system

Abstract

We provide the system. [Solution] A means of receiving information, A generation means for generating a game-like training session based on that information, A means of collecting user biometric data, An evaluation method that analyzes collected data and assesses user skills, A means of providing feedback to users based on evaluation results, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] Conventional soft skill training is costly and time - consuming, thus having the problem of hindering business productivity. Also, these trainings have the problem that it is difficult to objectively evaluate the skills of participants, and it is not easy to compare and improve employees. In particular, in order to respond to a changing business environment, it is required to improve soft skills in an efficient and attractive way.

Means for Solving the Problems

[0005] This invention provides a dynamic training environment by having a receiving means for receiving information and a generating means for generating game-style training sessions based on that information. Furthermore, it includes a collection means for collecting user biometric data and an evaluation means for analyzing the collected data to assess the user's skills. This allows participants to objectively understand their own performance and receive personalized advice tailored to their progress through a provisioning means utilizing generative algorithms. This enables efficient and effective soft skills training, contributing to accelerating transformation in business operations.

[0006] "Information" refers to user actions and choices, as well as data acquired from external sources, which forms the basis for the system to generate training sessions.

[0007] A "receiving means" is a component that has the function of receiving information from an external source and is used when data is brought into the system.

[0008] A "generation means" is a device that has the function of dynamically creating a game-style training session tailored to a specific purpose based on the information it receives.

[0009] A "collection device" is a component that acquires a user's biometric data and is used to monitor the user's status in real time.

[0010] "Evaluation methods" refer to processes and algorithms used to analyze collected data and determine and evaluate users' skills and performance.

[0011] A "means of delivery" refers to a function that presents evaluation results obtained from analysis to the user and provides feedback and advice.

[0012] A "generative algorithm" refers to an algorithm that automatically generates personalized feedback and next steps based on the user's performance data. [Brief explanation of the drawing]

[0013] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] The 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.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a game-based training system for users to efficiently and effectively improve their soft skills. Implementing this system involves a combination of server, terminal, and user interaction.

[0035] First, the user accesses the system and registers an account. Once registration is complete, the server stores the user's information in a database and prepares to provide a personalized training program based on the user's needs.

[0036] Next, the server generates a relevant game-style training session based on the user's selection of a training category through their device. For example, users can choose from categories such as "Communication Skills" or "Stress Management." Based on the user's selection, the server generates content including an appropriate game scenario and objectives and sends it to the device.

[0037] When a user activates the booster on their device, it makes decisions based on various situations the user experiences as the game progresses. The device provides real-time feedback to the user as the game progresses. During this time, the data collection system acquires the user's biometric data through a biofeedback device. The device sends this data to a server to monitor the user's emotions and stress levels.

[0038] The server analyzes the user's in-game behavior data and biofeedback data, and uses evaluation tools to perform a comprehensive performance assessment. Once the assessment is complete, the server uses a generative algorithm to generate personalized feedback and advice for the user. This advice is displayed to the user via their device, providing guidance on the next training session and areas for improvement.

[0039] For example, if a user selects "teamwork skills," the system presents a game scenario that requires players to solve problems collaboratively. The user's heart rate and skin electrical activity are monitored in real time, and immediate feedback is provided based on increases or decreases in stress levels. This allows users to hone their skills in a fun and effective way.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user accesses the system using a device and logs into their account by entering their authentication information on the login screen. The server verifies the user's authentication information, and access is authorized.

[0043] Step 2:

[0044] After the user logs into their account, a list of training categories is displayed on their device. The user then selects a category that suits their needs, such as communication skills or stress management.

[0045] Step 3:

[0046] When a user selects a training category, the server creates a booster in the appropriate game format based on the selected category. The server then sends the content of the generated booster to the user's device.

[0047] Step 4:

[0048] The user starts the booster on their device. The device provides the user with the game rules, displays an interactive scenario, and waits for input.

[0049] Step 5:

[0050] As the game progresses, the user makes choices based on the presented scenario. The device provides real-time feedback to the user and records the user's choices.

[0051] Step 6:

[0052] The device uses biofeedback to collect the user's biometric data (such as heart rate and skin electrical activity) during gameplay. The collected data is then sent to a server.

[0053] Step 7:

[0054] The server analyzes user behavior data and biofeedback data. Based on the analysis results, it evaluates user performance using evaluation tools.

[0055] Step 8:

[0056] Based on the analysis results, the server uses generative algorithms to generate optimal feedback and advice for the user. This generated feedback and advice is then sent to the device.

[0057] Step 9:

[0058] The device displays evaluation results and feedback to the user, suggesting approaches and areas for improvement for the next training session. Based on the feedback, the user can continue further training.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In recent years, improving soft skills has become increasingly important in individuals' professional lives, but there is a lack of appropriate training systems to achieve this efficiently and effectively. Furthermore, existing training methods struggle to provide personalized feedback tailored to the individual needs of users. Consequently, there is a growing need for training systems that allow for enjoyable learning through games and provide real-time feedback based on individual progress.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes receiving means for receiving information, storage means for storing individual information, and generating means for generating a game-like training course based on that information. This makes it possible to dynamically provide training content based on the training category selected by the user and to receive individualized advice according to their progress.

[0064] A "receiving means" refers to a device or function that receives information input from a user and processes it appropriately within the system.

[0065] A "generation means" is a device or function that creates a game-style training course based on received information or stored data.

[0066] "Storage means" refers to a device or function that stores data for recording individual user information and progress, and for use in subsequent processing and evaluation.

[0067] "Means of provision" refers to devices or functions that display appropriate training content to the user based on the training category selected by the user.

[0068] "Collection means" refers to devices and functions that acquire users' biometric and behavioral data and utilize them for evaluation and feedback performed by the system.

[0069] A "tracking device" is a device or function that records a user's behavior and reactions in real time during a game or training session, and uses this information to provide feedback.

[0070] "Evaluation tools" refer to devices or functions that analyze collected user data to determine abilities and learning progress.

[0071] A "generative algorithm" is a set of computational procedures for automatically creating personalized advice and feedback based on the user's progress and needs.

[0072] This system provides game-based training to help users efficiently and effectively improve their soft skills. The main components of the system are a server, terminals, and biofeedback devices as data collection tools.

[0073] When a user accesses a terminal and registers an account, the server stores the user's information in a database and prepares a training program tailored to their individual needs. Based on the training category selected by the user from the terminal, the server uses a generative AI model to generate a training session with game scenarios and goals. Software such as Python and TENSORFLOW® are used for this generation.

[0074] For example, if a user selects "teamwork skills," the system provides a game scenario themed around collaborative problem-solving. The user begins training on their device, making decisions in various situations while receiving real-time feedback. The device sends user behavior data to a server, which analyzes it and evaluates performance.

[0075] Biofeedback devices collect the user's biometric data, such as heart rate and skin electrical activity. This data is also sent to a server and used to monitor the user's stress level and emotional state. The server combines this data to comprehensively evaluate performance and generate feedback. This feedback is personalized to the user using a generation algorithm and displayed on the device as specific advice for improvement.

[0076] Examples of prompt statements that can be input to a generative AI model include the following:

[0077] "Create a scenario where users work as a team to solve a problem. Include specific communication tips to support situations where collaboration is lacking."

[0078] In this way, the system provides users with a training environment where they can learn while having fun, and efficiently supports the improvement of their soft skills.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user accesses the device and registers an account. The device provides user information (name, email address, password, etc.) as input. The device sends this information to the server, which stores the received information in a database. This lays the foundation for preparing a personalized training program for the user.

[0082] Step 2:

[0083] The user selects a training category via their device. The input is a category selection, such as "Communication Skills" or "Stress Management." The device sends this selection information to the server. Based on this information, the server uses a generative AI model to set appropriate game scenarios and goals, and creates a training session. The output is the training content provided to the user.

[0084] Step 3:

[0085] The server generates training content and sends it to the device. The device receives it and displays it to the user. The user starts a session based on the presented training content. Specifically, the user makes selections and decisions according to the scenario on the screen.

[0086] Step 4:

[0087] As the user progresses through a training session, a biofeedback device collects biometric data such as heart rate and skin electrical activity in real time. This biometric data is then transmitted from the collection device to the terminal and then to a server.

[0088] Step 5:

[0089] The server analyzes the received biometric data and the user's behavioral data during the session. This allows for the determination of the user's stress level and emotions. The analysis output includes an overall performance evaluation.

[0090] Step 6:

[0091] Based on the analysis results, the server uses a generation algorithm to generate personalized feedback for the user. This feedback includes specific areas for improvement for the next training session. The generated feedback is sent to the terminal and displayed to the user in real time.

[0092] Step 7:

[0093] Users review feedback on their devices and use it as a reference for future training sessions. Specifically, they examine the advice and comments displayed on the device screen and use them as clues to improve their own behavior.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] Conventional skill training systems fail to adequately improve skills related to collaborative work between users and machines. This can lead to inefficiencies in machine operation and potentially cause significant operational disruptions. Furthermore, a lack of flexibility in efficiently training specific skills is a significant challenge.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes receiving means for receiving information, collecting means for collecting the user's biometric data, and means for improving machine control techniques and dialogue capabilities during training sessions. This enables the user to efficiently perform collaborative tasks with the machine and improve specific skills.

[0099] "Means of receiving information" refers to a device or process that acquires data or parameters provided from an external source and converts them into a format usable within the system.

[0100] A "game-based training session" is a learning activity that incorporates entertainment elements and allows users to participate in order to improve specific skills.

[0101] "Means of collecting user biometric data" refers to devices or processes for acquiring data that reflects the user's physical and emotional state.

[0102] "An evaluation method for assessing user skills" refers to a system element that measures user performance according to a set of criteria and presents it as an indicator.

[0103] "Means of providing feedback" refers to functions that offer users advice and information to help them improve their skills.

[0104] "Means for improving machine control technology and dialogue capabilities" refers to functions that enhance the skills necessary for users to work collaboratively with machines and support efficient communication.

[0105] "Means for developing the ability to efficiently carry out work in coordination with machine operation" refers to a system that develops the ability to optimize work while coordinating with multiple machines.

[0106] To implement this invention, a system is constructed in which a server, a mobile terminal, and a factory robot work together. The server receives information and, based on data provided by the user, generates game-like training sessions. This provides specialized scenarios tailored to the skill training selected by the user. For example, in communication skills training, a scenario in a virtual factory setting is executed, allowing the user to learn to work collaboratively with a robot.

[0107] The mobile device serves as an interface for users to interact with the server. Users utilize this device to register an account, select training categories, and receive feedback. Furthermore, the device collects biometric data from biofeedback devices and transmits it to the server. This data, including heart rate and stress levels, is used to understand the user's emotions and level of tension.

[0108] The server analyzes collected biometric data and user behavior data within the game, and performs a comprehensive performance evaluation using evaluation tools. Based on the evaluation results, it utilizes a generative AI model to generate individualized feedback tailored to the user's progress. This feedback helps improve the robot's operation skills and conversational abilities, and can indicate areas for improvement for the next session.

[0109] A concrete example would be a scenario in a virtual factory where a robot operator works with a robot to efficiently install parts. An example of a prompt might be, "Generate a script in which a robot operator gives instructions to a partner robot to achieve a common goal in a virtual factory scenario." In this way, users can practically learn and develop the skills necessary in various situations.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] Users register an account using their mobile device. In this step, the personal information entered by the user is received and sent to the server. The server stores the received data in a database and prepares a training program based on the user's needs.

[0113] Step 2:

[0114] The user selects a training category through their device. Based on this input, the server generates a game scenario suitable for specific skill training. The generated scenario is sent to the device and presented to the user.

