real time. The instruction unit is implemented by the control unit 46A of the robot 414, which provides optimal training content according to each staff member's skill level and learning progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A generation unit that creates scenario-based training programs, A simulation unit that performs a simulation based on the training program created by the generation unit, A feedback unit provides real-time feedback to the simulation performed by the aforementioned simulation unit, The system includes a guidance unit that provides individualized guidance based on the feedback provided by the aforementioned feedback unit. A system characterized by the following features. (Note 2) The generating unit is Generating customer support scenarios using AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned simulation unit, Based on the generated scenarios, simulations are performed to allow staff to receive training in an environment that closely resembles actual work. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is We evaluate staff performance and provide real-time feedback and advice on areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned leadership, We provide training content tailored to each staff member's skill level and learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate scenarios to simulate how to respond to customer inquiries and the procedures for handling complaints. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is It estimates the user's emotions and adjusts the difficulty of the scenario based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is Analyze past training data and automatically generate the most effective scenario. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating a scenario, customize it according to specific business conditions and customer types. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts the sequence of scenarios based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating scenarios, we incorporate customer support scenarios from different industries to provide diverse training. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating scenarios, the system provides individually optimized scenarios by referencing the staff's past performance data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation's progress based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned simulation unit, During the simulation, unexpected situations that occur in real time are automatically generated to hone response capabilities. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned simulation unit, By running multiple scenarios simultaneously during the simulation, multitasking capabilities can be improved. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned simulation unit, It estimates the user's emotions and adjusts the timing of the simulation's feedback based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned simulation unit, During the simulation, incorporate scenarios involving interactions with customers who have different language and cultural backgrounds. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned simulation unit, During the simulation, we refer to the staff's past response history to provide more realistic scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is During the feedback process, specific improvement measures will be proposed and incorporated into the next simulation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is During feedback sessions, we refer to staff members' past performance data to provide individually optimized advice. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback format based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is During feedback sessions, we evaluate staff responses from multiple perspectives using different evaluation criteria. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, refer to the success stories of other staff members and propose specific improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned leadership, It estimates the user's emotions and adjusts the teaching method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned leadership, During instruction, we refer to the staff member's past learning history to provide the most suitable training content. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned leadership, During instruction, provide intensive training that focuses on specific skill sets. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned leadership, The system estimates the user's emotions and adjusts the frequency of guidance based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned leadership, During instruction, combine different training methods to promote effective learning. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned leadership, During training, incorporate collaborative training with other staff members to improve teamwork. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A generation unit that creates scenario-based training programs, A simulation unit that performs a simulation based on the training program created by the generation unit, A feedback unit provides real-time feedback to the simulation performed by the aforementioned simulation unit, The system comprises a guidance unit that provides individualized guidance based on the feedback provided by the aforementioned feedback unit. A system characterized by the following features.

2. The generating unit is Generating customer support scenarios using AI. The system according to feature 1.

3. The aforementioned simulation unit, Based on the generated scenarios, simulations are performed to allow staff to receive training in an environment that closely resembles actual work. The system according to feature 1.

4. The aforementioned feedback unit is We evaluate staff performance and provide real-time feedback and advice on areas for improvement. The system according to feature 1.

5. The aforementioned leadership, We provide training content tailored to each staff member's skill level and learning progress. The system according to feature 1.

6. The generating unit is Generate scenarios to simulate how to respond to customer inquiries and the procedures for handling complaints. The system according to feature 1.

7. The generating unit is The system estimates the user's emotions and adjusts the difficulty of the scenario based on those emotions. The system according to feature 1.

8. The generating unit is Analyze past training data and automatically generate the most effective scenario. The system according to feature 1.

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