[0115] Step 3:

[0116] When a user starts a game, biofeedback devices collect biometric data. The device transmits this biometric data to a server in real time. The server analyzes data such as heart rate and stress level to understand the user's current state.

[0117] Step 4:

[0118] The server uses a generative AI model to combine user behavioral data and biometric data to perform a comprehensive performance evaluation. By analyzing each piece of data, it identifies the user's skill level and areas for improvement.

[0119] Step 5:

[0120] Based on the evaluation results, the server generates feedback for the user. This feedback includes specific advice tailored to the user's progress and is displayed on the terminal. This allows the user to understand areas for improvement in the next training session.

[0121] Step 6:

[0122] The entire system utilizes accumulated feedback and user responses for retraining, improving the quality of training sessions and feedback. Based on this, the generative AI model is continuously refined to provide more sophisticated skill training.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] This invention provides a system that offers game-based training to help users effectively improve their soft skills, and combines this with emotion recognition functionality. The system utilizes a server, terminals, and various sensor devices. These elements work together to enable customized training for the user.

[0125] The user first accesses the system via their device and logs in by entering their authentication information. Once the login is approved, the server retrieves the user's training history and displays the appropriate training category on the dashboard. When the user selects a category, the server generates a training session in the corresponding game format and sends it to the device.

[0126] When a training session begins, the device presents the user with an interactive scenario. At this time, the emotion engine activates, collecting and analyzing the user's facial expressions, voice tone, and biometric data (heart rate and skin electrical activity) from biofeedback devices in real time. This allows for the estimation of the user's current emotional state.

[0127] The server comprehensively evaluates the user's skills based on analyzed emotional data and the user's choices and actions within the game. It then utilizes generative algorithms to generate personalized feedback and advice tailored to the user's emotional state. These results are then provided to the user via their device.

[0128] As a concrete example, let's say a user has selected "stress management" training. In this case, a tense situation is simulated during the game, and the emotion engine estimates the user's stress level. Based on biofeedback data and facial expression analysis, the server determines how stressed the user is and provides advice on relief techniques as needed, helping them acquire skills that can be used in real-life stressful situations.

[0129] Thus, training systems incorporating an emotion engine are designed to provide a more effective learning experience by deeply understanding the user's emotions and responding in real time.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The user launches the application on their device, and the login screen appears. The user enters their authentication information and attempts to log in.

[0133] Step 2:

[0134] The server receives and verifies the user's authentication information. If authentication is successful, it generates the user's dashboard and provides it to the terminal.

[0135] Step 3:

[0136] The user selects a training category from their device. For example, they might select "Stress Management" or "Communication Skills."

[0137] Step 4:

[0138] The server dynamically generates an appropriate game-style training session based on the user's selection and sends the session content to the terminal.

[0139] Step 5:

[0140] The user starts a training session on their device. The device presents the user with the game rules and instructions, and prompts them to start the game.

[0141] Step 6:

[0142] During gameplay, the emotion engine activates, and the device collects the user's facial expression data and voice tone using the camera and microphone. It also acquires biometric information (heart rate, skin electrical activity, etc.) from a biofeedback device.

[0143] Step 7:

[0144] The server analyzes collected facial expression data, biometric information, and user behavior within the game to estimate the user's emotional state. Based on the analysis results, the user's skill level is evaluated.

[0145] Step 8:

[0146] The server uses a generative algorithm to generate feedback and advice tailored to the user's emotional state and sends it to the device.

[0147] Step 9:

[0148] The device displays evaluation results and feedback to the user, and provides advice for the next training session. Based on the feedback, the user can choose to continue training or use other features.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] In modern society, improving soft skills is crucial for personal growth and professional success, but there is a lack of practical and effective training methods. Furthermore, providing real-time feedback tailored to individual emotional states and reactions is difficult, making it challenging to offer appropriate support to each user.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes receiving means for receiving information, generating means for generating simulated training sessions, and collecting means for collecting user biometric information. This makes it possible to dynamically provide customized training sessions for individual users and to provide real-time feedback while considering the user's current emotional state by utilizing biometric information collected using sensor devices.

[0154] A "receiving means" is an element that has the function of acquiring information from an external source and is used by a system to receive necessary data and instructions.

[0155] A "generation means" is an element that has the function of constructing instructions and commands based on acquired information and creating sessions and content based on a specific purpose.

[0156] A "collection method" refers to an element that has the function of collecting biometric information from users, and is used by the system to collect necessary data and understand the user's condition.

[0157] An "evaluation tool" is an element that processes collected information and has the function of analyzing the user's skills and status.

[0158] "Means of delivery" refers to elements that have the function of conveying appropriate feedback and instructions to users based on the evaluated results.

[0159] "Analysis means" refers to an element that analyzes collected biometric information and has the function of evaluating the user's emotions and state in real time.

[0160] A generative algorithm is a computational method used to generate appropriate responses or feedback based on specific conditions or data.

[0161] This invention is a system for users to effectively improve their soft skills, combining game-based training sessions with emotion recognition capabilities. The system utilizes a server, terminals, and sensor devices. Each component is described in detail below.

[0162] The server receives information and generates simulated training sessions using a generative AI model. These sessions are finely customized based on the user's past training history and current skill level. The server leverages generative algorithms to understand the user's situation in real time and provide optimal feedback.

[0163] The terminal serves as the interface with the user. It presents the game session sent from the server to the user and displays various instructions and feedback. The user makes choices and interacts with the system through the terminal.

[0164] The sensor device plays a role in collecting biometric data. Specifically, it acquires data such as heart rate and skin electrical activity in real time and uses it to analyze the user's emotions and stress levels. This data is essential for the server to understand and evaluate the user's emotional state.

[0165] For example, if a user selects "stress management" training, the system simulates a stressful situation and measures the user's stress level using a sensor. The server then analyzes this data and provides appropriate stress reduction techniques as feedback. As a result, the user can improve their ability to cope with real-world stressful situations.

[0166] An example of a prompt is "Design a generative algorithm to assess the user's stress level and generate appropriate feedback." Based on such prompts, the system provides a training experience optimized for each individual user.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user logs into the system via a terminal. The user enters their authentication information into the terminal as input, which the server receives. The server then performs the authentication process by referencing a database, retrieving the user profile and training history. The output displays a list of training categories the user has access to.

[0170] Step 2:

[0171] The user selects a training category of interest on their device. This selection is sent to the server as input. The server generates a simulated training session based on the selected category. By utilizing the generated AI model, a customized, game-like training session is created. The generated session data is then sent back to the device as output.

[0172] Step 3:

[0173] The terminal presents the user with a training session. The input at this stage is session data sent from the server. The terminal displays an interactive scenario on the screen and provides voice instructions via speakers or a headset. The output is the information the user needs to participate in the training.

[0174] Step 4:

[0175] The sensor device starts operating and collects the user's biometric data. The input is biometric information such as the user's heart rate and skin electrical activity. The collected data is sent to a server for analysis. The server uses an emotion engine to analyze the data in real time and estimate the user's emotional state. The output is a digital profile that constitutes the user's emotional state.

[0176] Step 5:

[0177] The server evaluates the user's skills based on the analysis results. The inputs are the analysis results obtained from the emotion engine and the user's behavioral data during training. A generative algorithm is used to evaluate the user's skill level and areas for improvement. The evaluation results are generated as output.

[0178] Step 6:

[0179] The server sends the generated evaluation results and feedback to the terminal. The input is the evaluation results obtained in step 5. As output, the user receives appropriate feedback and advice via the terminal, which they use to decide on their next action.

[0180] (Application Example 2)

[0181] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0182] In modern brick-and-mortar stores, while developing employees with customer service skills is crucial, traditional methods struggle to provide effective training that takes individual emotional states into account. Specifically, there is a lack of means for employees to receive real-time feedback and improve their skills while understanding their own emotional state.

[0183] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0184] In this invention, the server includes receiving means for receiving information, generating means for generating game-like training sessions, and means for providing scenarios for improving customer service skills. This enables employees to effectively improve their abilities through interactive customer service training tailored to their individual emotional states.

[0185] "Receiving means" refers to a device or mechanism for acquiring information from an external source and processing it within the system.

[0186] A "generation means" is a function or system that creates a game-style training session tailored to a specific purpose based on acquired information.

[0187] "Means of data collection" refers to devices and technologies for acquiring a user's biometric information, and this includes sensor devices, etc.

[0188] "Evaluation means" refers to a process or mechanism for analyzing and evaluating a user's abilities and status based on collected data.

[0189] "Means of provision" refers to a system or function for providing information and feedback to users based on evaluation results.

[0190] A "scenario" refers to a training scene or situational setting designed to help a user improve a specific skill.

[0191] "Emotion recognition means" refers to technology or devices that analyze a user's voice and facial expressions to determine their emotional state.

[0192] A "generation algorithm" is a computational method or program that automatically generates appropriate advice and content according to the user's progress.

[0193] This invention is a system aimed at improving the customer service skills of employees in physical stores. It consists primarily of a server, terminals, and various sensor devices. Specific embodiments are described below.

[0194] First, the server receives data regarding customer service training requests from the user's terminal in order to receive information. A smartphone is used as the terminal, and the user selects a specific scenario and logs in via this smartphone. After receiving the information, the server uses a generation mechanism to create a game-style training session and creates a training session based on the scenario selected by the user.

[0195] Next, the smartphone's camera and microphone are used as data collection tools to gather the user's biometric information, specifically voice and facial expression data. This data is analyzed in real time, and the user's emotional state is evaluated through emotion recognition tools. External emotion recognition software such as Microsoft® Azure® Face API and Google® Cloud Speech-to-Text are used for the evaluation.

[0196] As an evaluation method, emotional data and user behavior data within the game are analyzed on the server. Based on the results of this analysis, a generation algorithm is used to provide personalized feedback tailored to the user's progress. This allows users to receive real-time advice necessary to improve their customer service skills.

[0197] For example, if a user selects the "product description" scenario, the device simulates this situation, and the server determines the user's level of nervousness and confidence. Based on the data analyzed by the emotion recognition engine, the server provides timely advice on how to approach the situation, supporting the user's growth.

[0198] An example of a prompt message could be, "Analyze the user's facial expressions and tone of voice in real time, and generate feedback based on the customer service scenario." By utilizing such prompts, the system can provide more accurate feedback and adjust its training content.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The user logs in to their smartphone and selects a specific customer service scenario. The user's selection information is sent to the server as input. The server receives this information and prepares a game-style training session based on the scenario. As output, data related to the selected scenario is generated.

[0202] Step 2:

[0203] The server generates a game-style training session and sends the data to the terminal. The input is the scenario selected by the user, and the server dynamically creates an interactive scenario based on this information. The output is the training session scenario displayed on the user's terminal.

[0204] Step 3:

[0205] The device uses the smartphone's camera and microphone to collect the user's biometric information, specifically facial expressions and voice data. Voice and video data of the user are collected as input. The biometric information is transmitted to the emotion recognition engine in real time.

[0206] Step 4:

[0207] The server uses an emotion recognition engine to analyze the received biometric data and evaluate the user's emotional state. User voice and video data are used as input. The evaluation result outputs the user's emotional state.

[0208] Step 5:

[0209] The server generates personalized feedback using a generative algorithm based on the evaluated emotional state and the user's in-game behavior data. Emotional evaluation data and user behavior data are used as input. The output is the feedback and advice provided to the user.

[0210] Step 6:

[0211] The server sends the generated feedback to the user's terminal, which then displays it to the user. Feedback information is sent from the server to the terminal as input. Based on this information, the user can reflect on and adjust their skills. Specific advice is displayed on the user's screen as output.

[0212] 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.

[0213] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0219] 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.

[0220] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0221] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0222] 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.

[0223] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0224] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0225] The 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.

[0226] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0227] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0228] This invention is a game-based training system for users to efficiently and effectively improve their soft skills. Implementing this system involves a combination of server, terminal, and user interaction.

[0229] First, the user accesses the system and registers an account. Once registration is complete, the server stores the user's information in a database and prepares to provide a personalized training program based on the user's needs.

[0230] Next, the server generates a relevant game-style training session based on the user's selection of a training category through their device. For example, users can choose from categories such as "Communication Skills" or "Stress Management." Based on the user's selection, the server generates content including an appropriate game scenario and objectives and sends it to the device.

[0231] When a user activates the booster on their device, it makes decisions based on various situations the user experiences as the game progresses. The device provides real-time feedback to the user as the game progresses. During this time, the data collection system acquires the user's biometric data through a biofeedback device. The device sends this data to a server to monitor the user's emotions and stress levels.

[0232] The server analyzes the user's in-game behavior data and biofeedback data, and uses evaluation tools to perform a comprehensive performance assessment. Once the assessment is complete, the server uses a generative algorithm to generate personalized feedback and advice for the user. This advice is displayed to the user via their device, providing guidance on the next training session and areas for improvement.

[0233] For example, if a user selects "teamwork skills," the system presents a game scenario that requires players to solve problems collaboratively. The user's heart rate and skin electrical activity are monitored in real time, and immediate feedback is provided based on increases or decreases in stress levels. This allows users to hone their skills in a fun and effective way.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user accesses the system using a device and logs into their account by entering their authentication information on the login screen. The server verifies the user's authentication information, and access is authorized.

[0237] Step 2:

[0238] After the user logs into their account, a list of training categories is displayed on their device. The user then selects a category that suits their needs, such as communication skills or stress management.

[0239] Step 3:

[0240] When a user selects a training category, the server creates a booster in the appropriate game format based on the selected category. The server then sends the content of the generated booster to the user's device.

[0241] Step 4:

[0242] The user starts the booster on their device. The device provides the user with the game rules, displays an interactive scenario, and waits for input.

[0243] Step 5:

[0244] As the game progresses, the user makes choices based on the presented scenario. The device provides real-time feedback to the user and records the user's choices.

[0245] Step 6:

[0246] The device uses biofeedback to collect the user's biometric data (such as heart rate and skin electrical activity) during gameplay. The collected data is then sent to a server.

[0247] Step 7:

[0248] The server analyzes user behavior data and biofeedback data. Based on the analysis results, it evaluates user performance using evaluation tools.

[0249] Step 8:

[0250] Based on the analysis results, the server uses generative algorithms to generate optimal feedback and advice for the user. This generated feedback and advice is then sent to the device.

[0251] Step 9:

[0252] The device displays evaluation results and feedback to the user, suggesting approaches and areas for improvement for the next training session. Based on the feedback, the user can continue further training.

[0253] (Example 1)

[0254] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0255] In recent years, improving soft skills has become increasingly important in individuals' professional lives, but there is a lack of appropriate training systems to achieve this efficiently and effectively. Furthermore, existing training methods struggle to provide personalized feedback tailored to the individual needs of users. Consequently, there is a growing need for training systems that allow for enjoyable learning through games and provide real-time feedback based on individual progress.

[0256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0257] In this invention, the server includes receiving means for receiving information, storage means for storing individual information, and generating means for generating a game-like training course based on that information. This makes it possible to dynamically provide training content based on the training category selected by the user and to receive individualized advice according to their progress.

[0258] A "receiving means" refers to a device or function that receives information input from a user and processes it appropriately within the system.

[0259] A "generation means" is a device or function that creates a game-style training course based on received information or stored data.

[0260] "Storage means" refers to a device or function that stores data for recording individual user information and progress, and for use in subsequent processing and evaluation.

[0261] "Means of provision" refers to devices or functions that display appropriate training content to the user based on the training category selected by the user.

[0262] "Collection means" refers to devices and functions that acquire users' biometric and behavioral data and utilize them for evaluation and feedback performed by the system.

[0263] A "tracking device" is a device or function that records a user's behavior and reactions in real time during a game or training session, and uses this information to provide feedback.

[0264] "Evaluation tools" refer to devices or functions that analyze collected user data to determine abilities and learning progress.

[0265] A "generative algorithm" is a set of computational procedures for automatically creating personalized advice and feedback based on the user's progress and needs.

[0266] This system provides game-based training to help users efficiently and effectively improve their soft skills. The main components of the system are a server, terminals, and biofeedback devices as data collection tools.

[0267] When a user accesses a device and registers an account, the server stores the user's information in a database and prepares a training program tailored to their individual needs. Based on the training category selected by the user from the device, the server uses a generative AI model to generate a training session with game scenarios and goals. Software such as Python or TensorFlow is used for this generation.

[0268] For example, if a user selects "teamwork skills," the system provides a game scenario themed around collaborative problem-solving. The user begins training on their device, making decisions in various situations while receiving real-time feedback. The device sends user behavior data to a server, which analyzes it and evaluates performance.

[0269] Biofeedback devices collect the user's biometric data, such as heart rate and skin electrical activity. This data is also sent to a server and used to monitor the user's stress level and emotional state. The server combines this data to comprehensively evaluate performance and generate feedback. This feedback is personalized to the user using a generation algorithm and displayed on the device as specific advice for improvement.

[0270] Examples of prompt statements that can be input to a generative AI model include the following:

[0271] "Create a scenario where users work as a team to solve a problem. Include specific communication tips to support situations where collaboration is lacking."

[0272] In this way, the system provides users with a training environment where they can learn while having fun, and efficiently supports the improvement of their soft skills.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The user accesses the device and registers an account. The device provides user information (name, email address, password, etc.) as input. The device sends this information to the server, which stores the received information in a database. This lays the foundation for preparing a personalized training program for the user.

[0276] Step 2:

[0277] The user selects a training category via their device. The input is a category selection, such as "Communication Skills" or "Stress Management." The device sends this selection information to the server. Based on this information, the server uses a generative AI model to set appropriate game scenarios and goals, and creates a training session. The output is the training content provided to the user.

[0278] Step 3:

[0279] The server generates training content and sends it to the device. The device receives it and displays it to the user. The user starts a session based on the presented training content. Specifically, the user makes selections and decisions according to the scenario on the screen.

[0280] Step 4:

[0281] As the user progresses through the training session, a biofeedback device as a collection means acquires biometric data such as heart rate and skin electrical activity in real time. These biometric data are transmitted from the collection means to the terminal and then to the server.

[0282] Step 5:

[0283] The server analyzes the received biometric data and the user's behavioral data during the session. Thereby, the stress level and emotion of the user are determined. The output of the analysis includes an overall evaluation of performance.

[0284] Step 6:

[0285] Based on the analysis results, the server uses a generation algorithm to generate individualized feedback for the user. This feedback includes specific improvement points for the next training. The generated feedback is transmitted to the terminal and displayed to the user in real time.

[0286] Step 7:

[0287] The user checks the feedback on the terminal and uses it as a reference for subsequent trainings. Specifically, the user examines the advice and comments displayed on the terminal screen and uses them as clues to improve their own behavior.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] In a conventional skill training system, the improvement of skills focusing on the collaborative work between the user and the machine has not been sufficiently achieved. Therefore, it lacks efficiency in machine operation and may cause serious business obstacles. Furthermore, the lack of flexibility for efficiently training specific skills is an issue.

[0291] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0292] In this invention, the server includes receiving means for receiving information, collecting means for collecting the user's biometric data, and means for improving machine control techniques and dialogue capabilities during training sessions. This enables the user to efficiently perform collaborative tasks with the machine and improve specific skills.

[0293] "Means of receiving information" refers to a device or process that acquires data or parameters provided from an external source and converts them into a format usable within the system.

[0294] A "game-based training session" is a learning activity that incorporates entertainment elements and allows users to participate in order to improve specific skills.

[0295] "Means of collecting user biometric data" refers to devices or processes for acquiring data that reflects the user's physical and emotional state.

[0296] "An evaluation method for assessing user skills" refers to a system element that measures user performance according to a set of criteria and presents it as an indicator.

[0297] "Means of providing feedback" refers to functions that offer users advice and information to help them improve their skills.

[0298] "Means for improving machine control technology and dialogue capabilities" refers to functions that enhance the skills necessary for users to work collaboratively with machines and support efficient communication.

[0299] "Means for developing the ability to efficiently carry out work in coordination with machine operation" refers to a system that develops the ability to optimize work while coordinating with multiple machines.

[0300] To implement this invention, a system is constructed in which a server, a mobile terminal, and a factory robot work together. The server receives information and, based on data provided by the user, generates game-like training sessions. This provides specialized scenarios tailored to the skill training selected by the user. For example, in communication skills training, a scenario in a virtual factory setting is executed, allowing the user to learn to work collaboratively with a robot.

[0301] The mobile device serves as an interface for users to interact with the server. Users utilize this device to register an account, select training categories, and receive feedback. Furthermore, the device collects biometric data from biofeedback devices and transmits it to the server. This data, including heart rate and stress levels, is used to understand the user's emotions and level of tension.

[0302] The server analyzes collected biometric data and user behavior data within the game, and performs a comprehensive performance evaluation using evaluation tools. Based on the evaluation results, it utilizes a generative AI model to generate individualized feedback tailored to the user's progress. This feedback helps improve the robot's operation skills and conversational abilities, and can indicate areas for improvement for the next session.

[0303] A concrete example would be a scenario in a virtual factory where a robot operator works with a robot to efficiently install parts. An example of a prompt might be, "Generate a script in which a robot operator gives instructions to a partner robot to achieve a common goal in a virtual factory scenario." In this way, users can practically learn and develop the skills necessary in various situations.

[0304] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0305] Step 1:

[0306] The user registers an account using a mobile terminal. In this step, the personal information entered by the user is received and sent to the server. The server stores the received data in a database and prepares a training program based on the user's needs.

[0307] Step 2:

[0308] The user selects a training category through the terminal. Based on this input, the server generates a game scenario suitable for specific skill training. The generated scenario is sent to the terminal and presented to the user.

[0309] Step 3:

[0310] When the user starts the game, biometric data is collected by a biofeedback device. The terminal sends this biometric data to the server in real-time. The server analyzes data such as heart rate and stress level to understand the user's current state.

[0311] Step 4:

[0312] The server uses the generated AI model to combine the user's behavioral data and biometric data for a comprehensive performance evaluation. By analyzing each data, the user's skill level and issues are clarified.

[0313] Step 5:

[0314] Based on the evaluation results, the server generates feedback for the user. The feedback includes specific advice according to the user's progress and is displayed on the terminal. Thus, the user can know what to improve in the next training.

[0315] Step 6:

[0316] The entire system utilizes accumulated feedback and user responses for retraining, improving the quality of training sessions and feedback. Based on this, the generative AI model is continuously refined to provide more sophisticated skill training.

[0317] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0318] This invention provides a system that offers game-based training to help users effectively improve their soft skills, and combines this with emotion recognition functionality. The system utilizes a server, terminals, and various sensor devices. These elements work together to enable customized training for the user.

[0319] The user first accesses the system via their device and logs in by entering their authentication information. Once the login is approved, the server retrieves the user's training history and displays the appropriate training category on the dashboard. When the user selects a category, the server generates a training session in the corresponding game format and sends it to the device.

[0320] When a training session begins, the device presents the user with an interactive scenario. At this time, the emotion engine activates, collecting and analyzing the user's facial expressions, voice tone, and biometric data (heart rate and skin electrical activity) from biofeedback devices in real time. This allows for the estimation of the user's current emotional state.

[0321] The server comprehensively evaluates the user's skills based on analyzed emotional data and the user's choices and actions within the game. It then utilizes generative algorithms to generate personalized feedback and advice tailored to the user's emotional state. These results are then provided to the user via their device.

[0322] As a concrete example, let's say a user has selected "stress management" training. In this case, a tense situation is simulated during the game, and the emotion engine estimates the user's stress level. Based on biofeedback data and facial expression analysis, the server determines how stressed the user is and provides advice on relief techniques as needed, helping them acquire skills that can be used in real-life stressful situations.

[0323] Thus, training systems incorporating an emotion engine are designed to provide a more effective learning experience by deeply understanding the user's emotions and responding in real time.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The user launches the application on their device, and the login screen appears. The user enters their authentication information and attempts to log in.

[0327] Step 2:

[0328] The server receives and verifies the user's authentication information. If authentication is successful, it generates the user's dashboard and provides it to the terminal.

[0329] Step 3:

[0330] The user selects a training category from their device. For example, they might select "Stress Management" or "Communication Skills."

[0331] Step 4:

[0332] The server dynamically generates an appropriate game-style training session based on the user's selection and sends the session content to the terminal.

[0333] Step 5:

[0334] The user starts a training session on their device. The device presents the user with the game rules and instructions, and prompts them to start the game.

[0335] Step 6:

[0336] During gameplay, the emotion engine activates, and the device collects the user's facial expression data and voice tone using the camera and microphone. It also acquires biometric information (heart rate, skin electrical activity, etc.) from a biofeedback device.

[0337] Step 7:

[0338] The server analyzes collected facial expression data, biometric information, and user behavior within the game to estimate the user's emotional state. Based on the analysis results, the user's skill level is evaluated.

[0339] Step 8:

[0340] The server uses a generative algorithm to generate feedback and advice tailored to the user's emotional state and sends it to the device.

[0341] Step 9:

[0342] The device displays evaluation results and feedback to the user, and provides advice for the next training session. Based on the feedback, the user can choose to continue training or use other features.

[0343] (Example 2)

[0344] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0345] In modern society, improving soft skills is crucial for personal growth and professional success, but there is a lack of practical and effective training methods. Furthermore, providing real-time feedback tailored to individual emotional states and reactions is difficult, making it challenging to offer appropriate support to each user.

[0346] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0347] In this invention, the server includes receiving means for receiving information, generating means for generating simulated training sessions, and collecting means for collecting user biometric information. This makes it possible to dynamically provide customized training sessions for individual users and to provide real-time feedback while considering the user's current emotional state by utilizing biometric information collected using sensor devices.

[0348] A "receiving means" is an element that has the function of acquiring information from an external source and is used by a system to receive necessary data and instructions.

[0349] A "generation means" is an element that has the function of constructing instructions and commands based on acquired information and creating sessions and content based on a specific purpose.

[0350] A "collection method" refers to an element that has the function of collecting biometric information from users, and is used by the system to collect necessary data and understand the user's condition.

[0351] An "evaluation tool" is an element that processes collected information and has the function of analyzing the user's skills and status.

[0352] "Means of delivery" refers to elements that have the function of conveying appropriate feedback and instructions to users based on the evaluated results.

[0353] "Analysis means" refers to an element that analyzes collected biometric information and has the function of evaluating the user's emotions and state in real time.

[0354] A generative algorithm is a computational method used to generate appropriate responses or feedback based on specific conditions or data.

[0355] This invention is a system for users to effectively improve their soft skills, combining game-based training sessions with emotion recognition capabilities. The system utilizes a server, terminals, and sensor devices. Each component is described in detail below.

[0356] The server receives information and generates simulated training sessions using a generative AI model. These sessions are highly customized based on the user's past training history and current skill level. The server leverages generative algorithms to understand the user's progress in real time and provide optimal feedback.

[0357] The terminal serves as the interface with the user. It presents the game session sent from the server to the user and displays various instructions and feedback. The user makes choices and interacts with the system through the terminal.

[0358] The sensor device plays a role in collecting biometric data. Specifically, it acquires data such as heart rate and skin electrical activity in real time and uses it to analyze the user's emotions and stress levels. This data is essential for the server to understand and evaluate the user's emotional state.

[0359] For example, if a user selects "stress management" training, the system simulates a stressful situation and measures the user's stress level using a sensor. The server then analyzes this data and provides appropriate stress reduction techniques as feedback. As a result, the user can improve their ability to cope with real-world stressful situations.

[0360] An example of a prompt is "Design a generative algorithm to assess the user's stress level and generate appropriate feedback." Based on such prompts, the system provides a training experience optimized for each individual user.

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] The user logs into the system via a terminal. The user enters their authentication information into the terminal as input, which the server receives. The server then performs the authentication process by referencing a database, retrieving the user profile and training history. The output displays a list of training categories the user has access to.

[0364] Step 2:

[0365] The user selects a training category of interest on their device. This selection is sent to the server as input. The server generates a simulated training session based on the selected category. By utilizing the generated AI model, a customized, game-like training session is created. The generated session data is then sent back to the device as output.

[0366] Step 3:

[0367] The terminal presents the user with a training session. The input at this stage is session data sent from the server. The terminal displays an interactive scenario on the screen and provides voice instructions via speakers or a headset. The output is the information the user needs to participate in the training.

[0368] Step 4:

[0369] The sensor device starts operating and collects the user's biometric data. The input is biometric information such as the user's heart rate and skin electrical activity. The collected data is sent to a server for analysis. The server uses an emotion engine to analyze the data in real time and estimate the user's emotional state. The output is a digital profile that constitutes the user's emotional state.

[0370] Step 5:

[0371] The server evaluates the user's skills based on the analysis results. The inputs are the analysis results obtained from the emotion engine and the user's behavioral data during training. A generative algorithm is used to evaluate the user's skill level and areas for improvement. The evaluation results are generated as output.

[0372] Step 6:

[0373] The server sends the generated evaluation results and feedback to the terminal. The input is the evaluation results obtained in step 5. As output, the user receives appropriate feedback and advice via the terminal. The user uses this to decide on their next action.

[0374] (Application Example 2)

[0375] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0376] In modern brick-and-mortar stores, while developing employees with customer service skills is crucial, traditional methods struggle to provide effective training that takes individual emotional states into account. Specifically, there is a lack of means for employees to receive real-time feedback and improve their skills while understanding their own emotional state.

[0377] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0378] In this invention, the server includes receiving means for receiving information, generating means for generating game-like training sessions, and means for providing scenarios for improving customer service skills. This enables employees to effectively improve their abilities through interactive customer service training tailored to their individual emotional states.

[0379] "Receiving means" refers to a device or mechanism for acquiring information from an external source and processing it within the system.

[0380] A "generation means" is a function or system that creates a game-style training session tailored to a specific purpose based on acquired information.

[0381] "Means of data collection" refers to devices and technologies for acquiring a user's biometric information, and this includes sensor devices, etc.

[0382] "Evaluation means" refers to a process or mechanism for analyzing and evaluating a user's abilities and status based on collected data.

[0383] "Means of provision" refers to a system or function for providing information and feedback to users based on evaluation results.

[0384] A "scenario" refers to a training scene or situational setting designed to help a user improve a specific skill.

[0385] "Emotion recognition means" refers to technology or devices that analyze a user's voice and facial expressions to determine their emotional state.

[0386] A "generation algorithm" is a computational method or program that automatically generates appropriate advice and content according to the user's progress.

[0387] This invention is a system aimed at improving the customer service skills of employees in physical stores. It consists primarily of a server, terminals, and various sensor devices. Specific embodiments are described below.

[0388] First, the server receives data regarding customer service training requests from the user's terminal in order to receive information. A smartphone is used as the terminal, and the user selects a specific scenario and logs in via this smartphone. After receiving the information, the server uses a generation mechanism to create a game-style training session and creates a training session based on the scenario selected by the user.

[0389] Next, the smartphone's camera and microphone are used as data collection tools to gather the user's biometric information, specifically voice and facial expression data. This data is analyzed in real time, and the user's emotional state is evaluated through emotion recognition tools. External emotion recognition software such as Microsoft Azure Face API and Google Cloud Speech-to-Text is used for the evaluation.

[0390] As an evaluation method, emotional data and user behavior data within the game are analyzed on the server. Based on the results of this analysis, a generation algorithm is used to provide personalized feedback tailored to the user's progress. This allows users to receive real-time advice necessary to improve their customer service skills.

[0391] For example, if a user selects the "product description" scenario, the device simulates this situation, and the server determines the user's level of nervousness and confidence. Based on the data analyzed by the emotion recognition engine, the server provides timely advice on how to approach the situation, supporting the user's growth.

[0392] An example of a prompt message could be, "Analyze the user's facial expressions and tone of voice in real time, and generate feedback based on the customer service scenario." By utilizing such prompts, the system can provide more accurate feedback and adjust its training content.

[0393] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0394] Step 1:

[0395] The user logs in to their smartphone and selects a specific customer service scenario. The user's selection information is sent to the server as input. The server receives this information and prepares a game-style training session based on the scenario content. As output, data related to the selected scenario is generated.

[0396] Step 2:

[0397] The server generates a game-style training session and sends the data to the terminal. The input is the scenario selected by the user, and the server dynamically creates an interactive scenario based on this information. The output is the training session scenario displayed on the user's terminal.

[0398] Step 3:

[0399] The device uses the smartphone's camera and microphone to collect the user's biometric information, specifically facial expressions and voice data. Voice and video data of the user are collected as input. The biometric information is transmitted to the emotion recognition engine in real time.

[0400] Step 4:

[0401] The server uses an emotion recognition engine to analyze the received biometric information and evaluate the user's emotional state. User voice and video data are used as input. The evaluation result outputs the user's emotional state.

[0402] Step 5:

[0403] The server generates personalized feedback using a generative algorithm based on the evaluated emotional state and the user's in-game behavior data. Emotional evaluation data and user behavior data are used as input. The output is the feedback and advice provided to the user.

[0404] Step 6:

[0405] The server sends the generated feedback to the user's terminal, which then displays it to the user. Feedback information is sent from the server to the terminal as input. Based on this information, the user can reflect on and adjust their skills. Specific advice is displayed on the user's screen as output.

[0406] 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.

[0407] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0408] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] 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.

[0412] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0413] 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.

[0414] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0415] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0416] 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.

[0417] 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.

[0418] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0419] The 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.

[0420] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0421] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0422] This invention is a game-based training system for users to efficiently and effectively improve their soft skills. Implementing this system involves a combination of server, terminal, and user interaction.

[0423] First, the user accesses the system and registers an account. Once registration is complete, the server stores the user's information in a database and prepares to provide a personalized training program based on the user's needs.

[0424] Next, the server generates a relevant game-style training session based on the user's selection of a training category through their device. For example, users can choose from categories such as "Communication Skills" or "Stress Management." Based on the user's selection, the server generates content including an appropriate game scenario and objectives and sends it to the device.

[0425] When a user activates the booster on their device, it makes decisions based on various situations the user experiences as the game progresses. The device provides real-time feedback to the user as the game progresses. During this time, the data collection system acquires the user's biometric data through a biofeedback device. The device sends this data to a server to monitor the user's emotions and stress levels.

[0426] The server analyzes the user's in-game behavior data and biofeedback data, and uses evaluation tools to perform a comprehensive performance assessment. Once the assessment is complete, the server uses a generative algorithm to generate personalized feedback and advice for the user. This advice is displayed to the user via their device, providing guidance on the next training session and areas for improvement.

[0427] For example, if a user selects "teamwork skills," the system presents a game scenario that requires players to solve problems collaboratively. The user's heart rate and skin electrical activity are monitored in real time, and immediate feedback is provided based on increases or decreases in stress levels. This allows users to hone their skills in a fun and effective way.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The user accesses the system using a device and logs into their account by entering their authentication information on the login screen. The server verifies the user's authentication information, and access is authorized.

[0431] Step 2:

[0432] After the user logs into their account, a list of training categories is displayed on their device. The user then selects a category that suits their needs, such as communication skills or stress management.

[0433] Step 3:

[0434] When a user selects a training category, the server creates a booster in the appropriate game format based on the selected category. The server then sends the content of the generated booster to the user's device.

[0435] Step 4:

[0436] The user starts the booster on their device. The device provides the user with the game rules, displays an interactive scenario, and waits for input.

[0437] Step 5:

[0438] As the game progresses, the user makes choices based on the presented scenario. The device provides real-time feedback to the user and records the user's choices.

[0439] Step 6:

[0440] The device uses biofeedback to collect the user's biometric data (such as heart rate and skin electrical activity) during gameplay. The collected data is then sent to a server.

[0441] Step 7:

[0442] The server analyzes user behavior data and biofeedback data. Based on the analysis results, it evaluates user performance using evaluation tools.

[0443] Step 8:

[0444] Based on the analysis results, the server uses generative algorithms to generate optimal feedback and advice for the user. This generated feedback and advice is then sent to the device.

[0445] Step 9:

[0446] The device displays evaluation results and feedback to the user, suggesting approaches and areas for improvement for the next training session. Based on the feedback, the user can continue further training.

[0447] (Example 1)

[0448] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0449] In recent years, improving soft skills has become increasingly important in individuals' professional lives, but there is a lack of appropriate training systems to achieve this efficiently and effectively. Furthermore, existing training methods struggle to provide personalized feedback tailored to the individual needs of users. Consequently, there is a growing need for training systems that allow for enjoyable learning through games and provide real-time feedback based on individual progress.

[0450] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0451] In this invention, the server includes receiving means for receiving information, storage means for storing individual information, and generating means for generating a game-like training course based on that information. This makes it possible to dynamically provide training content based on the training category selected by the user and to receive individualized advice according to their progress.

[0452] A "receiving means" refers to a device or function that receives information input from a user and processes it appropriately within the system.

[0453] A "generation means" is a device or function that creates a game-style training course based on received information or stored data.

[0454] "Storage means" refers to a device or function that stores data for recording individual user information and progress, and for use in subsequent processing and evaluation.

[0455] "Means of provision" refers to devices or functions that display appropriate training content to the user based on the training category selected by the user.

[0456] "Collection means" refers to devices and functions that acquire users' biometric and behavioral data and utilize them for evaluation and feedback performed by the system.

[0457] A "tracking device" is a device or function that records a user's behavior and reactions in real time during a game or training session, and uses this information to provide feedback.

[0458] "Evaluation tools" refer to devices or functions that analyze collected user data to determine abilities and learning progress.

[0459] A "generative algorithm" is a set of computational procedures for automatically creating personalized advice and feedback based on the user's progress and needs.

[0460] This system provides game-based training to help users efficiently and effectively improve their soft skills. The main components of the system are a server, terminals, and biofeedback devices as data collection tools.

[0461] When a user accesses a device and registers an account, the server stores the user's information in a database and prepares a training program tailored to their individual needs. Based on the training category selected by the user from the device, the server uses a generative AI model to generate a training session with game scenarios and goals. Software such as Python or TensorFlow is used for this generation.

[0462] For example, if a user selects "teamwork skills," the system provides a game scenario themed around collaborative problem-solving. The user begins training on their device, making decisions in various situations while receiving real-time feedback. The device sends user behavior data to a server, which analyzes it and evaluates performance.

[0463] Biofeedback devices collect the user's biometric data, such as heart rate and skin electrical activity. This data is also sent to a server and used to monitor the user's stress level and emotional state. The server combines this data to comprehensively evaluate performance and generate feedback. This feedback is personalized to the user using a generation algorithm and displayed on the device as specific advice for improvement.

[0464] Examples of prompt statements that can be input to a generative AI model include the following:

[0465] "Create a scenario where users work as a team to solve a problem. Include specific communication tips to support situations where collaboration is lacking."

[0466] In this way, the system provides users with a training environment where they can learn while having fun, and efficiently supports the improvement of their soft skills.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] The user accesses the device and registers an account. The device provides user information (name, email address, password, etc.) as input. The device sends this information to the server, which stores the received information in a database. This lays the foundation for preparing a personalized training program for the user.

[0470] Step 2:

[0471] The user selects a training category via their device. The input is a category selection, such as "Communication Skills" or "Stress Management." The device sends this selection information to the server. Based on this information, the server uses a generative AI model to set appropriate game scenarios and goals, and creates a training session. The output is the training content provided to the user.

[0472] Step 3:

[0473] The server generates training content and sends it to the device. The device receives it and displays it to the user. The user starts a session based on the presented training content. Specifically, the user makes selections and decisions according to the scenario on the screen.

[0474] Step 4:

[0475] As the user progresses through a training session, a biofeedback device collects biometric data such as heart rate and skin electrical activity in real time. This biometric data is then transmitted from the collection device to the terminal and then to a server.

[0476] Step 5:

[0477] The server analyzes the received biometric data and the user's behavioral data during the session. This allows for the determination of the user's stress level and emotions. The analysis output includes an overall performance evaluation.

[0478] Step 6:

[0479] Based on the analysis results, the server uses a generation algorithm to generate personalized feedback for the user. This feedback includes specific areas for improvement for the next training session. The generated feedback is sent to the terminal and displayed to the user in real time.

[0480] Step 7:

[0481] Users review feedback on their devices and use it as a reference for future training sessions. Specifically, they examine the advice and comments displayed on the device screen and use them as clues to improve their own behavior.

[0482] (Application Example 1)

[0483] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0484] Conventional skill training systems fail to adequately improve skills related to collaborative work between users and machines. This can lead to inefficiencies in machine operation and potentially cause significant operational disruptions. Furthermore, a lack of flexibility in efficiently training specific skills is a significant challenge.

[0485] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0486] In this invention, the server includes receiving means for receiving information, collecting means for collecting the user's biometric data, and means for improving machine control techniques and dialogue capabilities during training sessions. This enables the user to efficiently perform collaborative tasks with the machine and improve specific skills.

[0487] "Means of receiving information" refers to a device or process that acquires data or parameters provided from an external source and converts them into a format usable within the system.

[0488] A "game-based training session" is a learning activity that incorporates entertainment elements and allows users to participate in order to improve specific skills.

[0489] "Means of collecting user biometric data" refers to devices or processes for acquiring data that reflects the user's physical and emotional state.

[0490] "An evaluation method for assessing user skills" refers to a system element that measures user performance according to a set of criteria and presents it as an indicator.

[0491] "Means of providing feedback" refers to functions that offer users advice and information to help them improve their skills.

[0492] "Means for improving machine control technology and communication capabilities" refers to functions that enhance the skills necessary for users to work collaboratively with machines and support efficient communication.

[0493] "Means for developing the ability to efficiently carry out work in coordination with machine operation" refers to a system that develops the ability to optimize work while coordinating with multiple machines.

[0494] To implement this invention, a system is constructed in which a server, a mobile terminal, and a factory robot work together. The server receives information and, based on data provided by the user, generates game-like training sessions. This provides specialized scenarios tailored to the skill training selected by the user. For example, in communication skills training, a scenario in a virtual factory setting is executed, allowing the user to learn to work collaboratively with a robot.

[0495] The mobile device serves as an interface for users to interact with the server. Users utilize this device to register an account, select training categories, and receive feedback. Furthermore, the device collects biometric data from biofeedback devices and transmits it to the server. This data, including heart rate and stress levels, is used to understand the user's emotions and level of tension.

[0496] The server analyzes collected biometric data and user behavior data within the game, and performs a comprehensive performance evaluation using evaluation tools. Based on the evaluation results, it utilizes a generative AI model to generate individualized feedback tailored to the user's progress. This feedback helps improve the robot's operation skills and conversational abilities, and can indicate areas for improvement for the next session.

[0497] A concrete example would be a scenario in a virtual factory where a robot operator works with a robot to efficiently install parts. An example of a prompt might be, "Generate a script in which a robot operator gives instructions to a partner robot to achieve a common goal in a virtual factory scenario." In this way, users can practically learn and develop the skills necessary in various situations.

[0498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0499] Step 1:

[0500] Users register an account using their mobile device. In this step, the personal information entered by the user is received and sent to the server. The server stores the received data in a database and prepares a training program based on the user's needs.

[0501] Step 2:

[0502] The user selects a training category through their device. Based on this input, the server generates a game scenario suitable for specific skill training. The generated scenario is sent to the device and presented to the user.

[0503] Step 3:

[0504] When a user starts a game, biofeedback devices collect biometric data. The device transmits this biometric data to a server in real time. The server analyzes data such as heart rate and stress level to understand the user's current state.

[0505] Step 4:

[0506] The server uses a generative AI model to combine user behavioral data and biometric data to perform a comprehensive performance evaluation. By analyzing each piece of data, it identifies the user's skill level and areas for improvement.

[0507] Step 5:

[0508] Based on the evaluation results, the server generates feedback for the user. This feedback includes specific advice tailored to the user's progress and is displayed on the terminal. This allows the user to understand areas for improvement in the next training session.

[0509] Step 6:

[0510] The entire system utilizes accumulated feedback and user responses for retraining, improving the quality of training sessions and feedback. Based on this, the generative AI model is continuously refined to provide more sophisticated skill training.

[0511] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0512] This invention provides a system that offers game-based training to help users effectively improve their soft skills, and combines this with emotion recognition functionality. The system utilizes a server, terminals, and various sensor devices. These elements work together to enable customized training for the user.

[0513] The user first accesses the system via their device and logs in by entering their authentication information. Once the login is approved, the server retrieves the user's training history and displays the appropriate training category on the dashboard. When the user selects a category, the server generates a training session in the corresponding game format and sends it to the device.

[0514] When a training session begins, the device presents the user with an interactive scenario. At this time, the emotion engine activates, collecting and analyzing the user's facial expressions, voice tone, and biometric data (heart rate and skin electrical activity) from biofeedback devices in real time. This allows for the estimation of the user's current emotional state.

[0515] The server comprehensively evaluates the user's skills based on analyzed emotional data and the user's choices and actions within the game. It then utilizes generative algorithms to generate personalized feedback and advice tailored to the user's emotional state. These results are then provided to the user via their device.

[0516] As a concrete example, let's say a user has selected "stress management" training. In this case, a tense situation is simulated during the game, and the emotion engine estimates the user's stress level. Based on biofeedback data and facial expression analysis, the server determines how stressed the user is and provides advice on relief techniques as needed, helping them acquire skills that can be used in real-life stressful situations.

[0517] Thus, training systems incorporating an emotion engine are designed to provide a more effective learning experience by deeply understanding the user's emotions and responding in real time.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The user launches the application on their device, and the login screen appears. The user enters their authentication information and attempts to log in.

[0521] Step 2:

[0522] The server receives and verifies the user's authentication information. If authentication is successful, it generates the user's dashboard and provides it to the terminal.

[0523] Step 3:

[0524] The user selects a training category from their device. For example, they might select "Stress Management" or "Communication Skills."

[0525] Step 4:

[0526] The server dynamically generates an appropriate game-style training session based on the user's selection and sends the session content to the terminal.

[0527] Step 5:

[0528] The user starts a training session on their device. The device presents the user with the game rules and instructions, and prompts them to start the game.

[0529] Step 6:

[0530] During gameplay, the emotion engine activates, and the device collects the user's facial expression data and voice tone using the camera and microphone. It also acquires biometric information (heart rate, skin electrical activity, etc.) from a biofeedback device.

[0531] Step 7:

[0532] The server analyzes collected facial expression data, biometric information, and user behavior within the game to estimate the user's emotional state. Based on the analysis results, the user's skill level is evaluated.

[0533] Step 8:

[0534] The server uses a generative algorithm to generate feedback and advice tailored to the user's emotional state and sends it to the device.

[0535] Step 9:

[0536] The device displays evaluation results and feedback to the user, and provides advice for the next training session. Based on the feedback, the user can choose to continue training or use other features.

[0537] (Example 2)

[0538] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0539] In modern society, improving soft skills is crucial for personal growth and professional success, but there is a lack of practical and effective training methods. Furthermore, providing real-time feedback tailored to individual emotional states and reactions is difficult, making it challenging to offer appropriate support to each user.

[0540] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0541] In this invention, the server includes receiving means for receiving information, generating means for generating simulated training sessions, and collecting means for collecting user biometric information. This makes it possible to dynamically provide customized training sessions for individual users and to provide real-time feedback while considering the user's current emotional state by utilizing biometric information collected using sensor devices.

[0542] A "receiving means" is an element that has the function of acquiring information from an external source and is used by a system to receive necessary data and instructions.

[0543] A "generation means" is an element that has the function of constructing instructions and commands based on acquired information and creating sessions and content based on a specific purpose.

[0544] A "collection method" refers to an element that has the function of collecting biometric information from users, and is used by the system to collect necessary data and understand the user's condition.

[0545] An "evaluation tool" is an element that processes collected information and has the function of analyzing the user's skills and status.

[0546] "Means of delivery" refers to elements that have the function of conveying appropriate feedback and instructions to users based on the evaluated results.

[0547] "Analysis means" refers to an element that analyzes collected biometric information and has the function of evaluating the user's emotions and state in real time.

[0548] A generative algorithm is a computational method used to generate appropriate responses or feedback based on specific conditions or data.

[0549] This invention is a system for users to effectively improve their soft skills, combining game-based training sessions with emotion recognition capabilities. The system utilizes a server, terminals, and sensor devices. Each component is described in detail below.

[0550] The server receives information and generates simulated training sessions using a generative AI model. These sessions are highly customized based on the user's past training history and current skill level. The server leverages generative algorithms to understand the user's progress in real time and provide optimal feedback.

[0551] The terminal serves as the interface with the user. It presents the game session sent from the server to the user and displays various instructions and feedback. The user makes choices and interacts with the system through the terminal.

[0552] The sensor device plays a role in collecting biometric data. Specifically, it acquires data such as heart rate and skin electrical activity in real time and uses it to analyze the user's emotions and stress levels. This data is essential for the server to understand and evaluate the user's emotional state.

[0553] For example, if a user selects "stress management" training, the system simulates a stressful situation and measures the user's stress level using a sensor. The server then analyzes this data and provides appropriate stress reduction techniques as feedback. As a result, the user can improve their ability to cope with real-world stressful situations.

[0554] An example of a prompt is "Design a generative algorithm to assess the user's stress level and generate appropriate feedback." Based on such prompts, the system provides a training experience optimized for each individual user.

[0555] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0556] Step 1:

[0557] The user logs into the system via a terminal. The user enters their authentication information into the terminal as input, which the server receives. The server then performs the authentication process by referencing a database, retrieving the user profile and training history. The output displays a list of training categories the user has access to.

[0558] Step 2:

[0559] The user selects a training category of interest on their device. This selection is sent to the server as input. The server generates a simulated training session based on the selected category. By utilizing the generated AI model, a customized, game-like training session is created. The generated session data is then sent back to the device as output.

[0560] Step 3:

[0561] The terminal presents the user with a training session. The input at this stage is session data sent from the server. The terminal displays an interactive scenario on the screen and provides voice instructions via speakers or a headset. The output is the information the user needs to participate in the training.

[0562] Step 4:

[0563] The sensor device starts operating and collects the user's biometric data. The input is biometric information such as the user's heart rate and skin electrical activity. The collected data is sent to a server for analysis. The server uses an emotion engine to analyze the data in real time and estimate the user's emotional state. The output is a digital profile that constitutes the user's emotional state.

[0564] Step 5:

[0565] The server evaluates the user's skills based on the analysis results. The inputs are the analysis results obtained from the emotion engine and the user's behavioral data during training. A generative algorithm is used to evaluate the user's skill level and areas for improvement. The evaluation results are generated as output.

[0566] Step 6:

[0567] The server sends the generated evaluation results and feedback to the terminal. The input is the evaluation results obtained in step 5. As output, the user receives appropriate feedback and advice via the terminal. The user uses this to decide on their next action.

[0568] (Application Example 2)

[0569] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0570] In modern brick-and-mortar stores, while developing employees with customer service skills is crucial, traditional methods struggle to provide effective training that takes individual emotional states into account. Specifically, there is a lack of means for employees to receive real-time feedback and improve their skills while understanding their own emotional state.

[0571] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0572] In this invention, the server includes receiving means for receiving information, generating means for generating game-like training sessions, and means for providing scenarios for improving customer service skills. This enables employees to effectively improve their abilities through interactive customer service training tailored to their individual emotional states.

[0573] "Receiving means" refers to a device or mechanism for acquiring information from an external source and processing it within the system.

[0574] A "generation means" is a function or system that creates a game-style training session tailored to a specific purpose based on acquired information.

[0575] "Means of data collection" refers to devices and technologies for acquiring a user's biometric information, and this includes sensor devices, etc.

[0576] "Evaluation means" refers to a process or mechanism for analyzing and evaluating a user's abilities and status based on collected data.

[0577] "Means of provision" refers to a system or function for providing information and feedback to users based on evaluation results.

[0578] A "scenario" refers to a training scene or situational setting designed to help a user improve a specific skill.

[0579] "Emotion recognition means" refers to technology or devices that analyze a user's voice and facial expressions to determine their emotional state.

[0580] A "generation algorithm" is a computational method or program that automatically generates appropriate advice and content according to the user's progress.

[0581] This invention is a system aimed at improving the customer service skills of employees in physical stores. It consists primarily of a server, terminals, and various sensor devices. Specific embodiments are described below.

[0582] First, the server receives data regarding customer service training requests from the user's terminal in order to receive information. A smartphone is used as the terminal, and the user selects a specific scenario and logs in via this smartphone. After receiving the information, the server uses a generation mechanism to create a game-style training session and creates a training session based on the scenario selected by the user.

[0583] Next, the smartphone's camera and microphone are used as data collection tools to gather the user's biometric information, specifically voice and facial expression data. This data is analyzed in real time, and the user's emotional state is evaluated through emotion recognition tools. External emotion recognition software such as Microsoft Azure Face API and Google Cloud Speech-to-Text is used for the evaluation.

[0584] As an evaluation method, emotional data and user behavior data within the game are analyzed on the server. Based on the results of this analysis, a generation algorithm is used to provide personalized feedback tailored to the user's progress. This allows users to receive real-time advice necessary to improve their customer service skills.

[0585] For example, if a user selects the "product description" scenario, the device simulates this situation, and the server determines the user's level of nervousness and confidence. Based on the data analyzed by the emotion recognition engine, the server provides timely advice on how to approach the situation, supporting the user's growth.

[0586] An example of a prompt message could be, "Analyze the user's facial expressions and tone of voice in real time, and generate feedback based on the customer service scenario." By utilizing such prompts, the system can provide more accurate feedback and adjust its training content.

[0587] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0588] Step 1:

[0589] The user logs in to their smartphone and selects a specific customer service scenario. The user's selection information is sent to the server as input. The server receives this information and prepares a game-style training session based on the scenario content. As output, data related to the selected scenario is generated.

[0590] Step 2:

[0591] The server generates a game-style training session and sends the data to the terminal. The input is the scenario selected by the user, and the server dynamically creates an interactive scenario based on this information. The output is the training session scenario displayed on the user's terminal.

[0592] Step 3:

[0593] The device uses the smartphone's camera and microphone to collect the user's biometric information, specifically facial expressions and voice data. Voice and video data of the user are collected as input. The biometric information is transmitted to the emotion recognition engine in real time.

[0594] Step 4:

[0595] The server uses an emotion recognition engine to analyze the received biometric information and evaluate the user's emotional state. User voice and video data are used as input. The evaluation result outputs the user's emotional state.

[0596] Step 5:

[0597] The server generates personalized feedback using a generative algorithm based on the evaluated emotional state and the user's in-game behavior data. Emotional evaluation data and user behavior data are used as input. The output is the feedback and advice provided to the user.

[0598] Step 6:

[0599] The server sends the generated feedback to the user's terminal, which then displays it to the user. Feedback information is sent from the server to the terminal as input. Based on this information, the user can reflect on and adjust their skills. Specific advice is displayed on the user's screen as output.

[0600] 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.

[0601] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0602] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0603] [Fourth Embodiment]

[0604] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0605] 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.

[0606] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0607] 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.

[0608] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0609] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0610] 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.

[0611] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0612] 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.

[0613] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0614] The 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.

[0615] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0616] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0617] This invention is a game-based training system for users to efficiently and effectively improve their soft skills. Implementing this system involves a combination of server, terminal, and user interaction.

[0618] First, the user accesses the system and registers an account. Once registration is complete, the server stores the user's information in a database and prepares to provide a personalized training program based on the user's needs.

[0619] Next, the server generates a relevant game-style training session based on the user's selection of a training category through their device. For example, users can choose from categories such as "Communication Skills" or "Stress Management." Based on the user's selection, the server generates content including an appropriate game scenario and objectives and sends it to the device.

[0620] When a user activates the booster on their device, it makes decisions based on various situations the user experiences as the game progresses. The device provides real-time feedback to the user as the game progresses. During this time, the data collection system acquires the user's biometric data through a biofeedback device. The device sends this data to a server to monitor the user's emotions and stress levels.

[0621] The server analyzes the user's in-game behavior data and biofeedback data, and uses evaluation tools to perform a comprehensive performance assessment. Once the assessment is complete, the server uses a generative algorithm to generate personalized feedback and advice for the user. This advice is displayed to the user via their device, providing guidance on the next training session and areas for improvement.

[0622] For example, if a user selects "teamwork skills," the system presents a game scenario that requires players to solve problems collaboratively. The user's heart rate and skin electrical activity are monitored in real time, and immediate feedback is provided based on increases or decreases in stress levels. This allows users to hone their skills in a fun and effective way.

[0623] The following describes the processing flow.

[0624] Step 1:

[0625] The user accesses the system using a device and logs into their account by entering their authentication information on the login screen. The server verifies the user's authentication information, and access is authorized.

[0626] Step 2:

[0627] After the user logs into their account, a list of training categories is displayed on their device. The user then selects a category that suits their needs, such as communication skills or stress management.

[0628] Step 3:

[0629] When a user selects a training category, the server creates a booster in the appropriate game format based on the selected category. The server then sends the content of the generated booster to the user's device.

[0630] Step 4:

[0631] The user starts the booster on their device. The device provides the user with the game rules, displays an interactive scenario, and waits for input.

[0632] Step 5:

[0633] As the game progresses, the user makes choices based on the presented scenario. The device provides real-time feedback to the user and records the user's choices.

[0634] Step 6:

[0635] The device uses biofeedback to collect the user's biometric data (such as heart rate and skin electrical activity) during gameplay. The collected data is then sent to a server.

[0636] Step 7:

[0637] The server analyzes user behavior data and biofeedback data. Based on the analysis results, it evaluates user performance using evaluation tools.

[0638] Step 8:

[0639] Based on the analysis results, the server uses generative algorithms to generate optimal feedback and advice for the user. This generated feedback and advice is then sent to the device.

[0640] Step 9:

[0641] The device displays evaluation results and feedback to the user, suggesting approaches and areas for improvement for the next training session. Based on the feedback, the user can continue further training.

[0642] (Example 1)

[0643] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0644] In recent years, improving soft skills has become increasingly important in individuals' professional lives, but there is a lack of appropriate training systems to achieve this efficiently and effectively. Furthermore, existing training methods struggle to provide personalized feedback tailored to the individual needs of users. Consequently, there is a growing need for training systems that allow for enjoyable learning through games and provide real-time feedback based on individual progress.

[0645] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0646] In this invention, the server includes receiving means for receiving information, storage means for storing individual information, and generating means for generating a game-like training course based on that information. This makes it possible to dynamically provide training content based on the training category selected by the user and to receive individualized advice according to their progress.

[0647] A "receiving means" refers to a device or function that receives information input from a user and processes it appropriately within the system.

[0648] A "generation means" is a device or function that creates a game-style training course based on received information or stored data.

[0649] "Storage means" refers to a device or function that stores data for recording individual user information and progress, and for use in subsequent processing and evaluation.

[0650] "Means of provision" refers to devices or functions that display appropriate training content to the user based on the training category selected by the user.

[0651] "Collection means" refers to devices and functions that acquire users' biometric and behavioral data and utilize them for evaluation and feedback performed by the system.

[0652] A "tracking device" is a device or function that records a user's behavior and reactions in real time during a game or training session, and uses this information to provide feedback.

[0653] "Evaluation tools" refer to devices or functions that analyze collected user data to determine abilities and learning progress.

[0654] A "generative algorithm" is a set of computational procedures for automatically creating personalized advice and feedback based on the user's progress and needs.

[0655] This system provides game-based training to help users efficiently and effectively improve their soft skills. The main components of the system are a server, terminals, and biofeedback devices as data collection tools.

[0656] When a user accesses a device and registers an account, the server stores the user's information in a database and prepares a training program tailored to their individual needs. Based on the training category selected by the user from the device, the server uses a generative AI model to generate a training session with game scenarios and goals. Software such as Python or TensorFlow is used for this generation.

[0657] For example, if a user selects "teamwork skills," the system provides a game scenario themed around collaborative problem-solving. The user begins training on their device, making decisions in various situations while receiving real-time feedback. The device sends user behavior data to a server, which analyzes it and evaluates performance.

[0658] Biofeedback devices collect the user's biometric data, such as heart rate and skin electrical activity. This data is also sent to a server and used to monitor the user's stress level and emotional state. The server combines this data to comprehensively evaluate performance and generate feedback. This feedback is personalized to the user using a generation algorithm and displayed on the device as specific advice for improvement.

[0659] Examples of prompt statements that can be input to a generative AI model include the following:

[0660] "Create a scenario where users work as a team to solve a problem. Include specific communication tips to support situations where collaboration is lacking."

[0661] In this way, the system provides users with a training environment where they can learn while having fun, and efficiently supports the improvement of their soft skills.

[0662] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0663] Step 1:

[0664] The user accesses the device and registers an account. The device provides user information (name, email address, password, etc.) as input. The device sends this information to the server, which stores the received information in a database. This lays the foundation for preparing a personalized training program for the user.

[0665] Step 2:

[0666] The user selects a training category via their device. The input is a category selection, such as "Communication Skills" or "Stress Management." The device sends this selection information to the server. Based on this information, the server uses a generative AI model to set appropriate game scenarios and goals, and creates a training session. The output is the training content provided to the user.

[0667] Step 3:

[0668] The server generates training content and sends it to the device. The device receives it and displays it to the user. The user starts a session based on the presented training content. Specifically, the user makes selections and decisions according to the scenario on the screen.

[0669] Step 4:

[0670] As the user progresses through a training session, a biofeedback device collects biometric data such as heart rate and skin electrical activity in real time. This biometric data is then transmitted from the collection device to the terminal and then to a server.

[0671] Step 5:

[0672] The server analyzes the received biometric data and the user's behavioral data during the session. This allows for the determination of the user's stress level and emotions. The analysis output includes an overall performance evaluation.

[0673] Step 6:

[0674] Based on the analysis results, the server uses a generation algorithm to generate personalized feedback for the user. This feedback includes specific areas for improvement for the next training session. The generated feedback is sent to the terminal and displayed to the user in real time.

[0675] Step 7:

[0676] Users review feedback on their devices and use it as a reference for future training sessions. Specifically, they examine the advice and comments displayed on the device screen and use them as clues to improve their own behavior.

[0677] (Application Example 1)

[0678] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0679] Conventional skill training systems fail to adequately improve skills related to collaborative work between users and machines. This can lead to inefficiencies in machine operation and potentially cause significant operational disruptions. Furthermore, a lack of flexibility in efficiently training specific skills is a significant challenge.

[0680] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0681] In this invention, the server includes receiving means for receiving information, collecting means for collecting the user's biometric data, and means for improving machine control techniques and dialogue capabilities during training sessions. This enables the user to efficiently perform collaborative tasks with the machine and improve specific skills.

[0682] "Means of receiving information" refers to a device or process that acquires data or parameters provided from an external source and converts them into a format usable within the system.

[0683] A "game-based training session" is a learning activity that incorporates entertainment elements and allows users to participate in order to improve specific skills.

[0684] "Means of collecting user biometric data" refers to devices or processes for acquiring data that reflects the user's physical and emotional state.

[0685] "An evaluation method for assessing user skills" refers to a system element that measures user performance according to a set of criteria and presents it as an indicator.

[0686] "Means of providing feedback" refers to functions that offer users advice and information to help them improve their skills.

[0687] "Means for improving machine control technology and communication capabilities" refers to functions that enhance the skills necessary for users to work collaboratively with machines and support efficient communication.

[0688] "Means for developing the ability to efficiently carry out work in coordination with machine operation" refers to a system that develops the ability to optimize work while coordinating with multiple machines.

[0689] To implement this invention, a system is constructed in which a server, a mobile terminal, and a factory robot work together. The server receives information and, based on data provided by the user, generates game-like training sessions. This provides specialized scenarios tailored to the skill training selected by the user. For example, in communication skills training, a scenario in a virtual factory setting is executed, allowing the user to learn to work collaboratively with a robot.

[0690] The mobile device serves as an interface for users to interact with the server. Users utilize this device to register an account, select training categories, and receive feedback. Furthermore, the device collects biometric data from biofeedback devices and transmits it to the server. This data, including heart rate and stress levels, is used to understand the user's emotions and level of tension.

[0691] The server analyzes collected biometric data and user behavior data within the game, and performs a comprehensive performance evaluation using evaluation tools. Based on the evaluation results, it utilizes a generative AI model to generate individualized feedback tailored to the user's progress. This feedback helps improve the robot's operation skills and conversational abilities, and can indicate areas for improvement for the next session.

[0692] A concrete example would be a scenario in a virtual factory where a robot operator works with a robot to efficiently install parts. An example of a prompt might be, "Generate a script in which a robot operator gives instructions to a partner robot to achieve a common goal in a virtual factory scenario." In this way, users can practically learn and develop the skills necessary in various situations.

[0693] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0694] Step 1:

[0695] Users register an account using their mobile device. In this step, the personal information entered by the user is received and sent to the server. The server stores the received data in a database and prepares a training program based on the user's needs.

[0696] Step 2:

[0697] The user selects a training category through their device. Based on this input, the server generates a game scenario suitable for specific skill training. The generated scenario is sent to the device and presented to the user.

[0698] Step 3:

[0699] When a user starts a game, biofeedback devices collect biometric data. The device transmits this biometric data to a server in real time. The server analyzes data such as heart rate and stress level to understand the user's current state.

[0700] Step 4:

[0701] The server uses a generative AI model to combine user behavioral data and biometric data to perform a comprehensive performance evaluation. By analyzing each piece of data, it identifies the user's skill level and areas for improvement.

[0702] Step 5:

[0703] Based on the evaluation results, the server generates feedback for the user. This feedback includes specific advice tailored to the user's progress and is displayed on the terminal. This allows the user to understand areas for improvement in the next training session.

[0704] Step 6:

[0705] The entire system utilizes accumulated feedback and user responses for retraining, improving the quality of training sessions and feedback. Based on this, the generative AI model is continuously refined to provide more sophisticated skill training.

[0706] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0707] This invention provides a system that offers game-based training to help users effectively improve their soft skills, and combines this with emotion recognition functionality. The system utilizes a server, terminals, and various sensor devices. These elements work together to enable customized training for the user.

[0708] The user first accesses the system via their device and logs in by entering their authentication information. Once the login is approved, the server retrieves the user's training history and displays the appropriate training category on the dashboard. When the user selects a category, the server generates a training session in the corresponding game format and sends it to the device.

[0709] When a training session begins, the device presents the user with an interactive scenario. At this time, the emotion engine activates, collecting and analyzing the user's facial expressions, voice tone, and biometric data (heart rate and skin electrical activity) from biofeedback devices in real time. This allows for the estimation of the user's current emotional state.

[0710] The server comprehensively evaluates the user's skills based on analyzed emotional data and the user's choices and actions within the game. It then utilizes generative algorithms to generate personalized feedback and advice tailored to the user's emotional state. These results are then provided to the user via their device.

[0711] As a concrete example, let's say a user has selected "stress management" training. In this case, a tense situation is simulated during the game, and the emotion engine estimates the user's stress level. Based on biofeedback data and facial expression analysis, the server determines how stressed the user is and provides advice on relief techniques as needed, helping them acquire skills that can be used in real-life stressful situations.

[0712] Thus, training systems incorporating an emotion engine are designed to provide a more effective learning experience by deeply understanding the user's emotions and responding in real time.

[0713] The following describes the processing flow.

[0714] Step 1:

[0715] The user launches the application on their device, and the login screen appears. The user enters their authentication information and attempts to log in.

[0716] Step 2:

[0717] The server receives and verifies the user's authentication information. If authentication is successful, it generates the user's dashboard and provides it to the terminal.

[0718] Step 3:

[0719] The user selects a training category from their device. For example, they might select "Stress Management" or "Communication Skills."

[0720] Step 4:

[0721] The server dynamically generates an appropriate game-style training session based on the user's selection and sends the session content to the terminal.

[0722] Step 5:

[0723] The user starts a training session on their device. The device presents the user with the game rules and instructions, and prompts them to start the game.

[0724] Step 6:

[0725] During gameplay, the emotion engine activates, and the device collects the user's facial expression data and voice tone using the camera and microphone. It also acquires biometric information (heart rate, skin electrical activity, etc.) from a biofeedback device.

[0726] Step 7:

[0727] The server analyzes collected facial expression data, biometric information, and user behavior within the game to estimate the user's emotional state. Based on the analysis results, the user's skill level is evaluated.

[0728] Step 8:

[0729] The server uses a generative algorithm to generate feedback and advice tailored to the user's emotional state and sends it to the device.

[0730] Step 9:

[0731] The device displays evaluation results and feedback to the user, and provides advice for the next training session. Based on the feedback, the user can choose to continue training or use other features.

[0732] (Example 2)

[0733] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0734] In modern society, improving soft skills is crucial for personal growth and professional success, but there is a lack of practical and effective training methods. Furthermore, providing real-time feedback tailored to individual emotional states and reactions is difficult, making it challenging to offer appropriate support to each user.

[0735] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0736] In this invention, the server includes receiving means for receiving information, generating means for generating simulated training sessions, and collecting means for collecting user biometric information. This makes it possible to dynamically provide customized training sessions for individual users and to provide real-time feedback while considering the user's current emotional state by utilizing biometric information collected using sensor devices.

[0737] A "receiving means" is an element that has the function of acquiring information from an external source and is used by a system to receive necessary data and instructions.

[0738] A "generation means" is an element that has the function of constructing instructions and commands based on acquired information and creating sessions and content based on a specific purpose.

[0739] A "collection method" refers to an element that has the function of collecting biometric information from users, and is used by the system to collect necessary data and understand the user's condition.

[0740] An "evaluation tool" is an element that processes collected information and has the function of analyzing the user's skills and status.

[0741] "Means of delivery" refers to elements that have the function of conveying appropriate feedback and instructions to users based on the evaluated results.

[0742] "Analysis means" refers to an element that analyzes collected biometric information and has the function of evaluating the user's emotions and state in real time.

[0743] A generative algorithm is a computational method used to generate appropriate responses or feedback based on specific conditions or data.

[0744] This invention is a system for users to effectively improve their soft skills, combining game-based training sessions with emotion recognition capabilities. The system utilizes a server, terminals, and sensor devices. Each component is described in detail below.

[0745] The server receives information and generates simulated training sessions using a generative AI model. These sessions are highly customized based on the user's past training history and current skill level. The server leverages generative algorithms to understand the user's progress in real time and provide optimal feedback.

[0746] The terminal serves as the interface with the user. It presents the game session sent from the server to the user and displays various instructions and feedback. The user makes choices and interacts with the system through the terminal.

[0747] The sensor device plays a role in collecting biometric data. Specifically, it acquires data such as heart rate and skin electrical activity in real time and uses it to analyze the user's emotions and stress levels. This data is essential for the server to understand and evaluate the user's emotional state.

[0748] For example, if a user selects "stress management" training, the system simulates a stressful situation and measures the user's stress level using a sensor. The server then analyzes this data and provides appropriate stress reduction techniques as feedback. As a result, the user can improve their ability to cope with real-world stressful situations.

[0749] An example of a prompt is "Design a generative algorithm to assess the user's stress level and generate appropriate feedback." Based on such prompts, the system provides a training experience optimized for each individual user.

[0750] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0751] Step 1:

[0752] The user logs into the system via a terminal. The user enters their authentication information into the terminal as input, which the server receives. The server then performs the authentication process by referencing a database, retrieving the user profile and training history. The output displays a list of training categories the user has access to.

[0753] Step 2:

[0754] The user selects a training category of interest on their device. This selection is sent to the server as input. The server generates a simulated training session based on the selected category. By utilizing the generated AI model, a customized, game-like training session is created. The generated session data is then sent back to the device as output.

[0755] Step 3:

[0756] The terminal presents the user with a training session. The input at this stage is session data sent from the server. The terminal displays an interactive scenario on the screen and provides voice instructions via speakers or a headset. The output is the information the user needs to participate in the training.

[0757] Step 4:

[0758] The sensor device starts operating and collects the user's biometric data. The input is biometric information such as the user's heart rate and skin electrical activity. The collected data is sent to a server for analysis. The server uses an emotion engine to analyze the data in real time and estimate the user's emotional state. The output is a digital profile that constitutes the user's emotional state.

[0759] Step 5:

[0760] The server evaluates the user's skills based on the analysis results. The inputs are the analysis results obtained from the emotion engine and the user's behavioral data during training. A generative algorithm is used to evaluate the user's skill level and areas for improvement. The evaluation results are generated as output.

[0761] Step 6:

[0762] The server sends the generated evaluation results and feedback to the terminal. The input is the evaluation results obtained in step 5. As output, the user receives appropriate feedback and advice via the terminal. The user uses this to decide on their next action.

[0763] (Application Example 2)

[0764] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0765] In modern brick-and-mortar stores, while developing employees with customer service skills is crucial, traditional methods struggle to provide effective training that takes individual emotional states into account. Specifically, there is a lack of means for employees to receive real-time feedback and improve their skills while understanding their own emotional state.

[0766] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0767] In this invention, the server includes receiving means for receiving information, generating means for generating game-like training sessions, and means for providing scenarios for improving customer service skills. This enables employees to effectively improve their abilities through interactive customer service training tailored to their individual emotional states.

[0768] "Receiving means" refers to a device or mechanism for acquiring information from an external source and processing it within the system.

[0769] A "generation means" is a function or system that creates a game-style training session tailored to a specific purpose based on acquired information.

[0770] "Means of data collection" refers to devices and technologies for acquiring a user's biometric information, and this includes sensor devices, etc.

[0771] "Evaluation means" refers to a process or mechanism for analyzing and evaluating a user's abilities and status based on collected data.

[0772] "Means of provision" refers to a system or function for providing information and feedback to users based on evaluation results.

[0773] A "scenario" refers to a training scene or situational setting designed to help a user improve a specific skill.

[0774] "Emotion recognition means" refers to technology or devices that analyze a user's voice and facial expressions to determine their emotional state.

[0775] A "generation algorithm" is a computational method or program that automatically generates appropriate advice and content according to the user's progress.

[0776] This invention is a system aimed at improving the customer service skills of employees in physical stores. It consists primarily of a server, terminals, and various sensor devices. Specific embodiments are described below.

[0777] First, the server receives data regarding customer service training requests from the user's terminal in order to receive information. A smartphone is used as the terminal, and the user selects a specific scenario and logs in via this smartphone. After receiving the information, the server uses a generation mechanism to create a game-style training session and creates a training session based on the scenario selected by the user.

[0778] Next, the smartphone's camera and microphone are used as data collection tools to gather the user's biometric information, specifically voice and facial expression data. This data is analyzed in real time, and the user's emotional state is evaluated through emotion recognition tools. External emotion recognition software such as Microsoft Azure Face API and Google Cloud Speech-to-Text is used for the evaluation.

[0779] As an evaluation method, emotional data and user behavior data within the game are analyzed on the server. Based on the results of this analysis, a generation algorithm is used to provide personalized feedback tailored to the user's progress. This allows users to receive real-time advice necessary to improve their customer service skills.

[0780] For example, if a user selects the "product description" scenario, the device simulates this situation, and the server determines the user's level of nervousness and confidence. Based on the data analyzed by the emotion recognition engine, the server provides timely advice on how to approach the situation, supporting the user's growth.

[0781] An example of a prompt message could be, "Analyze the user's facial expressions and tone of voice in real time, and generate feedback based on the customer service scenario." By utilizing such prompts, the system can provide more accurate feedback and adjust its training content.

[0782] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0783] Step 1:

[0784] The user logs in to their smartphone and selects a specific customer service scenario. The user's selection information is sent to the server as input. The server receives this information and prepares a game-style training session based on the scenario content. As output, data related to the selected scenario is generated.

[0785] Step 2:

[0786] The server generates a game-style training session and sends the data to the terminal. The input is the scenario selected by the user, and the server dynamically creates an interactive scenario based on this information. The output is the training session scenario displayed on the user's terminal.

[0787] Step 3:

[0788] The device uses the smartphone's camera and microphone to collect the user's biometric information, specifically facial expressions and voice data. Voice and video data of the user are collected as input. The biometric information is transmitted to the emotion recognition engine in real time.

[0789] Step 4:

[0790] The server uses an emotion recognition engine to analyze the received biometric information and evaluate the user's emotional state. User voice and video data are used as input. The evaluation result outputs the user's emotional state.

[0791] Step 5:

[0792] The server generates personalized feedback using a generative algorithm based on the evaluated emotional state and the user's in-game behavior data. Emotional evaluation data and user behavior data are used as input. The output is the feedback and advice provided to the user.

[0793] Step 6:

[0794] The server sends the generated feedback to the user's terminal, which then displays it to the user. Feedback information is sent from the server to the terminal as input. Based on this information, the user can reflect on and adjust their skills. Specific advice is displayed on the user's screen as output.

[0795] 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.

[0796] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0797] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0798] 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.

[0799] Figure 9 shows an 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.

[0800] 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.

[0801] 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.

[0802] 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, motorcycles, etc., 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, for example, based 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.

[0803] 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."

[0804] 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.

[0805] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0806] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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 the like 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.

[0815] 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 as being incorporated by reference.

[0816] The following is further disclosed regarding the embodiments described above.

[0817] (Claim 1)

[0818] A means of receiving information,

[0819] A generation means for generating a game-like training session based on that information,

[0820] A means of collecting user biometric data,

[0821] An evaluation method that analyzes collected data and assesses user skills,

[0822] A means of providing feedback to users based on evaluation results,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, wherein the generation means dynamically generates a game format for specific skill training based on the user's selection.

[0826] (Claim 3)

[0827] The system according to claim 1, wherein the providing means utilizes a generative algorithm to generate individualized advice according to the user's progress.

[0828] "Example 1"

[0829] (Claim 1)

[0830] A means of receiving information,

[0831] A generation means that generates a game-like training course based on that information,

[0832] A means of storing individual information,

[0833] A means of providing training content according to the selected category,

[0834] A means of collecting user biometric data,

[0835] A tracking system that tracks progress in real time and provides feedback,

[0836] An evaluation method that analyzes collected data and assesses the user's capabilities,

[0837] A generation means for generating feedback to the user based on the evaluation results,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, wherein the generation means dynamically generates a game format for training specific abilities based on the user's selection.

[0841] (Claim 3)

[0842] The system according to claim 1, wherein the generation means utilizes a generation algorithm to generate individual advice according to the user's progress.

[0843] "Application Example 1"

[0844] (Claim 1)

[0845] A means of receiving information,

[0846] A generation means for generating a game-like training session based on that information,

[0847] A means of collecting user biometric data,

[0848] An evaluation method that analyzes collected data and assesses user skills,

[0849] A means of providing feedback to users based on evaluation results,

[0850] Means to improve machine control technology and interaction skills in training sessions,

[0851] A means of developing the ability to efficiently carry out work in coordination with machine operation,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, wherein the generation means dynamically generates a game format for specific skill training based on the user's selection.

[0855] (Claim 3)

[0856] The system according to claim 1, wherein the providing means utilizes a generative algorithm to generate individualized advice according to the user's progress.

[0857] "Example 2 of combining an emotion engine"

[0858] (Claim 1)

[0859] A means of receiving information,

[0860] A generation means that generates a simulated training session based on that information,

[0861] A means of collecting user biometric information,

[0862] An evaluation method that analyzes collected information and assesses the user's abilities,

[0863] A means of providing a response to the user based on the evaluation results,

[0864] An analysis means for acquiring biological information in real time using a sensor device,

[0865] A generation means that generates a response according to the user's emotional state using a generative algorithm,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, wherein the generation means dynamically generates a simulated form for specific ability training based on the user's selection.

[0869] (Claim 3)

[0870] The system according to claim 1, wherein the providing means generates individual instructions according to the user's progress.

[0871] "Application example 2 when combining with an emotional engine"

[0872] (Claim 1)

[0873] A means of receiving information,

[0874] A generation means for generating a game-like training session based on that information,

[0875] A means of collecting user biometric information,

[0876] An evaluation method that analyzes collected data and assesses user capabilities,

[0877] A means of providing feedback to users based on evaluation results,

[0878] A means of providing scenarios to improve customer service skills,

[0879] An emotion recognition method for analyzing the user's voice and facial expressions,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, wherein the generation means dynamically generates a game format for training a specific ability based on the user's selection.

[0883] (Claim 3)

[0884] The system according to claim 1, wherein the providing means utilizes a generation algorithm to generate individual advice according to the user's progress. [Explanation of Symbols]

[0885] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving information, A generation means for generating a game-like training session based on that information, A means of collecting user biometric data, An evaluation method that analyzes collected data and assesses user skills, A means of providing feedback to users based on evaluation results, A system that includes this.

2. The system according to claim 1, wherein the generation means dynamically generates a game format for specific skill training based on the user's selection.

3. The system according to claim 1, wherein the providing means utilizes a generative algorithm to generate individual advice according to the user's progress.

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